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180 results for “Downscaling”
Local sea-level rise caused by climate change in the northwest Pacific marginal seas using dynamical downscaling
Open the record for dataset details and reuse information.
Data for exploring topography-based methods for downscaling subgrid precipitation for use in Earth System Models
<p>Topography exerts major control on land surface processes. To improve representation of topographic impacts on land surface processes, a new topography-based subgrid structure has been introduced to the Energy Exascale Earth System Model representing the subgrid heterogeneity of surface elevation. Four topography-based methods of downscaling grid precipitation to the subgrids have been explored. The data utilized for the study include precipitation, surface elevation, and height rise data derived from wind speed and Brunt Vaisala parameter and outputs of downscaled precipitation and statistical metrics calculated in this study. Results show that utilizing hypsometric elevation of the subgrid landscape within the model grid cell improves downscaling of precipitation in mountainous areas. Furthermore, accounting for blocking of airflow further improves precipitation downscaling slightly in mountainous regions consistently across multiple grid sizes.</p> <p>The data files include:</p> <ol> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/daily_prism_precip.zip?versionId=be97ca8d-182a-4f1e-9ae3-9da3f2b87e24">daily_prism_precip.zip</a>: high resolution precipitation data (4 km) obtained from PRISM [Daly et al. 1994, Daly et al. 2008].</li> <li> <a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/dem_4km4.nc">dem_4km4.nc</a>: 4 km surface elevation data derived from high resolution surface elevation data (90 m) obtained from HydroSHEDS [Lehner et al. 2008, Lehner and Grill 2013]</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">fr_number.zip</a>: Height rise of airflow calculated from wind speed and Brunt Vaisala parameter derived from the North American Regional Reanalysis data.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_128km.zip?versionId=5f64ec8c-4018-4d97-ae1d-eb6f15ccc564">output_from_dwnscaling_methods_at_128km.zip</a>: Output data of the downscaling methods at 128 km spatial resolution.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_96km.zip?versionId=b2c67f80-9794-41cb-9986-a4c7259ccf1c">output_from_dwnscaling_methods_at_96km.zip</a>: Output data of the downscaling methods at 96 km spatial resolution. </li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_64km.zip?versionId=b83230a0-308e-4f90-971b-6636a5add796">output_from_dwnscaling_methods_at_64km.zip</a>: Output data of the downscaling methods at 64 km spatial resolution.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_32km.zip?versionId=cc9021cc-c3b4-4c04-a558-752c151c49ba">output_from_dwnscaling_methods_at_32km.zip</a>: Output data of the downscaling methods at 32 km spatial resolution. </li> <li>ppt_spatial_downscaling_daily_data_flatten_withFr_test_filt0_v3rev_64.py: Python code used to calculate downscaled precipitation data from aggregated grid precipitation data.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/stns_precip_2015.csv">stns_precip_2015.csv</a>: Precipitation data at rain gauge stations in the Conterminous US extracted from the Daymet station-level input datasets are used for evaluation of the downscaled results </li> </ol> <p>Other datasets used to calculate wind speed and Brunt Vaisala parameter were extracted from the North American Regional Reanalysis (NARR) including wind speed, temperature, surface pressure, specific humidity and relative humidity [Mesinger et al. 2006].</p> <p> </p> <p><strong>References:</strong></p> <p>Daly, C., et al. (1994). "A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain." Journal of Applied Meteorology <strong>33</strong>(2): 140-158. </p> <p>Daly, C., et al. (2008). "Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States." International Journal of Climatology <strong>28</strong>(15): 2031-2064.</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>Mesinger, F., et al. (2006). "NORTH AMERICAN REGIONAL REANALYSIS." Bulletin of the American Meteorological Society <strong>87</strong>(3): 343-360.</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), 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 Kenny 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, 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, 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) 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>
Supplementary material 1 from: Seebens H, Kaplan E (2022) DASCO: A workflow to downscale alien species checklists using occurrence records and to re-allocate species distributions across realms. NeoBiota 74: 75-91. https://doi.org/10.3897/neobiota.74.81082
Manual of DASCO
Super Resolution ET: actual and downscaled
<p>Super Resolution ET: actual and downscaled</p>
Analysis of future heatwaves in the Pearl River Delta through CMIP6-WRF dynamical downscaling
<p>Heatwave datasets</p>
Uncertainties Inherent from Large-Scale Climate Projections in the Statistical Downscaling Projection of North Atlantic Tropical Cyclone Activity
Open the record for dataset details and reuse information.
2018-2020 Dataset [7/7] 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 7/7 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 2018-2019. 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>
Sample dataset 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 a sample of the input data 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". It allows the user to test and train the models on a reduced dataset (45GB).</p> <p>This sample dataset comprises ~3 years of normalized hourly data for both low-resolution predictors and high-resolution target variables. Data has been randomly picked from the whole dataset, from 2000 to 2020, with 70% of data coming from the original training dataset, 15% from the original validation dataset, and 15% from the original test dataset. 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 sample dataset also includes files relative 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> and unzip this zip file in the data folder.</p>
vae_downscaling
<p>VAE downcaling inferrence code</p>
Testdataset for Downscaling with different ML models
<p>This dataset consists of ERA5 and CERRA data for a training period (2014), a validation period (some months in 2017), and a testing period (some months in 2018) for training a UNET on colab.</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 6
<p>Future projections of precipitation by the BMlinear model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 10
<p>Extra data of the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia" that did not fit in their respective deposits:</p> <p>Future projections of:</p> <p>Precipitation by all CNN models (BMlinear, BM1, BM10, BMdense) forced by the UKESM1-0-LL GCM.</p> <p>2-meter maximum and minimum temperatures by the BM1 model forced by the NorESM2-MM and UKESM1-0-LL GCMs.</p> <p>2-meter mean temperature by the BMlinear and BM1 models forced by the NorESM2-MM and UKESM1-0-LL GCMs.</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 4
<p>Future projections of 2-meter mean temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
CLIM4cities - Downscaling Map
<p>This graphic was produced to demonstrate an example of downscaling, generated from the maps from CoLAB +ATLANTIC. The graphic was orginally posted on the CLIM4cities LinkedIn account where the concept of downscaling was introduced to visitors. Visit the page here: <a href="https://www.linkedin.com/company/101643967/admin/feed/posts/">CLIM4cities: Company Page Admin | LinkedIn</a>. </p>
ECOSTRESS Gridded Downscaled Soil Moisture Instantaneous L3 Global 70 m V002
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. A map of the acquisition coverage can be found on the [ECOSTRESS website](https://ecostress.jpl.nasa.gov/science).The ECOSTRESS Gridded Downscaled Soil Moisture Instantaneous L3 Global 70 m (ECO_L3G_SM) Version 2 data product provides instantaneous soil moisture (SM) estimates downscaled using linear regression. The linear regression uses up-sampled surface temperature (ST), normalized difference vegetation index (NDVI), and albedo as predictor variables and SM from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) as response variables for their relative outputs. Once the regression coefficients have been determined, they are applied to the 70 meter (m) ST, NDVI, and albedo as a first pass, which is then bias corrected using a GEOS-5 FP image. This data product is mosaicked from the L3 tiled SM ([ECO_L3T_SM](https://doi.org/10.5067/ECOSTRESS/ECO_L3T_SM.002)) product, is projected to a globally snapped 0.0006° grid, and has a spatial resolution of 70 m.The ECO_L3G_SM Version 2 data product contains three layers distributed in an HDF5 file including SM, cloud mask, and water masks.Known Issues* Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.* Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.* Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.* Missing Cloud Layer Alert: All users of ECOSTRESS Tiled and Gridded L3 Soil Moisture and Surface Energy Balance v002 products (ECO_L3T_SM, ECO_L3G_SM, ECO_L3T_SEB and ECO_L3G_SEB) should be aware that the ‘cloud mask’ layer may be unavailable for a select number of granules for the year 2023. Users are encouraged to get that information from the corresponding Level 2 Standard Cloud Mask products (ECO_L2_CLOUD and ECO_L2G_CLOUD) to assess if a pixel is clear or cloudy (see section 3 of the User Guide).* Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A [list of scenes](https://lpdaac.usgs.gov/documents/2249/obst_all.sort.gz) confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024.* The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved through image matching with a basemap. Metadata in the L1B_GEO file shows the success of this geolocation improvement, using categorizations "best", "good", "suspect", and "poor". We recommend that users use only "best" and "good" scenes
ECOSTRESS Gridded Downscaled Meteorology Instantaneous L3 Global 70 m V002
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. A map of the acquisition coverage can be found in Figure 2 on the [ECOSTRESS website](https://ecostress.jpl.nasa.gov/science).The ECOSTRESS Gridded Downscaled Meteorology Instantaneous L3 Global 70 m (ECO_L3G_MET) Version 2 data product provides instantaneous near-surface air temperature (Ta) and relative humidity (RH) estimates downscaled using linear regression. The linear regression uses up-sampled surface temperature (ST), normalized difference vegetation index (NDVI), and albedo as predictor variables and Ta or RH from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) as response variables for their relative outputs. Once the regression coefficients have been determined, they are applied to the 70 meter (m) ST, NDVI, and albedo as a first pass, which is then bias corrected using a GEOS-5 FP image. This data product is mosaicked from the L3 tiled MET (ECO_L3T_MET) product, projected to a globally snapped 0.0006° grid, and has a spatial resolution of 70 meters (m).The ECO_L3G_MET Version 2 data product contains four layers distributed in an HDF5 format file including Ta, RH, cloud mask, and water mask.Known Issues* Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.* Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.* Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.* Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A [list of scenes](https://lpdaac.usgs.gov/documents/2249/obst_all.sort.gz) confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024.* The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved through image matching with a basemap. Metadata in the L1B_GEO file shows the success of this geolocation improvement, using categorizations "best", "good", "suspect", and "poor". We recommend that users use only "best" and "good" scenes for evaluations where geolocation is important (e.g., comparison to field sites). For some scenes, this metadata is not reflected in the higher-level products (e.g., land surface temperature, evapotranspiration, etc.). While this metadata is always available in the geolocation product, to save users additional download, we have produced a [summary text file](https://lpdaac.usgs.gov/documents/2253/qa_20250423-present.txt) that includes the geolocation quality flags for all scenes from launch to present. At a la
AMSR2/GCOM-W1 downscaled surface soil moisture (LPRM) L2B V001 (LPRM_AMSR2_DS_SOILM2) at GES DISC
AMSR2/GCOM-W1 downscaled surface soil moisture (LPRM) L2B V001 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer 2 (AMSR2), using the Land Parameter Retrieval Model (LPRM). Each swath is packaged with associated geolocation fields. The data set covers the period from May 2012, when the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-1st Water GCOM-W1 satellite was launched, to the present. The spatial resolution of the data is based on a resampling of the nominally 46 and 31 km resolutions, respectively, of AMSR2's C and X bands (6.9/7.3 and 10.7 GHz, respectively) to 25 km by 25 km and then a downscaling, using the smoothing filter-based intensity modulation (SFIM) technique, to 10 km by 10 km grids.The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR2's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites.Input data are from the AMSR2 spatial-resolution-matched brightness temperatures (L1SGRTBR) product, archived at JAXA.
ECOSTRESS Tiled Downscaled Meteorology Instantaneous L3 Global 70 m V002
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. A map of the acquisition coverage can be found in Figure 2 on the [ECOSTRESS website](https://ecostress.jpl.nasa.gov/science).The ECOSTRESS Tiled Downscaled Meteorology Instantaneous L3 Global 70 m (ECO_L3T_MET) Version 2 data product provides instantaneous near-surface air temperature (Ta) and relative humidity (RH) estimates downscaled using linear regression. The linear regression uses up-sampled surface temperature (ST), normalized difference vegetation index (NDVI), and albedo as predictor variables and Ta or RH from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) as response variables for their relative outputs. Once the regression coefficients have been determined, they are applied to the 70 meter (m) ST, NDVI, and albedo as a first pass, which is then bias corrected using a GEOS-5 FP image. The downscaled meteorology estimates are recorded into the ECO_L3T_MET data product and tiled using a modified version of the Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution.The ECO_L3T_MET Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format with each data layer distributed as a separate COG. This product contains four layers including Ta, RH, cloud mask, and water mask.Known Issues* Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4, and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023.* Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.* Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.* Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A [list of scenes](https://lpdaac.usgs.gov/documents/2249/obst_all.sort.gz) confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024.* The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved through image matching with a basemap. Metadata in the L1B_GEO file shows the success of this geolocation improvement, using categorizations "best", "good", "suspect", and "poor". We recommend that users use only "best" and "good" scenes for evaluations where geolocation is important (e.g., comparison to field sites). For some scenes, this metadata is not reflected in the higher-level products (e.g., land surface temperature, evapotranspiration, etc.). While this metadata
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.