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815 results for “Forecasting”
Using Phenology to Forecast Species Distributions across the Eastern United States in the 2070s
Studies that use species distribution models (SDMs) to document the relationship between species’ geographic range and environmental conditions rarely consider functional traits, such as phenology, that strongly affect species’ demography and fitness. Using more than 120,000 herbarium specimens representing 360 plant species across the eastern United States, we created a novel “phenology-informed” SDM that integrates dynamic phenological responses to changing climates. Compared to standard SDMs based only on abiotic variables, our phenology-informed SDMs forecast significantly lower species habitat loss, and less species turnover within communities under climate change. These results suggest that phenotypic plasticity and/or local adaptation in phenology may help many species adjust their ecological niches and persist in their habitats during periods of rapid environmental change. By modeling historical data that link phenology, climate and species distributions, our findings reveal how species’ reproductive phenology mediates their geographic distributions along environmental gradients and affect regional biodiversity patterns under future climate changes. More importantly, our newly developed model also circumvents the need for mechanistic models that explicitly link traits to occurrences for each species, and could thus facilitate the deployment of trait-based SDMs across unprecedented spatial and taxonomic scales.
Sample data for "Machine learning for large-scale forecasting"
<p>This dataset includes sample data for the Netherlands to run the machine learning baseline as described in the paper titled <em>Machine learning for large-scale crop yield forecasting</em>, accessible at <a href="https://doi.org/10.1016/j.agsy.2020.103016">https://doi.org/10.1016/j.agsy.2020.103016</a>. The software implementation of the machine learning baseline is available at: <a href="https://github.com/BigDataWUR/MLforCropYieldForecasting">https://github.com/BigDataWUR/MLforCropYieldForecasting</a>.</p> <p><strong>Notes:</strong></p> <p>The NUTS classification (Nomenclature of territorial units for statistics) is a hierarchical system for dividing up the economic territory of the EU and the UK (see Eurostat, 2016) for more details).</p> <p>Data</p> <p>The dataset consists of 11 CSV files. They are formatted to work as sample inputs to the machine learning baseline.</p> <ol> <li><strong>Crop Area Fractions </strong>(NUTS2, NUTS1): We aggregated the predictions of the machine learning baseline from NUTS2 to national (NUTS0) level by weighting them on the modeled crop area. Cerrani and López Lozano (2017) have described in detail the algorithm used to model crop areas for different NUTS levels. The data comes from the MARS Crop Yield Forecasting System (MCYFS) of European Commission's Joint Research Centre (JRC) (see Lecerf et al., 2019).</li> <li><strong>Centroids (NUTS2)</strong>: Data includes latitude, longitude and distance to coast of the centroids of NUTS2 regions.</li> <li><strong>Meteo Daily Data and Meteo Dekadal Data </strong>(NUTS2): The data comes from MCYFS (see EC-JRC, 2020). By default, the implementation uses daily data.</li> <li><strong>Remote Sensing Data</strong> (NUTS2, see Copernicus Global Land Service, 2020): Data includes fraction of absorbed photosynthetically active radiation (FAPAR) aggregated to NUTS2.</li> <li><strong>Soil Data</strong>: Data includes soil moisture information that can be used to calculate soil water holding capacity. The data comes from MCYFS (see Lecerf et al., 2019).</li> <li><strong>WOFOST data </strong>(NUTS2): The World Food Studies (WOFOST) crop model (van Diepen et al., 1989; Supit et al., 1994; de Wit et al. 2019) is a simulation model for the quantitative analysis of the growth and production of annual field crops. It is a mechanistic, dynamic model that explains daily crop growth on the basis of the underlying processes, such as photosynthesis, respiration and how these processes are influenced by environmental conditions. The crop simulation is fed by weather, soil and crop data. Observed meteorological data is interpolated on a regular 25 km grid using a method based on the distance, altitude and climatic region similarity between the center of grid cells and weather stations (see Van der Goot, 1998). WOFOST runs on the intersection between the 25 km meteorological grid and soil units based on the European soil map (http://esdac.jrc.ec.europa.eu/). In order to have the output data aggregated to administrative regions such as countries or provinces, simulation units are further intersected with the boundaries of these regions. The outputs at soil unit (STU) level are aggregated to grid level in an area weighted manner. Gridded simulations are aggregated to lowest NUTS level 3 considering the arable land area of each grid, derived from GLOBCOVER and CORINE Land Cover (Cerrani and Lopez Lozano, 2017). From NUTS3 to higher levels, crop area fractions for the current year, retrieved from Eurostat, are used to weight and aggregate the output (Cerrani and Lopez Lozano, 2017).</li> <li><strong>GAES data</strong>: GAES data includes agro-climatic features of regions, such as elevation and slope (from USGS-EROS, 2021), field size (from Lesiv et al., 2019), irrigated (crop) areas (from EC-JRC, 2020) and crop areas (from EC-JRC, 2020).</li> <li><strong>National yield statistics </strong>(NUTS0): These are the official Eurostat national yield statistics (Eurostat, 2020a). We used these yield statistics as reference to compare the machine learning predictions aggregated to NUTS0 and the actual MCYFS forecasts (see van der Velde and Nisini, 2019).</li> <li><strong>Regional yield statistics </strong>(NUTS2): We used NUTS2 yield statistics as labels to train and evaluate machine learning algorithms. We got NUTS2 yield statistics from The Central Bureau of Statistics (CBS) of the Netherlands (NL-CBS, 2020).</li> <li><strong>Past MCYFS Yield Forecasts </strong>(NUTS0): These are actual forecasts made by MCYFS in the past (see van der Velde and Nisini, 2019). We used the official Eurostat national yield statistics (see point 7 above) as the reference to compare the machine learning predictions aggregated to NUTS0 and MCYFS forecasts.</li> </ol> <p><strong>Crop ID and name mapping</strong></p> <p>2 : grain maize</p> <p>6 : sugar beets</p> <p>7 : potatoes</p> <p>90 : soft wheat</p> <p>93 : sunflower</p> <p>95 : spring barley</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We would like to thank S. Niemeyer from the European Commission’s Joint Research Centre (JRC) for the permission to provide open access to the Netherlands data. Similarly, we would like to thank M. van der Velde, L. Nisini and I. Cerrani from JRC for sharing with us past MCYFS forecasts and Eurostat national yield statistics.</p>
Sample data for "A weakly supervised framework for high resolution crop yield forecasts"
<p>This dataset includes sample data for the United States to run the weakly supervised framework as described in the paper titled <em>A weakly supervised framework for high resolution crop yield forecasts</em>, accessible at </p> <table summary="Additional metadata"> <tbody> <tr> <td><a href="https://doi.org/10.48550/arXiv.2205.09016">https://doi.org/10.48550/arXiv.2205.09016</a></td> </tr> </tbody> </table> <p> </p> <p>The updated paper (including results from the US) is published in Environmental Research Letters:</p> <p><a href="https://doi.org/10.1088/1748-9326/acf50e">https://doi.org/10.1088/1748-9326/acf50e</a></p> <p> </p> <p>The software implementation of the machine learning baseline is available at: https://github.com/BigDataWUR/MLforCropYieldForecasting/tree/weaksup.</p> <p> </p> <p>Data</p> <p>1. County data (county-data.zip) for county-level strongly supervised models:</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p> <p>* CSSF_COUNTY_US.csv: Crop productivity indicators including total above-ground production (kg ha<sup>-1</sup>), total weight of storage organs (kg ha<sup>-1</sup>), development stage (0-2). Source: de Wit et al. (2022).</p> <p>* METEO_COUNTY_US.csv: Meteo data including maximum, minimum, average daily air temperature (℃); sum of daily precipitation (PREC) (mm); sum of daily evapotranspiration of short vegetation (ET0) (Penman-Monteith, Allen et al., (1998)) (mm); climate water balance = (PREC - ET0) (mm). Source: Boogaard et al. (2022).</p> <p>* REMOTE_SENSING_COUNTY_US.csv: Fraction of Absorbed Photosynthetically Active Radiation (Smoothed) (FAPAR). Source: Copernicus GLS (2020).</p> <p>* SOIL_COUNTY_US.csv: Soil water holding capacity. Source: WISE Soil Property Database (Batjes, 2016).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p> </p> <p>2. 10-km grid data (grid-data.zip) for grid-level strongly supervised models:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level (similar to county data above).</p> <p>* METEO_GRIDs_US.csv: Meteo data at 10km grid level (similar to county data above).</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level (similar to county data above).</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level (similar to county data above).</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021), Lobell et al. (2020).</p> <p> </p> <p>3. County labels and 10-km grid inputs (dscale-US.zip) for weak supervision:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level.</p> <p>* METEO_GRIDs_US.csv: Meteo indicators at 10km grid level.</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level.</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level.</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p>
Influence of weather forecast resolution on the circulation of Lake George, NY.
This dataset contains outputs of numerical modeling for Lake George, New York, hydrodynamics. These numerical simulations were generated to assess the impact of increasing the resolution of weather forecasts on the lake’s thermal state. This research focused on June 2017, when an increase of biological activity was associated to the deepening of the thermocline in the south of the lake. Increasing the resolution of the weather forecast led to a more accurate representation of the water temperature in the lake, including the deepening of the thermocline. The dataset was used in support of “The influence of weather forecast resolution on the circulation of Lake George, NY”.
Datasets from study: "Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability"
<p>This repository contains the datasets needed to reproduce the figures from manuscript: Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability.</p> <p>In this study, we examine the potential of land surface temperature and vegetation data, which are not routinely assimilated in NWP models, for enhancing temperature forecast skill. We build surrogate models for NWP using Long Short-Term Memory.</p>
Data for paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts"
<p>The forecasts and observation datasets are used in the paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts". https://doi.org/10.1016/j.jhydrol.2021.127301</p> <p>The forecast data is a subset of the "ensemble for machine learning dataset (ENS4ML)" from ECMWF. </p> <p>The Python codes are stored in Github: https://github.com/wentao-bnu/LeNet_CSG_Precip</p>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
Data-Driven Computational Intelligence Applied to Dengue Outbreak Forecasting: a case study at the scale of the city of Natal, RN-Brazil
<p><strong>The dataset comprises survey data from the following sources:dengue_incidence_data.csv: public data provided by Municipal Health Department of Natal, State of Rio Grande do Norte, Brazil; and data of Brazilian Notifiable Diseases Information System (Sinan). The objective of this paper was to analyze incidence data of dengue cases registered in each neighborhood of Natal city, weekly sampled (52 epidemiological weeks a year) between 2016 – 2019). </strong></p>
A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods
<p><strong>Description</strong><br> This repository contains a comprehensive solar irradiance, imaging, and forecasting dataset. <br> The goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. <br> The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. <br> In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data. <br> We also include sample codes of baseline models for benchmarking of more elaborated models.</p> <p><strong>Data usage</strong><br> The usage of the datasets and sample codes presented here is intended for research and development purposes only and implies explicit reference to the paper:<br> <em>Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494</em></p> <p>Although every effort was made to ensure the quality of the data, no guarantees or liabilities are implied by the authors or publishers of the data.</p> <p><strong>Sample code</strong><br> As part of the data release, we are also including the sample code written in Python 3. <br> The preprocessed data used in the scripts are also provided. <br> The code can be used to reproduce the results presented in this work and as a starting point for future studies. <br> Besides the standard scientific Python packages (numpy, scipy, and matplotlib), the code depends on pandas for time-series operations, pvlib for common solar-related tasks, and scikit-learn for Machine Learning models. <br> All required Python packages are readily available on Mac, Linux, and Windows and can be installed via, e.g., pip. </p> <p><strong>Units</strong><br> All time stamps are in UTC (YYYY-MM-DD HH:MM:SS).<br> All irradiance and weather data are in SI units.<br> Sky image features are derived from 8-bit RGB (256 color levels) data.<br> Satellite images are derived from 8-bit gray-scale (256 color levels) data.</p> <p><strong>Missing data</strong><br> The string "NAN" indicates missing data</p> <p><strong>File formats</strong><br> All time series data files as in CSV (comma separated values)<br> Images are given in tar.bz2 files</p> <p><strong>Files </strong></p> <ul> <li><em>Folsom_irradiance.csv</em> Primary One-minute GHI, DNI, and DHI data.</li> <li><em>Folsom_weather.csv </em> Primary One-minute weather data.</li> <li><em>Folsom_sky_images_{YEAR}.tar.bz2</em> Primary Tar archives with daytime sky images captured at 1-min intervals for the years 2014, 2015, and 2016, compressed with bz2.</li> <li><em>Folsom_NAM_lat{LAT}_lon{LON}.csv </em> Primary NAM forecasts for the four nodes nearest the target location. {LAT} and {LON} are replaced by the node’s coordinates listed in Table I in the paper. </li> <li><em>Folsom_sky_image_features.csv </em> Secondary Features derived from the sky images.</li> <li><em>Folsom_satellite.csv </em> Secondary 10 pixel by 10 pixel GOES-15 images centered in the target location. </li> <li><em>Irradiance_features_{horizon}.csv</em> Secondary Irradiance features for the different forecasting horizons ({horizon} 1⁄4 {intra-hour, intra-day, day-ahead}). </li> <li><em>Sky_image_features_intra-hour.csv</em> Secondary Sky image features for the intra-hour forecasting issuing times. </li> <li><em>Sat_image_features_intra-day.csv</em> Secondary Satellite image features for the intra-day forecasting issuing times. </li> <li><em>NAM_nearest_node_day-ahead.csv </em> Secondary NAM forecasts (GHI, DNI computed with the DISC algorithm, and total cloud cover) for the nearest node to the target location prepared for day-ahead forecasting.</li> <li><em>Target_{horizon}.csv</em> Secondary Target data for the different forecasting horizons.</li> <li>F<em>orecast_{horizon}.py </em> Code Python script used to create the forecasts for the different horizons. </li> <li><em>Postprocess.py</em> Code Python script used to compute the error metric for all the forecasts.</li> </ul> <p> </p>
Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil: Numerical Wave Experiment in the South of Brazil (NWESB).
<p>This dataset corresponds to the input files of the test domains used for the simulations of the coupled GFS (Global Forecast System) and WAVEWATCH III models in the waters of the South Atlantic Ocean and in waters of the Brazilian Southeastern during the passage of a cold front and the presence of strong pressure gradient between a low-pressure system and a high-pressure system. In the files generated by WAVEWATCH III, wave fields are presented from 2016-03-25 14:00:00, which is the date from when the model it stabilizes. Also contained in this dataset are the files of the GFS model wind fields, the bathymetry files (eTOPO1) and the files of the bathymetry entries in WAVEWATCH III.</p> <p>All files with suffix 2 correspond to the geographic region 70°W to 4°W longitude and 55°S to 13°S latitude and all files with suffix 3 correspond to the geographic region 70°W at 20°W longitude and 55°S at 13°S latitude.</p> <p><strong>ww3-2.inp</strong> and <strong>Bathymetry2.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-2 domain. <strong>gfs-2.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-2 domain. <strong>ww3-2.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-2 domain.</p> <p><strong>ww3-3.inp</strong> and <strong>Bathymetry3.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-3 domain. <strong>gfs-3.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-3 domain. <strong>ww3-3.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-3 domain.</p> <p>The GFS model files contain data every 6 hours and the WAVEWATCH III model files contain data every 1 hour. All files have a spatial resolution of 0.25° (27.78 km).</p> <p> </p> <p><strong>Other data that complement this dataset:</strong></p> <p><strong><a href="https://figshare.com/articles/figure/Complementary_figures_of_Parameter_adjustments_of_the_GFS_WAVEWATCH_III_coupled_models_in_Southern_Brazil/16726375"><em>Complementary figures of Parameter adjustments of the GFS – WAVEWATCH III coupled models in Southern Brazil.</em></a></strong></p> <p><em><strong><a href="https://figshare.com/articles/dataset/Dataset_for_the_adjustment_of_a_wave_forecasting_system_for_the_deep_waters_of_the_South_Atlantic_Ocean_and_for_the_southern_coast_of_Brazil_Output_files_in_GrADS_format_/16767058">Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil (Output files in GrADS format).</a></strong></em></p> <p> </p> <p> </p>
Third Uniform California Earthquake Rupture Forecast (UCERF3) Fault System Solutions
<p>Data files for the Third Uniform California Earthquake Rupture Forecast (UCERF3), as described in <a href="https://doi.org/10.1785/0120130164">https://doi.org/10.1785/0120130164</a>.<br> <br> These data are stored in the original UCERF3 Fault System Solution file format, which uses binary files within zip containers. This format is being revised, and updates to this dataset will be published when the new and more user friendly format is finalized. See <a href="https://opensha.org/File-Formats">https://opensha.org/File-Formats</a> for more information.<br> <br> File descriptions:<br> <br> <strong>Branch Averaged Files</strong></p> <p>These files contain branch-averaged fault system solutions, where rupture properties (magnitude, rake, rate of occurrence, etc) are averaged across all UCERF3 logic tree branches, according to each branch's weighting in the final model. This is the simplest version of the model, and can be used as a quick approximation to mean hazard. One file exists for each fault model, and these files are compatible with the time-dependent version of UCERF3.</p> <ul> <li><em>branch_averaged_ucerf3_sol_FM3_1.zip</em> - fault model 3.1 branch averaged fault system solution</li> <li><em>branch_averaged_ucerf3_sol_FM3_2.zip</em> - fault model 3.2 branch averaged fault system solution</li> </ul> <p><strong>Full Model (Compound Solutions)</strong></p> <p>These files contain the full UCERF3 logic tree, and can be used to extract data for individual logic tree branches (e.g., for use in hazard calculations that consider all epistemic uncertainties).</p> <ul> <li><em>full_ucerf3_compound_sol.zip</em> - full compound solution file with information on all 1,440 time-independent logic tree branches</li> <li><em>full_ucerf3_compound_sol_with_individual_runs.zip</em> - same as above, but also containing rates for each of 10 simulated annealing inversion runs for each logic tree branch (total of 14,400 inversions)</li> </ul> <p><strong>True Mean Solutions</strong></p> <p>A different type of branch averaged solution, the “true mean” solution, is also available. They are similar to the branch averaged fault system solution described above, but instead use duplicate versions of each rupture whenever a key property (rake, magnitude, area) changes. This retains all variability allowing for quick reproduction of mean UCERF3 results with a minimum set of ruptures. The MeanUCERF3 ERF implemented in <a href="https://opensha.org">OpenSHA</a> uses these files and also allows the user to apply various approximations to further reduce the rupture count.</p> <p>Note: These solutions are not compatible with time dependent UCERF3 calculations as multiple instances of each subsection may exist, resulting in rate partitioning between instances and incorrect recurrence intervals for renewal model calculations.</p> <ul> <li><em>true_mean_ucerf3_sol.zip</em> - true mean fault system solution, across both fault models</li> <li><em>true_mean_ucerf3_sol_FM3_1.zip</em> - true mean fault system solution, only for fault model 3.1</li> <li><em>true_mean_ucerf3_sol_FM3_2.zip</em> - true mean fault system solution, only for fault model 3.2</li> </ul> <p><strong>Metadata</strong></p> <p>A copy of the original file format description is included in <em>file_format.md</em>, and is also <a href="https://opensha.org/File-Formats">available online here</a>. A CSV file that includes information on each gridded seismicity location is also included (<em>relm_gridded_region.csv</em>).</p>
PM2.5, PM10, NO2, O3 from Copernicus Air Quality Forecast March-June 2019, 2020 and 2021
<p>PM2.5, PM10, NO2, O3 Copernicus Air Quality Forecasts March-June 2019, 2020 and 2021 retrieved from the ADAM platform data cube (http://reliance.adamplatform.eu). Datasets are monthly averaged.</p> <p>The resulting extracted datasets are stored in netCDF format and cover Europe.</p>
WRF Forecast Data used for Verification of multi-resolution model forecasts of heavy rainfall events of 23rd-26th August 2017 over Nigeria
<p>A deterministic Weather Research and Forecasting model version 4.2 forecast of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) within the period 23<sup>rd</sup>-26<sup>th</sup> August 2017 over Nigeria. The model was setup to perform two nested domain simulations with 18 (parent domain), 6 and 2 km (hereafter WRF18, WRF6 and WRF2) horizontal resolutions. The outer domain covers West Africa and the innermost domain, which runs at convection-permitting scale, focuses on Nigeria. When interpreting the results, it is worthy of note that the data has been regridded to 18 km, which is 3 x the grid scale for WRF6 and 9 x the grid scale for WRF2. This means that there is a fair degree of smoothing that has been applied using a bilinear regridding process to get the models onto a level playing field. Only WRF18 retains its native grid and has not benefited from any additional smoothing.</p> <p>The WRF model setup is similar to the study of Gbode et al. (2019; DOI: https://doi.org/10.1007/s00704-018-2538-x) in terms of the model physics combination used in the model simulations. The parameterization schemes used are the Goddard (GD) WRF model microphysics (MP), the Mellor–Yamada–Janjic (MYJ) planetary boundary layer (PBL) and the Bett-Miller-Janjic (BMJ) cumulus convection (CU) parameterization schemes. This combination was found to reproduce realistic rainfall and temperature relative to gridded observations over West Africa. The GD is a six-class microphysics with graupel and modifications for ice/water saturation. MYJ is a local closure scheme that predicts turbulent kinetic energy and the BMJ CU is a profile adjustment scheme that relaxes both deep and shallow profiles toward a reference profile without explicit updraft, downdraft, or cloud entrainment. However, the CU scheme was turned off in the 2 km domain to explicitly represent convection.</p>
South East Australian Coastal Ocean Forecast System (SEA-COFS)
<p>A suite of high resolution hydrodynamic ocean models for south eastern Australia that form the South East Australian Coastal Ocean Forecast System (SEA-COFS).<br> <br> The modelling suite includes output from several different configurations of the Regional Ocean Modeling System hydrodynamic simulation of the East Australian Current (EAC) System. These various model configurations include free running hindcast models, data assimilating state estimates, ocean forecasts and various nested high resolution runs. There is also a biogeochemical configuration of the fennel model on the EAC parent grid.<br> <br> At the time of upload there were four model grids (check later versions for new and revised / extended grids).</p> <p>The SEA-COFS domain covers the southeastern Australia oceanic region from 25.1-41.5 S and 147.1-162.2 E.</p> <p><br> <strong>SEACOFS_EAC_Grid.nc</strong> Is the parent grid of the EAC Domain 2.5-6km resolution</p> <ul> <li>Kerry, C. G. and M. Roughan, (2020). A high-resolution, 22-year, free-running, hydrodynamic simulation of the East Australia Current System using the Regional Ocean Modeling System. UNSW.dataset. <a href="https://doi.org/10.26190/5e683944e1369"> 10.26190/5e683944e1369</a>. </li> <li>Kerry, C. G. and M. Roughan,<strong> </strong>Powell, Brian, Oke, Peter (2020). A high-resolution reanalysis of the East Australian Current System assimilating an unprecedented observational data set using 4D-Var data assimilation over a two-year period (2012-2013). Version 2017. UNSW.dataset. <a href="https://doi.org/10.26190/5ebe1f389dd87"> 10.26190/5ebe1f389dd87</a>. <br> Kerry, C. , Powell, B. Roughan, M. and Oke, P. (2016) <a href="http://www.geosci-model-dev.net/9/3779/2016/gmd-9-3779-2016.pdf">Development and evaluation of a high-resolution reanalysis of the East Australian Current region using the Regional Ocean Modelling System (ROMS 3.4) and Incremental Strong-Constraint 4-Dimensional Variational (IS4D-Var) data assimilation</a>. Geosci. Model Dev, 9, 3779-3801, 10.5194/gmd-9-3779-2016<br> </li> </ul> <p><strong>SEACOFS_CoffsHarbour_Grid.nc</strong> Coffs Harbour Region 0.75-1km resolution</p> <ul> <li>Kerry, C., Roughan, M., & Powell, B. (2020). <a href="https://doi.org/10.1016/j.jmarsys.2019.103286">Predicting the submesoscale circulation inshore of the East Australian Current</a>. Journal of Marine Systems (Vol. 204, p. 103286)</li> </ul> <p><strong>SEACOFS_HSM_Grid.nc</strong> - Hawkesbury Shelf Model (HSM) 750m resolution </p> <ul> <li>Ribbat N., M. Roughan, B. Powell, C. Kerry, S. Rao, (2020). A high-resolution (750m) free-running hydrodynamic simulation of the Hawkesbury Shelf region off Southeastern Australia (2012-2013) using the Regional Ocean Modeling System. UNSW.dataset. <a href="https://doi.org/10.26190/5ec35ca34752e">DOI: 10.26190/5ec35ca34752e</a>. <a href="https://researchdata.ands.org.au/high-resolution-750m-ocean-modeling/1460879">Data access and more information.</a></li> </ul> <p><strong>SEACOFS_Narooma_Grid.nc </strong> Narooma Model 0.75-1km resolution<br> <br> <strong>EAC_BGC Fennel Model </strong></p> <ul> <li>Rocha, C., Edwards, C. A., Roughan, M., Cetina-Heredia, P., & Kerry, C. (2019) <a href="https://doi.org/10.5194/gmd-12-441-2019">A high-resolution biogeochemical model (ROMS 3.4 + bio_Fennel) of the East Australian Current system</a> Geosci. Model Dev., 12, 441-456</li> </ul> <p> </p>
Data of "Towards a More Reliable Forecast of Ice Supersaturation: Concept of a One-Moment Ice Cloud Scheme that Avoids Saturation Adjustment"
<p>These are the data used for generating the figures in the ACP article "Towards a More Reliable Forecast of Ice Supersaturation: Concept of a One-Moment Ice Cloud Scheme that Avoids Saturation Adjustment" by Sperber and Gierens.</p> <p>The data sets labeled "Box" have been generated by the stochastic box model, "adj" refers to the parameterisation using saturation adjustment and data labeled "par" originate from the newly developed parameterisation.</p> <p>The label "const" followed by a number refers to simulations with a constant updraught of the speed specified by the number in cm/s. The label "cos" refers to the simulations in which the updraught velocity follows a cosine function in time.</p> <p>"a10" labels simulations with less initial clear sky humidity fluctuations of plus/minus 10% instead of plus/minus 25%. "al0028" labels simulations with a higher deposition rate of 0.0028 1/s instead of 0.0003 1/s. "step10" labels simulations with a longer time step of 10 minutes instead of 1 minute.</p> <p>"Box_const2_rh1.txt" contains data from a simulation similar to "Box_const2.txt" but with an initial mean relative humidity of 100% instead of 110%. "Box_het.txt" contains data from a simulation including heterogeneous nucleation. "Box_slow_nuc.txt" contains data from a simulation where the deposition rate increases over time from zero after nucleation in every air parcel. "Box_upvar.txt" contains data from a simulation, where the updraught velocity in every air parcel varies randomly between 1 cm/s and 3 cm/s and the deposition rate inside the air parcel depends on the updraught velocity at the time of nucleation.</p> <p> </p> <p>The columns in the "Box" files represent from left to right:</p> <p>1. Time since the simulation start in s</p> <p>2. Cloud fraction</p> <p>3. Mean relative humidity across all air parcels</p> <p>4. Mean specific humidity across all air parcels</p> <p>5. Mean specific ice content across all air parcels</p> <p>6. Mean relative humidity across all cloudy air parcels</p> <p>7. Mean relative humidity across all clear air parcels</p> <p>8. Mean equilibrium supersaturation</p> <p>9. Mean threshold relative humidity for homogeneous nucleation</p> <p>10. Mean deposition rate across all cloudy air parcels</p> <p>11. Mean updraught velocity</p> <p> </p> <p>The columns in the "adj" files represent from left to right:</p> <p>1. Time since the simulation start in s</p> <p>2. Cloud fraction</p> <p>3. Mean relative humidity</p> <p>4. Mean specific humidity</p> <p>5. Mean specific ice content</p> <p>6. In-cloud Humidity</p> <p>7. Clear sky humidity</p> <p> </p> <p>The columns in the "par" files represent from left to right:</p> <p>1. Time since the simulation start in s</p> <p>2. Cloud fraction</p> <p>3. Mean relative humidity</p> <p>4. Mean specific humidity</p> <p>5. Mean specific ice content</p> <p>6. In-cloud Humidity</p> <p>7. Clear sky humidity</p> <p>8. Obsolete</p> <p>9. Equilibrium supersaturation</p>
State-of-the-art review of near-term freshwater forecasting literature published between 2017 and 2022
This data publication includes code and results from a systematic literature review on the current state of near-term forecasting of freshwater quality. The review aimed to address the following questions: (1) Freshwater variables, scales, models, and skill: Which freshwater variables and temporal scales are most commonly targeted for near-term forecasts, and what modeling methods are most commonly employed to develop these forecasts? How is the accuracy of freshwater quality forecasts assessed, and how accurate are they? How is uncertainty typically incorporated into water quality forecast output? (2) Forecast infrastructure and workflows: Are iterative, automated workflows commonly employed in near-term freshwater quality forecasting? How are forecasts validated and archived? (3) Human dimensions: What is the stated motivation for development of most near-term freshwater quality forecasts, and who are the most common end users (if any)? How are end users engaged in forecast development? An initial search was conducted for published papers presenting freshwater quality forecasts from 1 January 2017 to 17 February 2022 in the Web of Science Core Collection. Results were subsequently analyzed in three stages. First, paper titles were screened for relevance. Second, an initial screen was conducted to assess whether each paper presented a near-term freshwater quality forecast. Third, papers that passed the initial screen were analyzed using a standardized matrix to assess the state of near-term freshwater quality forecasting and identify areas of recent progress and ongoing challenges. Additional details regarding the systematic literature search and review are presented in the Methods section of the metadata.
Impact of urban and shipping emissions on NASA-Unified Weather Research and Forecasting model results
<p>This dataset supports Huang et al. (2019, JGR-Atmospheres): "Impact of aerosols from urban and shipping emission sources on terrestrial carbon uptake and evapotranspiration: a case study in East Asia". The file named "NUWRFout.tar.gz" contains NUWRF base and sensitivity simulation results on 31 May 2016. The file named "LIS_soil_LAI.zip" contains model grid information, soil conditions and leaf area index (LAI) at NUWRF initialization times in late May 2016.</p>
Evaluating demand forecasting models using multi-criteria decision-making approach
<p>The datasets added include the raw data, ANP weights calculations and TOPSIS ranking calculations for the demonstration case in the article titled: Evaluating demand forecasting models using multi-criteria decision-making approach.</p> <p>The files include a data explanation text file.</p>
Rye microgrid load and generation data, and meteorological forecasts.
<p>This dataset contains timeseries for Rye Microgrid, Trondheim, Norway. The timeseries include solar and wind power generation, consumption and historical weather forecasts.</p> <p>From <a href="https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021">https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021</a>:</p> <p><em>"The Rye microgrid is a pilot within the EU research project REMOTE. It is a small microgrid placed at Langørgen, in the outskirts of Trondheim, and is a small energy system designed to supply electricity to a modern farm and three households. The REMOTE projects goal for Rye Microgrid is to run the system in islanded mode.</em></p> <p><em>The system has two sources of generation – a wind turbine and a rack of PV panels. In addition, the system has two storages – a battery with high charge and discharge response, but with limited storage and losses, and a hydrogen energy system, with lower charge and discharge rates, higher losses and storage capacity. When you want to charge the hydrogen system, electricity is used to run an electrolyser that makes hydrogen from water and stores the resulting hydrogen in a tank. The process can be reversed by producing electricity from hydrogen using a fuel cell. (...)</em></p> <p><em>Morover, when local production or discharges from storages are not sufficient to cover the demand, the microgrid can draw electricity from the grid at some costs."</em></p> <p> </p> <p>For further details, see: <a href="https://www.remote-euproject.eu/remote18/rem18-cont/uploads/2019/03/REMOTE-D2.2.pdf">https://www.remote-euproject.eu/remote18/rem18-cont/uploads/2019/03/REMOTE-D2.2.pdf</a> and <a href="https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021">https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021</a></p> <p>rye_generation_and_load.csv is a comma-separated csv-file with the following columns (all values in <em>kW </em>and time as UTC):</p> <ul> <li>Consumption: Consumption of loads in system (residential and agriculture).</li> <li>Solar: Total production from all solar PV racks.</li> <li>Wind: Power production from wind turbine.</li> </ul> <p>met_data.h5: Contains historical weather forecasts data from The Norwegian Meteorological Institute (met.no) updated every 6 hours for the given location. The file is in hdf5 format. The forecasts include the following parameters: air_pressure_at_sea_level [Pa], air_temperature_2m [K], cloud_area_fraction [pu], integral_of_surface_downwelling_shortwave_flux_in_air_wrt_time [J/m<sup>2</sup>s], wind_direction_10m [deg], wind_speed_10m [m/s]</p> <p>The structure of the file is as follows:</p> <ul> <li>lat63_41_lon10_11 (coordinates) <ul> <li>[forecasted parameter] <ul> <li>forecast <ul> <li>2020-01-01T00Z (time forecast was issued) <ul> <li>axis0 (columns, index where each represent a point in a geographical grid. For example if axis=0,1,2,3, the tables contains the forecasts for the four closes points to the microgrid.)</li> <li>axis1 (rows, timestamps)</li> <li>block0_items (equal to axis0)</li> <li>block0_values (matrix, forecast values)</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in Dec 2023.
<p>Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in NHESS journal in Dec 2023.</p> <p>For further technical details read the file READMEdata in the zipfile. The script MAKEFIG remakes all the figures from the data.</p> <p>For scientific details read the associated article preprint on the NHESS egusphere website.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.