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180 results for “Downscaling”
Data and R code from: Downscaling species to individual-level networks reveals the importance of population-level processes in mediating generalized community-wide interaction patterns
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Data from: Evaluation of different bias correction methods for dynamical downscaled future projections of the California Current Upwelling System
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Model output for: Attributing causes of future climate change in the California Current System with multi-model downscaling
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The Effects of Climate Downscaling Technique and Observational Dataset on Modeled Ecological Responses: Supporting Data Tables
These data have been prepared as a supplement to Pourmokhtarian et al. (2016; full citation below), where complete details on methods can be found. We evaluated three downscaling methods: the delta method (or the change factor method); monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD); and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8th degree resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (8 ensembles of 6 runs). This dataset consists of three files - 1) a zip archive file of all raw daily downscaled AOGCMs (csv format; years 1960-2099; delta method 2012-2099 only) which were used as input for PnET-BGC model, 2) a zip archive file of all PnET-BGC output files for each model run (csv format; years 1000-2100), and 3) a pdf document file that describes the content of the input and output files. Data were also used from the following Hubbard Brook longterm datasests: Daily Streamflow Watershed 6: http://dx.doi.org/10.6073/pasta/727ee240e0b1e10c92fa28641bedb0a3 Chemistry of Streamwater at the Hubbard Brook Experimental Forest, Watershed 6: http://dx.doi.org/10.6073/pasta/2ec152b0ab1d4e64aa40f4aa9bc492ac Daily Precipitation Watershed 6: http://dx.doi.org/10.6073/pasta/17c8ff8b160bf7893ef39f75a02652e5 Daily Maximum/Minimum Temperature Data: http://dx.doi.org/10.6073/pasta/2a4ab5522ce15f28196a6035802b09e8 Daily Solar Radiation Data: http://dx.doi.org/10.6073/pasta/2fa098a5aa191c64e622b253c0fee5af These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest
GCAM Version 2 Reference Scenario with Water Constraints Downscaled with Demeter to 5-arcmin (Irrigated, Rain-fed)
<p>GCAM Version 2 Reference Scenario with Water Constraints Downscaled with Demeter to 5-arcmin resolution for year 2015 for irrigated and rain-fed GCAM crop breakout along with forest, urban, sparse, snow, shrub land classes. This run was generated for use by the `teleconnect` package (see <a href="https://github.com/IMMM-SFA/teleconnect">https://github.com/IMMM-SFA/teleconnect</a>). The following is the full README found in the zipped data resource:</p> <blockquote> <p>GCAM v5.2 to Demeter </p> <p>Title:<br> Demeter output for GCAM v5.2 with water constraints - Reference scenario</p> <p>Description:<br> Demeter run conducted using the base layer combining Mirca and Modis v6 type 5 to generate rain-fed and irrigated crops constrained to Modis crop area. GCAM projection split RockIceDesert into snow and sparse land classes.</p> <p>Building the Demeter base layer for use with GCAM allocated land classes and use types:<br> Described in the readme_gcam-reg32basin235_modis-v6-2010_mirca2000_5arcmin.pdf document the docs directory of this data archive.</p> <p>GCAM Version: https://github.com/JGCRI/gcam-core/tree/gcam-v5.2 ; https://doi.org/10.5281/zenodo.3528353 </p> <p>GCAM Reference:<br> Calvin, K., Patel, P., Clarke, L., Asrar, G., Bond-Lamberty, B., Cui, R. Y., Di Vittorio, A., Dorheim, K., Edmonds, J., Hartin, C., Hejazi, M., Horowitz, R., Iyer, G., Kyle, P., Kim, S., Link, R., McJeon, H., Smith, S. J., Snyder, A., Waldhoff, S., and Wise, M.: GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems, Geosci. Model Dev., 12, 677–698, https://doi.org/10.5194/gmd-12-677-2019, 2019.</p> <p>Demeter Reference:<br> Vernon, C.R., Le Page, Y., Chen, M., Huang, M., Calvin, K.V., Kraucunas, I.P. and Braun, C.J., 2018. Demeter – A Land Use and Land Cover Change Disaggregation Model. Journal of Open Research Software, 6(1), p.15. DOI: http://doi.org/10.5334/jors.208</p> <p>Run:<br> GCAM reference scenario with water constraints conducted by Sonny Kim (skim@pnnl.gov) originally retrieved from PNNL's Constance here: /pic/projects/GCAM/water_market/database_basexdbGCAM51WaterConstr. </p> <p>Contents:<br> teleconnect_agu2019<br> -- config_gcam5p1_watconstr_ref.ini (Demeter configuration file) <br> -- code (code to run Demeter pre-, run, and post-processing)<br> ---- README.txt (Description of run order and process for Demeter on Constance)<br> ---- demeter_preprocess.py (Python script to extract land data from the GCAM database and split RockIceDesert into snow and sparse)<br> ---- demeter_postprocessing.py (Python script to create fractional output of Demeter's native output in square kilometers)<br> ---- run_demeter.py (Python script to run Demeter)<br> ---- run_demeter_gcam5p1_watconstr_ref.sh (sbatch script to submit a Demeter run on Constance)<br> ---- run_postprocessing.sh (sbatch script to submit a post-processing run on Constance)<br> ---- run_preprocessing.sh (sbatch script to submit a pre-processing run on Constance)<br> ---- slurm-11504861.out (Slurm output from Demeter run)<br> -- GCAM <br> ---- database_basexdbGCAM51WaterConstr (GCAM output database)<br> -- inputs (input files used by Demeter) <br> ---- allocation <br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_constraint_alloc.csv (weighting of constraints)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_observed_alloc.csv (reclassification table for observed land classes to Demeter final land classes)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_order_alloc.csv (processing order for land classes)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_projected_alloc.csv (reclassification table for GCAM land classes to Demeter final land classes)<br> ------ gcam_regbasin_modis_v6_type5_mirca_5arcmin_transition_alloc.csv (transition order for land classes)<br> ---- constraints<br> ------ 000_nutrientavail_hswd_5arcmin.csv (nutrient availability constraint weighted by grid cell)<br> ------ 001_soilquality_hswd_5arcmin.csv (soil quality constraint weighted by grid cell)<br> ---- observed<br> ------ gcam_reg32_basin235_modis_v6_2010_mirca_2000_5arcmin_sqdeg_wgs84_11Jul2019.csv (Demeter base layer)<br> ---- projected<br> ------gcam_5p1_watconst_reference.csv (output from demeter_preprocess.py from GCAM output)<br> ------gcam_5p1_watconst_reference_split.csv (output from demeter_preprocess.py from GCAM output with RockIceDesert split into snow and sparse land classes)<br> ---- reference (see https://github.com/IMMM-SFA/demeter)<br> ------ aezcoord.csv<br> ------ countrycoord.csv<br> ------ gcam_basin_lookup.csv<br> ------ gcam_regions_32.csv<br> ------ limits.csv<br> ------ query_land_reg32_basin235_gcam5p0.xml (land allocatio query)<br> ------ regioncoord.csv<br> -- for_teleconnect<br> ---- usa_demeter.csv (file used by the `teleconnect model` containing only 5-arcmin grid cells that are in GCAM region 1 (USA))<br> -- outputs (output files from Demeter run)<br> ---- ref_watconstr_2019-11-07_07h20m46s (output Demeter run directory)<br> ------ log_files (log file directory)<br> -------- logfile_ref_watconstr_2019-11-07_07h20m46s.log (log file from Demeter run)<br> ------ spatial_landcover_tabular<br> -------- landcover_2015_fraction.csv (fraction of land cover per grid cell per land class for 2015) <br> -------- landcover_2015_sqkm.csv (square kilometers of land cover per grid cell per land class for 2015) <br> -------- landcover_2015_timestep.csv (square kilometers of land cover per grid cell per land class for 2015) <br> -- docs <br> ---- readme_gcam-reg32basin235_modis-v6-2010_mirca2000_5arcmin.pdf (creation of the Demeter base layer)</p> </blockquote>
AWE-GEN-2d downscaled climate simulations for Omo-Turkana and Zambezi river basins
<p>AWE-GEN-2d downscaled climate simulations generated for the DAFNE project</p> <p>Refer to the README.txt for data access and description.</p>
WRF data for downscaling, used in Learned multi-resolution dynamical downscaling for precipitation
<p>This study uses regional climate model (RCM) simulated precipitation at low and high spatial resolution, to develop convolution neural network (CNN) based approaches, that can emulate high resolution modeled data using low resolution modeled data with cheaper computational resource than running dynamical downscaling at the high spatial resolution. Specifically, we define two types of CNNs, one that stacks variables directly and one that encodes each variable before stacking, and train each CNN type both with a conventional loss function, such as Mean Square Error (MSE), and with a conditional generative adversarial network (CGAN), for a total of four CNN variants. We compare the four new CNN-derived high resolution precipitation with precipitation generated from a bi-linear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique, using the original high resolution precipitation from the RCM as ground truth. We find that SR technique produces similar results to the interpolator with smoother spatial and temporal distributions and smaller data variabilities and extremes than ground truth shows. While the new CNNs trained by MSE generate better results over some regions than the interpolator and SR technique, their predictions are still not as close as ground truth. The CNNs trained by CGAN generate more realistic and physically reasonable results. This advanced technique improves not only the data variability in time and space, and but also the extremes, such as intense and long-lasting events, based on event-feature tracking algorithm.</p>
Data from: Downscaling pollen-transport networks to the level of individuals
1. Most plant-pollinator network studies are conducted at species level whereas little is known about network patterns at the individual level. In fact, nodes in traditional species-based interaction networks are aggregates of individuals establishing the actual links observed in nature. Thus, emergent properties of interaction networks might be the result of mechanisms acting at the individual level. 2. Pollen loads carried by insect flower-visitors from two mountain communities were studied to construct pollen-transport networks. For the first time, these community-wide pollen-transport networks were downscaled from species-species (sp-sp) to individuals-species (i-sp) in order to explore specialization, network patterns and niche variation at both interacting levels. We used a null model approach to account for network size differences inherent to the downscaling process. Specifically, our objectives were: (i) to investigate whether network structure changes with downscaling, (ii) to evaluate the incidence and magnitude of individual specialization in pollen use, and (iii) to identify potential ecological factors influencing the observed degree of individual specialization. 3. Network downscaling revealed a high specialization of pollinator individuals, which was masked and unexplored in sp-sp networks. The average number of interactions per node, connectance, interaction diversity and degree of nestedness decreased in i-sp networks, because generalized pollinator species were composed of specialized and idiosyncratic conspecific individuals. An analysis with 21 pollinator species representative of two communities showed that mean individual pollen resource niche was only c. 46% of the total species niche. 4.The degree of individual specialization was associated to inter- and intraspecific overlap in pollen use and it was higher for abundant than for rare species. Such niche heterogeneity depends on individual differences in foraging behaviour and likely has implications for community dynamics and species stability. 5. Our findings highlight the importance of taking inter-individual variation into account when studying higher–order structures such as interaction networks. We argue that exploring individual-based networks will improve our understanding of species-based networks and will enhance the link between network analysis, foraging theory and evolutionary biology.
TroDSIF: an improved spatially downscaled solar-induced chlorophyll fluorescence product of TROPOMI dataset
<p>TroDSIF is an improved spatially downscaled solar-induced chlorophyll fluorescence product of TROPOMI dataset at far-red band (wavelength at 740 nm), with a spatial resolution of 500 m and a temporal resolution of 16 days under clear-sky condition.<br>The original TROPOMI SIF was retrieved by Guanter et al., which was available at https://doi.org/10.5270/esa-s5p_innovation-sif-20180501_20210320-v2.1-202104.</p>
A Spatially Downscaled TROPOMI SIF Product at 0.005 Degree Resolution in China during 2019-2020
<p><span>To improve the spatial resolutions of TROPOMI SIF, a 0.005-degree SIF product during 2019~2020 in China was generated based on a proposed downscaling strategy correcting the predicted bias with Random Forest (RF) model by TROPOMI SIF, MODIS reflectance, and ERA5 reanalysis datasets. This bias-corrected downscaled SIF product (namely BCSIF) has been validated with the original TROPOMI SIF and the long-term tower-based SIF, also evaluated by correlating with the GPP data, which verified its reliability and applicability for GPP monitoring.</span></p>
SensRes dataset for downscaling soil maps
<p>This dataset contains soil information and sensor data from agricultural fields in Denmark, Lithuania, Northern Ireland, the Netherlands, and Turkey to downscale coarse-resolution maps to high resolution in the SensRes project (EJP SOIL).</p> <p> It includes 1455 soil samples with data on soil organic carbon and soil texture fractions (clay, silt, and sand). For each sampling site, the dataset also contains rasters of Sentinel-2 bare soil images, aerial images (RGB), and maps from Electromagnetic Induction and Gamma sensors. The soil data is provided in .txt file format, while the sensor data is available in .tif format. There are also shapefiles from each field in .shp format. </p> <p>The SensRes project developed a framework for downscaling soil maps, which was published as an R package (<a href="https://github.com/anbm-dk/soilscaler/tree/main">https://github.com/anbm-dk/soilscaler/tree/main</a>), and this dataset contains the required local inputs to apply the downscaling process. Part of the soil information present in this dataset has also been used in the STEROPES EJP SOIL project. </p>
Statistical downscaling of significant wave height: data
<p>The cfsr file contains the Climate Forecast System Reanalysis wind data over the North Atlantic and homere contains the Homere sea state data at a location in the Bay of Biscay (latitude=25.4N, longitude=1.6W). data_prep contains the processed data, which are used to run the statistical downscaling model.</p> <p>All files are in RDS format ( R data Serialization). </p>
An improved downscaled sun-induced chlorophyll fluorescence (DSIF) product of GOME-2 dataset
<p>The downscaled solar-induced chlorophyll fluorescence (DSIF) product (wavelength at 740 nm) had a spatial resolution of 0.05° and a temporal resolution of 8 days.</p> <p>The difference between version 1:</p> <p>(1) Before spatial downscaling, we upscaled SIF from instantaneous clear-sky observations to all-sky sums (according to https://doi.org/10.1016/j.agrformet.2021.108439), while use cos (SZA) instead of PAR.</p> <p>(2) After spatial downscaling, we used the ratio of ERA5 PAR to PAR under clear-sky to convert clear-sky SIF to all-sky SIF.</p> <p> </p>
Chem. Downscaling Models
<p>Trained models from the paper: "Downscaling Atmospheric Chemistry Simulations with Physically Consistent Deep Learning" (2022) by Geiss, A., S. J. Silva, and J. C. Hardin</p>
Data from: Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD
Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950–2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850–2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity.
Dataset for "MA-MGAN: Mixed Attention Markovian Generative Adversarial Network for Meteorological Downscaling"
<p>Dataset for "MA-MGAN: Mixed Attention Markovian Generative Adversarial Network for Meteorological Downscaling", including the training set and testing set used by the model, as well as the experimental results in the main text and supplementary Information.</p>
Fire Weather Index for Europe from Downscaled and Bias-Corrected CMIP6 Model Outputs
<p>This dataset contains the Canadian Forest Fire Weather Index (FWI) calculated from six downscaled and bias-corrected CMIP6 model outputs. The models included are:</p> <ul> <li>ACCESS-CM2 (Ziehn et al. 2020)</li> <li>CanESM5 (Swart et al. 2019)</li> <li>CNRM-ESM2-1 (Séférian et al. 2019)</li> <li>EC-EARTH3 (EC-Earth Consortium 2019)</li> <li>MPI-ESM1-2-HR (von Storch et al. 2017)</li> <li>MRI-ESM2-0 (Yukimoto et al. 2019)</li> </ul> <p>The dataset encompasses four Shared Socio-economic Pathway (SSP) projections:</p> <ul> <li>SSP1-2.6</li> <li>SSP2-4.5</li> <li>SSP3-7.0</li> <li>SSP5-8.5</li> </ul> <p>Each model output has been downscaled to a resolution of 0.0703135°, corresponding to approximately 9km×9km grids before the FWI calculation. The data covers Europe spatially and temporally spans from 1950 to 2080, offering comprehensive insights into past, present, and future fire weather conditions.</p> <p>This dataset supports the manuscript titled <strong>"The fire weather in Europe: large-scale trends towards higher danger" </strong>by Hetzer et al., currently under review in ERL. Detailed instructions for accessing the data can be found in the included README file. </p> <p>Note: Downloads are password protected. Please use "FWI_2024" for access. </p> <p>Funding: The authors acknowledge the financial support of the European Union’s Horizon 2020 research and innovation action for the FirEUrisk project under grant agreement ID: 101003890.</p> <p> </p> <p> </p> <p> </p> <p> </p>
2000-2002 Dataset [1/7] 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 1/7 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 2000-2002. 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>This part 1/7 of the dataset also includes files related to metadata, static data, normalization, and plotting.</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>
2009-2011 Dataset [4/7] 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 4/7 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 2009-2011. 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>
2012-2014 Dataset [5/7] 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 5/7 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 2012-2014. 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>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.