The FULLSET Data Product for the FLUXNET2015 Release includes all variables for the data product, including all quality and uncertainty variables, plus a few selected variables from the intermediate data processing steps in the data processing pipeline.
ERAI Data Product
Auxiliary data product containing full record (1989-2014) of downscaled micrometeorological variables (as related to the site’s measured variables) using the ERA-Interim reanalysis data product (details in the document about the data processing pipeline).
AUXMETEO Data Product
Auxiliary data product containing results from the downscaling of micrometeorological variables using the ERA-Interim reanalysis data product. Variables in this files relate to the linear regression and error/correlation estimates for each data variable used in the downscaling.
Variables (see list below): TA, PA, VPD, WS, P, SW_IN, LW_IN, LW_IN_JSB
Parameters:
- ERA_SLOPE: slope of linear regression
- ERA_INTERCEPT: intercept point of linear regression
- ERA_RMSE: root mean square error between site data and downscaled data
- ERA_CORRELATION: correlation coefficient of linear fit (R-Squared == ERA_CORRELATION * ERA_CORRELATION)
AUXNEE Data Product
Auxiliary data product with variables resulting from the processing of NEE (mainly related to USTAR filtering) and generation of RECO and GPP. Variables in this product include success/failure of execution of USTAR filtering methods, USTAR thresholds applied to different versions of variables, and percentile/threshold pairs with best model efficiency results.
Variables:
- USTAR_MP_METHOD: Moving Point Test USTAR threshold method run
- USTAR_CP_METHOD: Change Point Detection USTAR threshold method run
- NEE_USTAR50_[UT]: NEE using 50-percentile ofUSTAR thresholds from bootstrapping at USTAR filtering step using method UT (CUT, VUT)
- NEE_[UT]_REF: Reference NEE, using model efficiency approach, using method UT (CUT, VUT)
- [PROD]_[ALG]_[UT]_REF: Reference product PROD (RECO or GPP), using model efficiency approach, using algorithm ALG(NT, DT) for partitioning and method UT (CUT, VUT)
Parameters:
- SUCCESS_RUN: 1 if run of method (USTAR_MP_METHOD or USTAR_CP_METHOD) was successful, 0 otherwise
- USTAR_PERCENTILE: percentile of USTAR thresholds from bootstrapping at USTAR filtering step
- USTAR_THRESHOLD: USTAR threshold value corresponding to USTAR_PERCENTILE
- [RR]_USTAR_PERCENTILE: percentile of USTAR thresholds from bootstrapping at USTAR filtering step at resolution RR (HH, DD, WW, MM, YY)
- [RR]_USTAR_THRESHOLD: USTAR threshold value corresponding to USTAR_PERCENTILE at resolution RR (HH, DD, WW, MM, YY)
Variables in the FULLSET Data Product (all variables)
| Variable | Units | Description |
| TIMEKEEPING | ||
| TIMESTAMP | YYYYMMDDHHMM | ISO timestamp – short format |
| TIMESTAMP_START | YYYYMMDDHHMM | ISO timestamp start of averaging period – short format |
| TIMESTAMP_END | YYYYMMDDHHMM | ISO timestamp end of averaging period – short format |
| MICROMETEOROLOGICAL | ||
| TA_F_MDS | Air temperature, gapfilled using MDS method | |
| HH | deg C | |
| DD | deg C | average from half-hourly data |
| WW-YY | deg C | average from daily data |
| TA_F_MDS_QC | Quality flag for TA_F_MDS | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| TA_F_MDS_NIGHT | Average nighttime TA_F_MDS | |
| HH | not produced | |
| DD | deg C | average from half-hourly data |
| WW-YY | deg C | average from daily data |
| TA_F_MDS_NIGHT_SD | Standard deviation for TA_F_MDS_NIGHT | |
| HH | not produced | |
| DD | deg C | from half-hourly data |
| WW-YY | deg C | average SD from daily data |
| TA_F_MDS_NIGHT_QC | Quality flag for TA_F_MDS_NIGHT | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| TA_F_MDS_DAY | Average daytime TA_F_MDS | |
| HH | not produced | |
| DD | deg C | average from half-hourly data |
| WW-YY | deg C | average from daily data |
| TA_F_MDS_DAY_SD | Standard deviation for TA_F_MDS_DAY | |
| HH | not produced | |
| DD | deg C | from half-hourly data |
| WW-YY | deg C | average SD from daily data |
| TA_F_MDS_DAY_QC | Quality flag for TA_F_MDS_DAY | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| TA_ERA | Air temperature, downscaled from ERA, linearly regressed using measured only site data | |
| HH | deg C | |
| DD | deg C | average from half-hourly data |
| WW-YY | deg C | average from daily data |
| TA_ERA_NIGHT | Average nighttime TA_ERA | |
| HH | not produced | |
| DD | deg C | average from half-hourly data |
| WW-YY | deg C | average from daily data |
| TA_ERA_NIGHT_SD | Standard deviation for TA_ERA_NIGHT | |
| HH | not produced | |
| DD | deg C | from half-hourly data |
| WW-YY | deg C | average SD from daily data |
| TA_ERA_DAY | Average daytime TA_ERA | |
| HH | not produced | |
| DD | deg C | average from half-hourly data |
| WW-YY | deg C | average from daily data |
| TA_ERA_DAY_SD | Standard deviation for TA_ERA_DAY | |
| HH | not produced | |
| DD | deg C | from half-hourly data |
| WW-YY | deg C | average SD from daily data |
| TA_F | Air temperature, consolidated from TA_F_MDS and TA_ERA | |
| HH | deg C | TA_F_MDS used if TA_F_MDS_QC is 0 or 1 |
| DD | deg C | average from half-hourly data |
| WW-YY | deg C | average from daily data |
| TA_F_QC | Quality flag for TA_F | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| TA_F_NIGHT | Average nighttime TA_F | |
| HH | not produced | |
| DD | deg C | average from half-hourly data |
| WW-YY | deg C | average from daily data |
| TA_F_NIGHT_SD | Standard deviation for TA_F_NIGHT | |
| HH | not produced | |
| DD | deg C | from half-hourly data |
| WW-YY | deg C | average SD from daily data |
| TA_F_NIGHT_QC | Quality flag for TA_F_NIGHT | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| TA_F_DAY | Average daytime TA_F | |
| HH | not produced | |
| DD | deg C | average from half-hourly data |
| WW-YY | deg C | average from daily data |
| TA_F_DAY_SD | Standard deviation for TA_F_DAY | |
| HH | not produced | |
| DD | deg C | from half-hourly data |
| WW-YY | deg C | average SD from daily data |
| TA_F_DAY_QC | Quality flag for TA_F_DAY | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| SW_IN_POT | Shortwave radiation, incoming, potential (top of atmosphere) | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-MM | W m-2 | average from daily data |
| YY | W m-2 | not defined |
| SW_IN_F_MDS | Shortwave radiation, incoming, gapfilled using MDS (negative values set to zero, e.g., negative values from instrumentation noise) | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| SW_IN_F_MDS_QC | Quality flag for SW_IN_F_MDS | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| SW_IN_ERA | Shortwave radiation, incoming, downscaled from ERA, linearly regressed using measured only site data (negative values set to zero) | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| SW_IN_F | Shortwave radiation, incoming consolidated from SW_IN_F_MDS and SW_IN_ERA (negative values set to zero) | |
| HH | W m-2 | SW_IN_F_MDS used if SW_IN_F_MDS_QC is 0 or 1 |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| SW_IN_F_QC | Quality flag for SW_IN_F | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| LW_IN_F_MDS | Longwave radiation, incoming, gapfilled using MDS | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| LW_IN_F_MDS_QC | Quality flag for LW_IN_F_MDS | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| LW_IN_ERA | Longwave radiation, incoming, downscaled from ERA, linearly regressed using measured only site data | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| LW_IN_F | Longwave radiation, incoming, consolidated from LW_IN_F_MDS and LW_IN_ERA | |
| HH | W m-2 | LW_IN_F_MDS used if LW_IN_F_MDS_QC is 0 or 1 |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| LW_IN_F_QC | Quality flag for LW_IN_F | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| 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) | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| LW_IN_JSB_QC | Quality flag for LW_IN_JSB | |
| HH | nondimensional | highest from TA_F_MDS_QC, SW_IN_F_MDS_QC, and VPD_F_MDS_QC, poorest quality prevails |
| DD | nondimensional | fraction between 0-1, indicating percentage of calculated LW_IN starting from measured and good quality gapfill drivers data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage ofcalculated LW_IN starting from measured and good quality gapfill drivers data (average from daily data) |
| LW_IN_JSB_ERA | Longwave radiation, incoming, downscaled from ERA, linearly regressed using site level LW_IN_JSB calculated from measured only drivers | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| LW_IN_JSB_F | Longwave radiation, incoming, consolidated from LW_IN_JSB and LW_IN_JSB_ERA | |
| HH | W m-2 | LW_IN_JSB used if LW_IN_JSB_QC is 0 or 1 |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| LW_IN_JSB_F_QC | Quality flag for LW_IN_JSB_F | |
| HH | nondimensional | 0 = calculated from measured drivers; 1 = calculated from good quality gapfilled drivers; 2: downscaled from ERA |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| VPD_F_MDS | Vapor Pressure Deficit, gapfilled using MDS | |
| HH | hPa | |
| DD | hPa | average from half-hourly data |
| WW-YY | hPa | average from daily data |
| VPD_F_MDS_QC | Quality flag for VPD_F_MDS | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| VPD_ERA | Vapor Pressure Deficit, downscaled from ERA, linearly regressed using measured only site data | |
| HH | hPa | |
| DD | hPa | average from half-hourly data |
| WW-YY | hPa | average from daily data |
| VPD_F | Vapor Pressure Deficit consolidated from VPD_F_MDS and VPD_ERA | |
| HH | hPa | VPD_F_MDS used if VPD_F_MDS_QC is 0 or 1 |
| DD | hPa | average from half-hourly data |
| WW-YY | hPa | average from daily data |
| VPD_F_QC | Quality flag for VPD_F | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| PA | Atmospheric pressure | |
| HH | kPa | |
| DD-YY | kPa | not defined |
| PA_ERA | Atmospheric pressure, downscaled from ERA, linearly regressed using measured only site data | |
| HH | kPa | |
| DD | kPa | average from half-hourly data |
| WW-YY | kPa | average from daily data |
| PA_F | Atmospheric pressure consolidated from PA and PA_ERA | |
| HH | kPa | PA used if measured |
| DD | kPa | average from half-hourly data |
| WW-YY | kPa | average from daily data |
| PA_F_QC | Quality flag for PA_F | |
| HH | nondimensional | 0 = measured; 2 = downscaled from ERA |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured data (average from daily data) |
| P | Precipitation | |
| HH | mm | |
| DD-YY | not defined | |
| P_ERA | Precipitation, downscaled from ERA, linearly regressed using measured only site data | |
| HH | mm | (mm per dataset resolution: either hour or half-hour) |
| DD | mm d-1 | sum from half-hourly data (mm per day) |
| WW-MM
YY |
mm d-1
mm y-1 |
average from daily data (mm per day)
sum from daily data (mm per year) |
| P_F | Precipitation consolidated from P and P_ERA | |
| HH | mm | P used if measured (mm per dataset resolution: either hour or half-hour) |
| DD | mm d-1 | sum from half-hourly data (mm per day) |
| WW-MM
YY |
mm d-1
mm y-1 |
average from daily data (mm per day)
sum from daily data (mm per year) |
| P_F_QC | Quality flag for P_F | |
| HH | nondimensional | 0 = measured; 2 = downscaled from ERA |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured data (average from daily data) |
| WS | Wind speed | |
| HH | m s-1 | |
| DD-YY | m s-1 | not defined |
| WS_ERA | Wind speed, downscaled from ERA, linearly regressed using measured only site data | |
| HH | m s-1 | |
| DD | m s-1 | average from half-hourly data |
| WW-YY | m s-1 | average from daily data |
| WS_F | Wind speed, consolidated from WS and WS_ERA | |
| HH | m s-1 | WS used if measured |
| DD | m s-1 | average from half-hourly data |
| WW-YY | m s-1 | average from daily data |
| WS_F_QC | Quality flag of WS_F | |
| HH | nondimensional | 0 = measured; 2 = downscaled from ERA |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured data (average from daily data) |
| WD | Wind direction | |
| HH | Decimal degrees | |
| DD-YY | Decimal degrees | not defined |
| RH | Relative humidity, range 0-100 | |
| HH | % | |
| DD-YY | % | not defined |
| USTAR | Friction velocity | |
| HH | m s-1 | |
| DD | m s-1 | average from half-hourly data (only days with more than 50% records available) |
| WW-YY | m s-1 | average from daily data (only periods with more than 50% records available) |
| USTAR_QC | Quality flag of USTAR | |
| HH | nondimensional | not defined |
| DD | nondimensional | fraction between 0-1, indicating percentage of data available (measured) |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of data available (average from daily data) |
| NETRAD | Net radiation | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data (only days with more than 50% records available) |
| WW-YY | W m-2 | average from daily data (only periods with more than 50% records available) |
| NETRAD_QC | Quality flag of NETRAD | |
| HH | nondimensional | not defined |
| DD | nondimensional | fraction between 0-1, indicating percentage of data available (measured) |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of data available (average from daily data) |
| PPFD_IN | Photosynthetic photon flux density, incoming | |
| HH | µmolPhoton m-2 s-1 | |
| DD | µmolPhoton m-2 s-1 | average from half-hourly data (only days with more than 50% records available) |
| WW-YY | µmolPhoton m-2 s-1 | average from daily data (only periods with more than 50% records available) |
| PPFD_IN_QC | Quality flag of PPFD_IN | |
| HH | nondimensional | not defined |
| DD | nondimensional | fraction between 0-1, indicating percentage of data available (measured) |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of data available (average from daily data) |
| PPFD_DIF | Photosynthetic photon flux density, diffuse incoming | |
| HH | µmolPhoton m-2 s-1 | |
| DD | µmolPhoton m-2 s-1 | average from half-hourly data (only days with more than 50% records available) |
| WW-YY | µmolPhoton m-2 s-1 | average from daily data (only periods with more than 50% records available) |
| PPFD_DIF_QC | Quality flag of PPFD_DIF | |
| HH | nondimensional | not defined |
| DD | nondimensional | fraction between 0-1, indicating percentage of data available (measured) |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of data available (average from daily data) |
| PPFD_OUT | Photosynthetic photon flux density, outgoing | |
| HH | µmolPhoton m-2 s-1 | |
| DD | µmolPhoton m-2 s-1 | average from half-hourly data (only days with more than 50% records available) |
| WW-YY | µmolPhoton m-2 s-1 | average from daily data (only periods with more than 50% records available) |
| PPFD_OUT_QC | Quality flag of PPFD_OUT | |
| HH | nondimensional | not defined |
| DD | nondimensional | fraction between 0-1, indicating percentage of data available (measured) |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of data available (average from daily data) |
| SW_DIF | Shortwave radiation, diffuse incoming | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data (only days with more than 50% records available) |
| WW-YY | W m-2 | average from daily data (only periods with more than 50% records available) |
| SW_DIF_QC | Quality flag of SW_DIF | |
| HH | nondimensional | not defined |
| DD | nondimensional | fraction between 0-1, indicating percentage of data available (measured) |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of data available (average from daily data) |
| SW_OUT | Shortwave radiation, outgoing | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data (only days with more than 50% records available) |
| WW-YY | W m-2 | average from daily data (only periods with more than 50% records available) |
| SW_OUT_QC | Quality flag of SW_OUT | |
| HH | nondimensional | not defined |
| DD | nondimensional | fraction between 0-1, indicating percentage of data available (measured) |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of data available (average from daily data) |
| LW_OUT | Longwave radiation, outgoing | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data (only days with more than 50% records available) |
| WW-YY | W m-2 | average from daily data (only periods with more than 50% records available) |
| LW_OUT_QC | Quality flag of LW_OUT | |
| HH | nondimensional | not defined |
| DD | nondimensional | fraction between 0-1, indicating percentage of data available (measured) |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of data available (average from daily data) |
| CO2_F_MDS | CO2 mole fraction, gapfilled with MDS | |
| HH | umolCO2 mol-1 | |
| DD | umolCO2 mol-1 | average from half-hourly data |
| WW-YY | umolCO2 mol-1 | average from daily data |
| CO2_F_MDS_QC | Quality flag for CO2_F_MDS | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| TS_F_MDS_# | Soil temperature, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest) | |
| HH | deg C | |
| DD | deg C | average from half-hourly data |
| WW-YY | deg C | average from daily data |
| TS_F_MDS_#_QC | Quality flag for TS_F_MDS_# | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| SWC_F_MDS_# | Soil water content, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest) | |
| HH | % | |
| DD | % | average from half-hourly data |
| WW-YY | % | average from daily data |
| SWC_F_MDS_#_QC | Quality flag for SWC_F_MDS_# | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| ENERGY PROCESSING | ||
| G_F_MDS | Soil heat flux | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| G_F_MDS_QC | Quality flag of G_F_MDS | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| LE_F_MDS | Latent heat flux, gapfilled using MDS method | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| LE_F_MDS_QC | Quality flag for LE_F_MDS, LE_CORR, LE_CORR25, and LE_CORR75. | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| LE_CORR | Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| LE_CORR_25 | Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor, 25th percentile | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | not produced | |
| LE_CORR_75 | Latent heat flux, corrected LE_F_MDS by energy balance closure correction factor, 75th percentile | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | not produced | |
| LE_RANDUNC | Random uncertainty of LE, from measured only data | |
| HH | W m-2 | uses only data point where LE_F_MDS_QC is 0 and two hierarchical methods (see header and LE_RANDUNC_METHOD) |
| DD-YY | W m-2 | from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used |
| LE_RANDUNC_METHOD | Method used to estimate the random uncertainty of LE | |
| HH | nondimensional | 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) |
| DD-YY | not produced | |
| LE_RANDUNC_N | Number of half-hour data points used to estimate the random uncertainty of LE | |
| HH | nondimensional | |
| DD-YY | not produced | |
| LE_CORR_JOINTUNC | Joint uncertainty estimation for LE | |
| HH-DD | W m-2 | [SQRT(LE_RANDUNC^2 + ((LE_CORR75 – LE_CORR25) / 1.349)^2)] |
| WW-YY | not produced | |
| H_F_MDS | Sensible heat flux, gapfilled using MDS method | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| H_F_MDS_QC | Quality flag for H_F_MDS, H_CORR, H_CORR25, and H_CORR75. | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| H_CORR | Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | W m-2 | average from daily data |
| H_CORR_25 | Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor, 25th percentile | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | not produced | |
| H_CORR_75 | Sensible heat flux, corrected H_F_MDS by energy balance closure correction factor, 75th percentile | |
| HH | W m-2 | |
| DD | W m-2 | average from half-hourly data |
| WW-YY | not produced | |
| H_RANDUNC | Random uncertainty of H, from measured only data | |
| HH | W m-2 | uses only data point where H_F_MDS_QC is 0 and two hierarchical methods (see header and H_RANDUNC_METHOD) |
| DD-YY | W m-2 | from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used |
| H_RANDUNC_METHOD | Method used to estimate the random uncertainty of H | |
| HH | nondimensional | 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) |
| DD-YY | not produced | |
| H_RANDUNC_N | Number of half-hour data points used to estimate the random uncertainty of H | |
| HH | nondimensional | |
| DD-YY | not produced | |
| H_CORR_JOINTUNC | Joint uncertainty estimation for H | |
| HH-DD | W m-2 | [SQRT(H_RANDUNC^2 + ((H_CORR75 – H_CORR25) / 1.349)^2)] |
| WW-YY | not produced | |
| 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) | |
| HH | nondimensional | for ECB_CF Method 1 (minimum 5, maximum 93) |
| DD | nondimensional | for ECB_CF Method 1 (minimum 5, maximum 15) |
| WW–YY | nondimensional | fraction between 0-1, indicating percentages of half-hours used with respect to theoretical maximum number of half hours |
| EBC_CF_METHOD | Method used to calculate the energy balance closure correction factor | |
| HH-YY | nondimensional | 1 = ECB_CF Method 1, 2 = ECB_CF Method 2, 3 = ECB_CF Method 3. See general description for details |
| NET ECOSYSTEM EXCHANGE | ||
| NIGHT | Flag indicating nighttime interval based on SW_IN_POT | |
| HH | nondimensional | 0 = daytime, 1 = nighttime |
| DD-YY | not produced | |
| NIGHT_D | Number of half hours classified as nighttime in the period, i.e., when SW_IN_POT is 0 | |
| HH | not produced | |
| DD | nondimensional | number of half-hours |
| WW-MM | nondimensional | number of halfhours (average of the daily data) |
| YY | not produced | |
| DAY_D | Number of half hours classified as daytime in the period, i.e., when SW_IN_POT is greater than 0 | |
| HH | not produced | |
| DD | nondimensional | number of half-hours |
| WW-MM | nondimensional | number of halfhours (average of the daily data) |
| YY | not produced | |
| NIGHT_RANDUNC_N | Number of half hours classified as nighttime and used to calculate the aggregated random uncertainty | |
| HH | not produced | |
| DD | nondimensional | number of half-hours |
| WW-YY | nondimensional | number of halfhours (average of the daily data) |
| DAY_RANDUNC_N | Number of half hours classified as daytime and used to calculate the aggregated random uncertainty | |
| HH | not produced | |
| DD | nondimensional | number of half-hours |
| WW-YY | nondimensional | number of halfhours (average of the daily data) |
| 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 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| 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 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| NEE_CUT_REF_QC | Quality flag for NEE_CUT_REF | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_VUT_REF_QC | Quality flag for NEE_VUT_REF | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_CUT_REF_RANDUNC | Random uncertainty for NEE_CUT_REF, from measured only data | |
| HH | umolCO2 m-2 s-1 | uses only data points where NEE_CUT_REF_QC is 0 and two hierarchical methods – see header and NEE_CUT_REF_RANDUNC_METHOD |
| DD-MM | gC m-2 d-1 | from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used |
| YY | gC m-2 y-1 | from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used |
| NEE_VUT_REF_RANDUNC | Random uncertainty for NEE_VUT_REF, from measured only data | |
| HH | umolCO2 m-2 s-1 | uses only data points where NEE_VUT_REF_QC is 0 and two hierarchical methods – see header and NEE_VUT_REF_RANDUNC_METHOD |
| DD-MM | gC m-2 d-1 | from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used |
| YY | gC m-2 y-1 | from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used |
| NEE_CUT_REF_RANDUNC_METHOD | Method used to estimate the random uncertainty of NEE_CUT_REF | |
| HH | nondimensional | 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) |
| DD-YY | not produced | |
| NEE_VUT_REF_RANDUNC_METHOD | Method used to estimate the random uncertainty of NEE_VUT_REF | |
| HH | nondimensional | 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) |
| DD-YY | not produced | |
| NEE_CUT_REF_RANDUNC_N | Number of data points used to estimate the random uncertainty of NEE_CUT_REF | |
| HH | nondimensional | |
| DD-YY | not produced | |
| NEE_VUT_REF_RANDUNC_N | Number of data points used to estimate the random uncertainty of NEE_VUT_REF | |
| HH | nondimensional | |
| DD-YY | not produced | |
| NEE_CUT_REF_JOINTUNC | Joint uncertainty estimation for NEE_CUT_REF, including random uncertainty and USTAR filtering uncertainty | |
| HH | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_REF_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each half-hour |
| DD | gC m-2 d-1 | [SQRT(NEE_CUT_REF_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each day |
| WW | gC m-2 d-1 | [SQRT(NEE_CUT_REF_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each week |
| MM | gC m-2 d-1 | [SQRT(NEE_CUT_REF_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each month |
| YY | gC m-2 y-1 | [SQRT(NEE_CUT_REF_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each year |
| NEE_VUT_REF_JOINTUNC | Joint uncertainty estimation for NEE_VUT_REF, including random uncertainty and USTAR filtering uncertainty | |
| HH | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_REF_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each half-hour |
| DD | gC m-2 d-1 | [SQRT(NEE_VUT_REF_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each day |
| WW | gC m-2 d-1 | [SQRT(NEE_VUT_REF_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each week |
| MM | gC m-2 d-1 | [SQRT(NEE_VUT_REF_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each month |
| YY | gC m-2 y-1 | [SQRT(NEE_VUT_REF_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each year |
| NEE_CUT_USTAR50 | Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, from 50 percentile of USTAR threshold | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| NEE_VUT_USTAR50 | Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, from 50 percentile of USTAR threshold | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| NEE_CUT_USTAR50_QC | Quality flag for NEE_CUT_USTAR50 | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_VUT_USTAR50_QC | Quality flag for NEE_VUT_USTAR50 | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_CUT_USTAR50_RANDUNC | Random uncertainty for NEE_CUT_USTAR50, from measured only data | |
| HH | umolCO2 m-2 s-1 | uses only data points where NEE_CUT_USTAR50_QC is 0 and two hierarchical methods – see header and NEE_CUT_USTAR50_RANDUNC_METHOD |
| DD-MM | gC m-2 d-1 | from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used |
| YY | gC m-2 y-1 | from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used |
| NEE_VUT_USTAR50_RANDUNC | Random uncertainty for NEE_VUT_USTAR50, from measured only data | |
| HH | umolCO2 m-2 s-1 | uses only data points where NEE_VUT_USTAR50_QC is 0 and two hierarchical methods see header and NEE_VUT_USTAR50_RANDUNC_METHOD |
| DD-MM | gC m-2 d-1 | from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used |
| YY | gC m-2 y-1 | from random uncertainty of individual half-hours (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used |
| NEE_CUT_USTAR50_RANDUNC_METHOD | Method used to estimate the random uncertainty of NEE_CUT_USTAR50 | |
| HH | nondimensional | 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) |
| DD-YY | not produced | |
| NEE_VUT_USTAR50_RANDUNC_METHOD | Method used to estimate the random uncertainty of NEE_VUT_USTAR50 | |
| HH | nondimensional | 1 = RANDUNC Method 1 (direct SD method), 2 = RANDUNC Method 2 (median SD method) |
| DD-YY | not produced | |
| NEE_CUT_USTAR50_RANDUNC_N | Number of half-hour data points used to estimate the random uncertainty of NEE_CUT_USTAR50 | |
| HH | nondimensional | |
| DD-YY | not produced | |
| NEE_VUT_USTAR50_RANDUNC_N | Number of half-hour data points used to estimate the random uncertainty of NEE_VUT_USTAR50 | |
| HH | nondimensional | |
| DD-YY | not produced | |
| NEE_CUT_USTAR50_JOINTUNC | Joint uncertainty estimation for NEE_CUT_USTAR50, including random uncertainty and USTAR filtering uncertainty | |
| HH | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_USTAR50_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each half-hour |
| DD | gC m-2 d-1 | [SQRT(NEE_CUT_USTAR50_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each day |
| WW | gC m-2 d-1 | [SQRT(NEE_CUT_USTAR50_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each week |
| MM | gC m-2 d-1 | [SQRT(NEE_CUT_USTAR50_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each month |
| YY | gC m-2 y-1 | [SQRT(NEE_CUT_USTAR50_RANDUNC^2 + ((NEE_CUT_84 – NEE_CUT_16) / 2)^2)] for each year |
| NEE_VUT_USTAR50_JOINTUNC | Joint uncertainty estimation for NEE_VUT_USTAR50, including random uncertainty and USTAR filtering uncertainty | |
| HH | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_USTAR50_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each half-hour |
| DD | gC m-2 d-1 | [SQRT(NEE_VUT_USTAR50_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each day |
| WW | gC m-2 d-1 | [SQRT(NEE_VUT_USTAR50_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each week |
| MM | gC m-2 d-1 | [SQRT(NEE_VUT_USTAR50_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each month |
| YY | gC m-2 y-1 | [SQRT(NEE_VUT_USTAR50_RANDUNC^2 + ((NEE_VUT_84 – NEE_VUT_16) / 2)^2)] for each year |
| NEE_CUT_MEAN | Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, average from 40 NEE_CUT_XX versions | |
| HH | umolCO2 m-2 s-1 | average from 40 half-hourly NEE_CUT_XX |
| DD | gC m-2 d-1 | average from 40 daily NEE_CUT_XX |
| WW | gC m-2 d-1 | average from 40 weekly NEE_CUT_XX |
| MM | gC m-2 d-1 | average from 40 monthly NEE_CUT_XX |
| YY | gC m-2 y-1 | average from 40 yearly NEE_CUT_XX |
| NEE_VUT_MEAN | Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, average from 40 NEE_VUT_XX versions | |
| HH | umolCO2 m-2 s-1 | average from 40 half-hourly NEE_CUT_XX |
| DD | gC m-2 d-1 | average from 40 daily NEE_CUT_XX |
| WW | gC m-2 d-1 | average from 40 weekly NEE_CUT_XX |
| MM | gC m-2 d-1 | average from 40 monthly NEE_CUT_XX |
| YY | gC m-2 y-1 | average from 40 yearly NEE_CUT_XX |
| NEE_CUT_MEAN_QC | Quality flag for NEE_CUT_MEAN, fraction between 0-1 indicating percentage of good quality data | |
| HH | nondimensional | average of percentages of good data (NEE_CUT_XX_QC is 0 or 1) from 40 NEE_CUT_XX_QC |
| DD-YY | nondimensional | average of 40 NEE_CUT_XX_QC for the period |
| NEE_VUT_MEAN_QC | Quality flag for NEE_VUT_MEAN, fraction between 0-1 indicating percentage of good quality data | |
| HH | nondimensional | average of percentages of good data (NEE_VUT_XX_QC is 0 or 1) from 40 NEE_VUT_XX_QC |
| DD-YY | nondimensional | average of 40 NEE_VUT_XX_QC for the period |
| NEE_CUT_SE | Standard Error for NEE_CUT, calculated as SD(NEE_CUT_XX) / SQRT(40) | |
| HH | umolCO2 m-2 s-1 | SE from 40 half-hourly NEE_CUT_XX |
| DD | gC m-2 d-1 | SE from 40 daily NEE_CUT_XX |
| WW | gC m-2 d-1 | SE from 40 weekly NEE_CUT_XX |
| MM | gC m-2 d-1 | SE from 40 monthly NEE_CUT_XX |
| YY | gC m-2 y-1 | SE from 40 yearly NEE_CUT_XX |
| NEE_VUT_SE | Standard Error for NEE_VUT, calculated as SD(NEE_VUT_XX) / SQRT(40) | |
| HH | umolCO2 m-2 s-1 | SE from 40 half-hourly NEE_CUT_XX |
| DD | gC m-2 d-1 | SE from 40 daily NEE_CUT_XX |
| WW | gC m-2 d-1 | SE from 40 weekly NEE_CUT_XX |
| MM | gC m-2 d-1 | SE from 40 monthly NEE_CUT_XX |
| YY | gC m-2 y-1 | SE from 40 yearly NEE_CUT_XX |
| 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 | |
| HH | umolCO2 m-2 s-1 | XXth percentile from 40 half-hourly NEE_CUT_XX |
| DD | gC m-2 d-1 | XXth percentile from 40 daily NEE_CUT_XX |
| WW | gC m-2 d-1 | XXth percentile from 40 weekly NEE_CUT_XX |
| MM | gC m-2 d-1 | XXth percentile from 40 monthly NEE_CUT_XX |
| YY | gC m-2 y-1 | XXth percentile from 40 yearly NEE_CUT_XX |
| 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 | |
| HH | umolCO2 m-2 s-1 | XXth percentile from 40 half-hourly NEE_VUT_XX |
| DD | gC m-2 d-1 | XXth percentile from 40 daily NEE_VUT_XX |
| WW | gC m-2 d-1 | XXth percentile from 40 weekly NEE_VUT_XX |
| MM | gC m-2 d-1 | XXth percentile from 40 monthly NEE_VUT_XX |
| YY | gC m-2 y-1 | XXth percentile from 40 yearly NEE_VUT_XX |
| NEE_CUT_XX_QC | Quality flag for NEE_CUT_XX — XX = 05, 16, 25, 50, 75, 84, 95 | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_VUT_XX_QC | Quality flag for NEE_VUT_XX — XX = 05, 16, 25, 50, 75, 84, 95 | |
| HH | nondimensional | 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_CUT_REF_NIGHT | Average nighttime NEE, from NEE_CUT_REF | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | average from half-hourly data (where NIGHT is 1) |
| WW-YY | umolCO2 m-2 s-1 | average from daily data |
| NEE_VUT_REF_NIGHT | Average nighttime NEE, from NEE_VUT_REF | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | average from half-hourly data (where NIGHT is 1) |
| WW-YY | umolCO2 m-2 s-1 | average from daily data |
| NEE_CUT_REF_NIGHT_SD | Standard Deviation of the nighttime NEE, from the NEE_CUT_REF | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | from half-hourly data (where NIGHT is 1) |
| WW-YY | umolCO2 m-2 s-1 | from daily data |
| NEE_VUT_REF_NIGHT_SD | Standard Deviation of the nighttime NEE, from the NEE_VUT_REF | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | from half-hourly data (where NIGHT is 1) |
| WW-YY | umolCO2 m-2 s-1 | from daily data |
| NEE_CUT_REF_NIGHT_QC | Quality flag for NEE_CUT_REF_NIGHT | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_VUT_REF_NIGHT_QC | Quality flag for NEE_VUT_REF_NIGHT | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_CUT_REF_NIGHT_RANDUNC | Random uncertainty of NEE_CUT_REF_NIGHT, from the random uncertainty of the single nighttime half-hours | |
| HH | not produced | |
| DD-YY | umolCO2 m-2 s-1 | from random uncertainty of individual half-hours where NIGHT is 1 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the nighttime aggregation in the day. |
| NEE_VUT_REF_NIGHT_RANDUNC | Random uncertainty of NEE_VUT_REF_NIGHT, from the random uncertainty of the single nighttime half-hours | |
| HH | not produced | |
| DD-YY | umolCO2 m-2 s-1 | from random uncertainty of individual half-hours where NIGHT is 1 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the nighttime aggregation in the day. |
| NEE_CUT_REF_NIGHT_JOINTUNC | Joint uncertainty estimation for NEE_CUT_REF_NIGHT, including random uncertainty and USTAR filtering uncertainty | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_REF_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each day |
| WW | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_REF_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each week |
| MM | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_REF_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each month |
| YY | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_REF_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each year |
| NEE_VUT_REF_NIGHT_JOINTUNC | Joint uncertainty estimation for NEE_VUT_REF_NIGHT, including random uncertainty and USTAR filtering uncertainty | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_REF_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each day |
| WW | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_REF_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each week |
| MM | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_REF_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each month |
| YY | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_REF_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each year |
| NEE_CUT_REF_DAY | Average daytime NEE, from NEE_CUT_REF | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | average from half-hourly data (where NIGHT is 0) |
| WW-YY | umolCO2 m-2 s-1 | average from daily data |
| NEE_VUT_REF_DAY | Average daytime NEE, from NEE_VUT_REF | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | average from half-hourly data (where NIGHT is 0) |
| WW-YY | umolCO2 m-2 s-1 | average from daily data |
| NEE_CUT_REF_DAY_SD | Standard Deviation of the daytime NEE, from the NEE_CUT_REF | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | from half-hourly data (where NIGHT is 0) |
| WW-YY | umolCO2 m-2 s-1 | from daily data |
| NEE_VUT_REF_DAY_SD | Standard Deviation of the daytime NEE, from the NEE_VUT_REF | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | from half-hourly data (where NIGHT is 0) |
| WW-YY | umolCO2 m-2 s-1 | from daily data |
| NEE_CUT_REF_DAY_QC | Quality flag for NEE_CUT_REF_DAY | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_VUT_REF_DAY_QC | Quality flag for NEE_VUT_REF_DAY | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_CUT_REF_DAY_RANDUNC | Random uncertainty of NEE_CUT_REF_DAY, from the random uncertainty of the single daytime half-hours | |
| HH | not produced | |
| DD-YY | umolCO2 m-2 s-1 | from random uncertainty of individual half-hours where NIGHT is 0 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the daytime aggregation in the day. |
| NEE_VUT_REF_DAY_RANDUNC | Random uncertainty of NEE_VUT_REF_DAY, from the random uncertainty of the single daytime half-hours | |
| HH | not produced | |
| DD-YY | umolCO2 m-2 s-1 | from random uncertainty of individual half-hours where NIGHT is 0 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the daytime aggregation in the day. |
| NEE_CUT_REF_DAY_JOINTUNC | Joint uncertainty estimation for NEE_CUT_REF_DAY, including random uncertainty and USTAR filtering uncertainty | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_REF_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each day |
| WW | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_REF_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each week |
| MM | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_REF_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each month |
| YY | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_REF_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each year |
| NEE_VUT_REF_DAY_JOINTUNC | Joint uncertainty estimation for NEE_VUT_REF_DAY, including random uncertainty and USTAR filtering uncertainty | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_REF_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2)] for each day |
| WW | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_REF_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2)] for each week |
| MM | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_REF_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2)] for each month |
| YY | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_REF_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2)] for each year |
| NEE_CUT_USTAR50_NIGHT | Average nighttime NEE, from NEE_CUT_USTAR50 | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | average from half-hourly data (where NIGHT is 1) |
| WW-YY | umolCO2 m-2 s-1 | average from daily data |
| NEE_VUT_USTAR50_NIGHT | Average nighttime NEE, from NEE_VUT_USTAR50 | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | average from half-hourly data (where NIGHT is 1) |
| WW-YY | umolCO2 m-2 s-1 | average from daily data |
| NEE_CUT_USTAR50_NIGHT_SD | Standard Deviation of the nighttime NEE, from the NEE_CUT_USTAR50 | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | from half-hourly data (where NIGHT is 1) |
| WW-YY | umolCO2 m-2 s-1 | from daily data |
| NEE_VUT_USTAR50_NIGHT_SD | Standard Deviation of the nighttime NEE, from the NEE_VUT_USTAR50 | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | from half-hourly data (where NIGHT is 1) |
| WW-YY | umolCO2 m-2 s-1 | from daily data |
| NEE_CUT_USTAR50_NIGHT_QC | Quality flag for NEE_CUT_USTAR50_NIGHT | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_VUT_USTAR50_NIGHT_QC | Quality flag for NEE_VUT_USTAR50_NIGHT | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_CUT_USTAR50_NIGHT_RANDUNC | Random uncertainty of NEE_CUT_USTAR50_NIGHT, from the random uncertainty of the single nighttime half-hours | |
| HH | not produced | |
| DD-YY | umolCO2 m-2 s-1 | from random uncertainty of individual half-hours where NIGHT is 1 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the nighttime aggregation in the day. |
| NEE_VUT_USTAR50_NIGHT_RANDUNC | Random uncertainty of NEE_VUT_USTAR50_NIGHT, from the random uncertainty of the single nighttime half-hours | |
| HH | not produced | |
| DD-YY | umolCO2 m-2 s-1 | from random uncertainty of individual half-hours where NIGHT is 1 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the nighttime aggregation in the day. |
| NEE_CUT_USTAR50_NIGHT_JOINTUNC | Joint uncertainty estimation for NEE_CUT_USTAR50_NIGHT, including random uncertainty and USTAR filtering uncertainty | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each day |
| WW | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each week |
| MM | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each month |
| YY | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_CUT_84_NIGHT – NEE_CUT_16_NIGHT) / 2)^2)] for each year |
| NEE_VUT_USTAR50_NIGHT_JOINTUNC | Joint uncertainty estimation for NEE_VUT_USTAR50_NIGHT, including random uncertainty and USTAR filtering uncertainty | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each day |
| WW | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each week |
| MM | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each month |
| YY | umolCO2 m-2 s-1 | [SQRT(NEE_VUT_USTAR50_NIGHT_RANDUNC^2 + ((NEE_VUT_84_NIGHT – NEE_VUT_16_NIGHT) / 2)^2)] for each year |
| NEE_CUT_USTAR50_DAY | Average daytime NEE, from NEE_CUT_USTAR50 | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | average from half-hourly data (where NIGHT is 0) |
| WW-YY | umolCO2 m-2 s-1 | average from daily data |
| NEE_VUT_USTAR50_DAY | Average daytime NEE, from NEE_VUT_USTAR50 | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | average from half-hourly data (where NIGHT is 0) |
| WW-YY | umolCO2 m-2 s-1 | average from daily data |
| NEE_CUT_USTAR50_DAY_SD | Standard Deviation of the daytime NEE, from the NEE_CUT_USTAR50 | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | from half-hourly data (where NIGHT is 0) |
| WW-YY | umolCO2 m-2 s-1 | from daily data |
| NEE_VUT_USTAR50_DAY_SD | Standard Deviation of the daytime NEE, from the NEE_VUT_USTAR50 | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | from half-hourly data (where NIGHT is 0) |
| WW-YY | umolCO2 m-2 s-1 | from daily data |
| NEE_CUT_USTAR50_DAY_QC | Quality flag for NEE_CUT_USTAR50_DAY | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_VUT_USTAR50_DAY_QC | Quality flag for NEE_VUT_USTAR50_DAY | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_CUT_USTAR50_DAY_RANDUNC | Random uncertainty of NEE_CUT_USTAR50_DAY, from the random uncertainty of the single daytime half-hours | |
| HH | not produced | |
| DD-YY | umolCO2 m-2 s-1 | from random uncertainty of individual half-hours where NIGHT is 0 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the daytime aggregation in the day. |
| NEE_VUT_USTAR50_DAY_RANDUNC | Random uncertainty of NEE_VUT_USTAR50_DAY, from the random uncertainty of the single daytime half-hours | |
| HH | not produced | |
| DD-YY | umolCO2 m-2 s-1 | from random uncertainty of individual half-hours where NIGHT is 0 (rand(i)) = [SQRT(SUM(rand(i)^2)) / n], where n is the number of half-hours used to calculate the daytime aggregation in the day. |
| NEE_CUT_USTAR50_DAY_JOINTUNC | Joint uncertainty estimation for NEE_CUT_USTAR50_DAY, including random uncertainty and USTAR filtering uncertainty | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_USTAR50_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each day |
| WW | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_USTAR50_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each week |
| MM | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_USTAR50_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each month |
| YY | umolCO2 m-2 s-1 | [SQRT(NEE_CUT_USTAR50_DAY_RANDUNC^2 + ((NEE_CUT_84_DAY – NEE_CUT_16_DAY) / 2)^2)] for each year |
| NEE_VUT_USTAR50_DAY_JOINTUNC | Joint uncertainty estimation for NEE_VUT_USTAR50_DAY, including random uncertainty and USTAR filtering uncertainty | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | SQRT(NEE_VUT_USTAR50_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2) for each day |
| WW | umolCO2 m-2 s-1 | SQRT(NEE_VUT_USTAR50_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2) for each week |
| MM | umolCO2 m-2 s-1 | SQRT(NEE_VUT_USTAR50_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2) for each month |
| YY | umolCO2 m-2 s-1 | SQRT(NEE_VUT_USTAR50_DAY_RANDUNC^2 + ((NEE_VUT_84_DAY – NEE_VUT_16_DAY) / 2)^2) for each year |
| NEE_CUT_XX_NIGHT | NEE CUT nighttime 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 | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | XXth nighttime percentile from 40 daily NEE_CUT_XX_NIGHT |
| WW | umolCO2 m-2 s-1 | XXth nighttime percentile from 40 weekly NEE_CUT_XX_NIGHT |
| MM | umolCO2 m-2 s-1 | XXth nighttime percentile from 40 monthly NEE_CUT_XX_NIGHT |
| YY | umolCO2 m-2 s-1 | XXth nighttime percentile from 40 yearly NEE_CUT_XX_NIGHT |
| NEE_VUT_XX_NIGHT | NEE VUT nighttime 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 | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | XXth nighttime percentile from 40 daily NEE_VUT_XX_NIGHT |
| WW | umolCO2 m-2 s-1 | XXth nighttime percentile from 40 weekly NEE_VUT_XX_NIGHT |
| MM | umolCO2 m-2 s-1 | XXth nighttime percentile from 40 monthly NEE_VUT_XX_NIGHT |
| YY | umolCO2 m-2 s-1 | XXth nighttime percentile from 40 yearly NEE_VUT_XX_NIGHT |
| NEE_CUT_XX_NIGHT_QC | Quality flag for NEE_CUT_XX_NIGHT — XX = 05, 16, 25, 50, 75, 84, 95 | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_VUT_XX_NIGHT_QC | Quality flag for NEE_VUT_XX_NIGHT — XX = 05, 16, 25, 50, 75, 84, 95 | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_CUT_XX_DAY | NEE CUT daytime 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 | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | XXth daytime percentile from 40 daily NEE_CUT_XX_DAY |
| WW | umolCO2 m-2 s-1 | XXth daytime percentile from 40 weekly NEE_CUT_XX_DAY |
| MM | umolCO2 m-2 s-1 | XXth daytime percentile from 40 monthly NEE_CUT_XX_DAY |
| YY | umolCO2 m-2 s-1 | XXth daytime percentile from 40 yearly NEE_CUT_XX_DAY |
| NEE_VUT_XX_DAY | NEE VUT daytime 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 | |
| HH | not produced | |
| DD | umolCO2 m-2 s-1 | XXth daytime percentile from 40 daily NEE_VUT_XX_DAY |
| WW | umolCO2 m-2 s-1 | XXth daytime percentile from 40 weekly NEE_VUT_XX_DAY |
| MM | umolCO2 m-2 s-1 | XXth daytime percentile from 40 monthly NEE_VUT_XX_DAY |
| YY | umolCO2 m-2 s-1 | XXth daytime percentile from 40 yearly NEE_VUT_XX_DAY |
| NEE_CUT_XX_DAY_QC | Quality flag for NEE_CUT_XX_DAY — XX = 05, 16, 25, 50, 75, 84, 95 | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| NEE_VUT_XX_DAY_QC | Quality flag for NEE_VUT_XX_DAY — XX = 05, 16, 25, 50, 75, 84, 95 | |
| HH | not produced | |
| DD | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data |
| WW-YY | nondimensional | fraction between 0-1, indicating percentage of measured and good quality gapfill data (average from daily data) |
| PARTITIONING | ||
| NIGHTTIME | ||
| 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 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| RECO_NT_VUT_USTAR50 | Ecosystem Respiration, from Nighttime partitioning method, based on NEE_VUT_USTAR50 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| RECO_NT_VUT_MEAN | Ecosystem Respiration, from Nighttime partitioning method, average from RECO versions, each from corresponding NEE_VUT_XX version | |
| HH | umolCO2 m-2 s-1 | average from 40 half-hourly RECO_NT_VUT_XX |
| DD | gC m-2 d-1 | average from 40 daily RECO_NT_VUT_XX |
| WW | gC m-2 d-1 | average from 40 weekly RECO_NT_VUT_XX |
| MM | gC m-2 d-1 | average from 40 monthly RECO_NT_VUT_XX |
| YY | gC m-2 y-1 | average from 40 yearly RECO_NT_VUT_XX |
| RECO_NT_VUT_SE | Standard Error for Ecosystem Respiration, calculated as (SD(RECO_NT_VUT_XX) / SQRT(40)) | |
| HH | umolCO2 m-2 s-1 | SE from 40 half-hourly RECO_NT_CUT_XX |
| DD | gC m-2 d-1 | SE from 40 daily RECO_NT_VUT_XX |
| WW | gC m-2 d-1 | SE from 40 weekly RECO_NT_VUT_XX |
| MM | gC m-2 d-1 | SE from 40 monthly RECO_NT_VUT_XX |
| YY | gC m-2 y-1 | SE from 40 yearly RECO_NT_VUT_XX |
| RECO_NT_VUT_XX | Ecosystem Respiration, from Nighttime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95) | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| 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 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| RECO_NT_CUT_USTAR50 | Ecosystem Respiration, from Nighttime partitioning method, based on NEE_CUT_USTAR50 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| RECO_NT_CUT_MEAN | Ecosystem Respiration, from Nighttime partitioning method, average from RECO versions, each from corresponding NEE_CUT_XX version | |
| HH | umolCO2 m-2 s-1 | average from 40 half-hourly RECO_NT_CUT_XX |
| DD | gC m-2 d-1 | average from 40 daily RECO_NT_CUT_XX |
| WW | gC m-2 d-1 | average from 40 weekly RECO_NT_CUT_XX |
| MM | gC m-2 d-1 | average from 40 monthly RECO_NT_CUT_XX |
| YY | gC m-2 y-1 | average from 40 yearly RECO_NT_CUT_XX |
| RECO_NT_CUT_SE | Standard Error for Ecosystem Respiration, calculated as (SD(RECO_NT_CUT_XX) / SQRT(40)) | |
| HH | umolCO2 m-2 s-1 | SE from 40 half-hourly RECO_NT_CUT_XX |
| DD | gC m-2 d-1 | SE from 40 daily RECO_NT_CUT_XX |
| WW | gC m-2 d-1 | SE from 40 weekly RECO_NT_CUT_XX |
| MM | gC m-2 d-1 | SE from 40 monthly RECO_NT_CUT_XX |
| YY | gC m-2 y-1 | SE from 40 yearly RECO_NT_CUT_XX |
| RECO_NT_CUT_XX | Ecosystem Respiration, from Nighttime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95) | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| 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 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| GPP_NT_VUT_USTAR50 | Gross Primary Production, from Nighttime partitioning method, based on NEE_VUT_USTAR50 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| GPP_NT_VUT_MEAN | Gross Primary Production, from Nighttime partitioning method, average from GPP versions, each from corresponding NEE_VUT_XX version | |
| HH | umolCO2 m-2 s-1 | average from 40 half-hourly GPP_NT_VUT_XX |
| DD | gC m-2 d-1 | average from 40 daily GPP_NT_VUT_XX |
| WW | gC m-2 d-1 | average from 40 weekly GPP_NT_VUT_XX |
| MM | gC m-2 d-1 | average from 40 monthly GPP_NT_VUT_XX |
| YY | gC m-2 y-1 | average from 40 yearly GPP_NT_VUT_XX |
| GPP_NT_VUT_SE | Standard Error for Gross Primary Production, calculated as (SD(GPP_NT_VUT_XX) / SQRT(40)) | |
| HH | umolCO2 m-2 s-1 | SE from 40 half-hourly GPP_NT_VUT_XX |
| DD | gC m-2 d-1 | SE from 40 daily GPP_NT_VUT_XX |
| WW | gC m-2 d-1 | SE from 40 weekly GPP_NT_VUT_XX |
| MM | gC m-2 d-1 | SE from 40 monthly GPP_NT_VUT_XX |
| YY | gC m-2 y-1 | SE from 40 yearly GPP_NT_VUT_XX |
| GPP_NT_VUT_XX | Gross Primary Production, from Nighttime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95) | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| 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 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| GPP_NT_CUT_USTAR50 | Gross Primary Production, from Nighttime partitioning method, based on NEE_CUT_USTAR50 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| GPP_NT_CUT_MEAN | Gross Primary Production, from Nighttime partitioning method, average from GPP versions, each from corresponding NEE_CUT_XX version | |
| HH | umolCO2 m-2 s-1 | average from 40 half-hourly GPP_NT_CUT_XX |
| DD | gC m-2 d-1 | average from 40 daily GPP_NT_CUT_XX |
| WW | gC m-2 d-1 | average from 40 weekly GPP_NT_CUT_XX |
| MM | gC m-2 d-1 | average from 40 monthly GPP_NT_CUT_XX |
| YY | gC m-2 y-1 | average from 40 yearly GPP_NT_CUT_XX |
| GPP_NT_CUT_SE | Standard Error for Gross Primary Production, calculated as (SD(GPP_NT_CUT_XX) / SQRT(40)) | |
| HH | umolCO2 m-2 s-1 | SE from 40 half-hourly GPP_NT_CUT_XX |
| DD | gC m-2 d-1 | SE from 40 daily GPP_NT_CUT_XX |
| WW | gC m-2 d-1 | SE from 40 weekly GPP_NT_CUT_XX |
| MM | gC m-2 d-1 | SE from 40 monthly GPP_NT_CUT_XX |
| YY | gC m-2 y-1 | SE from 40 yearly GPP_NT_CUT_XX |
| GPP_NT_CUT_XX | Gross Primary Production, from Nighttime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95) | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| DAYTIME | ||
| 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 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| RECO_DT_VUT_USTAR50 | Ecosystem Respiration, from Daytime partitioning method, based on NEE_VUT_USTAR50 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| RECO_DT_VUT_MEAN | Ecosystem Respiration, from Daytime partitioning method, average from RECO versions, each from corresponding NEE_VUT_XX version | |
| HH | umolCO2 m-2 s-1 | average from 40 half-hourly RECO_DT_VUT_XX |
| DD | gC m-2 d-1 | average from 40 daily RECO_DT_VUT_XX |
| WW | gC m-2 d-1 | average from 40 weekly RECO_DT_VUT_XX |
| MM | gC m-2 d-1 | average from 40 monthly RECO_DT_VUT_XX |
| YY | gC m-2 y-1 | average from 40 yearly RECO_DT_VUT_XX |
| RECO_DT_VUT_SE | Standard Error for Ecosystem Respiration, calculated as (SD(RECO_DT_VUT_XX) / SQRT(40)) | |
| HH | umolCO2 m-2 s-1 | SE from 40 half-hourly RECO_DT_CUT_XX |
| DD | gC m-2 d-1 | SE from 40 daily RECO_DT_VUT_XX |
| WW | gC m-2 d-1 | SE from 40 weekly RECO_DT_VUT_XX |
| MM | gC m-2 d-1 | SE from 40 monthly RECO_DT_VUT_XX |
| YY | gC m-2 y-1 | SE from 40 yearly RECO_DT_VUT_XX |
| RECO_DT_VUT_XX | Ecosystem Respiration, from Daytime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95) | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| 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 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| RECO_DT_CUT_USTAR50 | Ecosystem Respiration, from Daytime partitioning method, based on NEE_CUT_USTAR50 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| RECO_DT_CUT_MEAN | Ecosystem Respiration, from Daytime partitioning method, average from RECO versions, each from corresponding NEE_CUT_XX version | |
| HH | umolCO2 m-2 s-1 | average from 40 half-hourly RECO_DT_CUT_XX |
| DD | gC m-2 d-1 | average from 40 daily RECO_DT_CUT_XX |
| WW | gC m-2 d-1 | average from 40 weekly RECO_DT_CUT_XX |
| MM | gC m-2 d-1 | average from 40 monthly RECO_DT_CUT_XX |
| YY | gC m-2 y-1 | average from 40 yearly RECO_DT_CUT_XX |
| RECO_DT_CUT_SE | Standard Error for Ecosystem Respiration, calculated as (SD(RECO_DT_CUT_XX) / SQRT(40)) | |
| HH | umolCO2 m-2 s-1 | SE from 40 half-hourly RECO_DT_CUT_XX |
| DD | gC m-2 d-1 | SE from 40 daily RECO_DT_CUT_XX |
| WW | gC m-2 d-1 | SE from 40 weekly RECO_DT_CUT_XX |
| MM | gC m-2 d-1 | SE from 40 monthly RECO_DT_CUT_XX |
| YY | gC m-2 y-1 | SE from 40 yearly RECO_DT_CUT_XX |
| RECO_DT_CUT_XX | Ecosystem Respiration, from Daytime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95) | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| 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 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| GPP_DT_VUT_USTAR50 | Gross Primary Production, from Daytime partitioning method, based on NEE_VUT_USTAR50 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| GPP_DT_VUT_MEAN | Gross Primary Production, from Daytime partitioning method, average from GPP versions, each from corresponding NEE_VUT_XX version | |
| HH | umolCO2 m-2 s-1 | average from 40 half-hourly GPP_DT_VUT_XX |
| DD | gC m-2 d-1 | average from 40 daily GPP_DT_VUT_XX |
| WW | gC m-2 d-1 | average from 40 weekly GPP_DT_VUT_XX |
| MM | gC m-2 d-1 | average from 40 monthly GPP_DT_VUT_XX |
| YY | gC m-2 y-1 | average from 40 yearly GPP_DT_VUT_XX |
| GPP_DT_VUT_SE | Standard Error for Gross Primary Production, calculated as (SD(GPP_DT_VUT_XX) / SQRT(40)) | |
| HH | umolCO2 m-2 s-1 | SE from 40 half-hourly GPP_DT_VUT_XX |
| DD | gC m-2 d-1 | SE from 40 daily GPP_DT_VUT_XX |
| WW | gC m-2 d-1 | SE from 40 weekly GPP_DT_VUT_XX |
| MM | gC m-2 d-1 | SE from 40 monthly GPP_DT_VUT_XX |
| YY | gC m-2 y-1 | SE from 40 yearly GPP_DT_VUT_XX |
| GPP_DT_VUT_XX | Gross Primary Production, from Daytime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95) | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| 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 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| GPP_DT_CUT_USTAR50 | Gross Primary Production, from Daytime partitioning method, based on NEE_CUT_USTAR50 | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| GPP_DT_CUT_MEAN | Gross Primary Production, from Daytime partitioning method, average from GPP versions, each from corresponding NEE_CUT_XX version | |
| HH | umolCO2 m-2 s-1 | average from 40 half-hourly GPP_DT_CUT_XX |
| DD | gC m-2 d-1 | average from 40 daily GPP_DT_CUT_XX |
| WW | gC m-2 d-1 | average from 40 weekly GPP_DT_CUT_XX |
| MM | gC m-2 d-1 | average from 40 monthly GPP_DT_CUT_XX |
| YY | gC m-2 y-1 | average from 40 yearly GPP_DT_CUT_XX |
| GPP_DT_CUT_SE | Standard Error for Gross Primary Production, calculated as (SD(GPP_DT_CUT_XX) / SQRT(40)) | |
| HH | umolCO2 m-2 s-1 | SE from 40 half-hourly GPP_DT_CUT_XX |
| DD | gC m-2 d-1 | SE from 40 daily GPP_DT_CUT_XX |
| WW | gC m-2 d-1 | SE from 40 weekly GPP_DT_CUT_XX |
| MM | gC m-2 d-1 | SE from 40 monthly GPP_DT_CUT_XX |
| YY | gC m-2 y-1 | SE from 40 yearly GPP_DT_CUT_XX |
| GPP_DT_CUT_XX | Gross Primary Production, from Daytime partitioning method (with XX = 05, 16, 25, 50, 75, 84, 95) | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| SUNDOWN | ||
| RECO_SR | Ecosystem Respiration, from Sundown Respiration partitioning method | |
| HH | umolCO2 m-2 s-1 | |
| DD | gC m-2 d-1 | calculated from half-hourly data |
| WW-MM | gC m-2 d-1 | average from daily data |
| YY | gC m-2 y-1 | sum from daily data |
| RECO_SR_N | Fraction between 0-1, indicating the percentage of data avaiable in the averaging period to parametrize the respiration model | |
| HH | not produced | |
| DD-YY | nondimensional | percentage of data available |