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165 results for “energy balance”
Dataset: The effects of class balance on the training energy consumption of logistic regression models
<p>Two synthetic datasets for binary classification, generated with the Random Radial Basis Function generator from WEKA. They are the same shape and size (104.952 instances, 185 attributes), but the "balanced" dataset has 52,13% of its instances belonging to class c0, while the "unbalanced" one only has 4,04% of its instances belonging to class c0. Therefore, this set of datasets is primarily meant to study how class balance influences the behaviour of a machine learning model.</p>
Output from the Glacier Energy and Mass Balance (GEMB v1.0) forced with 3-hourly ERA5 fields and gridded to 10km, Greenland and Antarctica 1979-2024
<p>These model output of firn air content (FAC) and surface mass balance (SMB) are from version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB is a column model of ice sheet and glacier surface-atmospheric energy and mass exchange as well as firn state. GEMB has been integrated into the open-source Ice-Sheet and Sea-level System Model which can be downloaded at https://issm.jpl.nasa.gov/. Here, GEMB is forced with 3-hourly ERA5 output from 1979 through end of 2024. For Greenland and its periphery, the ERA5 surface temperature and downwelling longwave radiation forcing are spatially bias-corrected for each month. All values are adjusted by the difference between the RACMO2.3 and the ERA5 1980-2015 monthly means. The GEMB output is bilinearly interpolated onto a 10km grid, from the native ISSM grid, and the output is given as 5-day output or as monthly.</p>
Understanding monsoon controls on the energy and mass balance of glaciers in the Central and Eastern Himalaya (Data Sets and Codes)
<p>This repository contains AWS datasets for the modelling periods considered in the analysis presented in the research paper, together with ablation measurements, pre-processed forcing data, T&C model codes, outputs and scripts for analysing outputs. When previously published elsewhere, references and links to the full, original datasets are provided under References.</p> <p>Matlab scripts for executing the T&C model are provided and should work stand-alone on any machine with a Matlab version 2019b or later installed.</p>
Estimating surface water availability in high mountain rock slopes using a numerical energy balance model
<p>Model output, forcing data and physical parameters used to estimate water and energy balance. The model was calibrated with field measurements from a study site in the Mont-Blanc massif, at 3842 m a.s.l, at a slope of 55 deegrees and aspect azimut of 150 degrees (south-east). The different ModelOutput files are from simulations at different elevastions (from 4800 m to 2700 m at steps of 300 m). We used the CryoGrid community model (version 1.0) toolbox (Westermann et al., 2022) to simulate the 1D ground thermal regime and ice/water balance, and estimate the availability of surface water and its potential for infiltration in rock fractures. The S2M-SAFRAN dataset combines output from a numerical weather prediction model and <em>in situ</em> observations, and was originally developed for operational needs to estimate avalanche hazard in mountainous areas (Durand et al., 1993). The S2M-SAFRAN dataset that we used is available for various mountain areas, at elevation steps of 300 m, and with an hourly resolution between the years 1958 to 2021 (Vernay et al., 2022). It includes most parameters that are required for modeling with CryoGrid: Relative humidity, air T, incoming long wavelength radiation, incoming short wavelength solar radiation, and wind speed. To complete the forcing data we used top of the atmosphere incident solar radiation from ERA5 global reanalysis dataset (Hersbach et al., 2020).</p>
Two Source Energy Balance Model Inputs and Outputs from Drone Surveys at Majadas de Tietar in May 2021
<p><strong>MONSOON PROJECT SURVEY DATA OUTPUTS: Majadas de Tietar Tree-Grass Savanna Ecosystem 05/05/2021-20/05/2021</strong></p> <p>Here we make available high resolution (0.82 cm) energy and water flux maps from unmanned aerial system (UAS) data collected using a Micasense Altum in May 2021. We use the Two Source Energy Balance Model (via pyTSEB) and include model inputs and outputs. We use the Priestley Taylor (TSEB hereafter) and Dual Time Difference (DTD hereafter) methods in pyTSEB, the details of which can be found here pyTSEB https://pytseb.readthedocs.io/en/latest/index.html. The data collection method largely follows https://www.mdpi.com/2072-4292/13/7/1286, however a new paper detailing these surveys in Majadas is under review (as of November 2021). </p> <p>This upload includes the following gridded datasets:</p> <p><strong>Model inputs</strong></p> <p>Zipfiles are named according to their collection date (<strong>DDMMYYYY.7z</strong>). Within each zipfile are the datasets corresponding to different flight times UTC +2 (<strong>hhmm_DDMMYY</strong>). Within each survey folder are rasters with descriptive filenames using the following format:</p> <p><em>Product type_Resolution_survey area_date_flight time.tif</em></p> <p>The following prefixes denote the Product types:</p> <ul> <li>CHM_... = Canopy Height Model (m)</li> <li>GFrac2_... = Green Fraction (0-1)</li> <li>MSpec_... = Raw multispectral dataset from Altum (Blue, Green, Red, NIR, Rededge, LWIR)</li> <li>TEmpK_... = Radiometric Surface Temperature (empirical calibration, K)</li> <li>TRawK_... = Radiometric Surface Temperature (no calibration, K)</li> <li>LST2_... = Radiometric Surface Temperature (calibrated using methods outlined here https://www.mdpi.com/2072-4292/12/7/1075, K)</li> <li>Grass_... = grass vegetation mask</li> <li>Tree_... = tree vegetation mask</li> </ul> <p>We also supply the config files used to generate TSEB and DTD. To run these you will need to edit the filepaths according to your own system. </p> <p><strong>Model Outputs</strong></p> <p><strong>Majadas_TSEB_EMP_outputs.7z</strong> = Two Source Energy Balance (pyTSEB) model outputs (using the Priestley-Taylor method), using radiometric temperature datasets calibrated empirically. </p> <p><strong>Majadas_DTD_EMP_outputs.7z</strong> = TSEB Dual Time Difference model outputs (from pyTSEB) using radiometric temperature datasets calibrated empirically. </p> <p><strong>DTD_ET.7z</strong> = Evapotranspiration rasters (calculated using DTD latent heat data) in g m<sup>-2</sup> s<sup>-1</sup></p> <p><strong>File names are descriptive</strong>:</p> <p><em>Model type_radiometric temperature method_vegetation type_survey area_date_flighttime.tif</em></p> <p>Model type = DTD or TSEB</p> <ul> <li>Radiometric temperature method = always empirical calibration here</li> <li>vegetation type = grass, tree, or merge (which is both tree and grass)</li> <li>Survey area = N (north, or Nitrogen fertiliser treatment), C (central, or Control fertiliser treatment), S (south, or Nitrogen and Phosphorus fertiliser treatment)</li> <li>date = in DDMMYY format</li> <li>flight time = takeoff time for the drone (hhmm) (UTC+2)</li> </ul> <p>To find the exact local time of survey times, please see the table in flight_data3.csv</p>
Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design
<p>Dataset related to the article: Virtanen, E.A., Lappalainen, J., Nurmi, M., Viitasalo, M., Tikanmäki, M., Heinonen, J., Atlaskin, E., Kallasvuo, M., Tikkanen, H., Moilanen, A. (2022) Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design. Renewable and Sustainable Energy Reviews 158, 112087.</p> <p>Dataset includes suitability maps for offshore windfarms, where priority values are scaled between 0-1 (note the reversed value scale): analysis solution (A) economy, (B) society, (C) biodiversity, (D) restrictions, (E) A+B+C without restrictions and (F) A+B+C with restrictions. Dataset includes also the conflict map (and R script), where each three main solutions (A, B, C) are mapped onto an RGB color composite map. </p> <p>Additional details can be found from the published article: <a href="https://doi.org/10.1016/j.rser.2022.112087">https://doi.org/10.1016/j.rser.2022.112087</a></p>
Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate
<p>This repository includes raw datasets, Python scripts, and output data products associated with the MRes project '<span>Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate</span>', by Josh Abrahams, University of Leeds. </p>
Scripts and datas for "Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance"
<p>Input and output datasets used for a global reconstruction of the eddy kinetic energy (EKE) dissipation rate in relation to a submitted work :</p> <p><strong>R. Torres, R. Waldman, J. Mak and R. Séférian </strong>: <em>Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance</em>.</p> <p>Inputs datas include a merge of 2 datasets from the World Ocean Atlas 2018 (WOA18, Garcia et al., 2019) and cover the 1995-2017 (95B7) period. Folder structure for the surface altimetry L4 datasets from the EU-Copernicus Marine Services (2021) is kept empty in order to limit the archive size. Datas can be download <a href="https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_L4_MY_008_047/services">here</a>.</p> <p>Optional datasets include CMEMS MDT product (<em>CMEMS/SEALEVEL_GLO_PHY_MDT_008_063/P20Y</em>) downloaded <a href="https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_MDT_008_063/">here</a> and ocean masks (<em>misc/basins/doi_10.5281</em>) from Martinez-Moreno et al. (2021).</p> <p>In addition, simulation outputs from the NEMO-OMIP2 model runned with the GEOMETRIC parameterization are processed (mainly time-averaged) and stored in <em>CNRM/runs/omip2_LR.Geom_Emin0-alpha01_1cyc-trd/post</em>. These files are used in the uncertainties and errors quantification.</p> <p>Outputs and published results are stored in each individual product post-processing folder while final EKE dissipation computation are located in the <em>EKE_dissipation_rate</em> folder since it results from a combination of multiple products.</p> <p>IPython notebooks for computing and plotting global maps are also provided :</p> <ul> <li><em>1-post_process_climato.ipynb</em> : compute from the climatology (e.g. WOA18 datas) the EKE dissipation timescales (units in days) and the surface modes with rough topography (LaCasce and Groeskamp, 2020).</li> <li><em>2-post_process_altimetry.ipynb</em> : compute from altimetry (CMEMS) datasets the EKE at surface and eventually coarsen the grid from 0.25 to 1 degree in order to match the climatology grid.</li> <li><em>3-compute_global_eke_dissipation.ipynb</em> : combine both outputs from the two above scripts to compute the global EKE dissipation. The script also plots new maps.</li> <li><em>0-plot_global_maps.ipynb</em> : plot the global maps for climatology and altimetry products.</li> <li><em>0-plot_lbekedis_ogcm.ipynb</em> : plot and analyse EKE timescale errors from the NEMO-OMIP2 simulation outputs.</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</p>
Iberian Summer Surface Temperature and Fluxes for Energy Balance
<p>This dataset holds selected postprocessed files for surface temperature and fluxes involved in the surface energy balance.</p><p>Four WRF experiments nested in ERA-Interim were prepared. The first one (N) was configured as in standard numerical downscaling experiments using the Noah LSM. The second one (D), with the same parameterizations, included a step of 3DVAR data assimilation every 6 hours. The third and the fourth ones (S and C) are similar to N and D but use a diffusive soil scheme instead of NOAH LSM. The experiments covered the period 2010-2014 after a year of spin-up (2019). </p><p>The following 3-hourly files are included:</p><ul><li>Tsoil: soil temperature for the first 2 top levels of the surface.</li><li>T2: 2 metre temperature. </li><li>Latent: Latent heat flux.</li><li>Sensible: Sensible heat flux.</li><li>NetSW: net short-wave radiation flux at the surface.</li><li>NetLW: net long-wave radiation flux at the surface.</li><li>GRDFLX: ground flux toward lower layers of the soil.</li></ul><p>The <i>N, D, C or S </i>characters in the file names indicate whether the files come from the WRF N, D, C or S experiments. The files include a table including the 3-hourly data for each grid point over the Iberian Peninsula: year | month | day | hour | V1 | ... | V2058 </p><p>The 2058 grid points included in each file are listed in the same order as in the file WRFmask_points_withoutUrban_withLandType.dat </p><p> </p><p> </p>
Energy Balance Flanders quarterly and monthly data and related auxiliary data
<p>This data set is used in the VITO pilot study of the UNECE Machine Learning project 2019-2020. </p>
Non-Closure of Surface Energy Balance Linked to Asymmetric Turbulent Transport of Scalars by Large Eddies
<p>This repository contains the dataset used in the manuscript of Liu, Gao, and Katul 2020. Please refer to the manuscript for the detailed description of the dataset.</p>
COSIPY distributed simulations of Mera Glacier mass and energy balance (20161101-20201101)
<p>The four netCDF files contain outputs from COSIPY model (Sauter et al., 2020) for Mera Glacier for the period 20161101 to 20201101. The model is run on a 0.003°*0.003° grid, and forced with meteological variables collected locally and distributed with constant gradients. The "constants.py" is the python file that contains the specific model settings.</p>
Nutrient balance and energy-acquisition effectiveness: Do birds adjust their fruit diet to achieve intake targets?
<p>1. According to diet-regulation hypotheses, animals select food to regulate the intake of macronutrients or maximise energy feeding efficiency. Specifically, the nutrient balance model proposes that foraging is primarily a process of balancing multiple nutrients to achieve a nutritional intake target, while the energy maximisation model proposes that foraging aims to maximise energy.</p> <p>2. Here, we evaluate the adjustment of fruit diets (the fruit-derived component of the diets) to nutritional and energy intake targets, characterizing the nutrient balance and energy maximisation strategies across fruit-eating bird species with different fruit-handling behaviours ("gulpers", which swallow whole fruits, and "mashers", which process the fruit in the beak) in subtropical Andean forests. Food-handling behaviour determines the food intake rate and, consequently, influences animal efficiency to obtain nutrients and energy.</p> <p>3. We used extensive field data from the diet of fruit-eating birds to test how species adjust their food intake. We used nutritional geometry to explore macronutrient balance and the effectiveness framework to explore energy-acquisition effectiveness.</p> <p>4. Observed diets showed a good fit with predictions of a diet balanced in macronutrient proportions. With few exceptions, diets clustered near an optimal macronutrient mixture and did not differ from each other in terms of maximising energy intake. Moreover, when comparing our results with a random diet based on local fruit availability, birds tended to fit better to the nutritional target, and less to the energy target, than expected from a random diet. Fruit-handling behaviour did not affect the ability of bird species to reach a nutritional target but it affected species energy acquisition, which was lower in mashers than in gulpers.</p> <p>5. This study explores for the first time different diet-regulation strategies in wild fruit-eating birds, and supports the argument that the diet reflects a specific regulation of macronutrients. Understanding why birds select fruits is a complex question requiring multiple considerations. The nutrient balance model explains the relevance of nutrient composition in the fruit selection by fruit-eating birds, although it is still necessary to determine its relative importance with respect to other dietary drivers.</p>
Parameter variability across different timescales in the energy balance-based model and its effect on evapotranspiration estimation
<p>Our dataset is for the manuscript "Parameter variability across different timescales in the energy balance-based model and its effect on evapotranspiration estimation". It includes the instantaneous and daily <em>z<sub>0m</sub></em>, <em>z<sub>0h</sub></em>, <em>g<sub>s</sub></em>, and <em>EBR</em>, which are derived from FLUXNET2015 dataset. The training and test datasets for building the data-driven parameter models are also uploaded.</p>
A Novel Surface Energy Balance Method for Thermal Inertia Studies of Terrestrial Analogs
<p>Thermophysical data collected from Woodhouse Mesa, AZ, USA in May 2021 and Sept 2022</p>
Dataset: Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model
<p>This dataset is a companion to the submitted WRR publication entitled ‘Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model’. The file structure is organized as follows:</p> <ul> <li><strong>ASO_50m_depth_surfaces</strong> - This folder contains the Airborne Snow Observatory lidar-derived snow depth products aggregated to 50m gridded spatial resolution. Each file is titled with a date such as ‘TB<em>YYYYMMDD</em>_SUPERsnow_depth.asc’. The coordinates are in UTM zone 11N and use the WGS84 coordinate system.</li> <li><strong>static_grids</strong> <ul> <li>Static grids are used in each of the subsequent folders and are not changed between years.</li> <li>init0000.ipw <ul> <li>Initialization file to begin the model run. Contains the digital elevation model in band 1, surface roughness raster in band 2, and zeroed images of snow properties in bands 3-7.</li> </ul> </li> <li>maxus.nc <ul> <li>netCDF file of 72 separate images of maximum upwind slope for all upwind directions from 0 (north) to 355 degrees in 5-degree increments. Derived using Adam Winstral’s Sx algorithm.</li> </ul> </li> <li>tuolx_dem_50m.ipw <ul> <li>Digital elevation model from ASO snow-free acquisition aggregated to 50m gridded spatial resolution. Same information as band 1 in the init0000.ipw file.</li> </ul> </li> <li>tuolx_hetchy_mask_50m.ipw <ul> <li>Basin mask of the Tuolumne River Basin above Hetch Hetchy Reservoir. Out-of-basin cells denoted as 0, and in-basin cells denoted as 1.</li> </ul> </li> <li>tuolx_vegheight_50m.ipw <ul> <li>Vegetation height raster in meters. Derived from NLCD dataset of vegetation type..</li> </ul> </li> <li>tuolx_vegk_50m.ipw <ul> <li>Emissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> <li>tuolx_vegnlcd_50m.ipw <ul> <li>Vegetation type from the National Land Cover Database.</li> </ul> </li> <li>tuolx_vegtau_50m.ipw <ul> <li>Fractional transmissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> </ul> </li> <li><strong>level1_raw_data</strong> <ul> <li>{Hourly data interpolated to nearest hour from downloaded raw data (CDEC/MesoWest)}</li> <li>air_temp_level1.csv</li> <li>precip_accum_level1.csv</li> <li>relative_humidity_level1.csv</li> <li>solar_radiation_level1.csv</li> <li>wind_direction_level1.csv</li> <li>wind_speed_level1.csv</li> </ul> </li> </ul> <p>The directories for each water year contain the configuration file for that year along with the vector meteorological data from measurement sites and site metadata in .csv format.</p> <ul> <li><strong>wy2013</strong></li> <li><strong>wy2014</strong></li> <li><strong>wy2015</strong></li> <li><strong>wy2016</strong> <ul> <li> <ul> <li>backup_config.ini {Initialization file used to distribute station data over a regular grid for each water year.}</li> <li>air_temp.csv</li> <li>cloud_factor.csv</li> <li>metadata.csv</li> <li>precip.csv</li> <li>vapor_pressure.csv</li> <li>wind_direction.csv</li> <li>wind_speed.csv</li> <li><strong>data/</strong> <ul> <li>[subdirectory containing all future created forcing grid files]</li> </ul> </li> <li><strong>runs/</strong> <ul> <li>[subdirectory containing all <em>iSnobal</em> output files in addition to reinitialization scripts for ASO snow depth updates]</li> </ul> </li> </ul> </li> </ul> </li> </ul>
Energy balance Ruditapes decussatus
<p>Growth and physiological performance in growth phenotypes of the carpet shell clam (<em>Ruditapes decussatus</em>) fed diets of variable lipid/carbohydrate ratios</p>
No evidence that the widespread environmental contaminant caffeine alters energy balance or stress responses in fish
<p>Anthropogenic sources of environmental pollution are ever-increasing as urban areas expand and more chemical compounds are used in daily life. The stimulant caffeine is one of the most consumed chemical compounds worldwide, and as a result, has been detected as an environmental contaminant in all types of major water sources on all continents. Exposure of wildlife to environmental pollutants can disrupt the energy balance of these organisms, as restoration of homeostasis is prioritised. In turn, energy allocated to other key biological processes such as growth or reproduction may be affected, consequently reducing the overall fitness of an individual. Therefore, we aimed to investigate if long-term exposure to environmentally relevant concentrations of caffeine had any energetic consequences on wildlife. Specifically, we exposed wild eastern mosquitofish (<em>Gambusia holbrooki</em>) to one of three nominal concentrations of caffeine (0, 100, and 10,000 ng/L) and assayed individuals for metabolic rate, general activity, antipredator and foraging behaviour, and body size as measures of energy expenditure or energy intake. We found no differences in any measured traits between any of the given exposure treatments, indicating that exposure to caffeine at current environmental levels may not adversely affect the energy balance and fitness of vulnerable freshwater fish.</p>
Effect of Infant Formula on Energy Balance
ClinicalTrials.gov study NCT01700205. IPD Sharing: NO. Countries: 1. Publications: 8.
Impaired Regulation of Energy Balance in Elderly People (Balance Study)
ClinicalTrials.gov study NCT00561145. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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Allen Brain Atlas
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OpenNeuro
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