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94 results for “Eddy covariance”
Data and code for: Combining eddy covariance towers, field measurements, and the MEMS 2 ecosystem model improves confidence in the climate impacts of bioenergy with carbon capture and storage
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Eight Mile Lake Research Watershed, Thaw Gradient, Ecosystem carbon balance: Eddy covariance CO2 flux data of a heterogenous landscape undergoing permafrost thaw.
In this larger study, we are asking the question: Is old carbon that comprises the bulk of the soil organic matter pool released in response to thawing of permafrost? We are answering this question by using a combination of field and laboratory experiments to measure radiocarbon isotope ratios in soil organic matter, soil respiration, and dissolved organic carbon, in tundra ecosystems. The objective of these proposed measurements is to develop a mechanistic understanding of the SOM sources contributing to C losses following permafrost thawing. We are making these measurements at an established tundra field site near Healy, Alaska in the foothills of the Alaska Range. Field measurements center on a natural experiment where permafrost has been observed to warm and thaw over the past several decades. This area represents a gradient of sites each with a different degree of change due to permafrost thawing. As such, this area is unique for addressing questions at the time and spatial scales relevant for change in arctic ecosystems. Understanding how landscape level physical and biological changes effect carbon cycling is important for estimating the carbon balance of an ecosystem undergoing permafrost thaw.
Surface carbon, water and energy fluxes measured by eddy covariance at 3 sites within the Alaska Peatlands Experiment and Bonanza Creek Experimental Forest 2013-2016
These data are simultaneous and continuous measurements of carbon, water and energy fluxes of the terrestrial landscape. These fluxes are major regulatory drivers of the boreal climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. At the APEX project site, within Bonanza Creek Experimental Forest, this monitoring is repeated across a chronosequence of permafrost degradation; the Black Spruce site is an area of stable permafrost with intact black spruce forest (APEX gamma site), the Thermokarst site is an active thermokarst zone with considerable tree mortality (APEX betaSW site), the Fen site is within a stable treeless fen with deep active layer depth (APEX apexcon,low, and ele sites). The main variables being monitored are the instananeous fluxes of CO2, water vapor and surface energy (shortwave, longwave and net radiation), secondary variables included photosynthetically active radiation (PAR), air and soil temperatures, rainfall, snow depth, soil moisture content, wind direction and speed, and average atmospheric concentrations of CO2 and H2O through the year. Our site naming scheme is as follows: 1) gamma = Black Spruce site = YF_2472, 2) betaSW = Thermokarst site= BC_5166, 3) (apexcon+apexele+apexlow) = Fen site = BC_FEN
Machine learning estimates of eddy covariance carbon flux in a scrub in the Mexican highland
<p>Arid and semi-arid ecosystems contain relatively high species diversity and are subject to intense use, in particular extensive cattle grazing, which has favoured the expansion and encroachment of perennial thorny shrubs into the grasslands, thus decreasing the value of the rangeland. However, these environments have been shown to positively impact global carbon dynamics. Machine learning and remote sensing had enhanced our knowledge about carbon dynamics, but they need to be further developed and adapted to particular analysis. We measured the net ecosystem exchange of C (NEE) with the Eddy Covariance (EC) method and estimated GPP in a thorny scrub at Bernal in Mexico. We tested the agreement between EC estimates and remotely sensed GPP estimates from MODIS, and also with two alternative modelling methods: ordinary least squares multiple regression (OLS) or ensembles of machine learning algorithms (EML). The variables used as predictors were Moderate Resolution Spectroradiometer (MODIS) spectral bands, vegetation indices and products, as well as gridded environmental variables. The Bernal site was a carbon sink despite it was overgrazed, the average NEE during fifteen months of 2017 and 2018 was -0.78 g C m<sup>-2</sup> d<sup>-1</sup> and the flux was negative or neutral during the measured months. The probability of agreement (θs) represented the agreement between observed and estimated values of GPP across the range of measurement. According to the mean value of θs, agreement was higher for the EML (0.6) followed by OLS (0.5) and then MODIS (0.24). This graphic metric was more informative than r<sup>2</sup> (0.98, 0.67, 0.58 respectively) to evaluate the model performance. This was particularly true for MODIS because the maximum θs of 4.3 was for measurements of 0.8 g C m<sup>-2</sup> d<sup>-1</sup> and then decreased steadily below 1 θs for measurements above 6.5 g C m<sup>-2</sup> d<sup>-1 </sup>for this scrub vegetation. In the case of EML and OLS the θs was stable across the range of measurement. We used an EML for the Ameriflux site US-SRM, which is similar in vegetation and climate, to predict GPP at Bernal, but θs was low (0.16) indicating the local specificity of this model. Although cacti were an important component of the vegetation, the night time flux was characterized by positive NEE, suggesting that the photosynthetic dark-cycle flux of cacti was lower than ecosystem respiration. The discrepancy between MODIS and EC GPP estimates stresses the need to understand the limitations of both methods.</p>
Eddy covariance flux data at the Wüstebach clear-cut site for the 2020 growing season
<p>Data related to the research article <em>Effects of Measurement Height and Low-Pass Filtering Corrections on Eddy-Covariance Flux Measurements Over a Forest Clearing with Complex Vegetation</em> published in <em>Boundary-Layer Meteorology</em>.</p>
Eddy covariance data of lower-cost and conventional setups and meteorological data, Wendhausen 2022 campaign
<p>Eddy covariance fluxes, together with ancillary meteorological data, spectra, stability and turbulence parameters, corresponding to the measurement campaign conducted in Wendhausen, Lehre, Lower Saxony (Germany) in 2022. The results were presented in the paper <em>Comparison between lower-cost and conventional eddy covariance set-ups for CO2 and evapotranspiration measurements above monocropping and agroforestry systems</em>, <a href="https://dx.doi.org/10.2139/ssrn.4632023" target="_blank" rel="noopener">http://dx.doi.org/10.2139/ssrn.4632023</a>.</p> <p>Spectra and co-spectra were averaged as presented in the paper. Flux data are not filtered or quality checked in these files, but the procedure followed for that is described in the paper as well.</p>
Dataset for "Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations"
<p>This dataset provides wetland methane (CH<sub>4</sub>) emissions, their uncertainties and underlying CH<sub>4</sub> flux densities north from 45 N using three different wetland maps. The data products are derived using data from several eddy covariance CH<sub>4</sub> flux sites, random forest machine learning algorithms and three prescribed wetland maps. The data are at 0.5 by 0.5 deg or 1 by 1 deg resolution, depending on the wetland map used. The dataset covers years 2013 and 2014. CH<sub>4</sub> flux densities are provided only for grid cells with > 5 % wetland coverage.</p> <p>The three data products are provided in netCDF format files (.nc). Please see more details in the attributes saved in the netCDF files.</p> <p>RF-DYPTOP.nc<br> Upscaling based on DYPTOP dynamic wetland map. At 1 by 1 deg resolution.</p> <p>RF-GLWD.nc<br> Upscaling using GLWD static wetland map. At 0.5 by 0.5 deg resolution.</p> <p>RF-PEATMAP.nc<br> Upscaling using PEATMAP static wetland map. At 0.5 by 0.5 deg resolution.</p> <p> </p> <p>This dataset is related to Peltola et al. (2019) manuscript submitted to Earth System Science Data. Please cite this publication if you use this dataset in your work.</p> <p>Peltola, O., Vesala, T., Gao, Y., Räty, O., Alekseychik, P., Aurela, M., Chojnicki, B., Desai, A. R., Dolman, A. J., Euskirchen, E. S., Friborg, T., Göckede, M., Helbig, M., Humphreys, E., Jackson, R. B., Jocher, G., Joos, F., Klatt, J., Knox, S. H., Kowalska, N., Kutzbach, L., Lienert, S., Lohila, A., Mammarella, I., Nadeau, D. F., Nilsson, M. B., Oechel, W. C., Peichl, M., Pypker, T., Quinton, W., Rinne, J., Sachs, T., Samson, M., Schmid, H. P., Sonnentag, O., Wille, C., Zona, D., and Aalto, T.: Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-28, in review, 2019.</p>
Eddy Covariance observations of three different sites from Estero El Soldado, Sonora, Mexico
<p>This database contains eddy covariance and micrometerogical measurements, it corresponds to three sites of study in Estero El Soldado (27.95413399, -110.9725342) Sonora, México. The flux tower was operating from June 12<sup>th</sup> 2018 to June 6<sup>st</sup> 2019. The dataset is divided in three sheets: site_1, site_2 and site_3 corresponding to the three sites where the tower was at each period. The three different scenarios consider a) bare soil and seawater column (site_1) from June 12th to October 12th 2018, n=134 days with 26 days of missing data, b) bare soil exposed some hours, seagrass present and shallow seawater column (site_2) from October 13th 2018 to February 24th 201, n= 135 days with 54 days of missing data and, 3) seagrass present and seawater column higher than previous case (site_3) from February 25th to June 6th 2019, with n=102 days with 20 days of missing data.</p> <p>A quality control filter was applied to the complete data set where outlier values were replaced with NaN. In addition to Mauder and Foken (2004) QC filter, we considered based on outliers the following cleaning : FCO2 values outside the range of -10 to 10 µmol m-2 s-1, as well as friction velocity (u*) >3 m s-1, shortwave incoming radiation >2000 W m-2, salinity <25 ppt and wind speed >25 m s-1.</p>
A dataset including 2-year eddy covariance and hyperspectral data at Yunxiao mangrove flux tower
<p>A dataset including 2-year eddy covariance and hyperspectral data at Yunxiao mangrove flux tower. The data is used for producing key findings in a manuscript under review.</p>
Agreement of multiple night- and daytime filtering approaches of eddy covariance-derived net ecosystem CO2 exchange over a mountain forest. Reproducible workflow.
<p>Datasets and python scripts to reproduce results from the publication <em>Agreement of multiple night- and daytime filtering approaches of eddy covariance-derived net ecosystem CO2 exchange over a mountain forest.</em></p> <p>See README.txt for a description of the single files.</p>
Dataset for "Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness"
<p>The micrometeorological dataset used in</p> <p>Tuovinen, J.-P., Aurela, M., Hatakka, J., Räsänen, A., Virtanen, T., Mikola, J., Ivakhov, V., Kondratyev, V. and Laurila, T.: Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness. <em>Biogeosciences Discussions</em>, https://doi.org/10.5194/bg-2018-155, 2018 (accepted for publication in <em>Biogeosciences</em>).</p> <p> </p>
Eddy covariance SAFE Flux Tower
<p><strong>Description: </strong></p> <p>The eddy covariance technique was used to record continuous, non-invasive measurements of CO2, H2O and energy exchange between the ecosystem and the atmosphere. The measuring system consists of a semi-open path infrared gas analyser LI-7200 (LI-COR, USA), and a CSAT3 Sonic Anemometer (Campbell Scientific, USA) at a measuring height of 52 m over a canopy height of ~25 m. Data were recorded at a frequency of 20 Hz that was treated using the post-processing software EddyPro® (v.7.0.6; www.licor.com/eddypro) to compute fluxes for each 30-minute averaging period. To treat the raw fluxes, primary data processing steps were applied, including spike removal (Vickers, 1997 J Atmos Ocean Technol), coordinate rotation, block averaging detrending of CO2, H2O and sonic temperature, time lag compensation using covariance maximisation detection method, random uncertainty estimation (Finkelstein et al. 2001 Journal of Geophysical Research Atmospheres), computation of turbulent fluxes and mean fluxes, spectral corrections (Moncrieff et al. 1997 J Hydrol Amst) using correction of low-pass filtering effects, planar fit rotation (Wilczak et al. 2001 Boundary Layer Meteorol) and quality flagging policy (Göckede et al. 2006 Boundary Layer Meteorol). Eddy covariance meteorological data from above and below canopy is available at DOI 10.5281/zenodo.3888374. Cells with -9999 represent not enough data collected, which can be regarded as NA.<br> <br> This data has been collected over a heavily logged landscape between 2012 - 2018, please note 2016 was removed from this dataset. Before 2015, the landscape was ~10 years recovering from it's previous round of logging (four times logged). During 2015 the landscape was salvaged logged, removing 75% of tree stand basal area.<br> <br> The first data sheet, named "Raw_data" contains all raw fluxes that have been treated by EddyPro, which have not been filtered or quality controlled.<br> <br> The second sheet, named "Daily_fluxes" contains daily mean fluxes of net ecosystem CO2 exchange (NEE), ecosystem respirationn (Reco) and gross primary productivity and their associated standard errors. Net ecosystem CO2 exchange (NEE) was calculated by adding the estimated CO2 storage flux to the observed CO2 flux. Data was subjecto quality control including the removal of quality flags 4 and 5 (Göckede et al. 2006 Boundary Layer Meteorol) and the application of a mean u* threshold of >0.29 m s-1 to the dataset, as established using the package "REddyProc" (v.1.2; (Wultzer et al. 2019 Biogeosciences)) in based on the Moving Point Method (Reichstein et al. 2005, GCB). Data was subsequently gap filled and partitioned, as descripted within the variable methods of this sheet. This data was part of an analysis of carbon fluxes within three periods of data collection: in 2012 – 2013, which captured the four-times logged ecosystem ~10 years after its previous round of logging, in 2015 during a new round of active salvage logging, and in 2017 – 2018 when the ecosystem was recovery 2-3 years after the salvage logging. Days with large standard errors for Reco (> ± 5 µmol m−2 s−1) were deemed as bad quality and removed from the dataset and we used only days that had four or more observed half-hourly values of NEE. Of the final dataset , 29.5% of the half-hourly values are original observed fluxes, and 70.5% gap-filled. Of the 455 days remaining after all filtering processes were applied, 65 days were during the 10-years recovery phase (2012-2013), 100 during the active salvage logging (2015) and 290 during the 2-3 years recovery from active salvage logging phase (2017-2018).</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/113"><strong>Changing carbon dioxide and water budgets from deforestation and habitat modification</strong></a></p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7307447">here</a></p> <p><strong>Files: </strong>This consists of 1 file: SAFE_EC_byYear.xlsx</p> <p><strong>SAFE_EC_byYear.xlsx</strong></p> <p>This file contains dataset metadata and 6 data tables:</p> <ol> <li> <p><strong>Raw_data_2012_2013</strong> (described in worksheet Raw_data_2012_2013)</p> <p>Description: EddyPro output of eddy covariance data collected at 52m at the top of the flux tower.</p> <p>Number of fields: 105</p> <p>Number of data rows: 24213</p> <p>Fields:</p> <ul> <li><strong>Location</strong>: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location)</li> <li><strong>date</strong>: Date of the end of the averaging period (Field type: date)</li> <li><strong>time</strong>: Time of the end of the averaging period (Field type: time)</li> <li><strong>DOY</strong>: decimal day of year (Field type: numeric)</li> <li><strong>daytime</strong>: Daytime or nightime, 1 = daytime, 0 = nighttime (Field type: numeric)</li> <li><strong>file_records</strong>: Number of valid records found in the raw file (or set of raw files) (Field type: numeric)</li> <li><strong>used_records</strong>: Number of valid records used for current the averaging period (Field type: numeric)</li> <li><strong>Tau</strong>: Corrected momentum flux (Field type: numeric)</li> <li><strong>qc_Tau</strong>: Quality flag for momentum flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_Tau</strong>: Random error for momentum flux, if selected (Field type: numeric)</li> <li><strong>H</strong>: Corrected sensible heat flux (Field type: numeric)</li> <li><strong>qc_H</strong>: Quality flag for sensible heat flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_H</strong>: Random error for momentum flux, if selected (Field type: numeric)</li> <li><strong>LE</strong>: Corrected latent heat flux (Field type: numeric)</li> <li><strong>qc_LE</strong>: Quality flag of latent heat flux based on Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_LE</strong>: Random error for latent heat flux, if selected (Field type: numeric)</li> <li><strong>co2_flux</strong>: CO2 flux (Field type: numeric)</li> <li><strong>qc_co2_flux</strong>: Quality flag for CO2 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_co2_flux</strong>: Random error of CO2 flux (Field type: numeric)</li> <li><strong>h2o_flux</strong>: H2O flux (Field type: numeric)</li> <li><strong>qc_h2o_flux</strong>: Quality flag of H20 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_h2o_flux</strong>: Random error of CO2 flux (Field type: numeric)</li> <li><strong>H_strg</strong>: Estimate of storage sensible heat flux (Field type: numeric)</li> <li><strong>LE_strg</strong>: Estimate of storage latent heat flux (Field type: numeric)</li> <li><strong>co2_strg</strong>: Estimate of storage CO2 flux (Field type: numeric)</li> <li><strong>h2o_strg</strong>: Estimate of storage H20 flux (Field type: numeric)</li> <li><strong>co2_v.adv</strong>: Estimate of vertical advection flux of CO2 (Field type: numeric)</li> <li><strong>h2o_v.adv</strong>: Estimate of vertical advection flux of H20 (Field type: numeric)</li> <li><strong>co2_molar_density</strong>: Measured or estimated molar density of gas (Field type: numeric)</li> <li><strong>co2_mole_fraction</strong>: Measured or estimated mole fraction of gas (Field type: numeric)</li> <li><strong>co2_mixing_ratio</strong>: Measured or estimated mixing ratio of gas (Field type: numeric)</li> <li><strong>co2_time_lag</strong>: Time lag used to synchronize gas time series (Field type: numeric)</li> <li><strong>co2_def_timelag</strong>: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric)</li> <li><strong>h2o_molar_density</strong>: Measured or estimated molar density of gas (Field type: numeric)</li> <li><strong>h2o_mole_fraction</strong>: Measured or estimated mole fraction of gas (Field type: numeric)</li> <li><strong>h2o_mixing_ratio</strong>: Measured or estimated mixing ratio of gas (Field type: numeric)</li> <li><strong>h2o_time_lag</strong>: Time lag used to synchronize gas time series (Field type: numeric)</li> <li><strong>h2o_def_timelag</strong>: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric)</li> <li><strong>sonic_temperature</strong>: Mean temperature of ambient air as measured by the anemometer (Field type: numeric)</li> <li><strong>air_temperature</strong>: Mean temperature of ambient air, either calculated from high frequency air temperature readings, or estimated from sonic temperature (Field type: numeric)</li> <li><strong>air_pressure</strong>: Mean pressure of ambient air, either calculated from high frequency air pressure readings, or estimated based on site altitude (barometric pressure) (Field type: numeric)</li> <li><strong>air_density</strong>: Density of ambient air (Field type: numeric)</li> <li><strong>air_heat_capacity</strong>: Specific heat at constant pressure of ambient air (Field type: numeric)</li> <li><strong>air_molar_volume</strong>: Molar volume of ambient air (Field type: numeric)</li> <li><strong>ET</strong>: Evapotranspiration flux (Field type: numeric)</li> <li><strong>water_vapor_density</strong>: Ambient mass density of water vapor (Field type: numeric)</li> <li><strong>e</strong>: Ambient water vapor partial pressure (Field type: numeric)</li> <li><strong>es</strong>: Ambient water vapor partial pressure at saturation (Field type: numeric)</li> <li><strong>specific_humidity</strong>: Ambient specific humidity on a mass basis (Field type: numeric)</li> <li><strong>RH</strong>: Ambient relative humidity (Field type: numeric)</li> <li><strong>VPD</strong>: Ambient water vapor pressure deficit (Field type: numeric)</li> <li><strong>Tdew</strong>: Ambient dew point temperature (Field type: numeric)</li> <li><strong>u_unrot</strong>: Wind component along the u anemometer axis (Field type: numeric)</li> <li><strong>v_unrot</strong>: Wind component along the v anemometer axis (Field type: numeric)</li> <li><strong>w_unrot</strong>: Wind component along the w anemometer axis (Field type: numeric)</li> <li><strong>u_rot</strong>: Rotated u wind component (mean wind speed) (Field type: numeric)</li> <li><strong>v_rot</strong>: Rotated v wind component (should be zero) (Field type: numeric)</li> <li><strong>w_rot</strong>: Rotated w wind component (should be zero) (Field type: numeric)</li> <li><strong>wind_speed</strong>: Mean wind speed (Field type: numeric)</li> <li><strong>max_wind_speed</strong>: Maximum instantaneous wind speed (Field type: numeric)</li> <li><strong>wind_dir</strong>: Direction from which the wind blows, with respect to Geographic or Magnetic north (Field type: numeric)</li> <li><strong>yaw</strong>: First rotation angle (Field type: numeric)</li> <li><strong>pitch</strong>: Second rotation angle (Field type: numeric)</li> <li><strong>u.</strong>: Friction velocity (Field type: numeric)</li> <li><strong>TKE</strong>: Turbulent kinetic energy (Field type: numeric)</li> <li><strong>L</strong>: Monin-Obukhov length (Field type: numeric)</li> <li><strong>X.z.d..L</strong>: Monin-Obukhov stability parameter - (z-d)/L (Field type: numeric)</li> <li><strong>bowen_ratio</strong>: Sensible heat flux to latent heat flux ratio (Field type: numeric)</li> <li><strong>T.</strong>: Scaling temperature (Field type: numeric)</li> <li><strong>model</strong>: Model for footprint estimation, 1- Kljun et al. (2004): A crosswind integrated parameterization of footprint estimations obtained with a 3D Lagrangian model by means of a scaling procedure.2 - Kormann and Meixner (2001): A crosswind integrated model based on the solution of the two dimensional advection-diffusion equation given by van Ulden (1978) and others for power-law profiles in wind velocity and eddy diffusivity, 3 - Hsieh et al. (2000): A crosswind integrated model based on the former model of Gash (1986) and on simulations with a Lagrangian stochastic model. (Field type: numeric)</li> <li><strong>x_peak</strong>: Along-wind distance providing <1% contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_offset</strong>: Along-wind distance providing the highest (peak) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_10.</strong>: Along-wind distance providing 10% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_30.</strong>: Along-wind distance providing 30% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_50.</strong>: Along-wind distance providing 50% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_70.</strong>: Along-wind distance providing 70% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_90.</strong>: Along-wind distance providing 90% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>un_Tau</strong>: Uncorrected momentum flux (Field type: numeric)</li> <li><strong>Tau_scf</strong>: Spectral correction factor for momentum flux (Field type: numeric)</li> <li><strong>un_H</strong>: Uncorrected sensible heat flux (Field type: numeric)</li> <li><strong>H_scf</strong>: Spectral correction factor for sensible heat flux (Field type: numeric)</li> <li><strong>un_LE</strong>: Uncorrected latent heat flux (Field type: numeric)</li> <li><strong>LE_scf</strong>: Spectral correction factor for latent heat flux (Field type: numeric)</li> <li><strong>un_co2_flux</strong>: Uncorrected gas flux (Field type: numeric)</li> <li><strong>co2_scf</strong>: Spectral correction factor for gas flux (Field type: numeric)</li> <li><strong>un_h2o_flux</strong>: Uncorrected gas flux (Field type: numeric)</li> <li><strong>h2o_scf</strong>: Spectral correction factor for gas flux (Field type: numeric)</li> <li><strong>spikes_hf</strong>: Hard flags for individual variables for spike test (Field type: numeric)</li> <li><strong>amplitude_resolution_hf</strong>: Hard flags for individual variables for amplitude resolution (Field type: numeric)</li> <li><strong>drop_out_hf</strong>: Hard flags for individual variables for drop-out test (Field type: numeric)</li> <li><strong>absolute_limits_hf</strong>: Hard flags for individual variables for absolute limits (Field type: numeric)</li> <li><strong>skewness_kurtosis_hf</strong>: Hard flags for individual variables for skewness and kurtosis (Field type: numeric)</li> <li><strong>skewness_kurtosis_sf</strong>: Soft flags for individual variables for skewness and kurtosis test (Field type: numeric)</li> <li><strong>discontinuities_hf</strong>: Hard flags for individual variables for discontinuities test (Field type: numeric)</li> <li><strong>discontinuities_sf</strong>: Soft flags for individual variables for discontinuities test (Field type: numeric)</li> <li><strong>timelag_hf</strong>: Hard flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>timelag_sf</strong>: Soft flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>attack_angle_hf</strong>: Hard flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>non_steady_wind_hf</strong>: Soft flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>u_spikes</strong>: Number of spikes detected and eliminated for rotated u wind component (Field type: numeric)</li> <li><strong>v_spikes</strong>: Number of spikes detected and eliminated forrotated v wind component (Field type: numeric)</li> <li><strong>w_spikes</strong>: Number of spikes detected and eliminated for rotated w wind component (Field type: numeric)</li> <li><strong>ts_spikes</strong>: Number of spikes detected and eliminated for ts variable (Field type: numeric)</li> <li><strong>co2_spikes</strong>: Number of spikes detected and eliminated for co2 variable (Field type: numeric)</li> <li><strong>h2o_spikes</strong>: Number of spikes detected and eliminated for h2o variable (Field type: numeric)</li> </ul> </li> <li> <p><strong>Raw_data_2014</strong> (described in worksheet Raw_data_2014)</p> <p>Description: EddyPro output of eddy covariance data collected at 52m at the top of the flux tower. There is a significant data gap, with some intermittent records available during the daytime, between 17/2/2014-17/06/2014 due to the problems in the power supply.</p> <p>Number of fields: 105</p> <p>Number of data rows: 17520</p> <p>Fields:</p> <ul> <li><strong>Location</strong>: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location)</li> <li><strong>date</strong>: Date of the end of the averaging period (Field type: date)</li> <li><strong>time</strong>: Time of the end of the averaging period (Field type: time)</li> <li><strong>DOY</strong>: decimal day of year (Field type: numeric)</li> <li><strong>daytime</strong>: Daytime or nightime, 1 = daytime, 0 = nighttime (Field type: numeric)</li> <li><strong>file_records</strong>: Number of valid records found in the raw file (or set of raw files) (Field type: numeric)</li> <li><strong>used_records</strong>: Number of valid records used for current the averaging period (Field type: numeric)</li> <li><strong>Tau</strong>: Corrected momentum flux (Field type: numeric)</li> <li><strong>qc_Tau</strong>: Quality flag for momentum flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_Tau</strong>: Random error for momentum flux, if selected (Field type: numeric)</li> <li><strong>H</strong>: Corrected sensible heat flux (Field type: numeric)</li> <li><strong>qc_H</strong>: Quality flag for sensible heat flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_H</strong>: Random error for momentum flux, if selected (Field type: numeric)</li> <li><strong>LE</strong>: Corrected latent heat flux (Field type: numeric)</li> <li><strong>qc_LE</strong>: Quality flag of latent heat flux based on Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_LE</strong>: Random error for latent heat flux, if selected (Field type: numeric)</li> <li><strong>co2_flux</strong>: CO2 flux (Field type: numeric)</li> <li><strong>qc_co2_flux</strong>: Quality flag for CO2 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_co2_flux</strong>: Random error of CO2 flux (Field type: numeric)</li> <li><strong>h2o_flux</strong>: H2O flux (Field type: numeric)</li> <li><strong>qc_h2o_flux</strong>: Quality flag of H20 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_h2o_flux</strong>: Random error of CO2 flux (Field type: numeric)</li> <li><strong>H_strg</strong>: Estimate of storage sensible heat flux (Field type: numeric)</li> <li><strong>LE_strg</strong>: Estimate of storage latent heat flux (Field type: numeric)</li> <li><strong>co2_strg</strong>: Estimate of storage CO2 flux (Field type: numeric)</li> <li><strong>h2o_strg</strong>: Estimate of storage H20 flux (Field type: numeric)</li> <li><strong>co2_v.adv</strong>: Estimate of vertical advection flux of CO2 (Field type: numeric)</li> <li><strong>h2o_v.adv</strong>: Estimate of vertical advection flux of H20 (Field type: numeric)</li> <li><strong>co2_molar_density</strong>: Measured or estimated molar density of gas (Field type: numeric)</li> <li><strong>co2_mole_fraction</strong>: Measured or estimated mole fraction of gas (Field type: numeric)</li> <li><strong>co2_mixing_ratio</strong>: Measured or estimated mixing ratio of gas (Field type: numeric)</li> <li><strong>co2_time_lag</strong>: Time lag used to synchronize gas time series (Field type: numeric)</li> <li><strong>co2_def_timelag</strong>: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric)</li> <li><strong>h2o_molar_density</strong>: Measured or estimated molar density of gas (Field type: numeric)</li> <li><strong>h2o_mole_fraction</strong>: Measured or estimated mole fraction of gas (Field type: numeric)</li> <li><strong>h2o_mixing_ratio</strong>: Measured or estimated mixing ratio of gas (Field type: numeric)</li> <li><strong>h2o_time_lag</strong>: Time lag used to synchronize gas time series (Field type: numeric)</li> <li><strong>h2o_def_timelag</strong>: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric)</li> <li><strong>sonic_temperature</strong>: Mean temperature of ambient air as measured by the anemometer (Field type: numeric)</li> <li><strong>air_temperature</strong>: Mean temperature of ambient air, either calculated from high frequency air temperature readings, or estimated from sonic temperature (Field type: numeric)</li> <li><strong>air_pressure</strong>: Mean pressure of ambient air, either calculated from high frequency air pressure readings, or estimated based on site altitude (barometric pressure) (Field type: numeric)</li> <li><strong>air_density</strong>: Density of ambient air (Field type: numeric)</li> <li><strong>air_heat_capacity</strong>: Specific heat at constant pressure of ambient air (Field type: numeric)</li> <li><strong>air_molar_volume</strong>: Molar volume of ambient air (Field type: numeric)</li> <li><strong>ET</strong>: Evapotranspiration flux (Field type: numeric)</li> <li><strong>water_vapor_density</strong>: Ambient mass density of water vapor (Field type: numeric)</li> <li><strong>e</strong>: Ambient water vapor partial pressure (Field type: numeric)</li> <li><strong>es</strong>: Ambient water vapor partial pressure at saturation (Field type: numeric)</li> <li><strong>specific_humidity</strong>: Ambient specific humidity on a mass basis (Field type: numeric)</li> <li><strong>RH</strong>: Ambient relative humidity (Field type: numeric)</li> <li><strong>VPD</strong>: Ambient water vapor pressure deficit (Field type: numeric)</li> <li><strong>Tdew</strong>: Ambient dew point temperature (Field type: numeric)</li> <li><strong>u_unrot</strong>: Wind component along the u anemometer axis (Field type: numeric)</li> <li><strong>v_unrot</strong>: Wind component along the v anemometer axis (Field type: numeric)</li> <li><strong>w_unrot</strong>: Wind component along the w anemometer axis (Field type: numeric)</li> <li><strong>u_rot</strong>: Rotated u wind component (mean wind speed) (Field type: numeric)</li> <li><strong>v_rot</strong>: Rotated v wind component (should be zero) (Field type: numeric)</li> <li><strong>w_rot</strong>: Rotated w wind component (should be zero) (Field type: numeric)</li> <li><strong>wind_speed</strong>: Mean wind speed (Field type: numeric)</li> <li><strong>max_wind_speed</strong>: Maximum instantaneous wind speed (Field type: numeric)</li> <li><strong>wind_dir</strong>: Direction from which the wind blows, with respect to Geographic or Magnetic north (Field type: numeric)</li> <li><strong>yaw</strong>: First rotation angle (Field type: numeric)</li> <li><strong>pitch</strong>: Second rotation angle (Field type: numeric)</li> <li><strong>u.</strong>: Friction velocity (Field type: numeric)</li> <li><strong>TKE</strong>: Turbulent kinetic energy (Field type: numeric)</li> <li><strong>L</strong>: Monin-Obukhov length (Field type: numeric)</li> <li><strong>X.z.d..L</strong>: Monin-Obukhov stability parameter - (z-d)/L (Field type: numeric)</li> <li><strong>bowen_ratio</strong>: Sensible heat flux to latent heat flux ratio (Field type: numeric)</li> <li><strong>T.</strong>: Scaling temperature (Field type: numeric)</li> <li><strong>model</strong>: Model for footprint estimation, 1- Kljun et al. (2004): A crosswind integrated parameterization of footprint estimations obtained with a 3D Lagrangian model by means of a scaling procedure.2 - Kormann and Meixner (2001): A crosswind integrated model based on the solution of the two dimensional advection-diffusion equation given by van Ulden (1978) and others for power-law profiles in wind velocity and eddy diffusivity, 3 - Hsieh et al. (2000): A crosswind integrated model based on the former model of Gash (1986) and on simulations with a Lagrangian stochastic model. (Field type: numeric)</li> <li><strong>x_peak</strong>: Along-wind distance providing <1% contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_offset</strong>: Along-wind distance providing the highest (peak) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_10.</strong>: Along-wind distance providing 10% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_30.</strong>: Along-wind distance providing 30% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_50.</strong>: Along-wind distance providing 50% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_70.</strong>: Along-wind distance providing 70% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_90.</strong>: Along-wind distance providing 90% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>un_Tau</strong>: Uncorrected momentum flux (Field type: numeric)</li> <li><strong>Tau_scf</strong>: Spectral correction factor for momentum flux (Field type: numeric)</li> <li><strong>un_H</strong>: Uncorrected sensible heat flux (Field type: numeric)</li> <li><strong>H_scf</strong>: Spectral correction factor for sensible heat flux (Field type: numeric)</li> <li><strong>un_LE</strong>: Uncorrected latent heat flux (Field type: numeric)</li> <li><strong>LE_scf</strong>: Spectral correction factor for latent heat flux (Field type: numeric)</li> <li><strong>un_co2_flux</strong>: Uncorrected gas flux (Field type: numeric)</li> <li><strong>co2_scf</strong>: Spectral correction factor for gas flux (Field type: numeric)</li> <li><strong>un_h2o_flux</strong>: Uncorrected gas flux (Field type: numeric)</li> <li><strong>h2o_scf</strong>: Spectral correction factor for gas flux (Field type: numeric)</li> <li><strong>spikes_hf</strong>: Hard flags for individual variables for spike test (Field type: numeric)</li> <li><strong>amplitude_resolution_hf</strong>: Hard flags for individual variables for amplitude resolution (Field type: numeric)</li> <li><strong>drop_out_hf</strong>: Hard flags for individual variables for drop-out test (Field type: numeric)</li> <li><strong>absolute_limits_hf</strong>: Hard flags for individual variables for absolute limits (Field type: numeric)</li> <li><strong>skewness_kurtosis_hf</strong>: Hard flags for individual variables for skewness and kurtosis (Field type: numeric)</li> <li><strong>skewness_kurtosis_sf</strong>: Soft flags for individual variables for skewness and kurtosis test (Field type: numeric)</li> <li><strong>discontinuities_hf</strong>: Hard flags for individual variables for discontinuities test (Field type: numeric)</li> <li><strong>discontinuities_sf</strong>: Soft flags for individual variables for discontinuities test (Field type: numeric)</li> <li><strong>timelag_hf</strong>: Hard flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>timelag_sf</strong>: Soft flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>attack_angle_hf</strong>: Hard flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>non_steady_wind_hf</strong>: Soft flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>u_spikes</strong>: Number of spikes detected and eliminated for rotated u wind component (Field type: numeric)</li> <li><strong>v_spikes</strong>: Number of spikes detected and eliminated forrotated v wind component (Field type: numeric)</li> <li><strong>w_spikes</strong>: Number of spikes detected and eliminated for rotated w wind component (Field type: numeric)</li> <li><strong>ts_spikes</strong>: Number of spikes detected and eliminated for ts variable (Field type: numeric)</li> <li><strong>co2_spikes</strong>: Number of spikes detected and eliminated for co2 variable (Field type: numeric)</li> <li><strong>h2o_spikes</strong>: Number of spikes detected and eliminated for h2o variable (Field type: numeric)</li> </ul> </li> <li> <p><strong>Raw_data_2015</strong> (described in worksheet Raw_data_2015)</p> <p>Description: EddyPro output of eddy covariance data collected at 52m at the top of the flux tower.</p> <p>Number of fields: 105</p> <p>Number of data rows: 17520</p> <p>Fields:</p> <ul> <li><strong>Location</strong>: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location)</li> <li><strong>date</strong>: Date of the end of the averaging period (Field type: date)</li> <li><strong>time</strong>: Time of the end of the averaging period (Field type: time)</li> <li><strong>DOY</strong>: decimal day of year (Field type: numeric)</li> <li><strong>daytime</strong>: Daytime or nightime, 1 = daytime, 0 = nighttime (Field type: numeric)</li> <li><strong>file_records</strong>: Number of valid records found in the raw file (or set of raw files) (Field type: numeric)</li> <li><strong>used_records</strong>: Number of valid records used for current the averaging period (Field type: numeric)</li> <li><strong>Tau</strong>: Corrected momentum flux (Field type: numeric)</li> <li><strong>qc_Tau</strong>: Quality flag for momentum flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_Tau</strong>: Random error for momentum flux, if selected (Field type: numeric)</li> <li><strong>H</strong>: Corrected sensible heat flux (Field type: numeric)</li> <li><strong>qc_H</strong>: Quality flag for sensible heat flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_H</strong>: Random error for momentum flux, if selected (Field type: numeric)</li> <li><strong>LE</strong>: Corrected latent heat flux (Field type: numeric)</li> <li><strong>qc_LE</strong>: Quality flag of latent heat flux based on Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_LE</strong>: Random error for latent heat flux, if selected (Field type: numeric)</li> <li><strong>co2_flux</strong>: CO2 flux (Field type: numeric)</li> <li><strong>qc_co2_flux</strong>: Quality flag for CO2 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_co2_flux</strong>: Random error of CO2 flux (Field type: numeric)</li> <li><strong>h2o_flux</strong>: H2O flux (Field type: numeric)</li> <li><strong>qc_h2o_flux</strong>: Quality flag of H20 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_h2o_flux</strong>: Random error of CO2 flux (Field type: numeric)</li> <li><strong>H_strg</strong>: Estimate of storage sensible heat flux (Field type: numeric)</li> <li><strong>LE_strg</strong>: Estimate of storage latent heat flux (Field type: numeric)</li> <li><strong>co2_strg</strong>: Estimate of storage CO2 flux (Field type: numeric)</li> <li><strong>h2o_strg</strong>: Estimate of storage H20 flux (Field type: numeric)</li> <li><strong>co2_v.adv</strong>: Estimate of vertical advection flux of CO2 (Field type: numeric)</li> <li><strong>h2o_v.adv</strong>: Estimate of vertical advection flux of H20 (Field type: numeric)</li> <li><strong>co2_molar_density</strong>: Measured or estimated molar density of gas (Field type: numeric)</li> <li><strong>co2_mole_fraction</strong>: Measured or estimated mole fraction of gas (Field type: numeric)</li> <li><strong>co2_mixing_ratio</strong>: Measured or estimated mixing ratio of gas (Field type: numeric)</li> <li><strong>co2_time_lag</strong>: Time lag used to synchronize gas time series (Field type: numeric)</li> <li><strong>co2_def_timelag</strong>: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric)</li> <li><strong>h2o_molar_density</strong>: Measured or estimated molar density of gas (Field type: numeric)</li> <li><strong>h2o_mole_fraction</strong>: Measured or estimated mole fraction of gas (Field type: numeric)</li> <li><strong>h2o_mixing_ratio</strong>: Measured or estimated mixing ratio of gas (Field type: numeric)</li> <li><strong>h2o_time_lag</strong>: Time lag used to synchronize gas time series (Field type: numeric)</li> <li><strong>h2o_def_timelag</strong>: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric)</li> <li><strong>sonic_temperature</strong>: Mean temperature of ambient air as measured by the anemometer (Field type: numeric)</li> <li><strong>air_temperature</strong>: Mean temperature of ambient air, either calculated from high frequency air temperature readings, or estimated from sonic temperature (Field type: numeric)</li> <li><strong>air_pressure</strong>: Mean pressure of ambient air, either calculated from high frequency air pressure readings, or estimated based on site altitude (barometric pressure) (Field type: numeric)</li> <li><strong>air_density</strong>: Density of ambient air (Field type: numeric)</li> <li><strong>air_heat_capacity</strong>: Specific heat at constant pressure of ambient air (Field type: numeric)</li> <li><strong>air_molar_volume</strong>: Molar volume of ambient air (Field type: numeric)</li> <li><strong>ET</strong>: Evapotranspiration flux (Field type: numeric)</li> <li><strong>water_vapor_density</strong>: Ambient mass density of water vapor (Field type: numeric)</li> <li><strong>e</strong>: Ambient water vapor partial pressure (Field type: numeric)</li> <li><strong>es</strong>: Ambient water vapor partial pressure at saturation (Field type: numeric)</li> <li><strong>specific_humidity</strong>: Ambient specific humidity on a mass basis (Field type: numeric)</li> <li><strong>RH</strong>: Ambient relative humidity (Field type: numeric)</li> <li><strong>VPD</strong>: Ambient water vapor pressure deficit (Field type: numeric)</li> <li><strong>Tdew</strong>: Ambient dew point temperature (Field type: numeric)</li> <li><strong>u_unrot</strong>: Wind component along the u anemometer axis (Field type: numeric)</li> <li><strong>v_unrot</strong>: Wind component along the v anemometer axis (Field type: numeric)</li> <li><strong>w_unrot</strong>: Wind component along the w anemometer axis (Field type: numeric)</li> <li><strong>u_rot</strong>: Rotated u wind component (mean wind speed) (Field type: numeric)</li> <li><strong>v_rot</strong>: Rotated v wind component (should be zero) (Field type: numeric)</li> <li><strong>w_rot</strong>: Rotated w wind component (should be zero) (Field type: numeric)</li> <li><strong>wind_speed</strong>: Mean wind speed (Field type: numeric)</li> <li><strong>max_wind_speed</strong>: Maximum instantaneous wind speed (Field type: numeric)</li> <li><strong>wind_dir</strong>: Direction from which the wind blows, with respect to Geographic or Magnetic north (Field type: numeric)</li> <li><strong>yaw</strong>: First rotation angle (Field type: numeric)</li> <li><strong>pitch</strong>: Second rotation angle (Field type: numeric)</li> <li><strong>u.</strong>: Friction velocity (Field type: numeric)</li> <li><strong>TKE</strong>: Turbulent kinetic energy (Field type: numeric)</li> <li><strong>L</strong>: Monin-Obukhov length (Field type: numeric)</li> <li><strong>X.z.d..L</strong>: Monin-Obukhov stability parameter - (z-d)/L (Field type: numeric)</li> <li><strong>bowen_ratio</strong>: Sensible heat flux to latent heat flux ratio (Field type: numeric)</li> <li><strong>T.</strong>: Scaling temperature (Field type: numeric)</li> <li><strong>model</strong>: Model for footprint estimation, 1- Kljun et al. (2004): A crosswind integrated parameterization of footprint estimations obtained with a 3D Lagrangian model by means of a scaling procedure.2 - Kormann and Meixner (2001): A crosswind integrated model based on the solution of the two dimensional advection-diffusion equation given by van Ulden (1978) and others for power-law profiles in wind velocity and eddy diffusivity, 3 - Hsieh et al. (2000): A crosswind integrated model based on the former model of Gash (1986) and on simulations with a Lagrangian stochastic model. (Field type: numeric)</li> <li><strong>x_peak</strong>: Along-wind distance providing <1% contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_offset</strong>: Along-wind distance providing the highest (peak) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_10.</strong>: Along-wind distance providing 10% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_30.</strong>: Along-wind distance providing 30% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_50.</strong>: Along-wind distance providing 50% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_70.</strong>: Along-wind distance providing 70% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_90.</strong>: Along-wind distance providing 90% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>un_Tau</strong>: Uncorrected momentum flux (Field type: numeric)</li> <li><strong>Tau_scf</strong>: Spectral correction factor for momentum flux (Field type: numeric)</li> <li><strong>un_H</strong>: Uncorrected sensible heat flux (Field type: numeric)</li> <li><strong>H_scf</strong>: Spectral correction factor for sensible heat flux (Field type: numeric)</li> <li><strong>un_LE</strong>: Uncorrected latent heat flux (Field type: numeric)</li> <li><strong>LE_scf</strong>: Spectral correction factor for latent heat flux (Field type: numeric)</li> <li><strong>un_co2_flux</strong>: Uncorrected gas flux (Field type: numeric)</li> <li><strong>co2_scf</strong>: Spectral correction factor for gas flux (Field type: numeric)</li> <li><strong>un_h2o_flux</strong>: Uncorrected gas flux (Field type: numeric)</li> <li><strong>h2o_scf</strong>: Spectral correction factor for gas flux (Field type: numeric)</li> <li><strong>spikes_hf</strong>: Hard flags for individual variables for spike test (Field type: numeric)</li> <li><strong>amplitude_resolution_hf</strong>: Hard flags for individual variables for amplitude resolution (Field type: numeric)</li> <li><strong>drop_out_hf</strong>: Hard flags for individual variables for drop-out test (Field type: numeric)</li> <li><strong>absolute_limits_hf</strong>: Hard flags for individual variables for absolute limits (Field type: numeric)</li> <li><strong>skewness_kurtosis_hf</strong>: Hard flags for individual variables for skewness and kurtosis (Field type: numeric)</li> <li><strong>skewness_kurtosis_sf</strong>: Soft flags for individual variables for skewness and kurtosis test (Field type: numeric)</li> <li><strong>discontinuities_hf</strong>: Hard flags for individual variables for discontinuities test (Field type: numeric)</li> <li><strong>discontinuities_sf</strong>: Soft flags for individual variables for discontinuities test (Field type: numeric)</li> <li><strong>timelag_hf</strong>: Hard flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>timelag_sf</strong>: Soft flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>attack_angle_hf</strong>: Hard flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>non_steady_wind_hf</strong>: Soft flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>u_spikes</strong>: Number of spikes detected and eliminated for rotated u wind component (Field type: numeric)</li> <li><strong>v_spikes</strong>: Number of spikes detected and eliminated forrotated v wind component (Field type: numeric)</li> <li><strong>w_spikes</strong>: Number of spikes detected and eliminated for rotated w wind component (Field type: numeric)</li> <li><strong>ts_spikes</strong>: Number of spikes detected and eliminated for ts variable (Field type: numeric)</li> <li><strong>co2_spikes</strong>: Number of spikes detected and eliminated for co2 variable (Field type: numeric)</li> <li><strong>h2o_spikes</strong>: Number of spikes detected and eliminated for h2o variable (Field type: numeric)</li> </ul> </li> <li> <p><strong>Raw_data_2017</strong> (described in worksheet Raw_data_2017)</p> <p>Description: EddyPro output of eddy covariance data collected at 52m at the top of the flux tower.</p> <p>Number of fields: 105</p> <p>Number of data rows: 18990</p> <p>Fields:</p> <ul> <li><strong>Location</strong>: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location)</li> <li><strong>date</strong>: Date of the end of the averaging period (Field type: date)</li> <li><strong>time</strong>: Time of the end of the averaging period (Field type: time)</li> <li><strong>DOY</strong>: decimal day of year (Field type: numeric)</li> <li><strong>daytime</strong>: Daytime or nightime, 1 = daytime, 0 = nighttime (Field type: numeric)</li> <li><strong>file_records</strong>: Number of valid records found in the raw file (or set of raw files) (Field type: numeric)</li> <li><strong>used_records</strong>: Number of valid records used for current the averaging period (Field type: numeric)</li> <li><strong>Tau</strong>: Corrected momentum flux (Field type: numeric)</li> <li><strong>qc_Tau</strong>: Quality flag for momentum flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_Tau</strong>: Random error for momentum flux, if selected (Field type: numeric)</li> <li><strong>H</strong>: Corrected sensible heat flux (Field type: numeric)</li> <li><strong>qc_H</strong>: Quality flag for sensible heat flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_H</strong>: Random error for momentum flux, if selected (Field type: numeric)</li> <li><strong>LE</strong>: Corrected latent heat flux (Field type: numeric)</li> <li><strong>qc_LE</strong>: Quality flag of latent heat flux based on Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_LE</strong>: Random error for latent heat flux, if selected (Field type: numeric)</li> <li><strong>co2_flux</strong>: CO2 flux (Field type: numeric)</li> <li><strong>qc_co2_flux</strong>: Quality flag for CO2 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_co2_flux</strong>: Random error of CO2 flux (Field type: numeric)</li> <li><strong>h2o_flux</strong>: H2O flux (Field type: numeric)</li> <li><strong>qc_h2o_flux</strong>: Quality flag of H20 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_h2o_flux</strong>: Random error of CO2 flux (Field type: numeric)</li> <li><strong>H_strg</strong>: Estimate of storage sensible heat flux (Field type: numeric)</li> <li><strong>LE_strg</strong>: Estimate of storage latent heat flux (Field type: numeric)</li> <li><strong>co2_strg</strong>: Estimate of storage CO2 flux (Field type: numeric)</li> <li><strong>h2o_strg</strong>: Estimate of storage H20 flux (Field type: numeric)</li> <li><strong>co2_v.adv</strong>: Estimate of vertical advection flux of CO2 (Field type: numeric)</li> <li><strong>h2o_v.adv</strong>: Estimate of vertical advection flux of H20 (Field type: numeric)</li> <li><strong>co2_molar_density</strong>: Measured or estimated molar density of gas (Field type: numeric)</li> <li><strong>co2_mole_fraction</strong>: Measured or estimated mole fraction of gas (Field type: numeric)</li> <li><strong>co2_mixing_ratio</strong>: Measured or estimated mixing ratio of gas (Field type: numeric)</li> <li><strong>co2_time_lag</strong>: Time lag used to synchronize gas time series (Field type: numeric)</li> <li><strong>co2_def_timelag</strong>: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric)</li> <li><strong>h2o_molar_density</strong>: Measured or estimated molar density of gas (Field type: numeric)</li> <li><strong>h2o_mole_fraction</strong>: Measured or estimated mole fraction of gas (Field type: numeric)</li> <li><strong>h2o_mixing_ratio</strong>: Measured or estimated mixing ratio of gas (Field type: numeric)</li> <li><strong>h2o_time_lag</strong>: Time lag used to synchronize gas time series (Field type: numeric)</li> <li><strong>h2o_def_timelag</strong>: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric)</li> <li><strong>sonic_temperature</strong>: Mean temperature of ambient air as measured by the anemometer (Field type: numeric)</li> <li><strong>air_temperature</strong>: Mean temperature of ambient air, either calculated from high frequency air temperature readings, or estimated from sonic temperature (Field type: numeric)</li> <li><strong>air_pressure</strong>: Mean pressure of ambient air, either calculated from high frequency air pressure readings, or estimated based on site altitude (barometric pressure) (Field type: numeric)</li> <li><strong>air_density</strong>: Density of ambient air (Field type: numeric)</li> <li><strong>air_heat_capacity</strong>: Specific heat at constant pressure of ambient air (Field type: numeric)</li> <li><strong>air_molar_volume</strong>: Molar volume of ambient air (Field type: numeric)</li> <li><strong>ET</strong>: Evapotranspiration flux (Field type: numeric)</li> <li><strong>water_vapor_density</strong>: Ambient mass density of water vapor (Field type: numeric)</li> <li><strong>e</strong>: Ambient water vapor partial pressure (Field type: numeric)</li> <li><strong>es</strong>: Ambient water vapor partial pressure at saturation (Field type: numeric)</li> <li><strong>specific_humidity</strong>: Ambient specific humidity on a mass basis (Field type: numeric)</li> <li><strong>RH</strong>: Ambient relative humidity (Field type: numeric)</li> <li><strong>VPD</strong>: Ambient water vapor pressure deficit (Field type: numeric)</li> <li><strong>Tdew</strong>: Ambient dew point temperature (Field type: numeric)</li> <li><strong>u_unrot</strong>: Wind component along the u anemometer axis (Field type: numeric)</li> <li><strong>v_unrot</strong>: Wind component along the v anemometer axis (Field type: numeric)</li> <li><strong>w_unrot</strong>: Wind component along the w anemometer axis (Field type: numeric)</li> <li><strong>u_rot</strong>: Rotated u wind component (mean wind speed) (Field type: numeric)</li> <li><strong>v_rot</strong>: Rotated v wind component (should be zero) (Field type: numeric)</li> <li><strong>w_rot</strong>: Rotated w wind component (should be zero) (Field type: numeric)</li> <li><strong>wind_speed</strong>: Mean wind speed (Field type: numeric)</li> <li><strong>max_wind_speed</strong>: Maximum instantaneous wind speed (Field type: numeric)</li> <li><strong>wind_dir</strong>: Direction from which the wind blows, with respect to Geographic or Magnetic north (Field type: numeric)</li> <li><strong>yaw</strong>: First rotation angle (Field type: numeric)</li> <li><strong>pitch</strong>: Second rotation angle (Field type: numeric)</li> <li><strong>u.</strong>: Friction velocity (Field type: numeric)</li> <li><strong>TKE</strong>: Turbulent kinetic energy (Field type: numeric)</li> <li><strong>L</strong>: Monin-Obukhov length (Field type: numeric)</li> <li><strong>X.z.d..L</strong>: Monin-Obukhov stability parameter - (z-d)/L (Field type: numeric)</li> <li><strong>bowen_ratio</strong>: Sensible heat flux to latent heat flux ratio (Field type: numeric)</li> <li><strong>T.</strong>: Scaling temperature (Field type: numeric)</li> <li><strong>model</strong>: Model for footprint estimation, 1- Kljun et al. (2004): A crosswind integrated parameterization of footprint estimations obtained with a 3D Lagrangian model by means of a scaling procedure.2 - Kormann and Meixner (2001): A crosswind integrated model based on the solution of the two dimensional advection-diffusion equation given by van Ulden (1978) and others for power-law profiles in wind velocity and eddy diffusivity, 3 - Hsieh et al. (2000): A crosswind integrated model based on the former model of Gash (1986) and on simulations with a Lagrangian stochastic model. (Field type: numeric)</li> <li><strong>x_peak</strong>: Along-wind distance providing <1% contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_offset</strong>: Along-wind distance providing the highest (peak) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_10.</strong>: Along-wind distance providing 10% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_30.</strong>: Along-wind distance providing 30% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_50.</strong>: Along-wind distance providing 50% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_70.</strong>: Along-wind distance providing 70% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_90.</strong>: Along-wind distance providing 90% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>un_Tau</strong>: Uncorrected momentum flux (Field type: numeric)</li> <li><strong>Tau_scf</strong>: Spectral correction factor for momentum flux (Field type: numeric)</li> <li><strong>un_H</strong>: Uncorrected sensible heat flux (Field type: numeric)</li> <li><strong>H_scf</strong>: Spectral correction factor for sensible heat flux (Field type: numeric)</li> <li><strong>un_LE</strong>: Uncorrected latent heat flux (Field type: numeric)</li> <li><strong>LE_scf</strong>: Spectral correction factor for latent heat flux (Field type: numeric)</li> <li><strong>un_co2_flux</strong>: Uncorrected gas flux (Field type: numeric)</li> <li><strong>co2_scf</strong>: Spectral correction factor for gas flux (Field type: numeric)</li> <li><strong>un_h2o_flux</strong>: Uncorrected gas flux (Field type: numeric)</li> <li><strong>h2o_scf</strong>: Spectral correction factor for gas flux (Field type: numeric)</li> <li><strong>spikes_hf</strong>: Hard flags for individual variables for spike test (Field type: numeric)</li> <li><strong>amplitude_resolution_hf</strong>: Hard flags for individual variables for amplitude resolution (Field type: numeric)</li> <li><strong>drop_out_hf</strong>: Hard flags for individual variables for drop-out test (Field type: numeric)</li> <li><strong>absolute_limits_hf</strong>: Hard flags for individual variables for absolute limits (Field type: numeric)</li> <li><strong>skewness_kurtosis_hf</strong>: Hard flags for individual variables for skewness and kurtosis (Field type: numeric)</li> <li><strong>skewness_kurtosis_sf</strong>: Soft flags for individual variables for skewness and kurtosis test (Field type: numeric)</li> <li><strong>discontinuities_hf</strong>: Hard flags for individual variables for discontinuities test (Field type: numeric)</li> <li><strong>discontinuities_sf</strong>: Soft flags for individual variables for discontinuities test (Field type: numeric)</li> <li><strong>timelag_hf</strong>: Hard flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>timelag_sf</strong>: Soft flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>attack_angle_hf</strong>: Hard flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>non_steady_wind_hf</strong>: Soft flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>u_spikes</strong>: Number of spikes detected and eliminated for rotated u wind component (Field type: numeric)</li> <li><strong>v_spikes</strong>: Number of spikes detected and eliminated forrotated v wind component (Field type: numeric)</li> <li><strong>w_spikes</strong>: Number of spikes detected and eliminated for rotated w wind component (Field type: numeric)</li> <li><strong>ts_spikes</strong>: Number of spikes detected and eliminated for ts variable (Field type: numeric)</li> <li><strong>co2_spikes</strong>: Number of spikes detected and eliminated for co2 variable (Field type: numeric)</li> <li><strong>h2o_spikes</strong>: Number of spikes detected and eliminated for h2o variable (Field type: numeric)</li> </ul> </li> <li> <p><strong>Raw_data_2018</strong> (described in worksheet Raw_data_2018)</p> <p>Description: EddyPro output of eddy covariance data collected at 52m at the top of the flux tower.</p> <p>Number of fields: 105</p> <p>Number of data rows: 12372</p> <p>Fields:</p> <ul> <li><strong>Location</strong>: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location)</li> <li><strong>date</strong>: Date of the end of the averaging period (Field type: date)</li> <li><strong>time</strong>: Time of the end of the averaging period (Field type: time)</li> <li><strong>DOY</strong>: decimal day of year (Field type: numeric)</li> <li><strong>daytime</strong>: Daytime or nightime, 1 = daytime, 0 = nighttime (Field type: numeric)</li> <li><strong>file_records</strong>: Number of valid records found in the raw file (or set of raw files) (Field type: numeric)</li> <li><strong>used_records</strong>: Number of valid records used for current the averaging period (Field type: numeric)</li> <li><strong>Tau</strong>: Corrected momentum flux (Field type: numeric)</li> <li><strong>qc_Tau</strong>: Quality flag for momentum flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_Tau</strong>: Random error for momentum flux, if selected (Field type: numeric)</li> <li><strong>H</strong>: Corrected sensible heat flux (Field type: numeric)</li> <li><strong>qc_H</strong>: Quality flag for sensible heat flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_H</strong>: Random error for momentum flux, if selected (Field type: numeric)</li> <li><strong>LE</strong>: Corrected latent heat flux (Field type: numeric)</li> <li><strong>qc_LE</strong>: Quality flag of latent heat flux based on Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_LE</strong>: Random error for latent heat flux, if selected (Field type: numeric)</li> <li><strong>co2_flux</strong>: CO2 flux (Field type: numeric)</li> <li><strong>qc_co2_flux</strong>: Quality flag for CO2 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_co2_flux</strong>: Random error of CO2 flux (Field type: numeric)</li> <li><strong>h2o_flux</strong>: H2O flux (Field type: numeric)</li> <li><strong>qc_h2o_flux</strong>: Quality flag of H20 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric)</li> <li><strong>rand_err_h2o_flux</strong>: Random error of CO2 flux (Field type: numeric)</li> <li><strong>H_strg</strong>: Estimate of storage sensible heat flux (Field type: numeric)</li> <li><strong>LE_strg</strong>: Estimate of storage latent heat flux (Field type: numeric)</li> <li><strong>co2_strg</strong>: Estimate of storage CO2 flux (Field type: numeric)</li> <li><strong>h2o_strg</strong>: Estimate of storage H20 flux (Field type: numeric)</li> <li><strong>co2_v.adv</strong>: Estimate of vertical advection flux of CO2 (Field type: numeric)</li> <li><strong>h2o_v.adv</strong>: Estimate of vertical advection flux of H20 (Field type: numeric)</li> <li><strong>co2_molar_density</strong>: Measured or estimated molar density of gas (Field type: numeric)</li> <li><strong>co2_mole_fraction</strong>: Measured or estimated mole fraction of gas (Field type: numeric)</li> <li><strong>co2_mixing_ratio</strong>: Measured or estimated mixing ratio of gas (Field type: numeric)</li> <li><strong>co2_time_lag</strong>: Time lag used to synchronize gas time series (Field type: numeric)</li> <li><strong>co2_def_timelag</strong>: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric)</li> <li><strong>h2o_molar_density</strong>: Measured or estimated molar density of gas (Field type: numeric)</li> <li><strong>h2o_mole_fraction</strong>: Measured or estimated mole fraction of gas (Field type: numeric)</li> <li><strong>h2o_mixing_ratio</strong>: Measured or estimated mixing ratio of gas (Field type: numeric)</li> <li><strong>h2o_time_lag</strong>: Time lag used to synchronize gas time series (Field type: numeric)</li> <li><strong>h2o_def_timelag</strong>: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric)</li> <li><strong>sonic_temperature</strong>: Mean temperature of ambient air as measured by the anemometer (Field type: numeric)</li> <li><strong>air_temperature</strong>: Mean temperature of ambient air, either calculated from high frequency air temperature readings, or estimated from sonic temperature (Field type: numeric)</li> <li><strong>air_pressure</strong>: Mean pressure of ambient air, either calculated from high frequency air pressure readings, or estimated based on site altitude (barometric pressure) (Field type: numeric)</li> <li><strong>air_density</strong>: Density of ambient air (Field type: numeric)</li> <li><strong>air_heat_capacity</strong>: Specific heat at constant pressure of ambient air (Field type: numeric)</li> <li><strong>air_molar_volume</strong>: Molar volume of ambient air (Field type: numeric)</li> <li><strong>ET</strong>: Evapotranspiration flux (Field type: numeric)</li> <li><strong>water_vapor_density</strong>: Ambient mass density of water vapor (Field type: numeric)</li> <li><strong>e</strong>: Ambient water vapor partial pressure (Field type: numeric)</li> <li><strong>es</strong>: Ambient water vapor partial pressure at saturation (Field type: numeric)</li> <li><strong>specific_humidity</strong>: Ambient specific humidity on a mass basis (Field type: numeric)</li> <li><strong>RH</strong>: Ambient relative humidity (Field type: numeric)</li> <li><strong>VPD</strong>: Ambient water vapor pressure deficit (Field type: numeric)</li> <li><strong>Tdew</strong>: Ambient dew point temperature (Field type: numeric)</li> <li><strong>u_unrot</strong>: Wind component along the u anemometer axis (Field type: numeric)</li> <li><strong>v_unrot</strong>: Wind component along the v anemometer axis (Field type: numeric)</li> <li><strong>w_unrot</strong>: Wind component along the w anemometer axis (Field type: numeric)</li> <li><strong>u_rot</strong>: Rotated u wind component (mean wind speed) (Field type: numeric)</li> <li><strong>v_rot</strong>: Rotated v wind component (should be zero) (Field type: numeric)</li> <li><strong>w_rot</strong>: Rotated w wind component (should be zero) (Field type: numeric)</li> <li><strong>wind_speed</strong>: Mean wind speed (Field type: numeric)</li> <li><strong>max_wind_speed</strong>: Maximum instantaneous wind speed (Field type: numeric)</li> <li><strong>wind_dir</strong>: Direction from which the wind blows, with respect to Geographic or Magnetic north (Field type: numeric)</li> <li><strong>yaw</strong>: First rotation angle (Field type: numeric)</li> <li><strong>pitch</strong>: Second rotation angle (Field type: numeric)</li> <li><strong>u.</strong>: Friction velocity (Field type: numeric)</li> <li><strong>TKE</strong>: Turbulent kinetic energy (Field type: numeric)</li> <li><strong>L</strong>: Monin-Obukhov length (Field type: numeric)</li> <li><strong>X.z.d..L</strong>: Monin-Obukhov stability parameter - (z-d)/L (Field type: numeric)</li> <li><strong>bowen_ratio</strong>: Sensible heat flux to latent heat flux ratio (Field type: numeric)</li> <li><strong>T.</strong>: Scaling temperature (Field type: numeric)</li> <li><strong>model</strong>: Model for footprint estimation, 1- Kljun et al. (2004): A crosswind integrated parameterization of footprint estimations obtained with a 3D Lagrangian model by means of a scaling procedure.2 - Kormann and Meixner (2001): A crosswind integrated model based on the solution of the two dimensional advection-diffusion equation given by van Ulden (1978) and others for power-law profiles in wind velocity and eddy diffusivity, 3 - Hsieh et al. (2000): A crosswind integrated model based on the former model of Gash (1986) and on simulations with a Lagrangian stochastic model. (Field type: numeric)</li> <li><strong>x_peak</strong>: Along-wind distance providing <1% contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_offset</strong>: Along-wind distance providing the highest (peak) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_10.</strong>: Along-wind distance providing 10% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_30.</strong>: Along-wind distance providing 30% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_50.</strong>: Along-wind distance providing 50% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_70.</strong>: Along-wind distance providing 70% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>x_90.</strong>: Along-wind distance providing 90% (cumulative) contribution to turbulent fluxes (Field type: numeric)</li> <li><strong>un_Tau</strong>: Uncorrected momentum flux (Field type: numeric)</li> <li><strong>Tau_scf</strong>: Spectral correction factor for momentum flux (Field type: numeric)</li> <li><strong>un_H</strong>: Uncorrected sensible heat flux (Field type: numeric)</li> <li><strong>H_scf</strong>: Spectral correction factor for sensible heat flux (Field type: numeric)</li> <li><strong>un_LE</strong>: Uncorrected latent heat flux (Field type: numeric)</li> <li><strong>LE_scf</strong>: Spectral correction factor for latent heat flux (Field type: numeric)</li> <li><strong>un_co2_flux</strong>: Uncorrected gas flux (Field type: numeric)</li> <li><strong>co2_scf</strong>: Spectral correction factor for gas flux (Field type: numeric)</li> <li><strong>un_h2o_flux</strong>: Uncorrected gas flux (Field type: numeric)</li> <li><strong>h2o_scf</strong>: Spectral correction factor for gas flux (Field type: numeric)</li> <li><strong>spikes_hf</strong>: Hard flags for individual variables for spike test (Field type: numeric)</li> <li><strong>amplitude_resolution_hf</strong>: Hard flags for individual variables for amplitude resolution (Field type: numeric)</li> <li><strong>drop_out_hf</strong>: Hard flags for individual variables for drop-out test (Field type: numeric)</li> <li><strong>absolute_limits_hf</strong>: Hard flags for individual variables for absolute limits (Field type: numeric)</li> <li><strong>skewness_kurtosis_hf</strong>: Hard flags for individual variables for skewness and kurtosis (Field type: numeric)</li> <li><strong>skewness_kurtosis_sf</strong>: Soft flags for individual variables for skewness and kurtosis test (Field type: numeric)</li> <li><strong>discontinuities_hf</strong>: Hard flags for individual variables for discontinuities test (Field type: numeric)</li> <li><strong>discontinuities_sf</strong>: Soft flags for individual variables for discontinuities test (Field type: numeric)</li> <li><strong>timelag_hf</strong>: Hard flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>timelag_sf</strong>: Soft flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>attack_angle_hf</strong>: Hard flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>non_steady_wind_hf</strong>: Soft flags for gas concentration for time lag test (Field type: numeric)</li> <li><strong>u_spikes</strong>: Number of spikes detected and eliminated for rotated u wind component (Field type: numeric)</li> <li><strong>v_spikes</strong>: Number of spikes detected and eliminated forrotated v wind component (Field type: numeric)</li> <li><strong>w_spikes</strong>: Number of spikes detected and eliminated for rotated w wind component (Field type: numeric)</li> <li><strong>ts_spikes</strong>: Number of spikes detected and eliminated for ts variable (Field type: numeric)</li> <li><strong>co2_spikes</strong>: Number of spikes detected and eliminated for co2 variable (Field type: numeric)</li> <li><strong>h2o_spikes</strong>: Number of spikes detected and eliminated for h2o variable (Field type: numeric)</li> </ul> </li> <li> <p><strong>Daily_fluxes</strong> (described in worksheet Daily_fluxes)</p> <p>Description: Daily mean fluxes of net ecosystem CO2 exchange (NEE), ecosystem respirationn (Reco) and gross primary productivity and their associated standard errors that have been subject to quality control, filtering, gapfilling and partitioning.</p> <p>Number of fields: 12</p> <p>Number of data rows: 455</p> <p>Fields:</p> <ul> <li><strong>Location</strong>: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location)</li> <li><strong>Year</strong>: Year of data collection (Field type: numeric)</li> <li><strong>DOY</strong>: Julian day of year of data collection (Field type: numeric)</li> <li><strong>Date</strong>: Date of data collection (Field type: date)</li> <li><strong>obs_counts_daytime</strong>: Count of net eocsystem CO2 exchange (NEE) obervations for that 24 hour period (Field type: numeric)</li> <li><strong>obs_counts_24hr</strong>: Count of net eocsystem CO2 exchange (NEE) obervations for that day - during daytime only (06:30 - 18:30), this indicates how many values were gapfilled for that daytime period, as nightime values are gapfilled whereby NEE = Reco (Field type: numeric)</li> <li><strong>NEE</strong>: Net ecosystem CO2 exchange (NEE) (Field type: numeric)</li> <li><strong>Reco</strong>: Ecosystem respiration (Reco) (Field type: numeric)</li> <li><strong>GPP</strong>: Gross primary productivity (GPP) (Field type: numeric)</li> <li><strong>NEE_stderror</strong>: Standard error of net ecosystem CO2 exchange (NEE) (Field type: numeric)</li> <li><strong>Reco_stderror</strong>: Standard error of ecosystem respiration (Reco) (Field type: numeric)</li> <li><strong>GPP_stderror</strong>: Standard error of gross primary productivity (GPP) (Field type: numeric)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2012-08-14 to 2018-11-30</p> <p><strong>Latitudinal extent: </strong>4.7173 to 4.7173</p> <p><strong>Longitudinal extent: </strong>117.6032 to 117.6032</p>
Methane fluxes measured by eddy covariance on Dutch peatlands
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Eddy covariance measurements in Sahelian semi-arid savanna
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Final eddy covariance dataset to support lessons from long-term monitoring of carbon gains and losses in cropping systems
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Evapotranspiration data from eddy-covariance flux-tower measurements and Landsat imagery in California’s Sierra Nevada from 1985 to 2019
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Evapotranspiration data of the TERENO sites Graswang and Fendt for 2013 and 2014 measured by eddy-covariance and lysimeters
<p>Further details on this data set can be found in the following papers:</p> <p>Mauder, M., Genzel, S., Fu, J., Kiese, R., Soltani, M., Steinbrecher, R., Kunstmann, H., Zeeman, M., Banerjee, T., Roo, F. De, De Roo, F., Kunstmann, H. and Zeeman, M.: Evaluation of energy balance closure adjustment methods by independent evapotranspiration estimates from lysimeters and hydrological simulations, Hydrol. Process., 32(October), 39–50, doi:10.1002/hyp.11397, 2018.</p> <p>Widmoser, P. and Michel, D.: Partial energy balance closure of eddy covariance evaporation measurements using concurrent lysimeter observations over grassland, Hydrol. Earth Syst. Sci. Discuss., (July), doi:10.5194/hess-2020-299, 2020.</p>
Technical note: Estimating light-use efficiency of benthic habitats using underwater O2 eddy covariance
<p>This datafile contains all data required to recreate figures presented in Attard KM & Glud RN (2020) Technical Note: Estimating light-use efficiency of benthic habitats using underwater O2 eddy covariance. Biogeosciences https://doi.org/10.5194/bg-2020-140 </p> <p>Paper abstract</p> <p>Light-use efficiency defines the ability of primary producers to convert sunlight energy to primary production and is computed as the ratio between the gross primary production and the intercepted photosynthetic active radiation. While this measure has been applied broadly within the atmospheric sciences to investigate resource-use efficiency in terrestrial habitats, it remains underused within the aquatic realm. This report provides a conceptual framework to compute hourly and daily light-use efficiency using underwater O<sub>2</sub> eddy covariance, a recent technological development that produces habitat-scale rates of primary production under unaltered in situ conditions. The analysis, tested on two benthic flux datasets, documents that hourly light-use efficiency may approach the maximum theoretical limit of 0.125 O<sub>2</sub> photon<sup>-1</sup> under low light conditions but it decreases rapidly towards the middle of the day and is typically tenfold lower on a 24 h basis. Overall, light-use efficiency provides a useful measure of habitat functioning and facilitates site comparison in time and space.</p>
An Excel spreadsheet including eddy covariance and meteorological data measured at northernmost restored mangrove ecosystem from 2017 to 2023.
<p>The data was measured using an open-path eddy covariance system in the northernmost restored mangrove ecosystem afforested in Zhejiang Province, China. The results will be published in a paper entitled "Net Carbon Uptake During the Wet Seasons Dominates the Northernmost Restored Mangrove Ecosystem in Southern China". Contact Xianglan Li at Beijing Normal University (xlli@bnu.edu.cn) if you have any question.</p>
Site-level CH4 flux data obtained from 35 chamber sites and 47 eddy covariance sites
<p>This dataset supports the manuscript "Quantifying Global Wetland Methane Emissions with In Situ Methane Flux Data and Machine Learning Approaches" currently under review. Before reusing this data, please contact the first author (chen4371@purdue.edu) for citations and recent updates.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.