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Variable Abbreviations

This list gives an overview of variables commonly used in eddy covariance ecosystem research.

Note: use the search bar above the table to find specific items.

 

wdt_ID Label Description Units (preferred) or format Category Used In
1 #gfRF Gap-filled using random forest same as input var diive
2 #odAL Outliers were removed using outlier detection: absolute limits same as input var diive
3 (FLUX)_QCF (suffix) measured (not gap-filled) data with the respective QCF flag applied
4 (footprint) model Model for footprint estimation - EddyPro (_full_output_ file)
5 (z-d)/L Monin-Obukhov stability parameter # EddyPro (_full_output_ file)
6 _CUT_ constant USTAR threshold across years
7 _fsd / _FSD (suffix) Standard deviation of datapoints used for gap filling (uncertainty) ReddyProc
8 _fsdu (suffix) Standard deviation across uStar thresholds (uncertainty, bias) ReddyProc
9 _fsdug (suffix) Combination of random uncertainty and uncertainty due to USTAR: sqrt(fsd^2 + fsdu^2) ReddyProc
10 _fwin (suffix) Full window length used for gap filling ReddyProc
11 _orig (suffix) Original values used for gap filling ReddyProc
12 _QCF0 (suffix) measured (not gap-filled) data of highest quality diive
13 _QCF01 (suffix) measured (not gap-filled) data of highest and OK quality diive
14 _SCF (suffix) Spectral correction factor for the respective flux #
15 _sd / _SD (suffix) Standard deviation ReddyProc
16 _SUT_ seasonal USTAR threshold, seasons are specified by the user
17 _Thres (suffix) the threshold of uStar values used to mark insufficient conditions m s-1 ReddyProc
18 _U05 (suffix) low estimate (5% quantile of the bootstrapped uncertainty distribution) ReddyProc
19 _U50 (suffix) median estimate (50% quantile of the bootstrapped uncertainty distribution) ReddyProc
20 _U95 (suffix) high estimate (95% quantile of the bootstrapped uncertainty distribution) ReddyProc
21 _uStar (suffix) estimate on the original unbootstrapped data ReddyProc
22 _VUT_ variable USTAR threshold for each year
23 abs_lim Hard flags for individual variables for absolute limits HFu/v/w/ts/co2 /h2o/ch4/n2 EddyPro (_full_output_ file)
24 AGC Automatic Gain Control LI-7500 % EC raw data files (ASCII)
25 AGC Mean value of AGC for LI‑7500RS or LI‑7200RS % EddyPro (_full_output_ file)
26 air_density Density of ambient air kg m-3 EddyPro (_full_output_ file)
27 air_heat_capactiy Specific heat at constant pressure of ambient air J K-1 kg-1 EddyPro (_full_output_ file)
28 air_molar_volume Molar volume of ambient air m3 mol-1 EddyPro (_full_output_ file)
29 air_pressure Mean pressure of ambient air, either calculated from high frequency air pressure readings, or estimated based on site altitude (barometric pressure) Pa EddyPro (_full_output_ file)
30 air_temperature Mean temperature of ambient air, either calculated from high frequency air temperature readings, or estimated from sonic temperature K EddyPro (_full_output_ file)
31 ALB Albedo, range 0-100 % Ameriflux
32 amp_res Hard flags for individual variables for amplitude resolution HFu/v/w/ts/co2 /h2o/ch4/n2 EddyPro (_full_output_ file)
33 APAR Absorbed PAR µmolPhoton m-2 s-1 Ameriflux
34 ATM Atmospheric -
35 attack_angle Hard flag for attack angle test HFu/v/w/ts/co2 /h2o/ch4/n2 EddyPro (_full_output_ file)
36 AVG Average -
37 AW All-wave radiation
38 AW_IN All-wave incoming radiation without correction (Pyrradiometer) W m–2
39 AW_OUT All-wave outgoing radiation without correction (Pyrradiometer) W m–2
40 BAK Backup measurement, e.g. used at ICOS station CH-DAV
41 BC Below Canopy -
42 BD Bulk density g cm-3
43 BICO Python script for BInary COnversion of EC raw data binary files to ASCII format - BICO
44 bowen_ratio Sensible heat flux to latent heat flux ratio # EddyPro (_full_output_ file)
45 BV Battery voltage V
46 BV_EC Battery Voltage EC system V
47 BV_iDL Internal Battery Voltage Logger V (location and replicate number is essential)
48 CH4 CH4 molar fraction (in humid air), wet mole fraction nmol mol-1, µmol mol-1
49 CH4_DRY CH4 dry mole fraction (in dry air), mixing ratio, ppb nmol mol-1 EC raw data files (ASCII)
50 CH4_MIXING_RATIO Methane (CH4) in mole fraction of dry air nmolCH4 mol-1 Ameriflux
51 CH4_QCL_CMB CH4 concentration from QCL for chamber system ppb (nmol CH4 mol-1)
52 CH4_QCL_EC CH4 concentration from QCL for eddy system ppb (nmol CH4 mol-1)
53 CH4_QCL_PRF CH4 concentration from QCL for profile system ppb (nmol CH4 mol-1)
54 CM Chamber -
55 CMB Chamber -
56 CNT Suffix for counts -
57 CO Carbon Monoxide (CO) mole fraction in wet air nmolCO mol-1 Ameriflux
58 CO2 Carbon Dioxide (CO2) in mole fraction of wet air umolCO2 mol-1 ICOS / Ameriflux
59 CO2_BOLE tree stem CO2 vol%
60 CO2_CONC CO2 concentration density, molar density mmol m-3 EC raw data files (ASCII)
61 CO2_DRY CO2 dry mole fraction (in dry air), mixing ratio, ppm (parts per million) µmol CO2 mol-1 EC raw data files (ASCII)
62 CO2_DRY Carbon Dioxide (CO2) in mole fraction of dry air umolCO2 mol-1 ICOS
63 CO2_DRY_n Profile level-specific carbon dioxide in mole fraction of dry air averaged over the whole respective half-hour (_n is an integer number going from 1 to n top-down) umolCO2 mol-1 ICOS
64 CO2_DRY_n_SE Standard error of profile level-specific carbon dioxide in mole fraction of dry air as estimenated over the whole respective half-hour (_n is an integer number going from 1 to n top-down) umolCO2 mol-1 ICOS
65 CO2_F_MDS CO2 mole fraction, gapfilled with MDS; µmolCO2 mol-1 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
66 CO2_F_MDS CO2 mole fraction, gapfilled with MDS µmolCO2 mol-1 ICOS / FLUXNET
67 CO2_F_MDS_QC Quality flag for CO2_F_MDS; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
68 CO2_F_MDS_QC Quality flag for CO2_F_MDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
69 CO2_HD10_FLAG Flag for the homogeneity test applied on differenced carbon dioxide 0: negligible evidences of error, IF HD10_STAT1) nondimensional ICOS
70 CO2_HD10_STAT Statistic of the homogeneity test applied on differenced carbon dioxide (percentage of data exceeding ñ10?) % ICOS
71 CO2_HD5_FLAG Flag for the homogeneity test applied on differenced carbon dioxide (0: negligible evidences of error, IF HD5_STAT4) nondimensional ICOS
72 CO2_HD5_STAT Statistic of the homogeneity test applied on differenced carbon dioxide (percentage of data exceeding ñ5?) % ICOS
73 CO2_HF10_FLAG Flag for the homogeneity test applied on carbon dioxide fluctuations (0: negligible evidences of error, IF HF10_STAT1) nondimensional ICOS
74 CO2_HF10_STAT Statistic of the homogeneity test applied on carbon dioxide fluctuations (percentage of data exceeding ñ10?) % ICOS
75 CO2_HF5_FLAG Flag for the homogeneity test applied on carbon dioxide fluctuations (0: negligible evidences of error, IF HF5_STAT4) nondimensional ICOS
76 CO2_HF5_STAT Statistic of the homogeneity test applied on carbon dioxide fluctuations (percentage of data exceeding ñ5?) % ICOS
77 CO2_IRGA70_CMB CO2 concentration from IRGA Li-7000 for chamber system µmol CO2 mol-1
78 CO2_IRGA70_EC CO2 concentration from IRGA Li-7000 for eddy system µmol CO2 mol-1
79 CO2_IRGA70_PRF CO2 concentration from IRGA Li-7000 for profile µmol CO2 mol-1
80 CO2_IRGA70_STM CO2 concentration from IRGA Li-7000, in stem µmol CO2 mol-1
81 CO2_IRGA72_CMB CO2 concentration from IRGA Li-7200 for chamber system µmol CO2 mol-1
82 CO2_IRGA72_EC CO2 concentration from IRGA Li-7200 for eddy system µmol CO2 mol-1
83 CO2_IRGA72_PRF CO2 concentration from IRGA Li-7200 for profile µmol CO2 mol-1
84 CO2_IRGA72_STM CO2 concentration from IRGA Li-7200, in stem µmol CO2 mol-1
85 CO2_IRGA75_CMB CO2 concentration from IRGA Li-7500 for chamber system µmol CO2 mol-1
86 CO2_IRGA75_EC CO2 concentration from IRGA Li-7500 for eddy system µmol CO2 mol-1
87 CO2_IRGA75_PRF CO2 concentration from IRGA Li-7500 for profile µmol CO2 mol-1
88 CO2_IRGA75_STM CO2 concentration from IRGA Li-7500, in stem µmol CO2 mol-1
89 CO2_KID_FLAG Flag for the CO2_KID_STAT (0: negligible evidences of error, IF KID_STAT50) nondimensional ICOS
90 CO2_KID_STAT Kurtosis Index of Differenced carbon dioxide nondimensional ICOS
91 CO2_MIXING_RATIO Carbon Dioxide (CO2) in mole fraction of dry air µmolCO2 mol-1 Ameriflux
92 CO2_SIGMA Standard deviation of carbon dioxide mole fraction in wet air µmolCO2 mol-1 Ameriflux
93 CO2_SIGMA Standard deviation of carbon dioxide in mole fraction of wet air umolCO2 mol-1 ICOS
94 CO2C13 Stable isotopic composition of CO2 - C13 (i.e., d13C of CO2) ‰ (permil) Ameriflux
95 COND_WATER Conductivity (i.e., electrical conductivity) of water µS cm-1 Ameriflux
96 COOLER_V Cooler voltage V EC raw data files (ASCII)
97 CUP Cup Anemometer -
98 D Depth -
99 D_SNOW Snow depth cm, m Ameriflux
100 DATA_SIZE Data size of instrument data block, number of bytes in instrument record bytes EC raw data files (ASCII)
101 DATE date yyyy-mm-dd
102 date Date of the end of the averaging period yyyy-mm-dd EddyPro (_full_output_ file)
103 DAY 2 digit day of month dd
104 DBH diameter of tree measured at breast height(1.3 m) cm
105 DBH Diameter of tree measured at breast height (1.3m) with continuous dendrometers cm Ameriflux
106 DENDRO Dendrometer mm
107 DIF Diffuse -
108 discontinuities Hard flags for individual variables for discontinuities test HFu/v/w/ts/co2 /h2o/ch4/n2 EddyPro (_full_output_ file)
109 discontinuities Soft flags for individual variables for discontinuities test HFu/v/w/ts/co2 /h2o/ch4/n2 EddyPro (_full_output_ file)
110 DNTP Time Difference between NTP Clock and Clock msec
111 DO Dissolved oxygen in water µmol L-1 Ameriflux
112 DOC Dissolved organic carbon mg l-1
113 DOY 3 digit day of year ddd
114 drop_out Hard flags for individual variables for drop-out test HFu/v/w/ts/co2 /h2o/ch4/n2 EddyPro (_full_output_ file)
115 DT Daytime (used in partitioning) - Fluxes
116 e Ambient water vapor partial pressure Pa EddyPro (_full_output_ file)
117 E0 activation energy parameter (K) in relationship between temperature and nighttime NEE from night-time partitioning ReddyProc
118 EBC_CF_METHOD Method used to calculate the energy balance closure correction factor; 1 = ECB_CF Method 1, 2 = ECB_CF Method 2, 3 = ECB_CF Method 3. See general description for details nondimensional ENERGY PROCESSING FLUXNET HH (half-hourly)
119 EBC_CF_METHOD Method used to calculate the energy balance closure correction factor. 1 = ECB_CF Method 1, 2 = ECB_CF Method 2, 3 = ECB_CF Method 3. See general description for details nondimensional ICOS / FLUXNET
120 EBC_CF_N Number of data points used to calculate energy closure balance correction factor. Driver data points within sliding window (ECB_CF Method 1) or number of ECB_CF data points (for ECB_CF Methods 2 and 3); for ECB_CF Method 1 (minimum 5, maximum 93) nondimensional ENERGY PROCESSING FLUXNET HH (half-hourly)
121 EBC_CF_N Number of data points used to calculate energy closure balance correction factor. Driver data points within sliding window (ECB_CF Method 1) or number of ECB_CF data points (for ECB_CF Methods 2 and 3). nondimensional ICOS / FLUXNET
122 EC Eddy covariance - Fluxes
123 EP EddyPro Flux Calculation Software -
124 es Ambient water vapor partial pressure at saturation Pa EddyPro (_full_output_ file)
125 ET Evapotranspiration flux mm hour-1 EddyPro (_full_output_ file)
126 ET_f Gap-filled evapotranspiration flux, calculated from gap-filled LE mmol H20 m-2 s-1 ReddyProc
127 EXT External -
128 extravar_mean Mean value of extravar (‡) EddyPro (_full_output_ file)
129 f (suffix) Original values and gaps filled with mean of selected datapoints (condition depending on gap filling method) ReddyProc
130 fall (suffix) All values considered as gaps (for uncertainty estimates) ReddyProc
131 fall_qc (suffix) All values considered as gaps (for uncertainty estimates) ReddyProc
132 FAPAR Fraction of absorbed PAR, range 0-100 % Ameriflux
133 FC Carbon Dioxide (CO2) turbulent flux (no storage correction) umolCO2 m-2 s-1 ICOS / Ameriflux
134 FC_FMR_FLAG Flag for the FMR test for FC (0: negligible evidences of error, IF FMR15) nondimensional ICOS
135 FC_FMR_STAT Fraction of Missing Records in raw, high-frequency, data used for FC flux estimation % ICOS
136 FC_LGD_FLAG Flag for the LGD test for FC (0: negligible evidences of error, IF LGD180) nondimensional ICOS
137 FC_LGD_STAT Longest Gap Duration in raw, high-frequency, data used for FC flux estimation seconds ICOS
138 FC_LSR_FLAG Flag for the LSR test for FC (0: negligible evidences of error, IF LSR_STAT>0.995; 1: moderate evidences of error, IF 0.99?LSR_STAT?0.995; 2: severe evidences of error, IF LSR_STAT nondimensional ICOS
139 FC_LSR_STAT Statistic of the Low Signal Resolution test for FC nondimensional ICOS
140 FC_M98_FLAG Flag of the FC_M98_STAT (0: negligible evidences of error, IF M_98_STAT3) nondimensional ICOS
141 FC_M98_STAT Statistic of the nonstationarity ratio test by Mahrt (1998) for FC nondimensional ICOS
142 FC_SCF_STAT Spectral correction factor for NEE nondimensional ICOS
143 FC_SSITC_TEST Results of the quality flagging for FC according to Foken et al 2004, based on a combination of Steady State and Integral Turbulence Characteristics tests by Foken and Wichura (1996) (i.e., 0, 1, 2) nondimensional Ameriflux
144 FC_SSITC_TEST Quality flagging for FC according to classification scheme by Foken et al (2004) and based on the combination of the results of Steady State and Integral Turbulence Characteristics tests by Foken and Wichura (1996) (0: high quality; 1:intermediate quality nondimensional ICOS
145 FCH4 Methane (CH4) turbulent flux (no storage correction) nmolCH4 m-2 s-1 Ameriflux
146 FCH4_L2_QCF CH4 flux quality-controlled with Level-2 flags nmol CH4 m-2 s-1 FLUX diive
147 FCH4_L3.1_L3.2_QCF CH4 flux quality-controlled with Level-2 and Level-3.2 flags, including Level-3.1 storage correction) nmol CH4 m-2 s-1 FLUX diive
148 FCH4_L3.1_L3.2_QCF0 CH4 highest-quality flux (QCF=0), quality-controlled with Level-2 and Level-3.2 flags, including Level-3.1 storage correction) nmol CH4 m-2 s-1 FLUX diive
149 FCH4_L3.1_L3.3_CUT_50_QCF CH4 flux quality-controlled with Level-2 and Level-3.2 flags, and after Level-3.3 USTAR filtering (CUT_50), including Level-3.1 storage correction nmol CH4 m-2 s-1 FLUX diive
150 FCH4_L3.1_L3.3_CUT_50_QCF_gfRF CH4 flux quality-controlled with Level-2 and Level-3.2 flags, and after Level-3.3 USTAR filtering (CUT_50), including Level-3.1 storage correction, gap-filled using random forest nmol CH4 m-2 s-1 FLUX diive
151 FCH4_L3.1_QCF CH4 flux quality-controlled with Level-2 flags, including Level-3.1 storage correction nmol CH4 m-2 s-1 FLUX diive
152 FCH4_SSITC_TEST Results of the quality flagging for FCH4 according to Foken et al 2004, based on a combination of Steady State and Integral Turbulence Characteristics tests by Foken and Wichura (1996) (i.e., 0, 1, 2) nondimensional Ameriflux
153 FETCH_70 Distance at which footprint cumulative probability is 70% m
154 FETCH_70 Distance at which cross-wind integrated footprint cumulative probability is 70% m ICOS
155 FETCH_80 Distance at which footprint cumulative probability is 80% m
156 FETCH_80 Distance at which cross-wind integrated footprint cumulative probability is 80% m ICOS
157 FETCH_90 Distance at which footprint cumulative probability is 90% m
158 FETCH_90 Distance at which cross-wind integrated footprint cumulative probability is 90% m ICOS
159 FETCH_FILTER Footprint quality flag (i.e., 0, 1): 0 and 1 indicate data measured when wind coming from direction that should be discarded and kept, respectively nondimensional Ameriflux
160 FETCH_MAX Distance at which footprint contribution is maximum m
161 FETCH_MAX Distance at which cross-wind integrated footprint contribution is maximum m ICOS
162 file_records Number of valid records found in the raw file (or set of raw files) # EddyPro (_full_output_ file)
163 filename Name of the raw file (or the first of a set) from which the dataset for the current averaging interval was extracted - EddyPro (_full_output_ file)
164 FIPAR Fraction of intercepted PAR, range 0-100 % Ameriflux
165 FIT_FLAG Fit flag flag EC raw data files (ASCII)
166 FLAG_(FLUX)_QCF quality control flag, overall quality flag for the respective flux 0=best, 1=medium, 2=bad data diive
167 FLAG_FCH4_L3.1_L3.3_CUT_50_QCF_gfRF_ISFILLED Flag showing whether the CH4 flux was gap-filled or measured 1=gap-filled, 0=measured FLAG diive
168 FLAG_FN2O_L3.1_L3.3_CUT_50_QCF_gfRF_ISFILLED Flag showing whether the N2O flux was gap-filled or measured 1=gap-filled, 0=measured FLAG diive
169 FLOW_VOLRATE Volume flow rate in the sampling line L min-1 EC raw data files (ASCII)
170 fmeth / FMETH (suffix) Method used for gap filling: 1 = similar meteo condition (sFillLUT with Rg, VPD, Tair) 2 = similar meteo (sFillLUT with Rg only) 3 = mean diurnal course (sFillMDC)) ReddyProc
171 FN2O Nitrous oxide (N2O) turbulent flux (no storage correction) nmolN2O m-2 s-1 Ameriflux
172 FN2O_L2_QCF N2O flux quality-controlled with Level-2 flags nmol N2O m-2 s-1 FLUX diive
173 FN2O_L3.1_L3.2_QCF N2O flux quality-controlled with Level-2 and Level-3.2 flags, including Level-3.1 storage correction) nmol N2O m-2 s-1 FLUX diive
174 FN2O_L3.1_L3.2_QCF0 N2O highest-quality flux (QCF=0), quality-controlled with Level-2 and Level-3.2 flags, including Level-3.1 storage correction) nmol N2O m-2 s-1 FLUX diive
175 FN2O_L3.1_L3.3_CUT_50_QCF N2O flux quality-controlled with Level-2 and Level-3.2 flags, and after Level-3.3 USTAR filtering (CUT_50), including Level-3.1 storage correction nmol N2O m-2 s-1 FLUX diive
176 FN2O_L3.1_L3.3_CUT_50_QCF_gfRF N2O flux quality-controlled with Level-2 and Level-3.2 flags, and after Level-3.3 USTAR filtering (CUT_50), including Level-3.1 storage correction, gap-filled using random forest nmol N2O m-2 s-1 FLUX diive
177 FN2O_L3.1_QCF N2O flux quality-controlled with Level-2 flags, including Level-3.1 storage correction nmol N2O m-2 s-1 FLUX diive
178 FN2O_SSITC_TEST Results of the quality flagging for FN2O according to Foken et al 2004, based on a combination of Steady State and Integral Turbulence Characteristics tests by Foken and Wichura (1996) (i.e., 0, 1, 2) nondimensional Ameriflux
179 FNO Nitric oxide (NO) turbulent flux (no storage correction) nmolNO m-2 s-1 Ameriflux
180 FNO_SSITC_TEST Results of the quality flagging for FNO according to Foken et al 2004, based on a combination of Steady State and Integral Turbulence Characteristics tests by Foken and Wichura (1996) (i.e., 0, 1, 2) nondimensional Ameriflux
181 FNO2 Nitrogen dioxide (NO2) turbulent flux (no storage correction) nmolNO2 m-2 s-1 Ameriflux
182 FNO2_SSITC_TEST Results of the quality flagging for FNO2 according to Foken et al 2004, based on a combination of Steady State and Integral Turbulence Characteristics tests by Foken and Wichura (1996) (i.e., 0, 1, 2) nondimensional Ameriflux
183 fnum (suffix) Number of datapoints used for gap filling # ReddyProc
184 FO3 Ozone (O3) turbulent flux (no storage correction) nmolO3 m-2 s-1 Ameriflux
185 FO3_SSITC_TEST Results of the quality flagging for FO3 according to Foken et al 2004, based on a combination of Steady State and Integral Turbulence Characteristics tests by Foken and Wichura (1996) (i.e., 0, 1, 2) nondimensional Ameriflux
186 FOG Fog presence
187 FOOTPRINT_80_SURF 2D footprint 80% isoplethe area m2 ICOS
188 FOOTPRINT_FLAG Footprint flag (0, 1, 2, 3). 0 = data used for footprint, 1 = 2D footprint model flag error, 2 = not all 2D footprint model isoplethes within specified domain, 3 = data removed because of invalid conditions for footprint model nondimensional ICOS
189 FOOTPRINT_TA_CONTR Cumulative footprint contribution of the footprint/target area intersection % ICOS
190 FOOTPRINT_TA_SURF Area of footprint/target area intersection m2 ICOS
191 FP Flux partitioning ReddyProc
192 FP__sd estimated standard devation of X ReddyProc
193 FP_alpha canopy light utilization efficiency and represents the initial slope of the light–response curve (daytime partitioning) ReddyProc
194 FP_beta maximum CO2 uptake rate of the canopy at light saturation (μmol m-2 s-1) (daytime partitioning) μmol m-2 s-1 ReddyProc
195 FP_dRecPar records until or after closest record that has a parameter estimate associated (daytime partitioning) ReddyProc
196 FP_E0 activation energy parameter (K) in relationship between temperature and nighttime NEE from day-time partitioning ReddyProc
197 FP_GPP2000 GPP at incoming radiation of 2000 Wm-2, more robust alternative to saturation FP_k ReddyProc
198 FP_k parameter controlling the VPD limitation of GPP (daytime partitioning) (daytime partitioning) ReddyProc
199 FP_qc quality flag of the estimated parameters: 0: good parameter fit, 1: some parameters out of range, required refit, 2: next parameter estimate is more than two weeks away (daytime partitioning) ReddyProc
200 FP_RRef respiration at reference temperature parameter (μmol m-2 s-1 as NEE) in relationship between temperature and nighttime NEE from day-time partitioning μmol m-2 s-1 ReddyProc
201 FP_RRef_Night same as FP_RRef using the same FP_E0, but from intermedate step based on night-time data ReddyProc
202 fqc / FQC (suffix) Quality flag assigned depending on gap filling method and window length: 0 = original, 1 = most reliable, 2 = medium, 3 = least reliable # ReddyProc
203 G Soil heat flux W m-2 Ameriflux
204 G_F_MDS Soil heat flux; W m-2 ENERGY PROCESSING FLUXNET HH (half-hourly)
205 G_F_MDS Soil heat flux W m-2 ICOS / FLUXNET
206 G_F_MDS_QC Quality flag of G_F_MDS; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ENERGY PROCESSING FLUXNET HH (half-hourly)
207 G_F_MDS_QC Quality flag of G_F_MDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
208 G_SF calibration/sensitivity factor of G uV (W m-2)-1
209 GA_DIAG_CODE GA diagnostic value # EC raw data files (ASCII)
210 GA_DIAG_FLAG Flag for gas analyzer (GA) instrumental diagnostics (0: negligible evidences of error; 2: severe evidences of error) nondimensional ICOS
211 gas_def_timelag Flag: whether the reported time lag is the default (T) or calculated (F) T/F EddyPro (_full_output_ file)
212 gas_flux Corrected gas flux µmol m-2 s-1(†) EddyPro (_full_output_ file)
213 gas_mixing_ratio Measured or estimated mixing ratio of gas µmol mol-1(†) EddyPro (_full_output_ file)
214 gas_molar_density Measured or estimated molar density of gas mmol m-3 EddyPro (_full_output_ file)
215 gas_mole_fraction Measured or estimated mole fraction of gas µmol mol-1(†) EddyPro (_full_output_ file)
216 gas_scf Spectral correction factor for gas flux # EddyPro (_full_output_ file)
217 gas_strg Estimate of storage gas flux µmol s-1 m-2(†) EddyPro (_full_output_ file)
218 gas_time_lag Time lag used to synchronize gas time series s EddyPro (_full_output_ file)
219 gas_v-adv Estimate of vertical advection flux µmol s-1 m-2(†) EddyPro (_full_output_ file)
220 GPP Gross Primary Productivity (nighttime-based) µmolCO2 m-2 s-1 Ameriflux
221 GPP_CUT_16_f Gross primary production, i.e. influx to the land surface (μmol m-2 s-1 as NEE) (nighttime-based), modelled from NEE_CUT_16_f μmol CO2 m-2 s-1
222 GPP_CUT_84_f Gross primary production, i.e. influx to the land surface (μmol m-2 s-1 as NEE) (nighttime-based), modelled from NEE_CUT_84_f μmol CO2 m-2 s-1
223 GPP_CUT_REF_f Gross primary production, i.e. influx to the land surface (μmol m-2 s-1 as NEE) (nighttime-based), modelled from NEE_CUT_REF_f μmol CO2 m-2 s-1
224 GPP_DT Gross primary production, i.e. influx to the land surface (μmol m-2 s-1 as NEE) estimated by day-time partitioning μmol m-2 s-1 ReddyProc
225 GPP_DT_CUT_05 Gross Primary Production, from Daytime partitioning method, percentile 05 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
226 GPP_DT_CUT_16 Gross primary production, i.e. influx to the land surface (μmol m-2 s-1 as NEE) estimated by day-time partitioning, modelled from NEE_CUT_16_f μmol m-2 s-1
227 GPP_DT_CUT_16 Gross Primary Production, from Daytime partitioning method, percentile 16 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
228 GPP_DT_CUT_25 Gross Primary Production, from Daytime partitioning method, percentile 25 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
229 GPP_DT_CUT_50 Gross Primary Production, from Daytime partitioning method, percentile 50 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
230 GPP_DT_CUT_75 Gross Primary Production, from Daytime partitioning method, percentile 75 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
231 GPP_DT_CUT_84 Gross primary production, i.e. influx to the land surface (μmol m-2 s-1 as NEE) estimated by day-time partitioning, modelled from NEE_CUT_84_f μmol m-2 s-1
232 GPP_DT_CUT_86 Gross Primary Production, from Daytime partitioning method, percentile 86 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
233 GPP_DT_CUT_95 Gross Primary Production, from Daytime partitioning method, percentile 95 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
234 GPP_DT_CUT_MEAN Gross Primary Production, from Daytime partitioning method, average from GPP versions, each from corresponding NEE_CUT_XX version; average from 40 half-hourly GPP_DT_CUT_XX µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
235 GPP_DT_CUT_MEAN Gross Primary Production, from Daytime partitioning method, average from GPP versions, each from corresponding NEE_CUT_XX version. average from 40 half-hourly GPP_DT_CUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
236 GPP_DT_CUT_REF Gross primary production, i.e. influx to the land surface (μmol m-2 s-1 as NEE) estimated by day-time partitioning, modelled from NEE_CUT_REF_f μmol m-2 s-1
237 GPP_DT_CUT_REF Gross Primary Production, from Daytime partitioning method, reference selected from GPP versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation; µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
238 GPP_DT_CUT_REF Gross Primary Production, from Daytime partitioning method, reference selected from GPP versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 ICOS / FLUXNET
239 GPP_DT_CUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_DT_CUT_XX) / SQRT(40)); SE from 40 half-hourly GPP_DT_CUT_XX µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
240 GPP_DT_CUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_DT_CUT_XX) / SQRT(40)). SE from 40 half-hourly GPP_DT_CUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
241 GPP_DT_CUT_USTAR50 Gross Primary Production, from Daytime partitioning method, based on NEE_CUT_USTAR50; µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
242 GPP_DT_CUT_USTAR50 Gross Primary Production, from Daytime partitioning method, based on NEE_CUT_USTAR50 µmolCO2 m-2 s-1 ICOS / FLUXNET
243 GPP_DT_CUT_XX Gross Primary Production, from Daytime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95); µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
244 GPP_DT_VUT_05 Gross Primary Production, from Daytime partitioning method, percentile 05 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
245 GPP_DT_VUT_16 Gross Primary Production, from Daytime partitioning method, percentile 16 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
246 GPP_DT_VUT_25 Gross Primary Production, from Daytime partitioning method, percentile 25 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
247 GPP_DT_VUT_50 Gross Primary Production, from Daytime partitioning method, percentile 50 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
248 GPP_DT_VUT_75 Gross Primary Production, from Daytime partitioning method, percentile 75 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
249 GPP_DT_VUT_86 Gross Primary Production, from Daytime partitioning method, percentile 86 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
250 GPP_DT_VUT_95 Gross Primary Production, from Daytime partitioning method, percentile 95 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
251 GPP_DT_VUT_MEAN Gross Primary Production, from Daytime partitioning method, average from GPP versions, each from corresponding NEE_VUT_XX version; average from 40 half-hourly GPP_DT_VUT_XX µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
252 GPP_DT_VUT_MEAN Gross Primary Production, from Daytime partitioning method, average from GPP versions, each from corresponding NEE_VUT_XX version. average from 40 half-hourly GPP_DT_VUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
253 GPP_DT_VUT_REF Gross Primary Production, from Daytime partitioning method, reference selected from GPP versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation; µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
254 GPP_DT_VUT_REF Gross Primary Production, from Daytime partitioning method, reference selected from GPP versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 ICOS / FLUXNET
255 GPP_DT_VUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_DT_VUT_XX) / SQRT(40)); SE from 40 half-hourly GPP_DT_VUT_XX µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
256 GPP_DT_VUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_DT_VUT_XX) / SQRT(40)). SE from 40 half-hourly GPP_DT_VUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
257 GPP_DT_VUT_USTAR50 Gross Primary Production, from Daytime partitioning method, based on NEE_VUT_USTAR50; µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
258 GPP_DT_VUT_USTAR50 Gross Primary Production, from Daytime partitioning method, based on NEE_VUT_USTAR50 µmolCO2 m-2 s-1 ICOS / FLUXNET
259 GPP_DT_VUT_XX Gross Primary Production, from Daytime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95); µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
260 GPP_f Gross primary production, i.e. influx to the land surface (μmol m-2 s-1 as NEE) (nighttime-based) μmol m-2 s-1 ReddyProc
261 GPP_NT_CUT_05 Gross Primary Production, from Nighttime partitioning method, percentile 05 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
262 GPP_NT_CUT_16 Gross Primary Production, from Nighttime partitioning method, percentile 16 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
263 GPP_NT_CUT_25 Gross Primary Production, from Nighttime partitioning method, percentile 25 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
264 GPP_NT_CUT_50 Gross Primary Production, from Nighttime partitioning method, percentile 50 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
265 GPP_NT_CUT_75 Gross Primary Production, from Nighttime partitioning method, percentile 75 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
266 GPP_NT_CUT_86 Gross Primary Production, from Nighttime partitioning method, percentile 86 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
267 GPP_NT_CUT_95 Gross Primary Production, from Nighttime partitioning method, percentile 95 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
268 GPP_NT_CUT_MEAN Gross Primary Production, from Nighttime partitioning method, average from GPP versions, each from corresponding NEE_CUT_XX version; average from 40 half-hourly GPP_NT_CUT_XX µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
269 GPP_NT_CUT_MEAN Gross Primary Production, from Nighttime partitioning method, average from GPP versions, each from corresponding NEE_CUT_XX version. average from 40 half-hourly GPP_NT_CUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
270 GPP_NT_CUT_REF Gross Primary Production, from Nighttime partitioning method, reference selected from GPP versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation; µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
271 GPP_NT_CUT_REF Gross Primary Production, from Nighttime partitioning method, reference selected from GPP versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 ICOS / FLUXNET
272 GPP_NT_CUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_NT_CUT_XX) / SQRT(40)); SE from 40 half-hourly GPP_NT_CUT_XX µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
273 GPP_NT_CUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_NT_CUT_XX) / SQRT(40)). SE from 40 half-hourly GPP_NT_CUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
274 GPP_NT_CUT_USTAR50 Gross Primary Production, from Nighttime partitioning method, based on NEE_CUT_USTAR50; µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
275 GPP_NT_CUT_USTAR50 Gross Primary Production, from Nighttime partitioning method, based on NEE_CUT_USTAR50 µmolCO2 m-2 s-1 ICOS / FLUXNET
276 GPP_NT_CUT_XX Gross Primary Production, from Nighttime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95); µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
277 GPP_NT_VUT_05 Gross Primary Production, from Nighttime partitioning method, percentile 05 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
278 GPP_NT_VUT_16 Gross Primary Production, from Nighttime partitioning method, percentile 16 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
279 GPP_NT_VUT_25 Gross Primary Production, from Nighttime partitioning method, percentile 25 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
280 GPP_NT_VUT_50 Gross Primary Production, from Nighttime partitioning method, percentile 50 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
281 GPP_NT_VUT_75 Gross Primary Production, from Nighttime partitioning method, percentile 75 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
282 GPP_NT_VUT_86 Gross Primary Production, from Nighttime partitioning method, percentile 86 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
283 GPP_NT_VUT_95 Gross Primary Production, from Nighttime partitioning method, percentile 95 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
284 GPP_NT_VUT_MEAN Gross Primary Production, from Nighttime partitioning method, average from GPP versions, each from corresponding NEE_VUT_XX version; average from 40 half-hourly GPP_NT_VUT_XX µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
285 GPP_NT_VUT_MEAN Gross Primary Production, from Nighttime partitioning method, average from GPP versions, each from corresponding NEE_VUT_XX version. average from 40 half-hourly GPP_NT_VUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
286 GPP_NT_VUT_REF Gross Primary Production, from Nighttime partitioning method, reference selected from GPP versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation; µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
287 GPP_NT_VUT_REF Gross Primary Production, from Nighttime partitioning method, reference selected from GPP versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 ICOS / FLUXNET
288 GPP_NT_VUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_NT_VUT_XX) / SQRT(40)); SE from 40 half-hourly GPP_NT_VUT_XX µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
289 GPP_NT_VUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_NT_VUT_XX) / SQRT(40)). SE from 40 half-hourly GPP_NT_VUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
290 GPP_NT_VUT_USTAR50 Gross Primary Production, from Nighttime partitioning method, based on NEE_VUT_USTAR50; µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
291 GPP_NT_VUT_USTAR50 Gross Primary Production, from Nighttime partitioning method, based on NEE_VUT_USTAR50 µmolCO2 m-2 s-1 ICOS / FLUXNET
292 GPP_NT_VUT_XX Gross Primary Production, from Nighttime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95); µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
293 GS Stomatal Conductance mmol H2O m-2 s-1
294 GWL Ground Water Level m
295 H Corrected sensible heat flux (no storage correction) W m-2
296 H Corrected sensible heat flux W m-2 EddyPro (_full_output_ file)
297 H Sensible heat turbulent flux (no storage correction, cleaned) W m-2 ICOS
298 H_CORR Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor; W m-2 ENERGY PROCESSING FLUXNET HH (half-hourly)
299 H_CORR Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor W m-2 ICOS / FLUXNET
300 H_CORR_25 Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor, 25th percentile; W m-2 ENERGY PROCESSING FLUXNET HH (half-hourly)
301 H_CORR_25 Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor, 25th percentile W m-2 ICOS / FLUXNET
302 H_CORR_75 Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor, 75th percentile; W m-2 ENERGY PROCESSING FLUXNET HH (half-hourly)
303 H_CORR_75 Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor, 75th percentile W m-2 ICOS / FLUXNET
304 H_CORR_JOINTUNC Joint uncertainty estimation for H; [SQRT(H_RANDUNC^2 + ((H_CORR75 - H_CORR25) / 1.349)^2)] W m-2 ENERGY PROCESSING FLUXNET HH (half-hourly)
305 H_CORR_JOINTUNC Joint uncertainty estimation for H. [SQRT(H_RANDUNC^2 + ((H_CORR75 - H_CORR25) / 1.349)^2)] W m-2 ICOS / FLUXNET
306 H_DATA_FLAG Flag for H (0: observed flux for which any quality control (QC) tests provided negligible evidences of error; 1: outlying flux rejected because at least one of the QC tests provided a moderate evidence of error; 2: flux removed because at least one of the nondimensional ICOS
307 H_F_MDS Sensible heat flux, gapfilled using MDS method; W m-2 ENERGY PROCESSING FLUXNET HH (half-hourly)
308 H_F_MDS Sensible heat flux, gapfilled using MDS method W m-2 ICOS / FLUXNET
309 H_F_MDS_QC Quality flag for H_F_MDS, H_CORR, H_CORR25, and H_CORR75.; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ENERGY PROCESSING FLUXNET HH (half-hourly)
310 H_F_MDS_QC Quality flag for H_F_MDS, H_CORR, H_CORR25, and H_CORR75. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
311 H_FMR_FLAG Flag for the FMR test for H (0: negligible evidences of error, IF FMR15) nondimensional ICOS
312 H_FMR_STAT Fraction of Missing Records in raw, high-frequency, data used for H flux estimation % ICOS
313 H_LGD_FLAG Flag for the LGD test for H (0: negligible evidences of error, IF LGD180) nondimensional ICOS
314 H_LGD_STAT Longest Gap Duration in raw, high-frequency, data used for H flux estimation seconds ICOS
315 H_LSR_FLAG Flag for the LSR test for H (0: negligible evidences of error, IF LSR_STAT>0.995; 1: moderate evidences of error, IF 0.99?LSR_STAT?0.995; 2: severe evidences of error, IF LSR_STAT nondimensional ICOS
316 H_LSR_STAT Statistic of the Low Signal Resolution test for H nondimensional ICOS
317 H_M98_FLAG Flag of the H_M98_STAT (0: negligible evidences of error, IF M_98_STAT3) nondimensional ICOS
318 H_M98_STAT Statistic of the nonstationarity ratio test by Mahrt (1998) for H nondimensional ICOS
319 H_OOR_FLAG Flag for H denoting values out of the physically plausible range (0: within range; 2: out of range). nondimensional ICOS
320 H_OUTLYING_FLAG Flag for H denoting outliers (0: no outlying flux; 1: outlying flux) nondimensional ICOS
321 H_RANDUNC Random uncertainty of H, from measured only data; uses only data point where H_F_MDS_QC is 0 and two hierarchical methods (see header and H_RANDUNC_METHOD) W m-2 ENERGY PROCESSING FLUXNET HH (half-hourly)
322 H_RANDUNC Random uncertainty of H, from measured only data. uses only data point where H_F_MDS_QC is 0 and two hierarchical methods (see header and H_RANDUNC_METHOD) W m-2 ICOS / FLUXNET
323 H_RANDUNC_METHOD Method used to estimate the random uncertainty of H; 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional ENERGY PROCESSING FLUXNET HH (half-hourly)
324 H_RANDUNC_METHOD Method used to estimate the random uncertainty of H. 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional ICOS / FLUXNET
325 H_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of H; nondimensional ENERGY PROCESSING FLUXNET HH (half-hourly)
326 H_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of H nondimensional ICOS / FLUXNET
327 H_scf Spectral correction factor for sensible heat flux # EddyPro (_full_output_ file)
328 H_SCF_STAT Spectral correction factor for H nondimensional ICOS
329 H_SSITC_TEST Results of the quality flagging for H according to Foken et al 2004, based on a combination of Steady State and Integral Turbulence Characteristics tests by Foken and Wichura (1996) (i.e., 0, 1, 2) nondimensional Ameriflux
330 H_SSITC_TEST Quality flagging for H according to classification scheme by Foken et al (2004) and based on the combination of the results of Steady State and Integral Turbulence Characteristics tests by Foken and Wichura (1996) (0: high quality; 1:intermediate quality; nondimensional ICOS
331 H_strg Estimate of storage sensible heat flux W m-2 EddyPro (_full_output_ file)
332 H_UNCLEANED Sensible heat turbulent flux (no storage correction, uncleaned) W m-2 ICOS
333 H2O H2O molar fraction (in humid air), wet mole fraction umol mol-1 EC raw data files (ASCII)
334 H2O Water (H2O) vapor in mole fraction of wet air mmolH2O mol-1 ICOS
335 H2O_CONC H2O concentration density, molar density mmol m-3 EC raw data files (ASCII)
336 H2O_DRY H2O dry mole fraction (in dry air), mixing ratio, ppt (parts per THOUSAND) mmol mol-1 EC raw data files (ASCII)
337 H2O_DRY Water (H2O) vapor in mole fraction of dry air mmolH2O mol-1 ICOS
338 H2O_HD10_FLAG Flag for the homogeneity test applied on differenced water vapor 0: negligible evidences of error, IF HD10_STAT1) nondimensional ICOS
339 H2O_HD10_STAT Statistic of the homogeneity test applied on differenced water vapor (percentage of data exceeding ñ10?) % ICOS
340 H2O_HD5_FLAG Flag for the homogeneity test applied on differenced water vapor (0: negligible evidences of error, IF HD5_STAT4) nondimensional ICOS
341 H2O_HD5_STAT Statistic of the homogeneity test applied on differenced water vapor (percentage of data exceeding ñ5?) % ICOS
342 H2O_HF10_FLAG Flag for the homogeneity test applied on water vapor fluctuations (0: negligible evidences of error, IF HF10_STAT1) nondimensional ICOS
343 H2O_HF10_STAT Statistic of the homogeneity test applied on water vapor fluctuations (percentage of data exceeding ñ10?) % ICOS
344 H2O_HF5_FLAG Flag for the homogeneity test applied on water vapor fluctuations (0: negligible evidences of error, IF HF5_STAT4) nondimensional ICOS
345 H2O_HF5_STAT Statistic of the homogeneity test applied on water vapor fluctuations (percentage of data exceeding ñ5?) % ICOS
346 H2O_IRGA70_CMB H2O concentration from IRGA Li-7000 for chamber system mmol H2O mol-1
347 H2O_IRGA70_EC H2O concentration from IRGA Li-7000 for eddy system mmol H2O mol-1
348 H2O_IRGA70_PRF H2O concentration from IRGA Li-7000 for profile mmol H2O mol-1
349 H2O_IRGA72_CMB H2O concentration from IRGA Li-7200 for chamber system mmol H2O mol-1
350 H2O_IRGA72_EC H2O concentration from IRGA Li-7200 for eddy system mmol H2O mol-1
351 H2O_IRGA72_PRF H2O concentration from IRGA Li-7200 for profile mmol H2O mol-1
352 H2O_IRGA75_CMB H2O concentration from IRGA Li-7500 for chamber system mmol H2O mol-1
353 H2O_IRGA75_EC H2O concentration from IRGA Li-7500 for eddy system mmol H2O mol-1
354 H2O_IRGA75_PRF H2O concentration from IRGA Li-7500 for profile mmol H2O mol-1
355 H2O_KID_FLAG Flag for the H2O_KID_STAT (0: negligible evidences of error, IF KID_STAT50) nondimensional ICOS
356 H2O_KID_STAT Kurtosis Index of Differenced water vapor nondimensional ICOS
357 H2O_MIXING_RATIO Water (H2O) vapor in mole fraction of dry air mmolH2O mol-1 Ameriflux
358 H2O_n Profile level-specific water vapor in mole fraction of wet air averaged over the whole respective half-hour (_n is an integer number going from 1 to n top-down) mmolH2O mol-1 ICOS
359 H2O_n_SE Standard error of profile level-specific water vapor in mole fraction of wet air as estimenated over the whole respective half-hour (_n is an integer number going from 1 to n top-down) mmolH2O mol-1 ICOS
360 H2O_QCL_EC H2O concentration from QCL for eddy system ppb (nmol H2O mol-1)
361 H2O_SIGMA Standard deviation of water vapor mole fraction mmolH2O mol-1 ICOS / Ameriflux
362 HEAT_ Heating (prefix, e.g. for sonic heating)
363 HOUR 2 digit hour of the day HH
364 HS100 Sonic anemometer Gill HS-100 -
365 HS50 Sonic anemometer Gill HS-50 -
366 IN Incoming -
367 INC_X Inclinometer x ° EC raw data files (ASCII)
368 INC_XY Inclinometer, alternatively x (odd record numbers) and y (even record numbers) ° EC raw data files (ASCII)
369 INC_Y Inclinometer y ° EC raw data files (ASCII)
370 INT Internal -
371 IRGA Infrared Gas Analyzer -
372 IRGA62 Closed-path IRGA LI-6262 -
373 IRGA70 IRGA LI-7000 -
374 IRGA72 Enclosed-path IRGA Li-7200
375 IRGA75 Open-path IRGA Li-7500 -
376 ITC_FLAG Flag for the ITC test (0: negligible evidences of error, IF ITC_STAT50) nondimensional ICOS
377 ITC_STAT Statistic of the Integral Turbulence Characteristics test (Foken and Wichura, 1996) % ICOS
378 IU instrument units, typically raw units e.g. mV ICOS
379 L Monin-Obukhov length M EddyPro (_full_output_ file)
380 LE Corrected latent heat flux W m-2 EddyPro (_full_output_ file)
381 LE Latent heat turbulent flux (no storage correction, cleaned) W m-2 ICOS
382 LE_CORR Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor; W m-2 ENERGY PROCESSING FLUXNET HH (half-hourly)
383 LE_CORR Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor W m-2 ICOS / FLUXNET
384 LE_CORR_25 Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor, 25th percentile; W m-2 ENERGY PROCESSING FLUXNET HH (half-hourly)
385 LE_CORR_25 Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor, 25th percentile W m-2 ICOS / FLUXNET
386 LE_CORR_75 Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor, 75th percentile; W m-2 ENERGY PROCESSING FLUXNET HH (half-hourly)
387 LE_CORR_75 Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor, 75th percentile W m-2 ICOS / FLUXNET
388 LE_CORR_JOINTUNC Joint uncertainty estimation for LE; [SQRT(LE_RANDUNC^2 + ((LE_CORR75 - LE_CORR25) / 1.349)^2)] W m-2 ENERGY PROCESSING FLUXNET HH (half-hourly)
389 LE_CORR_JOINTUNC Joint uncertainty estimation for LE. [SQRT(LE_RANDUNC^2 + ((LE_CORR75 - LE_CORR25) / 1.349)^2)] W m-2 ICOS / FLUXNET
390 LE_DATA_FLAG Flag for LE (0: observed flux for which any quality control (QC) tests provided negligible evidences of error; 1: outlying flux rejected because at least one of the QC tests provided a moderate evidence of error; 2: flux removed because at least one of th nondimensional ICOS
391 LE_f gapfilled latent heat flux, filtered by all quality checks W m-2
392 LE_F_MDS Latent heat flux, gapfilled using MDS method; W m-2 ENERGY PROCESSING FLUXNET HH (half-hourly)
393 LE_F_MDS Latent heat flux, gapfilled using MDS method W m-2 ICOS / FLUXNET
394 LE_F_MDS_QC Quality flag for LE_F_MDS, LE_CORR, LE_CORR25, and LE_CORR75.; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ENERGY PROCESSING FLUXNET HH (half-hourly)
395 LE_F_MDS_QC Quality flag for LE_F_MDS, LE_CORR, LE_CORR25, and LE_CORR75. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
396 LE_FMR_FLAG Flag for the FMR test for LE (0: negligible evidences of error, IF FMR15) nondimensional ICOS
397 LE_FMR_STAT Fraction of Missing Records in raw, high-frequency, data used for LE flux estimation % ICOS
398 LE_LGD_FLAG Flag for the LGD test for LE (0: negligible evidences of error, IF LGD180) nondimensional ICOS
399 LE_LGD_STAT Longest Gap Duration in raw, high-frequency, data used for LE flux estimation seconds ICOS
400 LE_LSR_FLAG Flag for the LSR test for LE (0: negligible evidences of error, IF LSR_STAT>0.995; 1: moderate evidences of error, IF 0.99?LSR_STAT?0.995; 2: severe evidences of error, IF LSR_STAT nondimensional ICOS
401 LE_LSR_STAT Statistic of the Low Signal Resolution test for LE nondimensional ICOS
402 LE_M98_FLAG Flag of the LE_M98_STAT (0: negligible evidences of error, IF M_98_STAT3) nondimensional ICOS
403 LE_M98_STAT Statistic of the nonstationarity ratio test by Mahrt (1998) for LE nondimensional ICOS
404 LE_OOR_FLAG Flag for LE denoting values out of the physically plausible range (0: within range; 2: out of range). nondimensional ICOS
405 LE_orig_QCF0 measured (not gap-filled) latent heat flux of highest quality W m-2
406 LE_OUTLYING_FLAG Flag for LE denoting outliers (0: no outlying flux; 1: outlying flux) nondimensional ICOS
407 LE_RANDUNC Random uncertainty of LE, from measured only data; uses only data point where LE_F_MDS_QC is 0 and two hierarchical methods (see header and LE_RANDUNC_METHOD) W m-2 ENERGY PROCESSING FLUXNET HH (half-hourly)
408 LE_RANDUNC Random uncertainty of LE, from measured only data. uses only data point where LE_F_MDS_QC is 0 and two hierarchical methods (see header and LE_RANDUNC_METHOD) W m-2 ICOS / FLUXNET
409 LE_RANDUNC_METHOD Method used to estimate the random uncertainty of LE; 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional ENERGY PROCESSING FLUXNET HH (half-hourly)
410 LE_RANDUNC_METHOD Method used to estimate the random uncertainty of LE. 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional ICOS / FLUXNET
411 LE_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of LE; nondimensional ENERGY PROCESSING FLUXNET HH (half-hourly)
412 LE_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of LE nondimensional ICOS / FLUXNET
413 LE_scf Spectral correction factor for latent heat flux # EddyPro (_full_output_ file)
414 LE_SCF_STAT Spectral correction factor for LE nondimensional ICOS
415 LE_SSITC_TEST Results of the quality flagging for LE according to Foken et al 2004, based on a combination of Steady State and Integral Turbulence Characteristics tests by Foken and Wichura (1996) (i.e., 0, 1, 2) nondimensional Ameriflux
416 LE_SSITC_TEST Quality flagging for LE according to classification scheme by Foken et al (2004) and based on the combination of the results of Steady State and Integral Turbulence Characteristics tests by Foken and Wichura (1996) (0: high quality; 1:intermediate quality nondimensional ICOS
417 LE_strg Estimate of storage latent heat flux W m-2 EddyPro (_full_output_ file)
418 LE_UNCLEANED Latent heat turbulent flux (no storage correction, uncleaned) W m-2 ICOS
419 LEAF_WET Leaf wetness, range 0-100 % Ameriflux
420 LGR Los Gatos laser (instrument) EC raw data files (ASCII)
421 LS Lightning strike unitless
422 LSD Lightning strike distance km
423 LW Longwave radiation W m-2
424 LW _IN_RAW Longwave Incoming Radiation without blackbody correction W m-2
425 LW _OUT_RAW Longwave Incoming Radiation without blackbody correction W m-2
426 LW_BC_IN Longwave radiation, below canopy incoming W m-2 Ameriflux
427 LW_BC_OUT Longwave radiation, below canopy outgoing W m-2 Ameriflux
428 LW_IN Longwave radiation, incoming W m-2 Ameriflux
429 LW_IN_ERA Longwave radiation, incoming, downscaled from ERA, linearly regressed using measured only site data; W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
430 LW_IN_ERA Longwave radiation, incoming, downscaled from ERA, linearly regressed using measured only site data W m-2 ICOS / FLUXNET
431 LW_IN_F Longwave radiation, incoming, consolidated from LW_IN_F_MDS and LW_IN_ERA; LW_IN_F_MDS used if LW_IN_F_MDS_QC is 0 or 1 W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
432 LW_IN_F Longwave radiation, incoming, consolidated from LW_IN_F_MDS and LW_IN_ERA W m-2 ICOS / FLUXNET
433 LW_IN_F_MDS Longwave radiation, incoming, gapfilled using MDS; W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
434 LW_IN_F_MDS Longwave radiation, incoming, gapfilled using MDS W m-2 ICOS / FLUXNET
435 LW_IN_F_MDS_QC Quality flag for LW_IN_F_MDS; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
436 LW_IN_F_MDS_QC Quality flag for LW_IN_F_MDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
437 LW_IN_F_QC Quality flag for LW_IN_F; 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
438 LW_IN_F_QC Quality flag for LW_IN_F. 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA nondimensional ICOS / FLUXNET
439 LW_IN_JSB Longwave radiation, incoming, calculated from TA_F_MDS, SW_IN_F_MDS, VPD_F_MDS and SW_IN_POT using the JSBACH algorithm (Sonke Zaehle); W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
440 LW_IN_JSB Longwave radiation, incoming, calculated from TA_F_MDS, SW_IN_F_MDS, VPD_F_MDS and SW_IN_POT using the JSBACH algorithm (Sonke Zaehle) W m-2 ICOS / FLUXNET
441 LW_IN_JSB_ERA Longwave radiation, incoming, downscaled from ERA, linearly regressed using site level LW_IN_JSB calculated from measured only drivers; W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
442 LW_IN_JSB_ERA Longwave radiation, incoming, downscaled from ERA, linearly regressed using site level LW_IN_JSB calculated from measured only drivers W m-2 ICOS / FLUXNET
443 LW_IN_JSB_F Longwave radiation, incoming, consolidated from LW_IN_JSB and LW_IN_JSB_ERA; LW_IN_JSB used if LW_IN_JSB_QC is 0 or 1 W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
444 LW_IN_JSB_F Longwave radiation, incoming, consolidated from LW_IN_JSB and LW_IN_JSB_ERA. LW_IN_JSB used if LW_IN_JSB_QC is 0 or 1 W m-2 ICOS / FLUXNET
445 LW_IN_JSB_F_QC Quality flag for LW_IN_JSB_F; 0 = calculated from measured drivers; 1 = calculated from good quality gapfilled drivers; 2: downscaled from ERA nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
446 LW_IN_JSB_F_QC Quality flag for LW_IN_JSB_F. 0 = calculated from measured drivers; 1 = calculated from good quality gapfilled drivers; 2: downscaled from ERA nondimensional ICOS / FLUXNET
447 LW_IN_JSB_QC Quality flag for LW_IN_JSB; highest from TA_F_MDS_QC, SW_IN_F_MDS_QC, and VPD_F_MDS_QC, poorest quality prevails nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
448 LW_IN_JSB_QC Quality flag for LW_IN_JSB. highest from TA_F_MDS_QC, SW_IN_F_MDS_QC, and VPD_F_MDS_QC, poorest quality prevails nondimensional ICOS / FLUXNET
449 LW_OUT Longwave radiation, outgoing W m-2 Ameriflux
450 LW_OUT Longwave radiation, outgoing; W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
451 LW_OUT Longwave radiation, outgoing W m-2 ICOS / FLUXNET
452 MAX Maximum -
453 max_wind_speed Maximum instantaneous wind speed m s-1 EddyPro (_full_output_ file)
454 MDS Marginal distribution sampling (Reichstein et al., 2005) - Fluxes
455 MEAS (suffix) measured
456 MET Meteorological Data -
457 MGMT_FERT_MIN_PARCEL Management event: application of mineral fertilizer 1=yes, 0=no MANAGEMENT PI datasets
458 MGMT_FERT_ORG Management event: application of organic fertilizer 1=yes, 0=no MANAGEMENT PI datasets
459 MGMT_GRAZING Management event: grazing 1=yes, 0=no MANAGEMENT PI datasets
460 MGMT_MOWING Management event: mowing 1=yes, 0=no MANAGEMENT PI datasets
461 MGMT_PESTICIDE_HERBICIDE Management event: application of pesticides and/or herbicides 1=yes, 0=no MANAGEMENT PI datasets
462 MGMT_SOILCULTIVATION Management event: soil cultivation (e.g., rolling, ploughing, harrowing, tillage) 1=yes, 0=no MANAGEMENT PI datasets
463 MGMT_SOWING Management event: sowing 1=yes, 0=no MANAGEMENT PI datasets
464 MIN Minimum -
465 MIN Minute -
466 MINUTE 2 digit minute of the day MM
467 MIRROR_RINGDOWNTIME Mirror ring-down time µs EC raw data files (ASCII)
468 MO_LENGTH Monin-Obukhov length m ICOS / Ameriflux
469 MONTH 2 digit month of year mm
470 MSW MeteoSwiss
471 N2O N2O molar fraction (in humid air), wet mole fraction nmol mol-1, µmol mol-1 EC raw data files (ASCII)
472 N2O_DRY N2O dry mole fraction (in dry air), mixing ratio, ppb nmol mol-1 EC raw data files (ASCII)
473 N2O_MIXING_RATIO Nitrous Oxide (N2O) in mole fraction of dry air nmolN2O mol-1 Ameriflux
474 N2O_QCL_CMB N2O concentration from QCL for chamber system ppb (nmol N2O mol-1)
475 N2O_QCL_EC N2O concentration from QCL for eddy system ppb (nmol N2O mol-1)
476 N2O_QCL_PRF N2O concentration from QCL for profile ppb (nmol N2O mol-1)
477 NDVI Normalized Difference Vegetation Index nondimensional Ameriflux
478 NEE Net ecosystem exchange of CO2 µmol CO2 m-2 s-1 Fluxes
479 NEE Net Ecosystem Exchange (cleaned) umolCO2 m-2 s-1 ICOS
480 NEE_CUT_05 NEE CUT 05 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
481 NEE_CUT_05_QC Quality flag for NEE_CUT_05. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
482 NEE_CUT_16 NEE CUT 16 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
483 NEE_CUT_16_f gap-filled NEE, filtered by all quality checks and the 16th percentile USTAR threshold (constant for all years) from FLUXNET analyses µmol CO2 m-2 s-1
484 NEE_CUT_16_orig measured (not gap-filled NEE), filtered by all quality checks and the 16th percentile USTAR threshold (constant for all years) from FLUXNET analyses µmol CO2 m-2 s-1
485 NEE_CUT_16_QC Quality flag for NEE_CUT_16. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
486 NEE_CUT_25 NEE CUT 25 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
487 NEE_CUT_25_QC Quality flag for NEE_CUT_25. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
488 NEE_CUT_50 NEE CUT 50 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
489 NEE_CUT_50_QC Quality flag for NEE_CUT_50. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
490 NEE_CUT_75 NEE CUT 75 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
491 NEE_CUT_75_QC Quality flag for NEE_CUT_75. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
492 NEE_CUT_84_f gap-filled NEE, filtered by all quality checks and the 84th percentile USTAR threshold (constant for all years) from FLUXNET analyses µmol CO2 m-2 s-1
493 NEE_CUT_84_orig measured (not gap-filled NEE), filtered by all quality checks and the 84th percentile USTAR threshold (constant for all years) from FLUXNET analyses µmol CO2 m-2 s-1
494 NEE_CUT_86 NEE CUT 86 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
495 NEE_CUT_86_QC Quality flag for NEE_CUT_86. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
496 NEE_CUT_95 NEE CUT 95 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
497 NEE_CUT_95_QC Quality flag for NEE_CUT_95. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
498 NEE_CUT_MEAN Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, average from 40 NEE_CUT_XX versions; average from 40 half-hourly NEE_CUT_XX µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
499 NEE_CUT_MEAN Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, average from 40 NEE_CUT_XX versions µmolCO2 m-2 s-1 ICOS / FLUXNET
500 NEE_CUT_MEAN_QC Quality flag for NEE_CUT_MEAN, fraction between 0-1 indicating percentage of good quality data; average of percentages of good data (NEE_CUT_XX_QC is 0 or 1) from 40 NEE_CUT_XX_QC nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
501 NEE_CUT_MEAN_QC Quality flag for NEE_CUT_MEAN, fraction between 0-1 indicating percentage of good quality data. average of percentages of good data (NEE_CUT_XX_QC is 0 or 1) from 40 NEE_CUT_XX_QC nondimensional ICOS / FLUXNET
502 NEE_CUT_REF Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, reference selected on the basis of the model efficiency (MEF). The MEF analysis is repeated for each time aggregation; µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
503 NEE_CUT_REF Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, reference selected on the basis of the model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 ICOS / FLUXNET
504 NEE_CUT_REF_f gap-filled NEE, filtered by all quality checks and the CUT_REF USTAR threshold (constant for all years) from FLUXNET analyses µmol CO2 m-2 s-1
505 NEE_CUT_REF_JOINTUNC Joint uncertainty estimation for NEE_CUT_REF, including random uncertainty and USTAR filtering uncertainty; [SQRT(NEE_CUT_REF_RANDUNC^2 + ((NEE_CUT_84 - NEE_CUT_16) / 2)^2)] for each half-hour µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
506 NEE_CUT_REF_JOINTUNC Joint uncertainty estimation for NEE_CUT_REF, including random uncertainty and USTAR filtering uncertainty. [SQRT(NEE_CUT_REF_RANDUNC^2 + ((NEE_CUT_84 - NEE_CUT_16) / 2)^2)] for each half-hour µmolCO2 m-2 s-1 ICOS / FLUXNET
507 NEE_CUT_REF_orig_QCF0 measured (not gap-filled) NEE of highest quality, filtered by all quality checks and the CUT_REF percentile USTAR threshold (constant for all years) from FLUXNET analyses µmol CO2 m-2 s-1
508 NEE_CUT_REF_QC Quality flag for NEE_CUT_REF; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
509 NEE_CUT_REF_QC Quality flag for NEE_CUT_REF. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
510 NEE_CUT_REF_RANDUNC Random uncertainty for NEE_CUT_REF, from measured only data; uses only data points where NEE_CUT_REF_QC is 0 and two hierarchical methods - see header and NEE_CUT_REF_RANDUNC_METHOD µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
511 NEE_CUT_REF_RANDUNC Random uncertainty for NEE_CUT_REF, from measured only data. uses only data points where NEE_CUT_REF_QC is 0 and two hierarchical methods - see header and NEE_CUT_REF_RANDUNC_METHOD µmolCO2 m-2 s-1 ICOS / FLUXNET
512 NEE_CUT_REF_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_CUT_REF; 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
513 NEE_CUT_REF_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_CUT_REF. 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional ICOS / FLUXNET
514 NEE_CUT_REF_RANDUNC_N Number of data points used to estimate the random uncertainty of NEE_CUT_REF; nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
515 NEE_CUT_REF_RANDUNC_N Number of data points used to estimate the random uncertainty of NEE_CUT_REF nondimensional ICOS / FLUXNET
516 NEE_CUT_SE Standard Error for NEE_CUT, calculated as SD(NEE_CUT_XX) / SQRT(40); SE from 40 half-hourly NEE_CUT_XX µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
517 NEE_CUT_SE Standard Error for NEE_CUT, calculated as SD(NEE_CUT_XX) / SQRT(40) from 40 half-hourly NEE_CUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
518 NEE_CUT_USTAR50 Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, from 50 percentile of USTAR threshold; µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
519 NEE_CUT_USTAR50 Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, from 50 percentile of USTAR threshold µmolCO2 m-2 s-1 ICOS / FLUXNET
520 NEE_CUT_USTAR50_JOINTUNC Joint uncertainty estimation for NEE_CUT_USTAR50, including random uncertainty and USTAR filtering uncertainty; [SQRT(NEE_CUT_USTAR50_RANDUNC^2 + ((NEE_CUT_84 - NEE_CUT_16) / 2)^2)] for each half-hour µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
521 NEE_CUT_USTAR50_JOINTUNC Joint uncertainty estimation for NEE_CUT_USTAR50, including random uncertainty and USTAR filtering uncertainty. [SQRT(NEE_CUT_USTAR50_RANDUNC^2 + ((NEE_CUT_84 - NEE_CUT_16) / 2)^2)] for each half-hour µmolCO2 m-2 s-1 ICOS / FLUXNET
522 NEE_CUT_USTAR50_QC Quality flag for NEE_CUT_USTAR50; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
523 NEE_CUT_USTAR50_QC Quality flag for NEE_CUT_USTAR50. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
524 NEE_CUT_USTAR50_RANDUNC Random uncertainty for NEE_CUT_USTAR50, from measured only data; uses only data points where NEE_CUT_USTAR50_QC is 0 and two hierarchical methods - see header and NEE_CUT_USTAR50_RANDUNC_METHOD µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
525 NEE_CUT_USTAR50_RANDUNC Random uncertainty for NEE_CUT_USTAR50, from measured only data. uses only data points where NEE_CUT_USTAR50_QC is 0 and two hierarchical methods - see header and NEE_CUT_USTAR50_RANDUNC_METHOD µmolCO2 m-2 s-1 ICOS / FLUXNET
526 NEE_CUT_USTAR50_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_CUT_USTAR50; 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
527 NEE_CUT_USTAR50_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_CUT_USTAR50 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional ICOS / FLUXNET
528 NEE_CUT_USTAR50_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of NEE_CUT_USTAR50; nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
529 NEE_CUT_USTAR50_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of NEE_CUT_USTAR50 nondimensional ICOS / FLUXNET
530 NEE_CUT_XX NEE CUT percentiles (approx. percentile indicated by XX, see doc.) calculated from the 40 estimates aggregated at the different time resolutions -- XX = 05, 16, 25, 50, 75, 84, 95; XXth percentile from 40 half-hourly NEE_CUT_XX µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
531 NEE_CUT_XX_QC Quality flag for NEE_CUT_XX -- XX = 05, 16, 25, 50, 75, 84, 95; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
532 NEE_DATA_FLAG Flag for NEE (i.e., 0: observed flux for which any quality control (QC) tests provided negligible evidences of error; 1: outlying flux rejected because at least one of the QC tests provided a moderate evidence of error; 2: flux removed because at least on nondimensional ICOS
533 NEE_OOR_FLAG Flag for NEE denoting values out of the physically plausible range (0: within range; 2: out of range). nondimensional ICOS
534 NEE_OUTLYING_FLAG Flag for NEE denoting outliers (0: no outlying flux; 1: outlying flux) nondimensional ICOS
535 NEE_U50_QCF0 measured (not gap-filled) NEE of highest quality, filtered by all quality checks and the median (50th percentile) USTAR threshold umol CO2 m-2 s-1 diive
536 NEE_UNCLEANED Net Ecosystem Exchange (uncleaned) umolCO2 m-2 s-1 ICOS
537 NEE_VUT_05 NEE VUT 05 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
538 NEE_VUT_05_QC Quality flag for NEE_VUT_05. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
539 NEE_VUT_16 NEE VUT 16 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
540 NEE_VUT_16_QC Quality flag for NEE_VUT_16. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
541 NEE_VUT_25 NEE VUT 25 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
542 NEE_VUT_25_QC Quality flag for NEE_VUT_25. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
543 NEE_VUT_50 NEE VUT 50 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
544 NEE_VUT_50_QC Quality flag for NEE_VUT_50. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
545 NEE_VUT_75 NEE VUT 75 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
546 NEE_VUT_75_QC Quality flag for NEE_VUT_75. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
547 NEE_VUT_86 NEE VUT 86 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
548 NEE_VUT_86_QC Quality flag for NEE_VUT_86. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
549 NEE_VUT_95 NEE VUT 95 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 ICOS / FLUXNET
550 NEE_VUT_95_QC Quality flag for NEE_VUT_95. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
551 NEE_VUT_MEAN Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, average from 40 NEE_VUT_XX versions; average from 40 half-hourly NEE_CUT_XX µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
552 NEE_VUT_MEAN Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, average from 40 NEE_VUT_XX versions µmolCO2 m-2 s-1 ICOS / FLUXNET
553 NEE_VUT_MEAN_QC Quality flag for NEE_VUT_MEAN, fraction between 0-1 indicating percentage of good quality data; average of percentages of good data (NEE_VUT_XX_QC is 0 or 1) from 40 NEE_VUT_XX_QC nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
554 NEE_VUT_MEAN_QC Quality flag for NEE_VUT_MEAN, fraction between 0-1 indicating percentage of good quality data. average of percentages of good data (NEE_VUT_XX_QC is 0 or 1) from 40 NEE_VUT_XX_QC nondimensional ICOS / FLUXNET
555 NEE_VUT_REF Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, reference selected on the basis of the model efficiency (MEF). The MEF analysis is repeated for each time aggregation; µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
556 NEE_VUT_REF Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, reference selected on the basis of the model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 ICOS / FLUXNET
557 NEE_VUT_REF_JOINTUNC Joint uncertainty estimation for NEE_VUT_REF, including random uncertainty and USTAR filtering uncertainty; [SQRT(NEE_VUT_REF_RANDUNC^2 + ((NEE_VUT_84 - NEE_VUT_16) / 2)^2)] for each half-hour µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
558 NEE_VUT_REF_JOINTUNC Joint uncertainty estimation for NEE_VUT_REF, including random uncertainty and USTAR filtering uncertainty. [SQRT(NEE_VUT_REF_RANDUNC^2 + ((NEE_VUT_84 - NEE_VUT_16) / 2)^2)] for each half-hour µmolCO2 m-2 s-1 ICOS / FLUXNET
559 NEE_VUT_REF_QC Quality flag for NEE_VUT_REF; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
560 NEE_VUT_REF_QC Quality flag for NEE_VUT_REF. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
561 NEE_VUT_REF_RANDUNC Random uncertainty for NEE_VUT_REF, from measured only data; uses only data points where NEE_VUT_REF_QC is 0 and two hierarchical methods - see header and NEE_VUT_REF_RANDUNC_METHOD µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
562 NEE_VUT_REF_RANDUNC Random uncertainty for NEE_VUT_REF, from measured only data. uses only data points where NEE_VUT_REF_QC is 0 and two hierarchical methods - see header and NEE_VUT_REF_RANDUNC_METHOD µmolCO2 m-2 s-1 ICOS / FLUXNET
563 NEE_VUT_REF_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_VUT_REF; 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
564 NEE_VUT_REF_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_VUT_REF. 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional ICOS / FLUXNET
565 NEE_VUT_REF_RANDUNC_N Number of data points used to estimate the random uncertainty of NEE_VUT_REF; nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
566 NEE_VUT_REF_RANDUNC_N Number of data points used to estimate the random uncertainty of NEE_VUT_REF nondimensional ICOS / FLUXNET
567 NEE_VUT_SE Standard Error for NEE_VUT, calculated as SD(NEE_VUT_XX) / SQRT(40); SE from 40 half-hourly NEE_CUT_XX µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
568 NEE_VUT_SE Standard Error for NEE_VUT, calculated as SD(NEE_VUT_XX) / SQRT(40) from 40 half-hourly NEE_VUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
569 NEE_VUT_USTAR50 Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, from 50 percentile of USTAR threshold; µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
570 NEE_VUT_USTAR50 Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, from 50 percentile of USTAR threshold µmolCO2 m-2 s-1 ICOS / FLUXNET
571 NEE_VUT_USTAR50_JOINTUNC Joint uncertainty estimation for NEE_VUT_USTAR50, including random uncertainty and USTAR filtering uncertainty; [SQRT(NEE_VUT_USTAR50_RANDUNC^2 + ((NEE_VUT_84 - NEE_VUT_16) / 2)^2)] for each half-hour µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
572 NEE_VUT_USTAR50_JOINTUNC Joint uncertainty estimation for NEE_VUT_USTAR50, including random uncertainty and USTAR filtering uncertainty. [SQRT(NEE_VUT_USTAR50_RANDUNC^2 + ((NEE_VUT_84 - NEE_VUT_16) / 2)^2)] for each half-hour µmolCO2 m-2 s-1 ICOS / FLUXNET
573 NEE_VUT_USTAR50_QC Quality flag for NEE_VUT_USTAR50; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
574 NEE_VUT_USTAR50_QC Quality flag for NEE_VUT_USTAR50. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
575 NEE_VUT_USTAR50_RANDUNC Random uncertainty for NEE_VUT_USTAR50, from measured only data; uses only data points where NEE_VUT_USTAR50_QC is 0 and two hierarchical methods see header and NEE_VUT_USTAR50_RANDUNC_METHOD µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
576 NEE_VUT_USTAR50_RANDUNC Random uncertainty for NEE_VUT_USTAR50, from measured only data uses only data points where NEE_VUT_USTAR50_QC is 0 and two hierarchical methods see header and NEE_VUT_USTAR50_RANDUNC_METHOD µmolCO2 m-2 s-1 ICOS / FLUXNET
577 NEE_VUT_USTAR50_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_VUT_USTAR50; 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
578 NEE_VUT_USTAR50_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_VUT_USTAR50 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional ICOS / FLUXNET
579 NEE_VUT_USTAR50_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of NEE_VUT_USTAR50; nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
580 NEE_VUT_USTAR50_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of NEE_VUT_USTAR50 nondimensional ICOS / FLUXNET
581 NEE_VUT_XX NEE VUT percentiles (approx. percentile indicated by XX, see doc.) calculated from the 40 estimates aggregated at the different time resolutions -- XX = 05, 16, 25, 50, 75, 84, 95; XXth percentile from 40 half-hourly NEE_VUT_XX µmolCO2 m-2 s-1 NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
582 NEE_VUT_XX_QC Quality flag for NEE_VUT_XX -- XX = 05, 16, 25, 50, 75, 84, 95; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
583 NETRAD Net Radiation W m-2
584 NETRAD Net radiation; W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
585 NETRAD Net radiation W m-2 ICOS / FLUXNET
586 NH4
587 NIGHT Flag indicating nighttime interval based on SW_IN_POT; 0 = daytime, 1 = nighttime nondimensional NET ECOSYSTEM EXCHANGE FLUXNET HH (half-hourly)
588 NIGHT Flag indicating nighttime interval based on SW_IN_POT. 0 = daytime, 1 = nighttime nondimensional ICOS / FLUXNET
589 NO Nitric oxide (NO) mole fraction in wet air nmolNO mol-1 Ameriflux
590 NO2 Nitrogen dioxide (NO2) mole fraction in wet air nmolNO2 mol-1 Ameriflux
591 NO3 Nitrate concentration µg g-1 soil
592 non_steady_wind Hard flag for non-steady horizontal test HFu/v/w/ts/co2 /h2o/ch4/n2 EddyPro (_full_output_ file)
593 NT Nighttime (used in partitioning) - Fluxes
594 NUMVALS Number of values #
595 O2 Oxygen Has to be seen when we have the sensors
596 O3 Ozone (O3) mole fraction in wet air nmolO3 mol-1 Ameriflux
597 OSO On Site Operation -
598 OUT Outgoing -
599 P Precipitation mm FLUXNET
600 P Precipitation; mm MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
601 P Precipitation mm ICOS / FLUXNET
602 P_ERA Precipitation, downscaled from ERA, linearly regressed using measured only site data; (mm per dataset resolution: either hour or half-hour) mm MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
603 P_ERA Precipitation, downscaled from ERA, linearly regressed using measured only site data. (mm per dataset resolution: either hour or half-hour) mm ICOS / FLUXNET
604 P_F Precipitation consolidated from P and P_ERA; P used if measured (mm per dataset resolution: either hour or half-hour) mm MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
605 P_F Precipitation consolidated from P and P_ERA. P used if measured (mm per dataset resolution: either hour or half-hour) mm ICOS / FLUXNET
606 P_F_QC Quality flag for P_F; 0 = measured; 2 = downscaled from ERA nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
607 P_F_QC Quality flag for P_F. 0 = measured; 2 = downscaled from ERA nondimensional ICOS / FLUXNET
608 PA Atmospheric Pressure Pa, kPa, hPa or other
609 PA Atmospheric pressure; kPa MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
610 PA Atmospheric pressure kPa ICOS / FLUXNET
611 PA_ERA Atmospheric pressure, downscaled from ERA, linearly regressed using measured only site data; kPa MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
612 PA_ERA Atmospheric pressure, downscaled from ERA, linearly regressed using measured only site data kPa ICOS / FLUXNET
613 PA_F Atmospheric pressure consolidated from PA and PA_ERA; PA used if measured kPa MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
614 PA_F Atmospheric pressure consolidated from PA and PA_ERA kPa ICOS / FLUXNET
615 PA_F_QC Quality flag for PA_F; 0 = measured; 2 = downscaled from ERA nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
616 PA_F_QC Quality flag for PA_F. 0 = measured; 2 = downscaled from ERA nondimensional ICOS / FLUXNET
617 PA_PRF Atmospheric Pressure in Profile Pa, kPa, hPa or other
618 PBLH Planetary boundary layer height m ICOS / Ameriflux
619 PCH4
620 pitch Second rotation angle ° (degrees) EddyPro (_full_output_ file)
621 PPFD Photosynthetic photon flux density, without additional suffix it is typically incoming µmolPhoton m-2 s-1
622 PPFD_DIF Photosynthetic photon flux density, diffuse incoming; µmolPhoton m-2 s-1 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
623 PPFD_DIF Photosynthetic photon flux density, diffuse incoming µmolPhoton m-2 s-1 ICOS / FLUXNET
624 PPFD_IN Photosynthetic photon flux density, incoming µmolPhoton m-2 s-1
625 PPFD_IN Photosynthetic photon flux density, incoming; µmolPhoton m-2 s-1 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
626 PPFD_IN Photosynthetic photon flux density, incoming µmolPhoton m-2 s-1 ICOS / FLUXNET
627 PPFD_OUT Photosynthetic photon flux density, outgoing µmolPhoton m-2 s-1
628 PPFD_OUT Photosynthetic photon flux density, outgoing; µmolPhoton m-2 s-1 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
629 PPFD_OUT Photosynthetic photon flux density, outgoing µmolPhoton m-2 s-1 ICOS / FLUXNET
630 PREC Precipitation mm
631 qc_gas_flux Quality flag for gas flux # EddyPro (_full_output_ file)
632 qc_H Quality flag for sensible heat flux # EddyPro (_full_output_ file)
633 qc_LE Quality flag latent heat flux # EddyPro (_full_output_ file)
634 qc_Tau Quality flag for momentum flux # EddyPro (_full_output_ file)
635 QCF_ (prefix) quality control flag for the respective variable #
636 QCF_LE quality control flag for latent heat flux (0=best, 1=OK, 2=bad), typically for LE_f #
637 QCF_NEE quality control flag for NEE (0=best, 1=OK, 2=bad), typically for NEE_CUT_REF_f #
638 R_ref respiration at reference temperature parameter (μmol m-2 s-1 as NEE) in relationship between temperature and nighttime NEE from night-time partitioning μmol CO2 m-2 s-1 ReddyProc
639 rand_err_gas_flux Random error for gas flux, if selected µmol s-1 m-2(†) EddyPro (_full_output_ file)
640 rand_err_H Random error for momentum flux, if selected W m-2 EddyPro (_full_output_ file)
641 rand_err_LE Random error for latent heat flux, if selected W m-2 EddyPro (_full_output_ file)
642 rand_err_Tau Random error for momentum flux, if selected kg m-1 s-2 EddyPro (_full_output_ file)
643 Reco_CUT_16 Ecosystem respiration, i.e. outflux from the land surface (μmol m-2 s-1 as NEE) (nighttime-based), modelled from NEE_CUT_16_f umol CO2 m-2 s-1
644 Reco_CUT_84 Ecosystem respiration, i.e. outflux from the land surface (μmol m-2 s-1 as NEE) (nighttime-based), modelled from NEE_CUT_84_f umol CO2 m-2 s-1
645 Reco_CUT_REF Ecosystem respiration, i.e. outflux from the land surface (μmol m-2 s-1 as NEE) (nighttime-based), modelled from NEE_CUT_REF_f umol CO2 m-2 s-1
646 RECO_DT_CUT_05 Ecosystem Respiration, from Daytime partitioning method, percentile 05 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
647 Reco_DT_CUT_16 Ecosystem respiration, i.e. outflux from the land surface (μmol m-2 s-1 as NEE) estimated by day-time partitioning, modelled from NEE_CUT_16_f umol CO2 m-2 s-1
648 RECO_DT_CUT_16 Ecosystem Respiration, from Daytime partitioning method, percentile 16 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
649 RECO_DT_CUT_25 Ecosystem Respiration, from Daytime partitioning method, percentile 25 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
650 RECO_DT_CUT_50 Ecosystem Respiration, from Daytime partitioning method, percentile 50 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
651 RECO_DT_CUT_75 Ecosystem Respiration, from Daytime partitioning method, percentile 75 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
652 Reco_DT_CUT_84 Ecosystem respiration, i.e. outflux from the land surface (μmol m-2 s-1 as NEE) estimated by day-time partitioning, modelled from NEE_CUT_84_f umol CO2 m-2 s-1
653 RECO_DT_CUT_86 Ecosystem Respiration, from Daytime partitioning method, percentile 86 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
654 RECO_DT_CUT_95 Ecosystem Respiration, from Daytime partitioning method, percentile 95 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
655 RECO_DT_CUT_MEAN Ecosystem Respiration, from Daytime partitioning method, average from RECO versions, each from corresponding NEE_CUT_XX version; average from 40 half-hourly RECO_DT_CUT_XX µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
656 RECO_DT_CUT_MEAN Ecosystem Respiration, from Daytime partitioning method, average from RECO versions, each from corresponding NEE_CUT_XX version. average from 40 half-hourly RECO_DT_CUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
657 Reco_DT_CUT_REF Ecosystem respiration, i.e. outflux from the land surface (μmol m-2 s-1 as NEE) estimated by day-time partitioning, modelled from NEE_CUT_REF_f umol CO2 m-2 s-1
658 RECO_DT_CUT_REF Ecosystem Respiration, from Daytime partitioning method, reference selected from RECO versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation; µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
659 RECO_DT_CUT_REF Ecosystem Respiration, from Daytime partitioning method, reference selected from RECO versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 ICOS / FLUXNET
660 RECO_DT_CUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_DT_CUT_XX) / SQRT(40)); SE from 40 half-hourly RECO_DT_CUT_XX µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
661 RECO_DT_CUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_DT_CUT_XX) / SQRT(40)). SE from 40 half-hourly RECO_DT_CUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
662 RECO_DT_CUT_USTAR50 Ecosystem Respiration, from Daytime partitioning method, based on NEE_CUT_USTAR50; µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
663 RECO_DT_CUT_USTAR50 Ecosystem Respiration, from Daytime partitioning method, based on NEE_CUT_USTAR50 µmolCO2 m-2 s-1 ICOS / FLUXNET
664 RECO_DT_CUT_XX Ecosystem Respiration, from Daytime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95); µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
665 RECO_DT_VUT_05 Ecosystem Respiration, from Daytime partitioning method, percentile 05 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
666 RECO_DT_VUT_16 Ecosystem Respiration, from Daytime partitioning method, percentile 16 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
667 RECO_DT_VUT_25 Ecosystem Respiration, from Daytime partitioning method, percentile 25 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
668 RECO_DT_VUT_50 Ecosystem Respiration, from Daytime partitioning method, percentile 50 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
669 RECO_DT_VUT_75 Ecosystem Respiration, from Daytime partitioning method, percentile 75 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
670 RECO_DT_VUT_86 Ecosystem Respiration, from Daytime partitioning method, percentile 86 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
671 RECO_DT_VUT_95 Ecosystem Respiration, from Daytime partitioning method, percentile 95 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
672 RECO_DT_VUT_MEAN Ecosystem Respiration, from Daytime partitioning method, average from RECO versions, each from corresponding NEE_VUT_XX version; average from 40 half-hourly RECO_DT_VUT_XX µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
673 RECO_DT_VUT_MEAN Ecosystem Respiration, from Daytime partitioning method, average from RECO versions, each from corresponding NEE_VUT_XX version. average from 40 half-hourly RECO_DT_VUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
674 RECO_DT_VUT_REF Ecosystem Respiration, from Daytime partitioning method, reference selected from RECO versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation; µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
675 RECO_DT_VUT_REF Ecosystem Respiration, from Daytime partitioning method, reference selected from RECO versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 ICOS / FLUXNET
676 RECO_DT_VUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_DT_VUT_XX) / SQRT(40)); SE from 40 half-hourly RECO_DT_VUT_XX µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
677 RECO_DT_VUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_DT_VUT_XX) / SQRT(40)). SE from 40 half-hourly RECO_DT_CUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
678 RECO_DT_VUT_USTAR50 Ecosystem Respiration, from Daytime partitioning method, based on NEE_VUT_USTAR50; µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
679 RECO_DT_VUT_USTAR50 Ecosystem Respiration, from Daytime partitioning method, based on NEE_VUT_USTAR50 µmolCO2 m-2 s-1 ICOS / FLUXNET
680 RECO_DT_VUT_XX Ecosystem Respiration, from Daytime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95); µmolCO2 m-2 s-1 DAYTIME PARTITIONING FLUXNET HH (half-hourly)
681 Reco_f Ecosystem respiration, i.e. outflux from the land surface (μmol m-2 s-1 as NEE) (nighttime-based) μmol CO2 m-2 s-1 ReddyProc
682 RECO_NT_CUT_05 Ecosystem Respiration, from Nighttime partitioning method, percentile 05 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
683 RECO_NT_CUT_16 Ecosystem Respiration, from Nighttime partitioning method, percentile 16 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
684 RECO_NT_CUT_25 Ecosystem Respiration, from Nighttime partitioning method, percentile 25 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
685 RECO_NT_CUT_50 Ecosystem Respiration, from Nighttime partitioning method, percentile 50 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
686 RECO_NT_CUT_75 Ecosystem Respiration, from Nighttime partitioning method, percentile 75 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
687 RECO_NT_CUT_86 Ecosystem Respiration, from Nighttime partitioning method, percentile 86 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
688 RECO_NT_CUT_95 Ecosystem Respiration, from Nighttime partitioning method, percentile 95 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
689 RECO_NT_CUT_MEAN Ecosystem Respiration, from Nighttime partitioning method, average from RECO versions, each from corresponding NEE_CUT_XX version; average from 40 half-hourly RECO_NT_CUT_XX µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
690 RECO_NT_CUT_MEAN Ecosystem Respiration, from Nighttime partitioning method, average from RECO versions, each from corresponding NEE_CUT_XX version. average from 40 half-hourly RECO_NT_CUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
691 RECO_NT_CUT_REF Ecosystem Respiration, from Nighttime partitioning method, reference selected from RECO versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation; µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
692 RECO_NT_CUT_REF Ecosystem Respiration, from Nighttime partitioning method, reference selected from RECO versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 ICOS / FLUXNET
693 RECO_NT_CUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_NT_CUT_XX) / SQRT(40)); SE from 40 half-hourly RECO_NT_CUT_XX µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
694 RECO_NT_CUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_NT_CUT_XX) / SQRT(40)). SE from 40 half-hourly RECO_NT_CUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
695 RECO_NT_CUT_USTAR50 Ecosystem Respiration, from Nighttime partitioning method, based on NEE_CUT_USTAR50; µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
696 RECO_NT_CUT_USTAR50 Ecosystem Respiration, from Nighttime partitioning method, based on NEE_CUT_USTAR50 µmolCO2 m-2 s-1 ICOS / FLUXNET
697 RECO_NT_CUT_XX Ecosystem Respiration, from Nighttime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95); µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
698 RECO_NT_VUT_05 Ecosystem Respiration, from Nighttime partitioning method, percentile 05 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
699 RECO_NT_VUT_16 Ecosystem Respiration, from Nighttime partitioning method, percentile 16 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
700 RECO_NT_VUT_25 Ecosystem Respiration, from Nighttime partitioning method, percentile 25 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
701 RECO_NT_VUT_50 Ecosystem Respiration, from Nighttime partitioning method, percentile 50 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
702 RECO_NT_VUT_75 Ecosystem Respiration, from Nighttime partitioning method, percentile 75 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
703 RECO_NT_VUT_86 Ecosystem Respiration, from Nighttime partitioning method, percentile 86 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
704 RECO_NT_VUT_95 Ecosystem Respiration, from Nighttime partitioning method, percentile 95 of the 40 estimations µmolCO2 m-2 s-1 ICOS / FLUXNET
705 RECO_NT_VUT_MEAN Ecosystem Respiration, from Nighttime partitioning method, average from RECO versions, each from corresponding NEE_VUT_XX version; average from 40 half-hourly RECO_NT_VUT_XX µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
706 RECO_NT_VUT_MEAN Ecosystem Respiration, from Nighttime partitioning method, average from RECO versions, each from corresponding NEE_VUT_XX version. average from 40 half-hourly RECO_NT_VUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
707 RECO_NT_VUT_REF Ecosystem Respiration, from Nighttime partitioning method, reference selected from RECO versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation; µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
708 RECO_NT_VUT_REF Ecosystem Respiration, from Nighttime partitioning method, reference selected from RECO versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 ICOS / FLUXNET
709 RECO_NT_VUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_NT_VUT_XX) / SQRT(40)); SE from 40 half-hourly RECO_NT_CUT_XX µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
710 RECO_NT_VUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_NT_VUT_XX) / SQRT(40)). SE from 40 half-hourly RECO_NT_CUT_XX µmolCO2 m-2 s-1 ICOS / FLUXNET
711 RECO_NT_VUT_USTAR50 Ecosystem Respiration, from Nighttime partitioning method, based on NEE_VUT_USTAR50; µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
712 RECO_NT_VUT_USTAR50 Ecosystem Respiration, from Nighttime partitioning method, based on NEE_VUT_USTAR50 µmolCO2 m-2 s-1 ICOS / FLUXNET
713 RECO_NT_VUT_XX Ecosystem Respiration, from Nighttime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95); µmolCO2 m-2 s-1 NIGHTTIME PARTITIONING FLUXNET HH (half-hourly)
714 RECO_SR Ecosystem Respiration, from Sundown Respiration partitioning method; µmolCO2 m-2 s-1 SUNDOWN FLUXNET HH (half-hourly)
715 RF random forest
716 Rg Shortwave radiation, incoming W m-2 ReddyProc
717 Rg_f Gap-filled shortwave radiation, incoming, typically gap-filled using MDS W m-2 ReddyProc
718 Rg_orig Measured (not gap-filled) shortwave radiation, incoming, typically gap-filled using MDS W m-2 ReddyProc
719 RH Relative humidity %
720 RH Ambient relative humidity 0 EddyPro (_full_output_ file)
721 RH Relative humidity, range 0-100; % MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
722 RH Relative humidity, range 0-100 % ICOS / FLUXNET
723 roll Third rotation angle ° (degrees) EddyPro (_full_output_ file)
724 RSSI Mean value of RSSI for LI‑7700, if present # EddyPro (_full_output_ file)
725 SA_DIAG_FLAG Flag for Sonic Anemometer (SA) instrumental diagnostics (0: negligible evidences of error; 2: severe evidences of error) nondimensional ICOS
726 SC Carbon Dioxide (CO2) storage flux umolCO2 m-2 s-1 ICOS
727 SC_OOR_FLAG Flag for SC denoting values out of the physically plausible range (0: within range; 2: out of range) nondimensional ICOS
728 SC_UNC Estimated uncertainty for SC umolCO2 m-2 s-1 ICOS
729 SC_UNC_FLAG SC QC flag for uncertainty (0: none; 1: flux data for which the estimated uncertainty exceeds the respective NEE absolute value by 3 times) nondimensional ICOS
730 SC_UNCLEANED Carbon dioxide storage flux (not QC filtered) umolCO2 m-2 s-1 ICOS
731 SDP Soil Dielectric Permittivity unitless
732 SH Sensible heat (H) storage flux (only if air temperature is measured along a profile) W m-2 ICOS
733 skw_kur Hard flags for individual variables for skewness and kurtosis HFu/v/w/ts/co2 /h2o/ch4/n2 EddyPro (_full_output_ file)
734 skw_kur Soft flags for individual variables for skewness and kurtosis test HFu/v/w/ts/co2 /h2o/ch4/n2 EddyPro (_full_output_ file)
735 SLE Latent heat (LE) storage flux (only if air temperature and H2O are measured along a profile) W m-2 ICOS
736 SLE_OOR_FLAG Flag for SLE denoting values out of the physically plausible range (0: within range; 2: out of range) nondimensional ICOS
737 SLE_UNC Estimated uncertainty for SLE W m-2 ICOS
738 SLE_UNC_FLAG SLE QC flag for uncertainty (0: none; 1: flux data for which the estimated uncertainty exceeds the respective NEE absolute value by 3 times) nondimensional ICOS
739 SLE_UNCLEANED Latent heat storage flux (not QC filtered) W m-2 ICOS
740 SN2O Nitrous oxide (N2O) storage flux nmol m-2 s-1
741 sonic_temperature Mean temperature of ambient air as measured by the anemometer K EddyPro (_full_output_ file)
742 specific_humidity Ambient specific humidity on a mass basis kg kg-1 EddyPro (_full_output_ file)
743 spikes Hard flags for individual variables for spike test HFu/v/w/ts/co2 /h2o/ch4/n2 EddyPro (_full_output_ file)
744 SW Shortwave radiation W m-2
745 SW_DIF Shortwave radiation, diffuse incoming; W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
746 SW_DIF Shortwave radiation, diffuse incoming W m-2 ICOS / FLUXNET
747 SW_IN Shortwave radiation, incoming W m-2
748 SW_IN_ERA Shortwave radiation, incoming, downscaled from ERA, linearly regressed using measured only site data (negative values set to zero); W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
749 SW_IN_ERA Shortwave radiation, incoming, downscaled from ERA, linearly regressed using measured only site data (negative values set to zero) W m-2 ICOS / FLUXNET
750 SW_IN_F Shortwave radiation, incoming consolidated from SW_IN_F_MDS and SW_IN_ERA (negative values set to zero); SW_IN_F_MDS used if SW_IN_F_MDS_QC is 0 or 1 W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
751 SW_IN_F Shortwave radiation, incoming consolidated from SW_IN_F_MDS and SW_IN_ERA (negative values set to zero). SW_IN_F_MDS used if SW_IN_F_MDS_QC is 0 or 1 W m-2 ICOS / FLUXNET
752 SW_IN_F_MDS Shortwave radiation, incoming, gapfilled using MDS (negative values set to zero, e.g., negative values from instrumentation noise); W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
753 SW_IN_F_MDS Shortwave radiation, incoming, gapfilled using MDS (negative values set to zero, e.g., negative values from instrumentation noise) W m-2 ICOS / FLUXNET
754 SW_IN_F_MDS_QC Quality flag for SW_IN_F_MDS; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
755 SW_IN_F_MDS_QC Quality flag for SW_IN_F_MDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
756 SW_IN_F_QC Quality flag for SW_IN_F; 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
757 SW_IN_F_QC Quality flag for SW_IN_F. 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA nondimensional ICOS / FLUXNET
758 SW_IN_POT Shortwave radiation, incoming, potential (top of atmosphere); W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
759 SW_IN_POT Shortwave radiation, incoming, potential (top of atmosphere) W m-2 ICOS / FLUXNET
760 SW_OUT Shortwave radiation, outgoing W m-2
761 SW_OUT Shortwave radiation, outgoing; W m-2 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
762 SW_OUT Shortwave radiation, outgoing W m-2 ICOS / FLUXNET
763 SWC Soil water content %
764 SWC_F_MDS_# Soil water content, gapfilled with MDS (numeric index "#" increases with the depth, 1 is shallowest); % MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
765 SWC_F_MDS_# Soil water content, gapfilled with MDS (numeric index "#" increases with the depth, 1 is shallowest) % ICOS / FLUXNET
766 SWC_F_MDS_#_QC Quality flag for SWC_F_MDS_#; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
767 SWC_F_MDS_#_QC Quality flag for SWC_F_MDS_#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
768 SWP Soil water potential kPa
769 T* Scaling temperature K EddyPro (_full_output_ file)
770 T_SONIC Sonic temperature deg C ICOS
771 T_SONIC_HD10_FLAG Flag for the homogeneity test applied on differenced sonic temperature 0: negligible evidences of error, IF HD10_STAT1) nondimensional ICOS
772 T_SONIC_HD10_STAT Statistic of the homogeneity test applied on differenced sonic temperature (percentage of data exceeding ñ10?) % ICOS
773 T_SONIC_HD5_FLAG Flag for the homogeneity test applied on differenced sonic temperature (0: negligible evidences of error, IF HD5_STAT4) nondimensional ICOS
774 T_SONIC_HD5_STAT Statistic of the homogeneity test applied on differenced sonic temperature (percentage of data exceeding ñ5?) % ICOS
775 T_SONIC_HF10_FLAG Flag for the homogeneity test applied on sonic temperature fluctuations (0: negligible evidences of error, IF HF10_STAT1) nondimensional ICOS
776 T_SONIC_HF10_STAT Statistic of the homogeneity test applied on sonic temperature fluctuations (percentage of data exceeding ñ10?) % ICOS
777 T_SONIC_HF5_FLAG Flag for the homogeneity test applied on sonic temperature fluctuations (0: negligible evidences of error, IF HF5_STAT4) nondimensional ICOS
778 T_SONIC_HF5_STAT Statistic of the homogeneity test applied on sonic temperature fluctuations (percentage of data exceeding ñ5?) % ICOS
779 T_SONIC_KID_FLAG Flag for the T_SONIC_KID_STAT (0: negligible evidences of error, IF KID_STAT50) nondimensional ICOS
780 T_SONIC_KID_STAT Kurtosis Index of Differenced sonic temperature nondimensional ICOS
781 T_SONIC_SIGMA Standard deviation of sonic temperature deg C ICOS
782 TA Air temperature °C
783 TA_ERA Air temperature, downscaled from ERA, linearly regressed using measured only site data deg C MICROMETEOROLOGICAL ICOS / FLUXNET / FLUXNET HH (half-hourly)
784 TA_F Air temperature, consolidated from TA_F_MDS and TA_ERA; TA_F_MDS used if TA_F_MDS_QC is 0 or 1 deg C MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
785 TA_F Air temperature, consolidated from TA_F_MDS and TA_ERA. TA_F_MDS used if TA_F_MDS_QC is 0 or 1 deg C ICOS / FLUXNET
786 TA_F_MDS Air temperature, gapfilled using MDS method deg C MICROMETEOROLOGICAL ICOS / FLUXNET / FLUXNET HH (half-hourly)
787 TA_F_MDS_QC Quality flag for TA_F_MDS; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
788 TA_F_MDS_QC Quality flag for TA_F_MDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
789 TA_F_QC Quality flag for TA_F; 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
790 TA_F_QC Quality flag for TA_F. 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA nondimensional ICOS / FLUXNET
791 Tair Air temperature °C ReddyProc
792 Tair_f Gap-filled air temperature, typically gap-filled using MDS °C ReddyProc
793 Tair_orig Measured (not gap-filled) air temperature °C ReddyProc
794 TAU Momentum flux kg m-1 s-2
795 Tau Corrected momentum flux kg m-1 s-2 EddyPro (_full_output_ file)
796 TAU Momentum flux kg m-1 s-2 ICOS
797 Tau_scf Spectral correction factor for momentum flux # EddyPro (_full_output_ file)
798 TAU_SSITC_TEST Quality flagging for TAU according to classification scheme by Foken et al (2004) and based on the combination of the results of Steady State and Integral Turbulence Characteristics tests by Foken and Wichura (1996) (0: high quality; 1:intermediate qualit nondimensional ICOS
799 TBIN body temperature of sensor measuring incoming signal deg C instrument metrics ETH raw data files
800 TBOUT body temperature of sensor measuring outgoing signal deg C instrument metrics ETH raw data files
801 Tdew Ambient dew point temperature K EddyPro (_full_output_ file)
802 TG Grass temperature deg C
803 time Time of the end of the averaging period HH:MM EddyPro (_full_output_ file)
804 time_lag Hard flags for gas concentration for time lag test HFu/v/w/ts/co2 /h2o/ch4/n2 EddyPro (_full_output_ file)
805 time_lag Soft flags for gas concentration for time lag test HFu/v/w/ts/co2 /h2o/ch4/n2 EddyPro (_full_output_ file)
806 TIMESTAMP ISO timestamp - short format YYYYMMDDHHMM TIMEKEEPING FLUXNET HH (half-hourly)
807 TIMESTAMP_END ISO timestamp end of averaging period - short format YYYYMMDDHHMM TIMEKEEPING FLUXNET HH (half-hourly)
808 TIMESTAMP_END ISO timestamp end of averaging period (up to a 12-digit integer as specified by the data's temporal resolution) yyyymmddHHMM ICOS
809 TIMESTAMP_END ISO timestamp end of averaging period - short format YYYYMMDDHHMM ICOS / FLUXNET
810 TIMESTAMP_START ISO timestamp start of averaging period - short format YYYYMMDDHHMM TIMEKEEPING FLUXNET HH (half-hourly)
811 TIMESTAMP_START ISO timestamp start of averaging period (up to a 12-digit integer as specified by the data's temporal resolution) yyyymmddHHMM ICOS
812 TIMESTAMP_START ISO timestamp start of averaging period - short format YYYYMMDDHHMM ICOS / FLUXNET
813 TIR thermal infrared °C
814 TKE Turbulent kinetic energy m2 s-2 EddyPro (_full_output_ file)
815 TLAG_ACTUAL actual time lag, typically between the turbulent departures of vertical wind and gas s EddyPro
816 TM tensiometer, measures soil moisture tension hPa
817 TPANEL Panel temperature deg C
818 TRH relative humidity temperature
819 TS_F_MDS_# Soil temperature, gapfilled with MDS (numeric index "#" increases with the depth, 1 is shallowest); deg C MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
820 TS_F_MDS_# Soil temperature, gapfilled with MDS (numeric index "#" increases with the depth, 1 is shallowest) deg C ICOS / FLUXNET
821 TS_F_MDS_#_QC Quality flag for TS_F_MDS_#; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
822 TS_F_MDS_#_QC Quality flag for TS_F_MDS_#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
823 u* Friction velocity m s-1 EddyPro (_full_output_ file)
824 u_rot Rotated u wind component (mean wind speed) m s-1 EddyPro (_full_output_ file)
825 U_SIGMA Standard deviation of lateral velocity fluctuations (towards main-wind direction after coordinates rotation) m s-1 ICOS
826 u_unrot Wind component along the u anemometer axis m s-1 EddyPro (_full_output_ file)
827 un_gas_flux Uncorrected gas flux µmol s-1 m-2(†) EddyPro (_full_output_ file)
828 un_H Uncorrected sensible heat flux W m-2 EddyPro (_full_output_ file)
829 un_LE Uncorrected latent heat flux W m-2 EddyPro (_full_output_ file)
830 un_Tau Uncorrected momentum flux kg m-1 s-2 EddyPro (_full_output_ file)
831 used_records Number of valid records used for current the averaging period # EddyPro (_full_output_ file)
832 USTAR Friction velocity m s-1
833 USTAR Friction velocity; m s-1 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
834 USTAR Friction velocity m s-1 ICOS / FLUXNET
835 Ustar_CUT_REF_Thres USTAR threshold used for _CUT_REF_ variables m s-1 ReddyProc
836 V Voltage V
837 v_rot Rotated v wind component (should be zero) m s-1 EddyPro (_full_output_ file)
838 V_SIGMA Standard deviation of lateral velocity fluctuations (cross main-wind direction after coordinates rotation) m s-1 ICOS
839 v_unrot Wind component along the v anemometer axis m s-1 EddyPro (_full_output_ file)
840 var_spikes Number of spikes detected and eliminated for variable var # EddyPro (_full_output_ file)
841 var_var Variance of variable var #NAME? EddyPro (_full_output_ file)
842 VEGH vegetation height m VEGETATION
843 VIN incoming signal in volts or millivolts V, mV ETH raw data files
844 VOUT outgoing signal in volts or millivolts V, mV ETH raw data files
845 VP vapor pressure
846 VPD Vapor pressure deficit kPa or hPa
847 VPD Ambient water vapor pressure deficit Pa EddyPro (_full_output_ file)
848 VPD_ERA Vapor Pressure Deficit, downscaled from ERA, linearly regressed using measured only site data; hPa MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
849 VPD_ERA Vapor Pressure Deficit, downscaled from ERA, linearly regressed using measured only site data hPa ICOS / FLUXNET
850 VPD_f Gap-filled vapor pressure deficit, typically gap-filled using MDS kPa or hPa
851 VPD_F Vapor Pressure Deficit consolidated from VPD_F_MDS and VPD_ERA; VPD_F_MDS used if VPD_F_MDS_QC is 0 or 1 hPa MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
852 VPD_F Vapor Pressure Deficit consolidated from VPD_F_MDS and VPD_ERA. VPD_F_MDS used if VPD_F_MDS_QC is 0 or 1 hPa ICOS / FLUXNET
853 VPD_F_MDS Vapor Pressure Deficit, gapfilled using MDS; hPa MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
854 VPD_F_MDS Vapor Pressure Deficit, gapfilled using MDS hPa ICOS / FLUXNET
855 VPD_F_MDS_QC Quality flag for VPD_F_MDS; 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
856 VPD_F_MDS_QC Quality flag for VPD_F_MDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional ICOS / FLUXNET
857 VPD_F_QC Quality flag for VPD_F; 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
858 VPD_F_QC Quality flag for VPD_F. 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA nondimensional ICOS / FLUXNET
859 VPD_orig Vapor pressure deficit calculated (not gap-filled) from measured TA and measured RH kPa or hPa
860 w/var_cov Covariance between w and variable var #NAME? EddyPro (_full_output_ file)
861 W_HD10_FLAG Flag for the homogeneity test applied on differenced vertical wind velocity (0: negligible evidences of error, IF HD10_STAT1) nondimensional ICOS
862 W_HD10_STAT Statistic of the homogeneity test applied on differenced vertical wind velocity (percentage of data exceeding æñ10?) % ICOS
863 W_HD5_FLAG Flag for the homogeneity test applied on differenced vertical wind velocity (0: negligible evidences of error, IF HD5_STAT4) nondimensional ICOS
864 W_HD5_STAT Statistic of the homogeneity test applied on differenced vertical wind velocity (percentage of data exceeding æñ5?) % ICOS
865 W_HF10_FLAG Flag for the homogeneity test applied on vertical wind velocity fluctuations (0: negligible evidences of error, IF HF10_STAT1) nondimensional ICOS
866 W_HF10_STAT Statistic of the homogeneity test applied on vertical wind velocity fluctuations (percentage of data exceeding æñ10?) % ICOS
867 W_HF5_FLAG Flag for the homogeneity test applied on vertical wind velocity fluctuations (0: negligible evidences of error, IF HF5_STAT4) nondimensional ICOS
868 W_HF5_STAT Statistic of the homogeneity test applied on vertical wind velocity fluctuations (percentage of data exceeding æñ5?) % ICOS
869 W_KID_FLAG Flag for the W_KID_STAT (0: negligible evidences of error, IF KID_STAT50) nondimensional ICOS
870 W_KID_STAT Kurtosis Index of Differenced vertical wind velocity nondimensional ICOS
871 w_rot Rotated w wind component (should be zero) m s-1 EddyPro (_full_output_ file)
872 W_SIGMA Standard deviation of vertical velocity fluctuations m s-1 ICOS
873 w_unrot Wind component along the w anemometer axis m s-1 EddyPro (_full_output_ file)
874 water_vapor_density Ambient mass density of water vapor kg m-3 EddyPro (_full_output_ file)
875 WD Wind direction; Decimal degrees MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
876 WD Wind direction Decimal degrees ICOS / FLUXNET
877 wind_dir Direction from which the wind blows, with respect to Geographic or Magnetic north ° (degrees) EddyPro (_full_output_ file)
878 wind_speed Mean wind speed m s-1 EddyPro (_full_output_ file)
879 WS Wind speed; m s-1 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
880 WS Wind speed m s-1 ICOS / FLUXNET
881 WS_ERA Wind speed, downscaled from ERA, linearly regressed using measured only site data; m s-1 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
882 WS_ERA Wind speed, downscaled from ERA, linearly regressed using measured only site data m s-1 ICOS / FLUXNET
883 WS_F Wind speed, consolidated from WS and WS_ERA; WS used if measured m s-1 MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
884 WS_F Wind speed, consolidated from WS and WS_ERA. WS used if measured m s-1 ICOS / FLUXNET
885 WS_F_QC Quality flag of WS_F; 0 = measured; 2 = downscaled from ERA nondimensional MICROMETEOROLOGICAL FLUXNET HH (half-hourly)
886 WS_F_QC Quality flag of WS_F. 0 = measured; 2 = downscaled from ERA nondimensional ICOS / FLUXNET
887 WSECT_FLAG Footprint quality flag indicating periods when wind was blowing from directions known to significantly affect the turbulent flow (0: negligible evidences of error; 2: severe evidences of error) nondimensional ICOS
888 WSG wind gust
889 x_10% Along-wind distance providing 10% (cumulative) contribution to turbulent fluxes m EddyPro (_full_output_ file)
890 x_30% Along-wind distance providing 30% (cumulative) contribution to turbulent fluxes m EddyPro (_full_output_ file)
891 x_50% Along-wind distance providing 50% (cumulative) contribution to turbulent fluxes m EddyPro (_full_output_ file)
892 x_70% Along-wind distance providing 70% (cumulative) contribution to turbulent fluxes m EddyPro (_full_output_ file)
893 x_90% Along-wind distance providing 90% (cumulative) contribution to turbulent fluxes m EddyPro (_full_output_ file)
894 x_offset Along-wind distance providing m EddyPro (_full_output_ file)
895 x_peak Along-wind distance providing the highest (peak) contribution to turbulent fluxes m EddyPro (_full_output_ file)
896 yaw First rotation angle ° (degrees) EddyPro (_full_output_ file)
897 ZL Monin-Obukhov stability parameter nondimensional ICOS
898 SF calibration/sensitivity factor uV (W m-2)-1
899 SEC soil electrical conductivity ds m-1
900 HFP heat flux plate
901 Lv Latent heat of vaporization J kg-1 diive / bigleaf
902 TIMESTAMP_START ISO timestamp start of averaging period - short format YYYYMMDDHHMM FLUXNET VARIABLES ICOS
903 TIMESTAMP_END ISO timestamp end of averaging period - short format YYYYMMDDHHMM FLUXNET VARIABLES ICOS
904 CO2_F_MDS CO2 mole fraction, gapfilled with MDS µmolCO2 mol-1 FLUXNET VARIABLES ICOS
905 CO2_F_MDS_QC Quality flag for CO2_F_MDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
906 EBC_CF_METHOD Method used to calculate the energy balance closure correction factor. 1 = ECB_CF Method 1, 2 = ECB_CF Method 2, 3 = ECB_CF Method 3. See general description for details nondimensional FLUXNET VARIABLES ICOS
907 EBC_CF_N Number of data points used to calculate energy closure balance correction factor. Driver data points within sliding window (ECB_CF Method 1) or number of ECB_CF data points (for ECB_CF Methods 2 and 3). nondimensional FLUXNET VARIABLES ICOS
908 G_F_MDS Soil heat flux W m-2 FLUXNET VARIABLES ICOS
909 G_F_MDS_QC Quality flag of G_F_MDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
910 GPP_DT_CUT_MEAN Gross Primary Production, from Daytime partitioning method, average from GPP versions, each from corresponding NEE_CUT_XX version. average from 40 half-hourly GPP_DT_CUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
911 GPP_DT_CUT_REF Gross Primary Production, from Daytime partitioning method, reference selected from GPP versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
912 GPP_DT_CUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_DT_CUT_XX) / SQRT(40)). SE from 40 half-hourly GPP_DT_CUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
913 GPP_DT_CUT_USTAR50 Gross Primary Production, from Daytime partitioning method, based on NEE_CUT_USTAR50 µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
914 GPP_DT_CUT_05 Gross Primary Production, from Daytime partitioning method, pecentile 05 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
915 GPP_DT_CUT_16 Gross Primary Production, from Daytime partitioning method, pecentile 16 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
916 GPP_DT_CUT_25 Gross Primary Production, from Daytime partitioning method, pecentile 25 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
917 GPP_DT_CUT_50 Gross Primary Production, from Daytime partitioning method, pecentile 50 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
918 GPP_DT_CUT_75 Gross Primary Production, from Daytime partitioning method, pecentile 75 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
919 GPP_DT_CUT_86 Gross Primary Production, from Daytime partitioning method, pecentile 86 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
920 GPP_DT_CUT_95 Gross Primary Production, from Daytime partitioning method, pecentile 95 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
921 GPP_DT_VUT_MEAN Gross Primary Production, from Daytime partitioning method, average from GPP versions, each from corresponding NEE_VUT_XX version. average from 40 half-hourly GPP_DT_VUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
922 GPP_DT_VUT_REF Gross Primary Production, from Daytime partitioning method, reference selected from GPP versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
923 GPP_DT_VUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_DT_VUT_XX) / SQRT(40)). SE from 40 half-hourly GPP_DT_VUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
924 GPP_DT_VUT_USTAR50 Gross Primary Production, from Daytime partitioning method, based on NEE_VUT_USTAR50 µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
925 GPP_DT_VUT_05 Gross Primary Production, from Daytime partitioning method, pecentile 05 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
926 GPP_DT_VUT_16 Gross Primary Production, from Daytime partitioning method, pecentile 16 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
927 GPP_DT_VUT_25 Gross Primary Production, from Daytime partitioning method, pecentile 25 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
928 GPP_DT_VUT_50 Gross Primary Production, from Daytime partitioning method, pecentile 50 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
929 GPP_DT_VUT_75 Gross Primary Production, from Daytime partitioning method, pecentile 75 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
930 GPP_DT_VUT_86 Gross Primary Production, from Daytime partitioning method, pecentile 86 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
931 GPP_DT_VUT_95 Gross Primary Production, from Daytime partitioning method, pecentile 95 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
932 GPP_NT_CUT_MEAN Gross Primary Production, from Nighttime partitioning method, average from GPP versions, each from corresponding NEE_CUT_XX version. average from 40 half-hourly GPP_NT_CUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
933 GPP_NT_CUT_REF Gross Primary Production, from Nighttime partitioning method, reference selected from GPP versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
934 GPP_NT_CUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_NT_CUT_XX) / SQRT(40)). SE from 40 half-hourly GPP_NT_CUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
935 GPP_NT_CUT_USTAR50 Gross Primary Production, from Nighttime partitioning method, based on NEE_CUT_USTAR50 µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
936 GPP_NT_CUT_05 Gross Primary Production, from Nighttime partitioning method, pecentile 05 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
937 GPP_NT_CUT_16 Gross Primary Production, from Nighttime partitioning method, pecentile 16 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
938 GPP_NT_CUT_25 Gross Primary Production, from Nighttime partitioning method, pecentile 25 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
939 GPP_NT_CUT_50 Gross Primary Production, from Nighttime partitioning method, pecentile 50 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
940 GPP_NT_CUT_75 Gross Primary Production, from Nighttime partitioning method, pecentile 75 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
941 GPP_NT_CUT_86 Gross Primary Production, from Nighttime partitioning method, pecentile 86 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
942 GPP_NT_CUT_95 Gross Primary Production, from Nighttime partitioning method, pecentile 95 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
943 GPP_NT_VUT_MEAN Gross Primary Production, from Nighttime partitioning method, average from GPP versions, each from corresponding NEE_VUT_XX version. average from 40 half-hourly GPP_NT_VUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
944 GPP_NT_VUT_REF Gross Primary Production, from Nighttime partitioning method, reference selected from GPP versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
945 GPP_NT_VUT_SE Standard Error for Gross Primary Production, calculated as (SD(GPP_NT_VUT_XX) / SQRT(40)). SE from 40 half-hourly GPP_NT_VUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
946 GPP_NT_VUT_USTAR50 Gross Primary Production, from Nighttime partitioning method, based on NEE_VUT_USTAR50 µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
947 GPP_NT_VUT_05 Gross Primary Production, from Nighttime partitioning method, pecentile 05 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
948 GPP_NT_VUT_16 Gross Primary Production, from Nighttime partitioning method, pecentile 16 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
949 GPP_NT_VUT_25 Gross Primary Production, from Nighttime partitioning method, pecentile 25 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
950 GPP_NT_VUT_50 Gross Primary Production, from Nighttime partitioning method, pecentile 50 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
951 GPP_NT_VUT_75 Gross Primary Production, from Nighttime partitioning method, pecentile 75 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
952 GPP_NT_VUT_86 Gross Primary Production, from Nighttime partitioning method, pecentile 86 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
953 GPP_NT_VUT_95 Gross Primary Production, from Nighttime partitioning method, pecentile 95 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
954 H_CORR Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor W m-2 FLUXNET VARIABLES ICOS
955 H_CORR_25 Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor, 25th percentile W m-2 FLUXNET VARIABLES ICOS
956 H_CORR_75 Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor, 75th percentile W m-2 FLUXNET VARIABLES ICOS
957 H_CORR_JOINTUNC Joint uncertainty estimation for H. [SQRT(H_RANDUNC^2 + ((H_CORR75 - H_CORR25) / 1.349)^2)] W m-2 FLUXNET VARIABLES ICOS
958 H_F_MDS Sensible heat flux, gapfilled using MDS method W m-2 FLUXNET VARIABLES ICOS
959 H_F_MDS_QC Quality flag for H_F_MDS, H_CORR, H_CORR25, and H_CORR75. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
960 H_RANDUNC Random uncertainty of H, from measured only data. uses only data point where H_F_MDS_QC is 0 and two hierarchical methods (see header and H_RANDUNC_METHOD) W m-2 FLUXNET VARIABLES ICOS
961 H_RANDUNC_METHOD Method used to estimate the random uncertainty of H. 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional FLUXNET VARIABLES ICOS
962 H_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of H nondimensional FLUXNET VARIABLES ICOS
963 LE_CORR Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor W m-2 FLUXNET VARIABLES ICOS
964 LE_CORR_25 Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor, 25th percentile W m-2 FLUXNET VARIABLES ICOS
965 LE_CORR_75 Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor, 75th percentile W m-2 FLUXNET VARIABLES ICOS
966 LE_CORR_JOINTUNC Joint uncertainty estimation for LE. [SQRT(LE_RANDUNC^2 + ((LE_CORR75 - LE_CORR25) / 1.349)^2)] W m-2 FLUXNET VARIABLES ICOS
967 LE_F_MDS Latent heat flux, gapfilled using MDS method W m-2 FLUXNET VARIABLES ICOS
968 LE_F_MDS_QC Quality flag for LE_F_MDS, LE_CORR, LE_CORR25, and LE_CORR75. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
969 LE_RANDUNC Random uncertainty of LE, from measured only data. uses only data point where LE_F_MDS_QC is 0 and two hierarchical methods (see header and LE_RANDUNC_METHOD) W m-2 FLUXNET VARIABLES ICOS
970 LE_RANDUNC_METHOD Method used to estimate the random uncertainty of LE. 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional FLUXNET VARIABLES ICOS
971 LE_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of LE nondimensional FLUXNET VARIABLES ICOS
972 LW_IN_ERA Longwave radiation, incoming, downscaled from ERA, linearly regressed using measured only site data W m-2 FLUXNET VARIABLES ICOS
973 LW_IN_F Longwave radiation, incoming, consolidated from LW_IN_F_MDS and LW_IN_ERA W m-2 FLUXNET VARIABLES ICOS
974 LW_IN_F_MDS Longwave radiation, incoming, gapfilled using MDS W m-2 FLUXNET VARIABLES ICOS
975 LW_IN_F_MDS_QC Quality flag for LW_IN_F_MDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
976 LW_IN_F_QC Quality flag for LW_IN_F. 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA nondimensional FLUXNET VARIABLES ICOS
977 LW_IN_JSB Longwave radiation, incoming, calculated from TA_F_MDS, SW_IN_F_MDS, VPD_F_MDS and SW_IN_POT using the JSBACH algorithm (Sonke Zaehle) W m-2 FLUXNET VARIABLES ICOS
978 LW_IN_JSB_ERA Longwave radiation, incoming, downscaled from ERA, linearly regressed using site level LW_IN_JSB calculated from measured only drivers W m-2 FLUXNET VARIABLES ICOS
979 LW_IN_JSB_F Longwave radiation, incoming, consolidated from LW_IN_JSB and LW_IN_JSB_ERA. LW_IN_JSB used if LW_IN_JSB_QC is 0 or 1 W m-2 FLUXNET VARIABLES ICOS
980 LW_IN_JSB_F_QC Quality flag for LW_IN_JSB_F. 0 = calculated from measured drivers; 1 = calculated from good quality gapfilled drivers; 2: downscaled from ERA nondimensional FLUXNET VARIABLES ICOS
981 LW_IN_JSB_QC Quality flag for LW_IN_JSB. highest from TA_F_MDS_QC, SW_IN_F_MDS_QC, and VPD_F_MDS_QC, poorest quality prevails nondimensional FLUXNET VARIABLES ICOS
982 LW_OUT Longwave radiation, outgoing W m-2 FLUXNET VARIABLES ICOS
983 NEE_CUT_MEAN Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, average from 40 NEE_CUT_XX versions µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
984 NEE_CUT_MEAN_QC Quality flag for NEE_CUT_MEAN, fraction between 0-1 indicating percentage of good quality data. average of percentages of good data (NEE_CUT_XX_QC is 0 or 1) from 40 NEE_CUT_XX_QC nondimensional FLUXNET VARIABLES ICOS
985 NEE_CUT_REF Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, reference selected on the basis of the model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
986 NEE_CUT_REF_JOINTUNC Joint uncertainty estimation for NEE_CUT_REF, including random uncertainty and USTAR filtering uncertainty. [SQRT(NEE_CUT_REF_RANDUNC^2 + ((NEE_CUT_84 - NEE_CUT_16) / 2)^2)] for each half-hour µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
987 NEE_CUT_REF_QC Quality flag for NEE_CUT_REF. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
988 NEE_CUT_REF_RANDUNC Random uncertainty for NEE_CUT_REF, from measured only data. uses only data points where NEE_CUT_REF_QC is 0 and two hierarchical methods - see header and NEE_CUT_REF_RANDUNC_METHOD µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
989 NEE_CUT_REF_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_CUT_REF. 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional FLUXNET VARIABLES ICOS
990 NEE_CUT_REF_RANDUNC_N Number of data points used to estimate the random uncertainty of NEE_CUT_REF nondimensional FLUXNET VARIABLES ICOS
991 NEE_CUT_SE Standard Error for NEE_CUT, calculated as SD(NEE_CUT_XX) / SQRT(40) from 40 half-hourly NEE_CUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
992 NEE_CUT_USTAR50 Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, from 50 percentile of USTAR threshold µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
993 NEE_CUT_USTAR50_JOINTUNC Joint uncertainty estimation for NEE_CUT_USTAR50, including random uncertainty and USTAR filtering uncertainty. [SQRT(NEE_CUT_USTAR50_RANDUNC^2 + ((NEE_CUT_84 - NEE_CUT_16) / 2)^2)] for each half-hour µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
994 NEE_CUT_USTAR50_QC Quality flag for NEE_CUT_USTAR50. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
995 NEE_CUT_USTAR50_RANDUNC Random uncertainty for NEE_CUT_USTAR50, from measured only data. uses only data points where NEE_CUT_USTAR50_QC is 0 and two hierarchical methods - see header and NEE_CUT_USTAR50_RANDUNC_METHOD µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
996 NEE_CUT_USTAR50_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_CUT_USTAR50 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional FLUXNET VARIABLES ICOS
997 NEE_CUT_USTAR50_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of NEE_CUT_USTAR50 nondimensional FLUXNET VARIABLES ICOS
998 NEE_CUT_05 NEE CUT 05 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
999 NEE_CUT_16 NEE CUT 16 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1000 NEE_CUT_25 NEE CUT 25 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1001 NEE_CUT_50 NEE CUT 50 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1002 NEE_CUT_75 NEE CUT 75 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1003 NEE_CUT_86 NEE CUT 86 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1004 NEE_CUT_95 NEE CUT 95 percentile calculated from the 40 NEE_CUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1005 NEE_CUT_05_QC Quality flag for NEE_CUT_05. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1006 NEE_CUT_16_QC Quality flag for NEE_CUT_16. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1007 NEE_CUT_25_QC Quality flag for NEE_CUT_25. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1008 NEE_CUT_50_QC Quality flag for NEE_CUT_50. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1009 NEE_CUT_75_QC Quality flag for NEE_CUT_75. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1010 NEE_CUT_86_QC Quality flag for NEE_CUT_86. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1011 NEE_CUT_95_QC Quality flag for NEE_CUT_95. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1012 NEE_VUT_MEAN Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, average from 40 NEE_VUT_XX versions µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1013 NEE_VUT_MEAN_QC Quality flag for NEE_VUT_MEAN, fraction between 0-1 indicating percentage of good quality data. average of percentages of good data (NEE_VUT_XX_QC is 0 or 1) from 40 NEE_VUT_XX_QC nondimensional FLUXNET VARIABLES ICOS
1014 NEE_VUT_REF Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, reference selected on the basis of the model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1015 NEE_VUT_REF_JOINTUNC Joint uncertainty estimation for NEE_VUT_REF, including random uncertainty and USTAR filtering uncertainty. [SQRT(NEE_VUT_REF_RANDUNC^2 + ((NEE_VUT_84 - NEE_VUT_16) / 2)^2)] for each half-hour µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1016 NEE_VUT_REF_QC Quality flag for NEE_VUT_REF. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1017 NEE_VUT_REF_RANDUNC Random uncertainty for NEE_VUT_REF, from measured only data. uses only data points where NEE_VUT_REF_QC is 0 and two hierarchical methods - see header and NEE_VUT_REF_RANDUNC_METHOD µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1018 NEE_VUT_REF_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_VUT_REF. 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional FLUXNET VARIABLES ICOS
1019 NEE_VUT_REF_RANDUNC_N Number of data points used to estimate the random uncertainty of NEE_VUT_REF nondimensional FLUXNET VARIABLES ICOS
1020 NEE_VUT_SE Standard Error for NEE_VUT, calculated as SD(NEE_VUT_XX) / SQRT(40) from 40 half-hourly NEE_VUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1021 NEE_VUT_USTAR50 Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, from 50 percentile of USTAR threshold µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1022 NEE_VUT_USTAR50_JOINTUNC Joint uncertainty estimation for NEE_VUT_USTAR50, including random uncertainty and USTAR filtering uncertainty. [SQRT(NEE_VUT_USTAR50_RANDUNC^2 + ((NEE_VUT_84 - NEE_VUT_16) / 2)^2)] for each half-hour µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1023 NEE_VUT_USTAR50_QC Quality flag for NEE_VUT_USTAR50. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1024 NEE_VUT_USTAR50_RANDUNC Random uncertainty for NEE_VUT_USTAR50, from measured only data uses only data points where NEE_VUT_USTAR50_QC is 0 and two hierarchical methods see header and NEE_VUT_USTAR50_RANDUNC_METHOD µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1025 NEE_VUT_USTAR50_RANDUNC_METHOD Method used to estimate the random uncertainty of NEE_VUT_USTAR50 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) nondimensional FLUXNET VARIABLES ICOS
1026 NEE_VUT_USTAR50_RANDUNC_N Number of half-hour data points used to estimate the random uncertainty of NEE_VUT_USTAR50 nondimensional FLUXNET VARIABLES ICOS
1027 NEE_VUT_05 NEE VUT 05 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1028 NEE_VUT_16 NEE VUT 16 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1029 NEE_VUT_25 NEE VUT 25 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1030 NEE_VUT_50 NEE VUT 50 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1031 NEE_VUT_75 NEE VUT 75 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1032 NEE_VUT_86 NEE VUT 86 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1033 NEE_VUT_95 NEE VUT 95 percentile calculated from the 40 NEE_VUT estimates µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1034 NEE_VUT_05_QC Quality flag for NEE_VUT_05. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1035 NEE_VUT_16_QC Quality flag for NEE_VUT_16. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1036 NEE_VUT_25_QC Quality flag for NEE_VUT_25. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1037 NEE_VUT_50_QC Quality flag for NEE_VUT_50. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1038 NEE_VUT_75_QC Quality flag for NEE_VUT_75. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1039 NEE_VUT_86_QC Quality flag for NEE_VUT_86. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1040 NEE_VUT_95_QC Quality flag for NEE_VUT_95. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1041 NETRAD Net radiation W m-2 FLUXNET VARIABLES ICOS
1042 NIGHT Flag indicating nighttime interval based on SW_IN_POT. 0 = daytime, 1 = nighttime nondimensional FLUXNET VARIABLES ICOS
1043 P Precipitation mm FLUXNET VARIABLES ICOS
1044 P_ERA Precipitation, downscaled from ERA, linearly regressed using measured only site data. (mm per dataset resolution: either hour or half-hour) mm FLUXNET VARIABLES ICOS
1045 P_F Precipitation consolidated from P and P_ERA. P used if measured (mm per dataset resolution: either hour or half-hour) mm FLUXNET VARIABLES ICOS
1046 P_F_QC Quality flag for P_F. 0 = measured; 2 = downscaled from ERA nondimensional FLUXNET VARIABLES ICOS
1047 PA Atmospheric pressure kPa FLUXNET VARIABLES ICOS
1048 PA_ERA Atmospheric pressure, downscaled from ERA, linearly regressed using measured only site data kPa FLUXNET VARIABLES ICOS
1049 PA_F Atmospheric pressure consolidated from PA and PA_ERA kPa FLUXNET VARIABLES ICOS
1050 PA_F_QC Quality flag for PA_F. 0 = measured; 2 = downscaled from ERA nondimensional FLUXNET VARIABLES ICOS
1051 PPFD_DIF Photosynthetic photon flux density, diffuse incoming µmolPhoton m-2 s-1 FLUXNET VARIABLES ICOS
1052 PPFD_IN Photosynthetic photon flux density, incoming µmolPhoton m-2 s-1 FLUXNET VARIABLES ICOS
1053 PPFD_OUT Photosynthetic photon flux density, outgoing µmolPhoton m-2 s-1 FLUXNET VARIABLES ICOS
1054 RECO_DT_CUT_MEAN Ecosystem Respiration, from Daytime partitioning method, average from RECO versions, each from corresponding NEE_CUT_XX version. average from 40 half-hourly RECO_DT_CUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1055 RECO_DT_CUT_REF Ecosystem Respiration, from Daytime partitioning method, reference selected from RECO versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1056 RECO_DT_CUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_DT_CUT_XX) / SQRT(40)). SE from 40 half-hourly RECO_DT_CUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1057 RECO_DT_CUT_USTAR50 Ecosystem Respiration, from Daytime partitioning method, based on NEE_CUT_USTAR50 µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1058 RECO_DT_CUT_05 Ecosystem Respiration, from Daytime partitioning method, pecentile 05 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1059 RECO_DT_CUT_16 Ecosystem Respiration, from Daytime partitioning method, pecentile 16 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1060 RECO_DT_CUT_25 Ecosystem Respiration, from Daytime partitioning method, pecentile 25 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1061 RECO_DT_CUT_50 Ecosystem Respiration, from Daytime partitioning method, pecentile 50 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1062 RECO_DT_CUT_75 Ecosystem Respiration, from Daytime partitioning method, pecentile 75 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1063 RECO_DT_CUT_86 Ecosystem Respiration, from Daytime partitioning method, pecentile 86 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1064 RECO_DT_CUT_95 Ecosystem Respiration, from Daytime partitioning method, pecentile 95 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1065 RECO_DT_VUT_MEAN Ecosystem Respiration, from Daytime partitioning method, average from RECO versions, each from corresponding NEE_VUT_XX version. average from 40 half-hourly RECO_DT_VUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1066 RECO_DT_VUT_REF Ecosystem Respiration, from Daytime partitioning method, reference selected from RECO versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1067 RECO_DT_VUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_DT_VUT_XX) / SQRT(40)). SE from 40 half-hourly RECO_DT_CUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1068 RECO_DT_VUT_USTAR50 Ecosystem Respiration, from Daytime partitioning method, based on NEE_VUT_USTAR50 µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1069 RECO_DT_VUT_05 Ecosystem Respiration, from Daytime partitioning method, pecentile 05 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1070 RECO_DT_VUT_16 Ecosystem Respiration, from Daytime partitioning method, pecentile 16 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1071 RECO_DT_VUT_25 Ecosystem Respiration, from Daytime partitioning method, pecentile 25 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1072 RECO_DT_VUT_50 Ecosystem Respiration, from Daytime partitioning method, pecentile 50 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1073 RECO_DT_VUT_75 Ecosystem Respiration, from Daytime partitioning method, pecentile 75 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1074 RECO_DT_VUT_86 Ecosystem Respiration, from Daytime partitioning method, pecentile 86 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1075 RECO_DT_VUT_95 Ecosystem Respiration, from Daytime partitioning method, pecentile 95 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1076 RECO_NT_CUT_MEAN Ecosystem Respiration, from Nighttime partitioning method, average from RECO versions, each from corresponding NEE_CUT_XX version. average from 40 half-hourly RECO_NT_CUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1077 RECO_NT_CUT_REF Ecosystem Respiration, from Nighttime partitioning method, reference selected from RECO versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1078 RECO_NT_CUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_NT_CUT_XX) / SQRT(40)). SE from 40 half-hourly RECO_NT_CUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1079 RECO_NT_CUT_USTAR50 Ecosystem Respiration, from Nighttime partitioning method, based on NEE_CUT_USTAR50 µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1080 RECO_NT_CUT_05 Ecosystem Respiration, from Nighttime partitioning method, pecentile 05 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1081 RECO_NT_CUT_16 Ecosystem Respiration, from Nighttime partitioning method, pecentile 16 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1082 RECO_NT_CUT_25 Ecosystem Respiration, from Nighttime partitioning method, pecentile 25 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1083 RECO_NT_CUT_50 Ecosystem Respiration, from Nighttime partitioning method, pecentile 50 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1084 RECO_NT_CUT_75 Ecosystem Respiration, from Nighttime partitioning method, pecentile 75 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1085 RECO_NT_CUT_86 Ecosystem Respiration, from Nighttime partitioning method, pecentile 86 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1086 RECO_NT_CUT_95 Ecosystem Respiration, from Nighttime partitioning method, pecentile 95 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1087 RECO_NT_VUT_MEAN Ecosystem Respiration, from Nighttime partitioning method, average from RECO versions, each from corresponding NEE_VUT_XX version. average from 40 half-hourly RECO_NT_VUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1088 RECO_NT_VUT_REF Ecosystem Respiration, from Nighttime partitioning method, reference selected from RECO versions using model efficiency (MEF). The MEF analysis is repeated for each time aggregation µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1089 RECO_NT_VUT_SE Standard Error for Ecosystem Respiration, calculated as (SD(RECO_NT_VUT_XX) / SQRT(40)). SE from 40 half-hourly RECO_NT_CUT_XX µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1090 RECO_NT_VUT_USTAR50 Ecosystem Respiration, from Nighttime partitioning method, based on NEE_VUT_USTAR50 µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1091 RECO_NT_VUT_05 Ecosystem Respiration, from Nighttime partitioning method, pecentile 05 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1092 RECO_NT_VUT_16 Ecosystem Respiration, from Nighttime partitioning method, pecentile 16 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1093 RECO_NT_VUT_25 Ecosystem Respiration, from Nighttime partitioning method, pecentile 25 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1094 RECO_NT_VUT_50 Ecosystem Respiration, from Nighttime partitioning method, pecentile 50 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1095 RECO_NT_VUT_75 Ecosystem Respiration, from Nighttime partitioning method, pecentile 75 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1096 RECO_NT_VUT_86 Ecosystem Respiration, from Nighttime partitioning method, pecentile 86 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1097 RECO_NT_VUT_95 Ecosystem Respiration, from Nighttime partitioning method, pecentile 95 of the 40 estimations µmolCO2 m-2 s-1 FLUXNET VARIABLES ICOS
1098 RH Relative humidity, range 0-100 % FLUXNET VARIABLES ICOS
1099 SW_DIF Shortwave radiation, diffuse incoming W m-2 FLUXNET VARIABLES ICOS
1100 SW_IN_ERA Shortwave radiation, incoming, downscaled from ERA, linearly regressed using measured only site data (negative values set to zero) W m-2 FLUXNET VARIABLES ICOS
1101 SW_IN_F Shortwave radiation, incoming consolidated from SW_IN_F_MDS and SW_IN_ERA (negative values set to zero). SW_IN_F_MDS used if SW_IN_F_MDS_QC is 0 or 1 W m-2 FLUXNET VARIABLES ICOS
1102 SW_IN_F_MDS Shortwave radiation, incoming, gapfilled using MDS (negative values set to zero, e.g., negative values from instrumentation noise) W m-2 FLUXNET VARIABLES ICOS
1103 SW_IN_F_MDS_QC Quality flag for SW_IN_F_MDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1104 SW_IN_F_QC Quality flag for SW_IN_F. 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA nondimensional FLUXNET VARIABLES ICOS
1105 SW_IN_POT Shortwave radiation, incoming, potential (top of atmosphere) W m-2 FLUXNET VARIABLES ICOS
1106 SW_OUT Shortwave radiation, outgoing W m-2 FLUXNET VARIABLES ICOS
1107 SWC_F_MDS_# Soil water content, gapfilled with MDS (numeric index "#" increases with the depth, 1 is shallowest) % FLUXNET VARIABLES ICOS
1108 SWC_F_MDS_#_QC Quality flag for SWC_F_MDS_#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1109 TA_ERA Air temperature, downscaled from ERA, linearly regressed using measured only site data deg C FLUXNET VARIABLES ICOS
1110 TA_F Air temperature, consolidated from TA_F_MDS and TA_ERA. TA_F_MDS used if TA_F_MDS_QC is 0 or 1 deg C FLUXNET VARIABLES ICOS
1111 TA_F_MDS Air temperature, gapfilled using MDS method deg C FLUXNET VARIABLES ICOS
1112 TA_F_MDS_QC Quality flag for TA_F_MDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1113 TA_F_QC Quality flag for TA_F. 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA nondimensional FLUXNET VARIABLES ICOS
1114 TS_F_MDS_# Soil temperature, gapfilled with MDS (numeric index "#" increases with the depth, 1 is shallowest) deg C FLUXNET VARIABLES ICOS
1115 TS_F_MDS_#_QC Quality flag for TS_F_MDS_#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1116 USTAR Friction velocity m s-1 FLUXNET VARIABLES ICOS
1117 VPD_ERA Vapor Pressure Deficit, downscaled from ERA, linearly regressed using measured only site data hPa FLUXNET VARIABLES ICOS
1118 VPD_F Vapor Pressure Deficit consolidated from VPD_F_MDS and VPD_ERA. VPD_F_MDS used if VPD_F_MDS_QC is 0 or 1 hPa FLUXNET VARIABLES ICOS
1119 VPD_F_MDS Vapor Pressure Deficit, gapfilled using MDS hPa FLUXNET VARIABLES ICOS
1120 VPD_F_MDS_QC Quality flag for VPD_F_MDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor nondimensional FLUXNET VARIABLES ICOS
1121 VPD_F_QC Quality flag for VPD_F. 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA nondimensional FLUXNET VARIABLES ICOS
1122 WD Wind direction Decimal degrees FLUXNET VARIABLES ICOS
1123 WS Wind speed m s-1 FLUXNET VARIABLES ICOS
1124 WS_ERA Wind speed, downscaled from ERA, linearly regressed using measured only site data m s-1 FLUXNET VARIABLES ICOS
1125 WS_F Wind speed, consolidated from WS and WS_ERA. WS used if measured m s-1 FLUXNET VARIABLES ICOS
1126 WS_F_QC Quality flag of WS_F. 0 = measured; 2 = downscaled from ERA nondimensional FLUXNET VARIABLES ICOS
1127 CO2_ABSLIM_NREX Total number of CO2 readings exceeding its absolute limits nondimensional AUXDATA VARIABLES ICOS
1128 CO2_DIAG_NREX Total number of records flagged for a bad gas analyser diagnostic for CO2 nondimensional AUXDATA VARIABLES ICOS
1129 CO2_NUM_SPIKES Total number of spikes detected and eliminated for CO2 (1 spike is intended here as less than 3 consecutive bad readings) nondimensional AUXDATA VARIABLES ICOS
1130 CO2_SPIKE_NREX Total number of single spikes for CO2 (1 spike is intended here as 1 record) nondimensional AUXDATA VARIABLES ICOS
1131 H2O_ABSLIM_NREX Total number of H2O readings exceeding its absolute limits nondimensional AUXDATA VARIABLES ICOS
1132 H2O_DIAG_NREX Total number of records flagged for a bad gas analyser diagnostic for H2O nondimensional AUXDATA VARIABLES ICOS
1133 H2O_NUM_SPIKES Total number of spikes detected and eliminated for H2O (1 spike is intended here as less than 3 consecutive bad readings) nondimensional AUXDATA VARIABLES ICOS
1134 H2O_SPIKE_NREX Total number of single spikes for H2O (1 spike is intended here as 1 record) nondimensional AUXDATA VARIABLES ICOS
1135 INST_LI7200_AGC_OR_RSSI Average signal strength for the LI-7200 gas analyzer % AUXDATA VARIABLES ICOS
1136 INST_LI7200_AUX_IN Total number of records flagged for a bad internal reference voltages gas analyzer diagnostic nondimensional AUXDATA VARIABLES ICOS
1137 INST_LI7200_CHOPPER Total number of records flagged for a bad chopper wheel temperature gas analyzer diagnostic nondimensional AUXDATA VARIABLES ICOS
1138 INST_LI7200_CO2_STR_LW Lower whisker of the CO2 signal strength for the gas analyzer (calculated as max(min(x), Q1 - 1.5 * IQR), where Q1 is the first quartile and IQR) % AUXDATA VARIABLES ICOS
1139 INST_LI7200_CO2_STR_NUM_20 Total number of CO2 signal strength data points which lie below 20% nondimensional AUXDATA VARIABLES ICOS
1140 INST_LI7200_CO2_STR_NUM_OUT Total number of any data points which lie beyond the extremes of the CO2 signal strength whiskers nondimensional AUXDATA VARIABLES ICOS
1141 INST_LI7200_CO2_STR_P50 Second quartile (median) of the volume CO2 signal strength for the gas analyzer % AUXDATA VARIABLES ICOS
1142 INST_LI7200_CO2_STR_UW Upper whisker of the CO2 signal strength for the gas analyzer (calculated as min(max(x), Q3 - 1.5 * IQR), where Q3 is the third quartile and IQR is the interquartile range) % AUXDATA VARIABLES ICOS
1143 INST_LI7200_DELTA_P Total number of records flagged for a bad differential pressure sensor gas analyzer diagnostic nondimensional AUXDATA VARIABLES ICOS
1144 INST_LI7200_DELTA_T Total number of times the difference between inlet and outlet temperatures is more than 3 degC nondimensional AUXDATA VARIABLES ICOS
1145 INST_LI7200_DETECTOR Total number of records flagged for a bad detector temperature in relation to the setpoint gas analyzer diagnostic nondimensional AUXDATA VARIABLES ICOS
1146 INST_LI7200_FLOW_PUMPDRIVE_MAX Maximum of the flow drive input to the pump of gas analyzer sampling line % AUXDATA VARIABLES ICOS
1147 INST_LI7200_FLOW_PUMPDRIVE_MIN Minimum of the flow drive input to the pump of gas analyzer sampling line % AUXDATA VARIABLES ICOS
1148 INST_LI7200_FLOW_PUMPDRIVE_P05 Fifth percentile of the flow drive input to the pump of gas analyzer sampling line % AUXDATA VARIABLES ICOS
1149 INST_LI7200_FLOW_PUMPDRIVE_P50 Second quartile (median) of the flow drive input to the pump of gas analyzer sampling line % AUXDATA VARIABLES ICOS
1150 INST_LI7200_FLOW_PUMPDRIVE_P95 NInety-fifth percentile of the flow drive input to the pump of gas analyzer sampling line % AUXDATA VARIABLES ICOS
1151 INST_LI7200_FLOW_VOLRATE_MAX Maximum of the volume flow rate in the gas analyzer sampling line liters min-1 AUXDATA VARIABLES ICOS
1152 INST_LI7200_FLOW_VOLRATE_MIN Minimum of the volume flow rate in the gas analyzer sampling line liters min-1 AUXDATA VARIABLES ICOS
1153 INST_LI7200_FLOW_VOLRATE_P05 Fifth percentile of the volume flow rate in the gas analyzer sampling line liters min-1 AUXDATA VARIABLES ICOS
1154 INST_LI7200_FLOW_VOLRATE_P50 Second quartile (median) of the volume flow rate in the gas analyzer sampling line liters min-1 AUXDATA VARIABLES ICOS
1155 INST_LI7200_FLOW_VOLRATE_P95 Ninety-fifth percentile of the volume flow rate in the gas analyzer sampling line liters min-1 AUXDATA VARIABLES ICOS
1156 INST_LI7200_H2O_STR_LW Lower whisker of the H2O signal strength for the gas analyzer (calculated as max(min(x), Q1 - 1.5 * IQR), where Q1 is the first quartile and IQR) % AUXDATA VARIABLES ICOS
1157 INST_LI7200_H2O_STR_NUM_20 Total number of H2O signal strength data points which lie below 20% nondimensional AUXDATA VARIABLES ICOS
1158 INST_LI7200_H2O_STR_NUM_OUT Total number of any data points which lie beyond the extremes of the H2O signal strength whiskers nondimensional AUXDATA VARIABLES ICOS
1159 INST_LI7200_H2O_STR_P50 Second quartile (median) of the volume H2O signal strength for the gas analyzer % AUXDATA VARIABLES ICOS
1160 INST_LI7200_H2O_STR_UW Upper whisker of the H2O signal strength for the gas analyzer (calculated as min(max(x), Q3 - 1.5 * IQR), where Q3 is the third quartile and IQR is the interquartile range) % AUXDATA VARIABLES ICOS
1161 INST_LI7200_HEAD_DETECT Total number of records flagged for a bad sensor head attached gas analyzer diagnostic nondimensional AUXDATA VARIABLES ICOS
1162 INST_LI7200_PLL Total number of records flagged for a bad phase lock loop gas analyzer diagnostic nondimensional AUXDATA VARIABLES ICOS
1163 INST_LI7200_SYNC Total number of records flagged for a bad sync gas analyzer diagnostic nondimensional AUXDATA VARIABLES ICOS
1164 INST_LI7200_T_GA_LW Lower whisker of the gas analyzer temperature (calculated as max(min(x), Q1 - 1.5 * IQR), where Q1 is the first quartile and IQR) deg C AUXDATA VARIABLES ICOS
1165 INST_LI7200_T_GA_NUM_OUT Total number of any data points which lie beyond the extremes of the gas analyzer temperature whiskers nondimensional AUXDATA VARIABLES ICOS
1166 INST_LI7200_T_GA_P25 First quartile of the gas analyzer temperature deg C AUXDATA VARIABLES ICOS
1167 INST_LI7200_T_GA_P50 Second quartile (median) of the gas analyzer temperature deg C AUXDATA VARIABLES ICOS
1168 INST_LI7200_T_GA_P75 Third quartile of the gas analyzer inlet thermocouple temperature deg C AUXDATA VARIABLES ICOS
1169 INST_LI7200_T_GA_UW Upper whisker of the gas analyzer temperature (calculated as min(max(x), Q3 - 1.5 * IQR), where Q3 is the third quartile and IQR is the interquartile range) deg C AUXDATA VARIABLES ICOS
1170 INST_LI7200_T_IN Total number of records flagged for a bad inlet thermocouple gas analyzer diagnostic nondimensional AUXDATA VARIABLES ICOS
1171 INST_LI7200_T_IN_DIFF Total number of times the inlet temperature differential is more than 0.5 degC nondimensional AUXDATA VARIABLES ICOS
1172 INST_LI7200_T_IN_LW Lower whisker of the gas analyzer inlet thermocouple temperature data (calculated as max(min(x), Q1 - 1.5 * IQR), where Q1 is the first quartile and IQR) deg C AUXDATA VARIABLES ICOS
1173 INST_LI7200_T_IN_NUM_OUT Total number of any data points which lie beyond the extremes of the gas analyzer inlet thermocouple temperature whiskers nondimensional AUXDATA VARIABLES ICOS
1174 INST_LI7200_T_IN_P25 First quartile of the gas analyzer inlet thermocouple temperature data deg C AUXDATA VARIABLES ICOS
1175 INST_LI7200_T_IN_P50 Second quartile (median) of the gas analyzer inlet thermocouple temperature data deg C AUXDATA VARIABLES ICOS
1176 INST_LI7200_T_IN_P75 Third quartile of the gas analyzer inlet thermocouple temperature data deg C AUXDATA VARIABLES ICOS
1177 INST_LI7200_T_IN_UW Upper whisker of the gas analyzer inlet thermocouple temperature data (calculated as min(max(x), Q3 - 1.5 * IQR), where Q3 is the third quartile and IQR is the interquartile range) deg C AUXDATA VARIABLES ICOS
1178 INST_LI7200_T_OUT Total number of records flagged for a bad outlet thermocouple gas analyzer diagnostic nondimensional AUXDATA VARIABLES ICOS
1179 INST_LI7200_T_OUT_DIFF Total number of times the outlet temperature differential is more than 0.5 degC nondimensional AUXDATA VARIABLES ICOS
1180 INST_LI7200_T_OUT_LW Lower whisker of the gas analyzer outlet thermocouple temperature data (calculated as max(min(x), Q1 - 1.5 * IQR), where Q1 is the first quartile and IQR) deg C AUXDATA VARIABLES ICOS
1181 INST_LI7200_T_OUT_NUM_OUT Total number of any data points which lie beyond the extremes of the gas analyzer outlet thermocouple temperature whiskers nondimensional AUXDATA VARIABLES ICOS
1182 INST_LI7200_T_OUT_P25 First quartile of the gas analyzer outlet thermocouple temperature data deg C AUXDATA VARIABLES ICOS
1183 INST_LI7200_T_OUT_P50 Second quartile (median) of the gas analyzer outlet thermocouple temperature data deg C AUXDATA VARIABLES ICOS
1184 INST_LI7200_T_OUT_P75 Third quartile of the gas analyzer outlet thermocouple temperature data deg C AUXDATA VARIABLES ICOS
1185 INST_LI7200_T_OUT_UW Upper whisker of the gas analyzer outlet thermocouple temperature data (calculated as min(max(x), Q3 - 1.5 * IQR), where Q3 is the third quartile and IQR is the interquartile range) deg C AUXDATA VARIABLES ICOS
1186 INST_LI7200_TUBE_HEAT_MAX Maximum of the Watts provided to the gas analyzer tube for heating W AUXDATA VARIABLES ICOS
1187 INST_LI7200_TUBE_HEAT_MIN Minimum of the Watts provided to the gas analyzer tube for heating W AUXDATA VARIABLES ICOS
1188 INST_LI7200_TUBE_HEAT_P05 Fifth percentile of the Watts provided to the gas analyzer tube for heating W AUXDATA VARIABLES ICOS
1189 INST_LI7200_TUBE_HEAT_P50 Second quartile (median) of the Watts provided to the gas analyzer tube for heating W AUXDATA VARIABLES ICOS
1190 INST_LI7200_TUBE_HEAT_P95 Ninety-fifth percentile of the Watts provided to the gas analyzer tube for heating W AUXDATA VARIABLES ICOS
1191 INST_LI7200_TUBE_HEAT_V_MAX Maximum of the volts provided to the gas analyzer tube for heating V AUXDATA VARIABLES ICOS
1192 INST_LI7200_TUBE_HEAT_V_MIN Minimum of the volts provided to the gas analyzer tube for heating V AUXDATA VARIABLES ICOS
1193 INST_LI7200_TUBE_HEAT_V_P05 Fifth percentile of the volts provided to the gas analyzer tube for heating V AUXDATA VARIABLES ICOS
1194 INST_LI7200_TUBE_HEAT_V_P50 Second quartile (median) of the volts provided to the gas analyzer tube for heating V AUXDATA VARIABLES ICOS
1195 INST_LI7200_TUBE_HEAT_V_P95 Ninety-fifth percentile of the volts provided to the gas analyzer tube for heating V AUXDATA VARIABLES ICOS
1196 INTS_HS_DIAG_NREX Total number of records flagged for a bad sonic anemometer diagnostic nondimensional AUXDATA VARIABLES ICOS
1197 INTS_HS_DIAG_NVM_FAIL Flag for errors with the non-volatile memory in the half-hour: 1 yes, 0 no, regardless of when it occurred nondimensional AUXDATA VARIABLES ICOS
1198 INTS_HS_DIAG_PRT_FAIL Flag for errors with the PRT temperature sensor in the half-hour: 1 yes, 0 no, regardless of when it occurred nondimensional AUXDATA VARIABLES ICOS
1199 INTS_HS_DIAG_T1_FAIL Half-hour fraction (as percentage of 30 minutes) in which the transducer pair 1 failed % AUXDATA VARIABLES ICOS
1200 INTS_HS_DIAG_T2_FAIL Half-hour fraction (as percentage of 30 minutes) in which the transducer pair 2 failed % AUXDATA VARIABLES ICOS
1201 INTS_HS_DIAG_T3_FAIL Half-hour fraction (as percentage of 30 minutes) in which the transducer pair 3 failed % AUXDATA VARIABLES ICOS
1202 INTS_HS_HEAT_ON Switch-on time (as percentage of the half-hour) of the sonic anemometer heating % AUXDATA VARIABLES ICOS
1203 INST_HS_INCL_V_P01 First percentile of the anemometer tilt angles as measured by the inclinometer along the vertical plane degrees AUXDATA VARIABLES ICOS
1204 INST_HS_INCL_V_P50 Second quartile (median) of the anemometer tilt angles as measured by the inclinometer along the vertical plane degrees AUXDATA VARIABLES ICOS
1205 INST_HS_INCL_V_P99 Ninety-ninth percentile of the anemometer tilt angles as measured by the inclinometer along the vertical plane degrees AUXDATA VARIABLES ICOS
1206 INST_HS_INCL_V_MAD Median absolute deviation (median of the absolute deviations from the median) of the anemometer tilt angles as measured by the inclinometer along the vertical plane degrees AUXDATA VARIABLES ICOS
1207 INST_HS_INCL_H_P01 First percentile of the anemometer tilt angles as measured by the inclinometer along the horizontal plane degrees AUXDATA VARIABLES ICOS
1208 INST_HS_INCL_H_P50 Second quartile (median) of the anemometer tilt angles as measured by the inclinometer along the horizontal plane degrees AUXDATA VARIABLES ICOS
1209 INST_HS_INCL_H_P99 Ninety-ninth percentile of the anemometer tilt angles as measured by the inclinometer along the horizontal plane degrees AUXDATA VARIABLES ICOS
1210 INST_HS_INCL_H_MAD Median absolute deviation (median of the absolute deviations from the median) of the anemometer tilt angles as measured by the inclinometer along the horizontal plane degrees AUXDATA VARIABLES ICOS
1211 T_SONIC_ABSLIM_NREX Total number of T_SONIC readings exceeding its absolute limits nondimensional AUXDATA VARIABLES ICOS
1212 T_SONIC_NUM_SPIKES Total number of spikes detected and eliminated for T_SONIC (1 spike is intended here as less than 3 consecutive bad readings) nondimensional AUXDATA VARIABLES ICOS
1213 T_SONIC_SPIKE_NREX Total number of single spikes for T_SONIC (1 spike is intended here as 1 record) nondimensional AUXDATA VARIABLES ICOS
1214 U_ABSLIM_NREX Total number of U readings exceeding its absolute limits nondimensional AUXDATA VARIABLES ICOS
1215 U_NUM_SPIKES Total number of spikes detected and eliminated for U (1 spike is intended here as less than 3 consecutive bad readings) nondimensional AUXDATA VARIABLES ICOS
1216 U_SPIKE_NREX Total number of single spikes for U (1 spike is intended here as 1 record) nondimensional AUXDATA VARIABLES ICOS
1217 V_ABSLIM_NREX Total number of V readings exceeding its absolute limits nondimensional AUXDATA VARIABLES ICOS
1218 V_NUM_SPIKES Total number of spikes detected and eliminated for V (1 spike is intended here as less than 3 consecutive bad readings) nondimensional AUXDATA VARIABLES ICOS
1219 V_SPIKE_NREX Total number of single spikes for V (1 spike is intended here as 1 record) nondimensional AUXDATA VARIABLES ICOS
1220 W_ABSLIM_NREX Total number of W readings exceeding its absolute limits nondimensional AUXDATA VARIABLES ICOS
1221 W_NUM_SPIKES Total number of spikes detected and eliminated for W (1 spike is intended here as less than 3 consecutive bad readings) nondimensional AUXDATA VARIABLES ICOS
1222 W_SPIKE_NREX Total number of single spikes for W (1 spike is intended here as 1 record) nondimensional AUXDATA VARIABLES ICOS
1223 TIMESTAMP_START ISO timestamp start of averaging period (up to a 12-digit integer as specified by the data's temporal resolution) yyyymmddHHMM FLUXES VARIABLES ICOS
1224 TIMESTAMP_END ISO timestamp end of averaging period (up to a 12-digit integer as specified by the data's temporal resolution) yyyymmddHHMM FLUXES VARIABLES ICOS
1225 CO2 Carbon Dioxide (CO2) in mole fraction of wet air µmolCO2 mol-1 FLUXES VARIABLES ICOS
1226 CO2_AL1_FLAG Flag for the autocorrelation at lag 1 for the X variable (0: negligible evidences of error, IF X_AL1_STAT>0.75; 1: moderate evidences of error, IF 0.5 nondimensional FLUXES VARIABLES ICOS
1227 CO2_AL1_STAT Autocorrelation at lag 1 for the X variable. Formerly (Rflux version < 3.2.0) named *_ACF_* nondimensional FLUXES VARIABLES ICOS
1228 CO2_DDI_FLAG Flag for the X_DDI_STAT (0: negligible evidences of error, IF X_DDI_STAT hz*60*5, where hz denoting the acquisition frequency) nondimensional FLUXES VARIABLES ICOS
1229 CO2_DDI_STAT Maximum number of binned data points for the X variable (number of bins proportional to the sample size) integer FLUXES VARIABLES ICOS
1230 CO2_DIP_FLAG Flag for the X_DIP_STAT (0: negligible evidences of error, IF X_DIP_STAT>0.1; 1: moderate evidences of error, IF 0.05 nondimensional FLUXES VARIABLES ICOS
1231 CO2_DIP_STAT P-value of the Hartigans’ Dip Test for Unimodality nondimensional FLUXES VARIABLES ICOS
1232 CO2_DRY Carbon Dioxide (CO2) in mole fraction of dry air µmolCO2 mol-1 FLUXES VARIABLES ICOS
1233 CO2_HD10_FLAG Flag for the homogeneity test applied on differenced X variable (0: negligible evidences of error, IF HD10_STAT1) nondimensional FLUXES VARIABLES ICOS
1234 CO2_HD10_STAT Statistic of the homogeneity test applied on differenced X variable (percentage of data exceeding µ±10σ) % FLUXES VARIABLES ICOS
1235 CO2_HD5_FLAG Flag for the homogeneity test applied on differenced X variable (0: negligible evidences of error, IF HD5_STAT4) nondimensional FLUXES VARIABLES ICOS
1236 CO2_HD5_STAT Statistic of the homogeneity test applied on differenced X variable (percentage of data exceeding µ±5σ) % FLUXES VARIABLES ICOS
1237 CO2_HF10_FLAG Flag for the homogeneity test (0: negligible evidences of error, IF HF10_STAT1) nondimensional FLUXES VARIABLES ICOS
1238 CO2_HF10_STAT Statistic of the homogeneity test applied on the X variable (percentage of data exceeding µ±10σ) % FLUXES VARIABLES ICOS
1239 CO2_HF5_FLAG Flag for the homogeneity test (0: negligible evidences of error, IF HF5_STAT4) nondimensional FLUXES VARIABLES ICOS
1240 CO2_HF5_STAT Statistic of the homogeneity test applied on the X variable (percentage of data exceeding µ±5σ) % FLUXES VARIABLES ICOS
1241 CO2_KID_FLAG Flag for the W_KID_STAT (0: negligible evidences of error, IF KID_STAT50) nondimensional FLUXES VARIABLES ICOS
1242 CO2_KID_STAT Kurtosis Index of differenced X variable nondimensional FLUXES VARIABLES ICOS
1243 CO2_SIGMA Standard deviation of carbon dioxide in mole fraction of wet air µmolCO2 mol-1 FLUXES VARIABLES ICOS
1244 FC Carbon Dioxide (CO2) turbulent flux (no storage correction) µmolCO2 m-2 s-1 FLUXES VARIABLES ICOS
1245 FC_FMR_FLAG Flag for the FMR test (0: negligible evidences of error, IF FMR15) nondimensional FLUXES VARIABLES ICOS
1246 FC_FMR_STAT Fraction of Missing Records in raw, high-frequency, data used for H flux estimation % FLUXES VARIABLES ICOS
1247 FC_LGD_FLAG Flag for the LGD test (0: negligible evidences of error, IF LGD180) nondimensional FLUXES VARIABLES ICOS
1248 FC_LGD_STAT Longest Gap Duration in raw, high-frequency, data used for H flux estimation seconds FLUXES VARIABLES ICOS
1249 FC_LSR_FLAG Flag for the LSR test (0: negligible evidences of error, IF LSR_STAT>0.995; 1: moderate evidences of error, IF 0.99≤LSR_STAT≤0.995; 2: severe evidences of error, IF LSR_STAT nondimensional FLUXES VARIABLES ICOS
1250 FC_LSR_STAT Statistic of the Low Signal Resolution test nondimensional FLUXES VARIABLES ICOS
1251 FC_M98_FLAG Flag of the X_M98_STAT (0: negligible evidences of error, IF M_98_STAT3) nondimensional FLUXES VARIABLES ICOS
1252 FC_M98_STAT Statistic of the nonstationarity ratio test by Mahrt (1998) nondimensional FLUXES VARIABLES ICOS
1253 FC_SCF_STAT Spectral correction factor for the X variable nondimensional FLUXES VARIABLES ICOS
1254
1255 FETCH_50 Distance at which cross-wind integrated footprint cumulative probability is 50% m FLUXES VARIABLES ICOS
1256 FETCH_70 Distance at which cross-wind integrated footprint cumulative probability is 70% m FLUXES VARIABLES ICOS
1257 FETCH_80 Distance at which cross-wind integrated footprint cumulative probability is 80% m FLUXES VARIABLES ICOS
1258 FETCH_90 Distance at which cross-wind integrated footprint cumulative probability is 90% m FLUXES VARIABLES ICOS
1259 FETCH_MAX Distance at which cross-wind integrated footprint contribution is maximum m FLUXES VARIABLES ICOS
1260 FETCH_OFFSET Offset distance: distance at which cross-wind integrated footprint contribution is 1% m FLUXES VARIABLES ICOS
1261 FOOTPRINT_80_SURF 2D footprint 80% isoplethe area m2 FLUXES VARIABLES ICOS
1262 FOOTPRINT_FLAG Footprint flag (0: data used; 1: footprint model error; 2: invalid conditions for footprint model; 3: missing concurrent NEE data (after QC)) nondimensional FLUXES VARIABLES ICOS
1263 FOOTPRINT_TA_CONTR Cumulative footprint contribution of the footprint/target area intersection,m2 m-2 % FLUXES VARIABLES ICOS
1264 FOOTPRINT_TA_SURF Area of footprint/target area intersection m2 FLUXES VARIABLES ICOS
1265 GA_DIAG_FLAG Gas analyzer (GA) Flag for instrumental diagnostics (0: negligible evidences of error; 2: severe evidences of error) nondimensional FLUXES VARIABLES ICOS
1266 H Sensible heat turbulent flux (no storage correction, cleaned) W m-2 FLUXES VARIABLES ICOS
1267
1268 H_FMR_FLAG Flag for the FMR test (0: negligible evidences of error, IF FMR15) nondimensional FLUXES VARIABLES ICOS
1269 H_FMR_STAT Fraction of Missing Records in raw, high-frequency, data used for H flux estimation % FLUXES VARIABLES ICOS
1270 H_LGD_FLAG Flag for the LGD test (0: negligible evidences of error, IF LGD180) nondimensional FLUXES VARIABLES ICOS
1271 H_LGD_STAT Longest Gap Duration in raw, high-frequency, data used for H flux estimation seconds FLUXES VARIABLES ICOS
1272 H_LSR_FLAG Flag for the LSR test (0: negligible evidences of error, IF LSR_STAT>0.995; 1: moderate evidences of error, IF 0.99≤LSR_STAT≤0.995; 2: severe evidences of error, IF LSR_STAT nondimensional FLUXES VARIABLES ICOS
1273 H_LSR_STAT Statistic of the Low Signal Resolution test nondimensional FLUXES VARIABLES ICOS
1274 H_M98_FLAG Flag of the X_M98_STAT (0: negligible evidences of error, IF M_98_STAT3) nondimensional FLUXES VARIABLES ICOS
1275 H_M98_STAT Statistic of the nonstationarity ratio test by Mahrt (1998) nondimensional FLUXES VARIABLES ICOS
1276 H_OOR_FLAG Flag for values out of the physically plausible range (0: within range; 1: out of range) nondimensional FLUXES VARIABLES ICOS
1277 H_OUTLYING_FLAG Flag for outliers (0: no outlying flux; 1: outlying flux) nondimensional FLUXES VARIABLES ICOS
1278 H_SCF_STAT Spectral correction factor for the X variable nondimensional FLUXES VARIABLES ICOS
1279 H_UNCLEANED Sensible heat turbulent flux (no storage correction, uncleaned) W m-2 FLUXES VARIABLES ICOS
1280 H2O Water (H2O) vapor in mole fraction of wet air mmolH2O mol-1 FLUXES VARIABLES ICOS
1281 H2O_AL1_FLAG Flag for the autocorrelation at lag 1 for the X variable (0: negligible evidences of error, IF X_AL1_STAT>0.75; 1: moderate evidences of error, IF 0.5 nondimensional FLUXES VARIABLES ICOS
1282 H2O_AL1_STAT Autocorrelation at lag 1 for the X variable. Formerly (Rflux version < 3.2.0) named *_ACF_* nondimensional FLUXES VARIABLES ICOS
1283 H2O_DDI_FLAG Flag for the X_DDI_STAT (0: negligible evidences of error, IF X_DDI_STAT hz*60*5, where hz denoting the acquisition frequency) nondimensional FLUXES VARIABLES ICOS
1284 H2O_DDI_STAT Maximum number of binned data points for the X variable (number of bins proportional to the sample size) integer FLUXES VARIABLES ICOS
1285 H2O_DIP_FLAG Flag for the X_DIP_STAT (0: negligible evidences of error, IF X_DIP_STAT>0.1; 1: moderate evidences of error, IF 0.05 nondimensional FLUXES VARIABLES ICOS
1286 H2O_DIP_STAT P-value of the Hartigans’ Dip Test for Unimodality nondimensional FLUXES VARIABLES ICOS
1287 H2O_DRY Water (H2O) vapor in mole fraction of dry air mmolH2O mol-1 FLUXES VARIABLES ICOS
1288 H2O_HD10_FLAG Flag for the homogeneity test applied on differenced X variable (0: negligible evidences of error, IF HD10_STAT1) nondimensional FLUXES VARIABLES ICOS
1289 H2O_HD10_STAT Statistic of the homogeneity test applied on differenced X variable (percentage of data exceeding µ±10σ) % FLUXES VARIABLES ICOS
1290 H2O_HD5_FLAG Flag for the homogeneity test applied on differenced X variable (0: negligible evidences of error, IF HD5_STAT4) nondimensional FLUXES VARIABLES ICOS
1291 H2O_HD5_STAT Statistic of the homogeneity test applied on differenced X variable (percentage of data exceeding µ±5σ) % FLUXES VARIABLES ICOS
1292 H2O_HF10_FLAG Flag for the homogeneity test (0: negligible evidences of error, IF HF10_STAT1) nondimensional FLUXES VARIABLES ICOS
1293 H2O_HF10_STAT Statistic of the homogeneity test applied on the X variable (percentage of data exceeding µ±10σ) % FLUXES VARIABLES ICOS
1294 H2O_HF5_FLAG Flag for the homogeneity test (0: negligible evidences of error, IF HF5_STAT4) nondimensional FLUXES VARIABLES ICOS
1295 H2O_HF5_STAT Statistic of the homogeneity test applied on the X variable (percentage of data exceeding µ±5σ) % FLUXES VARIABLES ICOS
1296 H2O_KID_FLAG Flag for the W_KID_STAT (0: negligible evidences of error, IF KID_STAT50) nondimensional FLUXES VARIABLES ICOS
1297 H2O_KID_STAT Kurtosis Index of differenced X variable nondimensional FLUXES VARIABLES ICOS
1298 H2O_SIGMA Standard deviation of water vapor mole fraction mmolH2O mol-1 FLUXES VARIABLES ICOS
1299 ITC_FLAG Flag for the ITC test (0: negligible evidences of error, IF ITC_STAT50) nondimensional FLUXES VARIABLES ICOS
1300 ITC_STAT Statistic of the Integral Turbulence Characteristics test (Foken and Wichura, 1996) % FLUXES VARIABLES ICOS
1301 LE Latent heat turbulent flux (no storage correction, cleaned) W m-2 FLUXES VARIABLES ICOS
1302 LE_FMR_FLAG Flag for the FMR test (0: negligible evidences of error, IF FMR15) nondimensional FLUXES VARIABLES ICOS
1303 LE_FMR_STAT Fraction of Missing Records in raw, high-frequency, data used for H flux estimation % FLUXES VARIABLES ICOS
1304 LE_LGD_FLAG Flag for the LGD test (0: negligible evidences of error, IF LGD180) nondimensional FLUXES VARIABLES ICOS
1305 LE_LGD_STAT Longest Gap Duration in raw, high-frequency, data used for H flux estimation seconds FLUXES VARIABLES ICOS
1306 LE_LSR_FLAG Flag for the LSR test (0: negligible evidences of error, IF LSR_STAT>0.995; 1: moderate evidences of error, IF 0.99≤LSR_STAT≤0.995; 2: severe evidences of error, IF LSR_STAT nondimensional FLUXES VARIABLES ICOS
1307 LE_LSR_STAT Statistic of the Low Signal Resolution test nondimensional FLUXES VARIABLES ICOS
1308 LE_M98_FLAG Flag of the X_M98_STAT (0: negligible evidences of error, IF M_98_STAT3) nondimensional FLUXES VARIABLES ICOS
1309 LE_M98_STAT Statistic of the nonstationarity ratio test by Mahrt (1998) nondimensional FLUXES VARIABLES ICOS
1310 LE_OOR_FLAG Flag for values out of the physically plausible range (0: within range; 1: out of range) nondimensional FLUXES VARIABLES ICOS
1311 LE_OUTLYING_FLAG Flag for outliers (0: no outlying flux; 1: outlying flux) nondimensional FLUXES VARIABLES ICOS
1312 LE_SCF_STAT Spectral correction factor for the X variable nondimensional FLUXES VARIABLES ICOS
1313 LE_UNCLEANED Latent heat turbulent flux (no storage correction, uncleaned) W m-2 FLUXES VARIABLES ICOS
1314 MISSING_LEV_ID_X Position indexes of missing storage profile levels. Reported as decimal number, must be converted to binary representation (1/0) to see the actual missing level(s), 1 = sampled, 0 = missing. Level positions from 1 to N, from left to right integer FLUXES VARIABLES ICOS
1315 MISSING_LEV_N_X Number of missing storage profile levels for a given half-hour integer FLUXES VARIABLES ICOS
1316 MO_LENGTH Monin-Obukhov length m FLUXES VARIABLES ICOS
1317 NEE Net Ecosystem Exchange (cleaned) µmolCO2 m-2 s-1 FLUXES VARIABLES ICOS
1318 NEE_OOR_FLAG Flag for values out of the physically plausible range (0: within range; 1: out of range) nondimensional FLUXES VARIABLES ICOS
1319 NEE_OUTLYING_FLAG Flag for outliers (0: no outlying flux; 1: outlying flux) nondimensional FLUXES VARIABLES ICOS
1320 NEE_UNCLEANED Net Ecosystem Exchange (uncleaned) µmolCO2 m-2 s-1 FLUXES VARIABLES ICOS
1321 PBLH Planetary boundary layer height, as estimated in the FFP function m FLUXES VARIABLES ICOS
1322 SA_DIAG_FLAG Sonic anemometer (SA) Flag for instrumental diagnostics (0: negligible evidences of error; 2: severe evidences of error) nondimensional FLUXES VARIABLES ICOS
1323 SC Carbon dioxide (CO2) storage flux µmolCO2 m-2 s-1 FLUXES VARIABLES ICOS
1324 SC_OOR_FLAG Flag for SC denoting values out of the physically plausible range (0: within range; 1: out of range) nondimensional FLUXES VARIABLES ICOS
1325 SC_UNC Estimated uncertainty for SC µmolCO2 m-2 s-1 FLUXES VARIABLES ICOS
1326 SC_UNC_FLAG SC QC flag for uncertainty (0: none; 1: flux data for which the estimated uncertainty exceeds the respective NEE absolute value by 3 times) nondimensional FLUXES VARIABLES ICOS
1327 SC_UNCLEANED Carbon dioxide storage flux, not QC filtered µmolCO2 m-2 s-1 FLUXES VARIABLES ICOS
1328 SH Sensible heat (H) storage flux (only if air temperature is measured along a profile) W m-2 FLUXES VARIABLES ICOS
1329 SH_OOR_FLAG Flag for SH denoting values out of the physically plausible range (0: within range; 1: out of range) nondimensional FLUXES VARIABLES ICOS
1330 SH_UNC Estimated uncertainty for SH W m-2 FLUXES VARIABLES ICOS
1331 SH_UNC_FLAG SC QC flag for uncertainty (0: none; 1: flux data for which the estimated uncertainty exceeds the respective NEE absolute value by 3 times) nondimensional FLUXES VARIABLES ICOS
1332 SH_UNCLEANED Sensible heat storage flux, not QC filtered W m-2 FLUXES VARIABLES ICOS
1333 SLE Latent heat (LE) storage flux (only if air temperature and H2O are measured along a profile) W m-2 FLUXES VARIABLES ICOS
1334 SLE_OOR_FLAG Flag for SLE denoting values out of the physically plausible range (0: within range; 1: out of range) nondimensional FLUXES VARIABLES ICOS
1335 SLE_UNC Estimated uncertainty for SLE W m-2 FLUXES VARIABLES ICOS
1336 SLE_UNC_FLAG SLE QC flag for uncertainty (0: none; 1: flux data for which the estimated uncertainty exceeds the respective NEE absolute value by 3 times) nondimensional FLUXES VARIABLES ICOS
1337 SLE_UNCLEANED Latent heat storage flux, not QC filtered W m-2 FLUXES VARIABLES ICOS
1338 SX_1P Single-point storage flux for the X variable. Approximation of the actual storage flux as estimated from a single air sampling point at the EC system height X specific FLUXES VARIABLES ICOS
1339 SX_FLAG Flag for the storage flux computation method for the X variable (0: flux from profile samplings; 1: flux from single-point; 2: linearly interpolated missing value; 3: linearly interpolated value discarded by the RFlux::cleanset SC QC procedure) nondimensional FLUXES VARIABLES ICOS
1340 T_SONIC Sonic temperature °C FLUXES VARIABLES ICOS
1341 T_SONIC_AL1_FLAG Flag for the autocorrelation at lag 1 for the X variable (0: negligible evidences of error, IF X_AL1_STAT>0.75; 1: moderate evidences of error, IF 0.5 nondimensional FLUXES VARIABLES ICOS
1342 T_SONIC_AL1_STAT Autocorrelation at lag 1 for the X variable. Formerly (Rflux version < 3.2.0) named *_ACF_* nondimensional FLUXES VARIABLES ICOS
1343 T_SONIC_DDI_FLAG Flag for the X_DDI_STAT (0: negligible evidences of error, IF X_DDI_STAT hz*60*5, where hz denoting the acquisition frequency) nondimensional FLUXES VARIABLES ICOS
1344 T_SONIC_DDI_STAT Maximum number of binned data points for the X variable (number of bins proportional to the sample size) integer FLUXES VARIABLES ICOS
1345 T_SONIC_DIP_FLAG Flag for the X_DIP_STAT (0: negligible evidences of error, IF X_DIP_STAT>0.1; 1: moderate evidences of error, IF 0.05 nondimensional FLUXES VARIABLES ICOS
1346 T_SONIC_DIP_STAT P-value of the Hartigans’ Dip Test for Unimodality nondimensional FLUXES VARIABLES ICOS
1347 T_SONIC_HD10_FLAG Flag for the homogeneity test applied on differenced X variable (0: negligible evidences of error, IF HD10_STAT1) nondimensional FLUXES VARIABLES ICOS
1348 T_SONIC_HD10_STAT Statistic of the homogeneity test applied on differenced X variable (percentage of data exceeding µ±10σ) % FLUXES VARIABLES ICOS
1349 T_SONIC_HD5_FLAG Flag for the homogeneity test applied on differenced X variable (0: negligible evidences of error, IF HD5_STAT4) nondimensional FLUXES VARIABLES ICOS
1350 T_SONIC_HD5_STAT Statistic of the homogeneity test applied on differenced X variable (percentage of data exceeding µ±5σ) % FLUXES VARIABLES ICOS
1351 T_SONIC_HF10_FLAG Flag for the homogeneity test (0: negligible evidences of error, IF HF10_STAT1) nondimensional FLUXES VARIABLES ICOS
1352 T_SONIC_HF10_STAT Statistic of the homogeneity test applied on the X variable (percentage of data exceeding µ±10σ) % FLUXES VARIABLES ICOS
1353 T_SONIC_HF5_FLAG Flag for the homogeneity test (0: negligible evidences of error, IF HF5_STAT4) nondimensional FLUXES VARIABLES ICOS
1354 T_SONIC_HF5_STAT Statistic of the homogeneity test applied on the X variable (percentage of data exceeding µ±5σ) % FLUXES VARIABLES ICOS
1355 T_SONIC_KID_FLAG Flag for the W_KID_STAT (0: negligible evidences of error, IF KID_STAT50) nondimensional FLUXES VARIABLES ICOS
1356 T_SONIC_KID_STAT Kurtosis Index of differenced X variable nondimensional FLUXES VARIABLES ICOS
1357 T_SONIC_SIGMA Standard deviation of sonic temperature °C FLUXES VARIABLES ICOS
1358 TAU Momentum flux kg m-1 s-2 FLUXES VARIABLES ICOS
1359 TAU_FMR_FLAG Flag for the FMR test (0: negligible evidences of error, IF FMR15) nondimensional FLUXES VARIABLES ICOS
1360 TAU_FMR_STAT Fraction of Missing Records in raw, high-frequency, data used for H flux estimation % FLUXES VARIABLES ICOS
1361 TAU_LGD_FLAG Flag for the LGD test (0: negligible evidences of error, IF LGD180) nondimensional FLUXES VARIABLES ICOS
1362 TAU_LGD_STAT Longest Gap Duration in raw, high-frequency, data used for H flux estimation seconds FLUXES VARIABLES ICOS
1363 TAU_LSR_FLAG Flag for the LSR test (0: negligible evidences of error, IF LSR_STAT>0.995; 1: moderate evidences of error, IF 0.99≤LSR_STAT≤0.995; 2: severe evidences of error, IF LSR_STAT nondimensional FLUXES VARIABLES ICOS
1364 TAU_LSR_STAT Statistic of the Low Signal Resolution test nondimensional FLUXES VARIABLES ICOS
1365 TAU_M98_FLAG Flag of the X_M98_STAT (0: negligible evidences of error, IF M_98_STAT3) nondimensional FLUXES VARIABLES ICOS
1366 TAU_M98_STAT Statistic of the nonstationarity ratio test by Mahrt (1998) nondimensional FLUXES VARIABLES ICOS
1367 TAU_OOR_FLAG Flag for values out of the physically plausible range (0: within range; 1: out of range) nondimensional FLUXES VARIABLES ICOS
1368 TAU_OUTLYING_FLAG Flag for outliers (0: no outlying flux; 1: outlying flux) nondimensional FLUXES VARIABLES ICOS
1369 TAU_SCF_STAT Spectral correction factor for the X variable nondimensional FLUXES VARIABLES ICOS
1370 TAU_UNCLEANED Momentum flux (no storage correction, uncleaned) kg m-1 s-2 kg m-1 s-2 FLUXES VARIABLES ICOS
1371 U_SIGMA Standard deviation of lateral velocity fluctuations (towards main-wind direction after coordinates rotation) m s-1 FLUXES VARIABLES ICOS
1372 USTAR Friction velocity m s-1 FLUXES VARIABLES ICOS
1373 V_SIGMA Standard deviation of lateral velocity fluctuations (cross main-wind direction after coordinates rotation) m s-1 FLUXES VARIABLES ICOS
1374 W_AL1_FLAG Flag for the autocorrelation at lag 1 for the X variable (0: negligible evidences of error, IF X_AL1_STAT>0.75; 1: moderate evidences of error, IF 0.5 nondimensional FLUXES VARIABLES ICOS
1375 W_AL1_STAT Autocorrelation at lag 1 for the X variable. Formerly (Rflux version < 3.2.0) named *_ACF_* nondimensional FLUXES VARIABLES ICOS
1376 W_DDI_FLAG Flag for the X_DDI_STAT (0: negligible evidences of error, IF X_DDI_STAT hz*60*5, where hz denoting the acquisition frequency) nondimensional FLUXES VARIABLES ICOS
1377 W_DDI_STAT Maximum number of binned data points for the X variable (number of bins proportional to the sample size) integer FLUXES VARIABLES ICOS
1378 W_DIP_FLAG Flag for the X_DIP_STAT (0: negligible evidences of error, IF X_DIP_STAT>0.1; 1: moderate evidences of error, IF 0.05 nondimensional FLUXES VARIABLES ICOS
1379 W_DIP_STAT P-value of the Hartigans’ Dip Test for Unimodality nondimensional FLUXES VARIABLES ICOS
1380 W_HD10_FLAG Flag for the homogeneity test applied on differenced X variable (0: negligible evidences of error, IF HD10_STAT1) nondimensional FLUXES VARIABLES ICOS
1381 W_HD10_STAT Statistic of the homogeneity test applied on differenced X variable (percentage of data exceeding µ±10σ) % FLUXES VARIABLES ICOS
1382 W_HD5_FLAG Flag for the homogeneity test applied on differenced X variable (0: negligible evidences of error, IF HD5_STAT4) nondimensional FLUXES VARIABLES ICOS
1383 W_HD5_STAT Statistic of the homogeneity test applied on differenced X variable (percentage of data exceeding µ±5σ) % FLUXES VARIABLES ICOS
1384 W_HF10_FLAG Flag for the homogeneity test (0: negligible evidences of error, IF HF10_STAT1) nondimensional FLUXES VARIABLES ICOS
1385 W_HF10_STAT Statistic of the homogeneity test applied on the X variable (percentage of data exceeding µ±10σ) % FLUXES VARIABLES ICOS
1386 W_HF5_FLAG Flag for the homogeneity test (0: negligible evidences of error, IF HF5_STAT4) nondimensional FLUXES VARIABLES ICOS
1387 W_HF5_STAT Statistic of the homogeneity test applied on the X variable (percentage of data exceeding µ±5σ) % FLUXES VARIABLES ICOS
1388 W_KID_FLAG Flag for the W_KID_STAT (0: negligible evidences of error, IF KID_STAT50) nondimensional FLUXES VARIABLES ICOS
1389 W_KID_STAT Kurtosis Index of differenced X variable nondimensional FLUXES VARIABLES ICOS
1390 W_SIGMA Standard deviation of vertical velocity fluctuations m s-1 FLUXES VARIABLES ICOS
1391 WD Wind direction decimal degrees FLUXES VARIABLES ICOS
1392 WS Wind speed m s-1 FLUXES VARIABLES ICOS
1393 WSECT_FLAG Footprint quality flag indicating periods when wind was blowing from directions known to significantly affect the turbulent flow (0: negligible evidences of error; 2: severe evidences of error) nondimensional FLUXES VARIABLES ICOS
1394 X_n Average concentrations of the X variable at each storage profile level (this are not the values used for the flux calculation, as are averaged over the full 30 minutes) ppt/ppm/ppb FLUXES VARIABLES ICOS
1395 X_n_SE Standard error of the average concentrations of the X variable at each storage profile level ppt/ppm/ppb FLUXES VARIABLES ICOS
1396 ZL Monin-Obukhov stability parameter nondimensional FLUXES VARIABLES ICOS
1397 BADM Biological, Ancillary, Disturbance and Metadata. BADM files provide a description, general background, geo-location, relevant publications, references and other information for the site. ICOS / FLUXNET
1398 _gfXG (suffix) Gap-filled using XGBoost. diive
1399 _gfMDS (suffix) Gap-filled using the MDS method (Reichstein et al., 2005). diive
Label Description Units (preferred) or format Category Used In

† Concentrations and fluxes for water vapor are provided as [mmol mol-1] and [mmol m-2 s-1] respectively.

‡ Units depend on the nature of the variable.

 

References

 

Last Updated on 20 Dec 2024 13:52