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180 results for “Downscaling”
Data and scripts for: Intercomparison of atmospheric datasets and PBL schemes for precipitation downscaling over a coastal mountain valley of northern British Columbia, Canada
<p>anl_6MYJdiv and anl_6MYNNdiv contains pairwise normalizations of dataset outputs (NAM/ERA5, NAM/NARR and ERA5/NARR) of total rainfall in 2017 for simulations with the MYJ and MYNN3 PBL schemes, that can be plotted by fig3_4.ncl. anl_MYJMYNN_div contains MYJ/MYNN3 spatial contours for each of ERA5, NAM -ANL and NARR outputs. anl_snow_MYJ contains MYJ output for total snow in 2017 by the ERA5, NAM-ANL and NARR datasets, for which values at discrete locations can be retrieved with yr2017snow.ncl, daily_ppt.ncl is script to extract modeled daily precipitation (dly_MYJ and dly_MYNN) from the various locations. Fig_ppt_monthly.R is the plotting script for observed and modeled precipitation time series from hydro31pt1pk.txt. nullwrf is array holder for plotting with ncl scripts. rivs_coasts.shp is shape file that is used in the spatial plots. </p>
Data from: Evaluation of different bias correction methods for dynamical downscaled future projections of the California Current Upwelling System
<p class="Abstract">Biases in global Earth System Models (ESMs) are an important source of errors when used to obtain boundary conditions for regional models. Here we examine historical and future conditions in the California Current System (CCS) using three different methods to force the regional model: (1) interpolation of ESM output to the regional grid with no bias correction; (2) a "seasonally-varying" delta method that obtains a season-dependent mean climate change signal from the ESM for a 30-year future period; and (3) a "time-varying" delta method that includes the interannual variability of the ESM over the 1980–2100 period. To compare these methods, we use a high-resolution (0.1˚) physical-biogeochemical regional model to dynamically downscale an ESM projection under the RCP8.5 emission scenario. Using different downscaling methods, the sign of future changes agrees for most of the physical and ecosystem variables, but the spatial patterns and magnitudes of these changes differ, with the seasonal- and time-varying delta simulations showing more similar changes. Not correcting the ESM forcing leads to amplification of biases in some ecosystem variables as well as misrepresentation of the California Undercurrent and CCS source waters. In the non-bias corrected and time-varying delta simulations, most of the ecosystem variables inherit trends and decadal variability from the ESM, while in the seasonally-varying delta simulation, the future variability reflects the observed historical variability (1980–2010). Our results demonstrate that bias correcting the forcing prior to downscaling improves historical simulations and that the bias correction method may impact the spatial and temporal variability of future projections. </p>
Jupyter Notebook and comprising data for GRL2023GL106264R: Understanding the Cascade: Removing GCM biases improves dynamically downscaled climate projections
<p>This notebook and attendant files allows users to interface with a small subset of the data used to create the data in GRL2023GL106264R. Also feel free to check out the overall description of the non-bias corrected dynamically downscaled GCMs in WUS-D3 here: https://zenodo.org/records/10635867. This DOI also contains version of WRF 4.1.3 allowing for yearly CH4, CO2, and N2O updates, as well as a 360-day calendar version.</p>
High-resolution and full coverage AOD downscaling based on the bagging model over the arid and semi-arid areas, NW China
<p>High-resolution and full coverage 250 m monthly AOD product over the arid and semi-arid areas, NW China. the scale factor is 1000.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Kortrijk Kennedy Park, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Kortrijk Kennedy Park (50°48'2"N 3°16'13" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Antwerp Berchem, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Antwerp Berchem (51°12'00"N 4°26'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Sint-Katelijne-Waver, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51°3'25"N 4°11'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Uccle KMI, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N, 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven City centre, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Leuven City Centre (50°52'48"N 4°42'0" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven Casa Blanca, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Casa Blanca neighbourhood Leuven (50°52'48"N, 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Leuven Casa Blanca, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of the Casa Blanca Neighbourhood Leuven (50°52'48"N 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Uccle KMI, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Leuven City centre, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of city centre of Leuven (50°52'48"N, 4°42'0"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Trained models and code accompanying 'Downscaling using Deep Convolutional Autoencoders, a case study for South East Asia'
<p>Trained models and code accompanying 'Downscaling using Deep Convolutional Autoencoders, a case study for South East Asia'.</p>
Datasets accompanying 'Downscaling using Deep Convolutional Autoencoders, a case study for South East Asia'
<p>Datasets required to replicate the experiment described in the paper: 'Downscaling using Deep Convolutional Autoencoders, a case study for South East Asia'</p> <p>This includes NetCDF files used to generate training data and processed climate data for model training, trained models and outputs from model predictions. It also contains the datasets used for comparisons (CORDEX and CMIP6)</p>
Data for impacts of topography-based subgrid scheme and downscaling of atmospheric forcing on modeling land surface processes in the conterminous US
<p>The effects of small-scale topography-induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). A topography-based subgrid structure and methods of downscaling of atmospheric forcing from the atmospheric grid to the subgrids of the land model grid (TGUs) have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to improve representation of the effects of small-scale topography-induced land surface heterogeneity on land surface processes. This study evaluates the impacts of the topography-based subgrid structure and downscaling of atmospheric forcing on modeling land surface processes in E3SM over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5-degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS-level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional-level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small-scale surface heterogeneity in ESMs and motivate future research to understand their effects on land-atmosphere interactions, streamflow, and water resources management over mountainous regions.</p> <p>The data utilized to evaluate effects of the topography-based subgrid structure and downscaling of atmospheric forcing in land surface modeling include a TGU level land surface data file, atmospheric forcing to drive the land model, ELM user name list configuration parameters, regionalization variables (topographic regions, snow fraction regions, water versus energy limited regions, and regions of season of maximum precipitation), model restart files for both ELM configurations, and model outputs (grid and subgrid levels), model outputs aggregated to TGUs and grid levels.</p> <p>The data files include:</p> <ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/daily_prism_precip.zip?versionId=be97ca8d-182a-4f1e-9ae3-9da3f2b87e24">DELM.zip</a>: Directory containing the following files relevant to the D ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_all_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_disag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_disag_daily_20220512.nc</li> <li>TGU-level monthly output file: tgu_level_output_all_disag_20221109.nc</li> </ol> <li> <a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/dem_4km4.nc">NoDELM.zip</a>: Directory containing the following files relevant to the NoD ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_no_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_nodisag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_nodisag_daily_20220512.nc </li> <li>TGU-level monthly output file: tgu_level_output_no_disag_20221109.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">shared.zip</a>: Directory containing the following files relevant to both the D ELM and NoD ELM configurations.</li> <ol> <li>Subgrid-based surface data file: MASKED.half_degree_merge.surfdata_0.5x0.5_simyr1850_c200924.pft17.10262022v2.nc</li> <li>Regionalization file used to generate regions based on snow fraction, water versus energy limited state, and seasons of maximum precipitation: half_deg_budyko_curve_analysis_20230104_disag.nc</li> <li>Topographic ratio file used to generate topography-based regions: grd_level_output_nodisag_run_11_new_20221109.nc</li> <li>TGU-level surface elevation data file where surface elevation data are derived from high resolution surface elevation data (90 m) obtained from HydroSHEDS [Lehner et al. 2008, Lehner and Grill 2013]: half_deg_subgrids_with_PFTs_and_stat_20210403.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">SNOTEL_files.zip</a>: Directory containing the following SNOTEL data related files used to evaluate model performance.</li> <ol> <li> SNOTEL list of stations file: SNOTEL_halfdegree_intersect4.csv</li> <li>SNOTEL data files/folders: csv</li> </ol> </ol> <p> </p> <p><strong>References</strong></p> <p>Lehner, B., et al. (2008). "New Global Hydrography Derived From Spaceborne Elevation Data." Eos, Transactions American Geophysical Union <strong>89</strong>(10): 93-94.</p> <p>Lehner, B. and G. Grill (2013). "Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems." Hydrological Processes <strong>27</strong>(15): 2171-2186.</p> <p> </p>
Supporting data for Emerging AI-based weather prediction models as downscaling tools
<p>Supporting data for "Emerging AI-based weather prediction models as downscaling tools" by Nikolay Koldunov, T. Rackow, Christian Lessig, S. Danilov, S. Cheedela, D. Sidorenko, Irina Sandu, Thomas Jung</p> <p><a href="https://t.co/PSUCUvh9lf" target="_blank" rel="noopener noreferrer nofollow"><span>https://</span>doi.org/10.48550/arXiv<span>.2406.17977</span></a></p>
Downscaling mutualistic networks from species to individuals reveals consistent interaction niches and roles within plant populations.
<p>Repository containing dataset and code for the manuscript entitled <em>Downscaling mutualistic networks from species to individuals reveals consistent interaction niches and roles within plant populations</em>.</p> <p>For this study, we compiled 46 empirical individual-based networks on plant-animal seed dispersal mutualism, encompassing 1037 plant individuals across 29 species from various regions. We compare the structure of individual-based networks to that of species-based networks and by extending the niche concept to interaction assemblages, we explore levels of individual plant specialization. We examine how individual variation influences network structure and how plant individuals "explore" the interaction niche of the population.</p> <p>Please refer to <strong>makefile.R</strong> for project outline, explanation and codes used, and to the <strong>README</strong> in networks folder for data structure and compilation.</p>
Downscaled 11 km CESM2 data used in CESM2 Greenland SMB evaluation paper (HIST-EC)
<p>Monthly output from CESM2 simulation HIST-EC over the period 1960-1999, downscaled to the 11 km RACMO grid using elevation class output.</p> <p>Variables: EFLX_LH_TOT, FGR, FIRA, FIRE, FLDS, FSA, FSDS, FSH, FSM, FSR, QICE, QICE_MELT, QRUNOFF, QSNOFRZ, QSNOMELT, QSOIL, RAIN, RAIN_FROM_ATM, RH2M, SNOW, SNOW_FROM_ATM, TG, TSA, TSKIN, U10</p>
Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features
<p>This dataset supports the study titled <em>"Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features"</em>, published in <em>Urban Climate</em> (<a href="https://doi.org/10.1016/j.uclim.2024.102102" target="_new" rel="noopener">DOI: 10.1016/j.uclim.2024.102102</a>).</p> <p> </p> <p><strong>Content Overview:</strong></p> <ul> <li> <p><strong>Building Label Data for Footprint Detection</strong>:</p> <ul> <li><em>Amsterdam_BDG_Label.rar</em></li> <li><em>MiamiDade_BDG_Label.rar</em></li> </ul> <p>These are the label datasets used for training the building detection segmentation models. They have been instrumental in accurately detecting building footprints in Amsterdam.</p> </li> <li> <p><strong>Amsterdam_3D_Buildings.rar</strong>: CityGML file of 3D building models for Amsterdam, derived from LiDAR data and U-Net3+ model.</p> </li> </ul> <ul> <li> <p><strong>Morphological Features.rar</strong>: Contains urban morphological features (in raster format) extracted from LiDAR data used in the study.</p> </li> <li> <p><strong>Training and Test Data for Air Temperature Estimation</strong>:</p> <ul> <li><em>Train_Test_AvgTemp_Amsterdam.rar</em></li> <li><em>Train_Test_MaxTemp_Amsterdam.rar</em></li> <li><em>Train_Test_MinTemp_Amsterdam.rar</em></li> </ul> <p>This dataset includes training and testing data for estimating air temperatures in three scenarios: average daily temperature, minimum daily temperature, and maximum daily temperature for the city of Amsterdam.</p> </li> </ul>
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.