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47 results for “geoTIFF”
STORM tropical cyclone wind speed return periods as global GeoTIFFs
<p>Global tropical cyclone wind speed return period maps.</p> <p>This dataset is derived with minimal processing from the following datasets created by Bloemendaal et al, which are released with a CC0 license:</p> <p>[1] Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2023): STORM tropical cyclone wind speed return periods. Version 4. 4TU.ResearchData. Dataset. <a href="https://doi.org/10.4121/12705164.v4">https://doi.org/10.4121/12705164.v4</a></p> <p>[2] Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan) et. al. (2023): STORM climate change tropical cyclone wind speed return periods. Version 4. 4TU.ResearchData. Dataset. <a href="https://doi.org/10.4121/14510817.v4">https://doi.org/10.4121/14510817.v4</a></p> <p>Datasets containing tropical cyclone maximum wind speed (in m/s) return periods, generated using the STORM datasets (see <a href="https://www.nature.com/articles/s41597-020-0381-2">https://www.nature.com/articles/s41597-020-0381-2</a>) and STORM climate change datasets (see <a href="https://figshare.com/s/397aff8631a7da2843fc">https://figshare.com/s/397aff8631a7da2843fc</a>). Return periods were empirically calculated using Weibull's plotting formula. The STORM_FIXED_RETURN_PERIOD dataset contains maximum wind speeds for a fixed set of return periods at 10 km resolution in every basin and for every climate model used here (see below).</p> <p>The GeoTIFFs provided in the datasets linked above have been mosaicked into single files with global extent for each climate model/return period using the following code: </p> <p><a href="https://github.com/nismod/open-gira/blob/88dc522dd267020b16927e1b51ba46c4df4da277/workflow/tropical-cyclone/STORM.smk">https://github.com/nismod/open-gira/blob/88dc522dd267020b16927e1b51ba46c4df4da277/workflow/tropical-cyclone/STORM.smk</a></p> <p>Files are named on the pattern: <code>STORM_FIXED_RETURN_PERIODS_{STORM_MODEL}_{STORM_RP}_YR_RP.tif</code></p> <p>STORM_MODEL is be one of constant, CMCC-CM2-VHR4, CNRM-CM6-1-HR, EC-Earth3P-HR or HadGEM3-GC31-HM. The "constant" files are for the present day, baseline climate scenario as explained in dataset [1]. The other files are for 2050, RCP8.5 under different models as explained in the paper linked from dataset [2].</p> <p>STORM_RP is one of 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000 or 10000.</p> <h3>Updates</h3> <p>2024-04-05: recreated GeoTIFFs to align precisely with source pixels, updated references to v4, uploaded GeoTIFFs within ZIP to meet Zenodo file limits.</p>
MODIS/Aqua+Terra Global Flood Product L3 NRT 250m 3-day GeoTIFF
The MODIS/Aqua+Terra Global Flood Product L3 Near Real Time (NRT) 250m 2-day GeoTIFF Product (MCDWD_L3_F2_NRT) (beta) provides maps of flooding globally. The Global Flood product is provided over 3 compositing periods (1-day, 2-day, and 3-day) to minimize the impact of clouds and more rigorously identify flood water (the best composite will depend on the cloudiness for a particular event). The MCDWD_L3_F3_NRT is 3-day product which is generated from current and previous two day’s data.The beta version of the product will be updated. For more information, visit product page at:https://earthdata.nasa.gov/earth-observation-data/near-real-time/mcdwd-nrt
SEDIMENT ANALYSIS NETWORK FOR DECISION SUPPORT (SANDS) LANDSAT GEOTIFF V1
The Sediment Analysis Network for Decision Support (SANDS) Landsat Geotiff dataset includes images for sediment redistribution after a hurricane on the coast of the Gulf of America and then creates a product based on the analysis from September 11, 2000 to September 8, 2008. This dataset consists of the set of daytime GeoTiff images from Landsat 5 and Landsat 7 provided to Geological Survey of Alabama for their analysis. Subsetted coordinates are 31-27N latitude and 90-84.25W longitude (Gulf of America coastline in Alabama and portions of Florida). These are seasonal data for storms.
SEDIMENT ANALYSIS NETWORK FOR DECISION SUPPORT (SANDS) MODIS GEOTIFF V1
The Sediment Analysis Network for Decision Support (SANDS) MODIS GeoTIFF dataset consists of the set of GeoTIFF images provided to the Geological Survey of Alabama for their analysis. These are seasonal data for storms. The Sediment Analysis Network for Decision Support (SANDS) analyzes GeoTIFF images to determine sediment redistribution after a hurricane on the Gulf coast and then creates a product based on the analysis.
MODIS/Aqua+Terra Global Flood Product L3 NRT 250m 1-day GeoTIFF
The MODIS/Aqua+Terra Global Flood Product L3 Near Real Time (NRT) 250m 1-day GeoTIFF (MCDWD_L3_F1_NRT) (beta) provides daily maps of flooding globally. The Global Flood product is provided over 3 compositing periods (1-day, 2-day, and 3-day) to minimize the impact of clouds and more rigorously identify flood water (the best composite will depend on the cloudiness for a particular event). The MCDWD_L3_F1_NRT is a 1-day product. The beta version of the product will be updated. For more information, visit product page at:https://earthdata.nasa.gov/earth-observation-data/near-real-time/mcdwd-nrt
MODIS/Aqua+Terra Global Flood Product L3 NRT 250m 1-day CS GeoTIFF
The MODIS/Aqua+Terra Global Flood Product L3 Near Real Time (NRT) 250m 1-day CS GeoTIFF Product (MCDWD_L3_F1C_NRT) (beta) provides daily maps of flooding globally. The Global Flood product is provided over 3 compositing periods (1-day, 2-day, and 3-day) to minimize the impact of clouds and more rigorously identify flood water (the best composite will depend on the cloudiness for a particular event). The MCDWD_L3_F1C_NRT is 1-day CS product that has cloud shadow masks applied to the water detections, to help remove cloud-shadow false positives. The beta version of the product will be updated. For more information, visit product page at:https://earthdata.nasa.gov/earth-observation-data/near-real-time/mcdwd-nrt
MODIS/Aqua+Terra Global Flood Product L3 NRT 250m 2-day GeoTIFF
The MODIS/Aqua+Terra Global Flood Product L3 Near Real Time (NRT) 250m 2-day GeoTIFF Product (MCDWD_L3_F2_NRT) (beta) provides maps of flooding globally. The Global Flood product is provided over 3 compositing periods (1-day, 2-day, and 3-day) to minimize the impact of clouds and more rigorously identify flood water (the best composite will depend on the cloudiness for a particular event). The MCDWD_L3_F2_NRT is 2-day product which is generated from current and previous day’s data.The beta version of the product will be updated. For more information, visit product page at:https://earthdata.nasa.gov/earth-observation-data/near-real-time/mcdwd-nrt
Nimbus Image Dissector Camera System Remapped Visible Imagery Daily L3, GeoTIFF V001
This data set (NmIDCS3G) consists of daily, global image composites constructed from Nimbus 3 and Nimbus 4 Image Dissector Camera System (IDCS) imagery for the region between 60 N and 60 S. Images were acquired between 23 April, 1969 - 04 January, 1971. Data are available as GeoTIFFs and browse images. For HDF5 formatted version of these data, see <a href="http://dx.doi.org/10.5067/NIMBUS/NmIDCS3H">Nimbus Image Dissector Camera System Remapped Visible Imagery Daily L3, HDF5</a>.
Nimbus Temperature-Humidity Infrared Radiometer 6.7 µm Water Vapor Remapped Digital Data Daily L3, GeoTIFF V001
This data set (NmTHIR67-3G) consists of daily, global composites of radiative temperatures obtained in the 6.7 µm water vapor window (6.5 µm - 7.0 µm) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. The THIR 6.7 µm window was used to map the water vapor distribution in the upper troposphere and stratosphere. Data files are GeoTIFF versions of the HDF-formatted equatorial projection file only from the Nimbus Temperature-Humidity Infrared Radiometer 6.7 µm Water Vapor Remapped Digital Data Daily, HDF5 (<a href="http://dx.doi.org/10.5067/NIMBUS/NmTHIR67-3H">NmTHIR67-3H</a>) data set.
Nimbus Advanced Vidicon Camera System Remapped Visible Imagery Daily L3, GeoTIFF V001
This data set (NmAVCS3G) consists of daily image composites constructed from Nimbus 1 (1964) and Nimbus 2 (1966) Advanced Vidicon Camera System (AVCS) imagery for the region between 60 N and 60 S. Data are provided as GeoTIFFs. For the HDF5 formatted version of these data, see <a href="http://dx.doi.org/10.5067/NIMBUS/NmAVCS3H">Nimbus Advanced Vidicon Camera System Remapped Visible Imagery Daily L3, HDF5</a>.
Nimbus Temperature-Humidity Infrared Radiometer 11.5 µm Remapped Digital Data Daily L3, GeoTIFF V001
This data set (NmTHIR115-3G) consists of daily, global composites of radiative temperatures obtained in the 11.5 µm window (10.5 µm - 12.5 µm) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 4 satellite. This window was used to measure cloud top or surface temperatures. Data files are GeoTIFF versions of the HDF-formatted equatorial projection file <em>only</em> from the Nimbus Temperature-Humidity Infrared Radiometer 11.5 µm Remapped Digital Data Daily L3, HDF5 (<a href="http://dx.doi.org/10.5067/NIMBUS/NmTHIR115-3H">NmTHIR115-3H</a>) data set.
Nimbus High Resolution Infrared Radiometer Remapped Digital Data Daily L3, GeoTIFF V001
This data set (NmHRIR3G) consists of daily composites constructed from Nimbus 1, Nimbus 2, and Nimbus 3 satellites High Resolution Infrared Radiometer (HRIR) data for the region between 60 N and 60 S. Measurements were obtained during 1964, 1966, and 1969. Data are available as GeoTIFFs and browse images. For the HDF5 formatted version of these data, see the <a href="http://dx.doi.org/10.5067/NIMBUS/NmHRIR3H">Nimbus High Resolution Infrared Radiometer Remapped Digital Data Daily L3, HDF5</a> data set.
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Churfirsten GeoTIFF - Erronous
<p>Multiscale elevation models centered on Churfirsten, Switzerland</p> <p>Resolutions: 30, 60, 120, 250, 500, 1,000, and 2,000 meters, 2,500 x 1,500 height samples each</p> <p>File format: GeoTIFF</p>
North America (SW) Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018</p> <p>Region: North America (southwest)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics.</p>
Amazon Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018 </p> <p>Region: Amazon </p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
Europe Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018 </p> <p>Region: Europe</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publications: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy (used in generation) classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
North America (SW) Gross Primary Productivity (GPP) Dataset 2018 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2018</p> <p>Region: North America (southwest)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics.</p>
North America (SW) Gross Primary Productivity (GPP) Dataset 2019 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2019</p> <p>Region: North America (southwest)</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics.</strong></p>
Amazon Gross Primary Productivity (GPP) Dataset 2019 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2019</p> <p>Region: Amazon</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p><strong>NOTE: As ArcPy classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </strong></p>
Europe Gross Primary Productivity (GPP) Dataset 2019 (GeoTIFF) - ZIP file
<p>Product: Gross Primary Productivity (GPP)</p> <p>Year: 2019</p> <p>Region: Europe</p> <p>Temporal Scale: 8 Days</p> <p>Spatial Resolution: 500 meters</p> <p>Method: Light-use-efficiency (LUE) approach.</p> <p>Referred Publications: https://www.sciencedirect.com/science/article/pii/S0048969718307149</p> <p>https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261</p> <p>This archive has been uploaded as a .zip file, due to repeated difficulties uploading single GeoTIFFs to Zenodo. </p> <p>NOTE: As ArcPy (used in generation) classifies 'no data' pixels as very low negative values, users should mask all data values below 0 to prevent these affecting statistics. </p>
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