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Dataset results
180 results for “Downscaling”
2015-2017 Dataset [6/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 6/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2015-2017. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2006-2008 Dataset [3/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 3/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2006-2008. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2003-2005 Dataset [2/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 2/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2003-2005. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
Downscaled Extreme Rainfall in Bangladesh
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Data for paper "Improved simulation of Compound Drought and Heat Extremes in Eastern China through CWRF downscaling"
<p>Data used in paper "<span>Improved simulation of Compound Drought and Heat Extremes in Eastern China through CWRF downscaling</span>".</p> <p>This dataset should be used with code on this link: <span>https://github.com/Oscarrrhhhh/CDHE_paper</span></p>
Dataset for downscaling, used in downscaled Spatiotemporal Precipitation Model Based on a Transformer Attention Mechanism
<p>In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct the neural network in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. This tailored module is instrumental in enhancing the model's ability to generate outcomes that are not only more true-to-life but also more consistent with physical laws. It adeptly mirrors the temporal and spatial fluctuations in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of four months. The datasets vary in their spatial resolutions, with one featuring a 50-kilometer resolution and the other a 12-kilometer resolution, both sourced from the Weather Research and Forecasting (WRF) Model.</p>
Downscaled climate time series
<p>Downscaled time series developed for the following paper: Fernández, Manquehual-Cheuque & Somos_Valenzuela (2024): Impact of Solar Radiation Management on Andean glacier-wide surface mass balance, npj Climate and Atmospheric Science, doi:<strong> </strong>10.1038/s41612-024-00807-x</p>
Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture
<p>Soil moisture is a key variable in the water, energy, and carbon cycles. Mapping sub-surface soil moisture with fine spatial resolution requires integrating downscaling approaches and process-based models. However, the effectiveness of hybrid methods, such as regression kriging (RK), in enhancing soil moisture estimates through process-based parameter predictions remains inconclusive. This study aims to integrate infiltration processes into downscaling models to predict 1-km multi-layer soil moisture, while comparing performance of nonlinear and linear models, and evaluating RK improvements. Random forests (RF) and generalized linear model (GLM) were used to downscale surface soil moisture (0–5 cm) from 36-km Soil Moisture Active Passive satellite products to 1 km across the Qinghai-Tibet Plateau. Next, the soil moisture analytical relationship (SMAR) model was applied to simulate infiltration processes and obtain site-scale parameters. RK variants (RFRK and GLMRK) were applied to jointly predict the spatial distribution of multiple infiltration parameters, which were used in SMAR at 1-km grids to estimate sub-surface soil moisture (5–40 cm). The results showed that parameter calibration significantly enhanced sub-surface soil moisture simulation, reducing root mean square error (RMSE) by 61.2% to 69.8%, from 0.09 to 0.03. RF outperformed GLM across all depth intervals, providing higher prediction accuracy (average RMSE, RF: 0.07; GLM: 0.09). Moreover, RK enhanced the Nash-Sutcliffe efficiency coefficient (RFRK: 0.34; GLMRK: 0.28) and coefficient of determination (RFRK: 0.5; GLMRK: 0.38) by 7.7%–13.3% and 2.2%–2.4%. This study provides a reference for mapping multi-layer soil moisture through the integration of data-driven and knowledge-driven approaches in regional-scale study areas.</p>
High-resolution Global Dataset of BaP Based on Downscaling (0.1° × 0.1°)
<p>This dataset contains the annual- and monthly-averaged BaP concentrations in the atmosphere based on downscaling by using Relative emission with a resolution of 0.1° × 0.1°.</p>
MATLAB scripts for reproducing figures in "Atlantic Tropical Cyclones Downscaled from Climate Reanalyses Show Increasing Activity Through the Late 19th and 20th Centuries"
<p>A set of MATLAB scripts that contain the data for all 4 figures of "<strong>Atlantic Tropical Cyclones Downscaled from Climate Reanalyses Show Increasing Activity Through the Late 19<sup>th</sup> and 20<sup>th</sup> Centuries" </strong> and allows the user to plot these data. Please read the very short ReadMe file before using the scripts. </p>
Transferability of single-image super resolution to geophysical downscaling
<p>Data to accompany paper "Transferability of single-image super resolution to geophysical downscaling" - Zhongyang Hu, Peter Kuipers Munneke, Stef Lhermitte, Yao Sun, Brice Noël, Melchior van Wessem, Lichao Mou, and Xiao Xiang Zhu. 2022</p> <p>RACMO2 27 km and 5.5 km simulations are freely available from (\url{https://www.projects.science.uu.nl/iceclimate/models/racmo-model.php#1-1}, IMAU, 2022; latest accessed on 3 October 2022) are provided by Van Wessem et al. (2018, 2016), for details and further usage, please refer to: </p> <p>Van Wessem, J.M., Ligtenberg, S.R.M., Reijmer, C.H., Van De Berg, W.J., Van Den Broeke, M.R., Barrand, N.E., Thomas, E.R., Turner, J., Wuite, J., Scambos, T.A. and Van Meijgaard, E., 2016. The modelled surface mass balance of the Antarctic Peninsula at 5.5 km horizontal resolution. <em>The Cryosphere</em>, <em>10</em>(1), pp.271-285.</p> <p>Van Wessem, J.M., Van De Berg, W.J., Noël, B.P., Van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C.L., Krüger, K., Lenaerts, J., Lhermitte, S. and Ligtenberg, S.R., 2018. Modelling the climate and surface mass balance of polar ice sheets using RACMO2–Part 2: Antarctica (1979–2016). <em>The Cryosphere</em>, <em>12</em>(4), pp.1479-1498.</p>
Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Super-Resolution Convolutional Neural Networks
<p>Datasets and source codes for the manuscript "Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Super-Resolution Convolutional Neural Networks" submitted to the journal "Artificial Intelligence for the Earth Systems" of the American Meteorological Society.</p>
Data and code: High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method
<p>Data and code supporting the research article:High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method - <br> Fasil M. Rettie, Sebastian Gayler, Tobias KD Weber, Kindie Tesfaye, Thilo Streck. Please, find detail description of the codes and datasets in readme file.</p>
A Deep-Learning-Based Approach to the Downscaling of Precipitation Data Observed in Taiwan
<p>The data set is provided by the authors for the observational precipitation data downscaling method presented in the paper.</p>
Data supporting 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)'
<p>This data set contains the data produced for the paper 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)'</p> <p>The data set contains output from LADDIE simulations, including basal melt rates.</p> <p>The main simulations used in the man text are:</p> <p>- Crosson-Dotson: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/CrossDots_0.5_tanh_Tdeep0.4_ztcl-500_050.nc">CrossDots_0.5_tanh_Tdeep0.4_ztcl-500_050.nc </a><br> - Filchner-Ronne: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/FRIS_1.0_linear_S134.8_T1-2.3_720.nc">FRIS_1.0_linear_S134.8_T1-2.3_720.nc </a><br> </p> <p>The additional simulations included in the Appendix are:</p> <p>- 3D forcing of Crosson-Dotson: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/CrossDots_0.5_mitgcm_2003_2008_100.nc">CrossDots_0.5_mitgcm_2003_2008_100.nc </a><br> - Tuning of Crosson-Dotson: XX_YY_ZZ.nc, where XX is the resolution in km, YY is the value for Cd,top, and ZZ is the value for Dmin<br> - Pine Island Ice Shelf: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/PIG.nc">PIG.nc </a><br> </p>
High quality figures of "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"
<p>This repository provides the figures for the publication "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain" in their original resolution, ensuring clarity and high-quality visual representations for readers.</p>
Data for "Dynamical Downscaling of Climate Simulations in the Tropics"
<p>Precipitation, radiation and vertical mass flux data. 'MPI' indicates conventional downscaling results. 'biascor' indicates bias-corrected downscaling results. 'sstcor' indicates SST-corrected downscaling results.</p>
Data from: Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD
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Data from: Downscaling pollen-transport networks to the level of individuals
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Data from: Evaluation of downscaled, gridded climate data for the conterminous United States
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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.