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42 results for “cloud cover”
Landsat bands (cloud free), tree cover (2000, 2010), bare-ground and surface water occurrence at 250 m based on GlobalForestWatch and USGS
<p>Landsat bands (cloud free) and tree cover (2000) based on Hansen et al. (2013), global surface water occurrence based on Pekel at al. (2016), and tree cover and bare-ground cover (2010) based the USGS land cover mapping projects (University of Maryland, Department of Geographical Sciences and USGS). All layers resampled to spatial resolution 1/480 d.d. (about 250 m) using gdalwarp with "average" resampling. Antarctica is not included. Original layers are available at 30 m resolution.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>lcv = theme: land cover,</li> <li>bareground = variable: occurrence of bareground,</li> <li>landsat.usgs = determination method: Landsat landcover at 30 m resolution project (https://landcover.usgs.gov/glc/),</li> <li>p = probability or fraction,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2010..2010 = time reference: year 2010,</li> <li>v1.0 = version number: 1.0,</li> </ul>
Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version
<p><strong>NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version >= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.</strong></p> <p> </p> <p>This is the data to the same-titled Data paper, which can be found at <a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>.</p> <p>Along with auxilary files for the <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">cloudremoval package</a>, and example scripts on how to access chunks of the data.</p> <p>The files contain:</p> <ol> <li><strong>python-cloudremoval-aux-data.tar.gz</strong> : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal</a></li> <li><strong>python-example-data-access.html </strong>: Example script how to access parts of the data using python</li> <li><strong>R-example-data-access.html</strong> : Example script how to access parts of the data using R</li> <li><strong>zenodo_01_original.tar.gz</strong> : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (<a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>), and for algorithm at <a href="https://doi.org/10.3390/rs5010110">https://doi.org/10.3390/rs5010110</a>.</li> <li><strong>zenodo_02_cloudremoval.tar.gz</strong> : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper.</li> </ol> <p> </p> <p>The maps are GeoTIFF with integer based values:</p> <p>0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata</p> <p> </p> <p>Version history:</p> <p>1.0.0 : initial upload<br> 1.0.1 : changes after revision of Data paper<br> 1.0.2 : added example scripts</p> <p> </p> <p> </p>
Automated vegetation cover estimation from close-range photogrammetric point clouds in mountain terrain for comparison of vegetation location properties - Dataset
<p>Vegetation cover data of the used plots, showing values for manually digitized, in-situ, and photogrammetric methods.</p>
Cloud-free snow cover area in the Pyrenees from MODIS
<p>This dataset contains the output of a gapfilling algorithm applied to MODIS snow products for the Pyrenees mountains as presented by Gascoin et al. (2015) and updated to the period 2000-Sep-01 to 2015-08-31 (15 hydrological years)</p> <ol> <li>Pirineos_gapfilled.tif: a multiband geotiff raster file in WGS84 UTM30N (EPSG:32630) at 500 m resolution with values 200 (snow) or 25 (no snow); <p>Corner Coordinates:<br> Upper Left ( 607750.000, 4789250.000) ( 1d40'21.72"W, 43d14'54.08"N)<br> Lower Left ( 607750.000, 4665250.000) ( 1d41'46.55"W, 42d 7'55.05"N)<br> Upper Right ( 973750.000, 4789250.000) ( 2d49'18.48"E, 43d 6'27.65"N)<br> Lower Right ( 973750.000, 4665250.000) ( 2d43' 8.76"E, 41d59'47.87"N)<br> Center ( 790750.000, 4727250.000) ( 0d32'47.24"E, 42d38'34.10"N)</p> </li> <li>Pirineos_gapfilled_dates.csv: a csv file indicating the date corresponding to each band (year, month, day)</li> <li>dem_Pirineos_UTM30_px500.tif: a geotiff raster of the elevation in WGS84 UTM30N (input of the gap-filling algorithm) with the same extent and resolution as 1.</li> <li>aspect_Pirineos_UTM30_px500.tif: a geotiff raster of the slope aspect in WGS84 UTM30N (input of the gap-filling algorithm) with the same extent and resolution as 1.</li> <li>Pirineos_gapfilled_probamap.png: a map of the mean annual number of snow days (snow cover duration) made from 1.</li> <li>Pirineos_gapfilled_scats.png: a plot of the timeseries of the daily snow cover area in km² over the Pyrenees mountain range from 2000-Sep-01 to 2015-08-31 made from 1.</li> </ol> <p><strong>Reference</strong></p> <p>Gascoin, S., Hagolle, O., Huc, M., Jarlan, L., Dejoux, J.-F., Szczypta, C., Marti, R., and Sánchez, R.: A snow cover climatology for the Pyrenees from MODIS snow products, Hydrol. Earth Syst. Sci., 19, 2337-2351, doi:10.5194/hess-19-2337-2015, 2015. http://doi.org/10.5194/hess-19-2337-2015</p> <p>Hall, D. K., V. V. Salomonson, and G. A. Riggs. 2006. MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/63NQASRDPDB0.</p> <p>Hall, D. K., V. V. Salomonson, and G. A. Riggs. 2006. MODIS/Aqua Snow Cover Daily L3 Global 500m Grid, Version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: http://dx.doi.org/10.5067/ZFAEMQGSR4XD.</p>
Supplementary data for "Mechanism of surface solar irradiance variability under broken cloud cover"
<p>Open Data for manuscript to be submitted in ACP: "Mechanisms of surface solar irradiance variability under broken clouds". Refer to the README for details. Most (larger) files within the .zip archives are gzipped. Use `gunzip` to decompress.</p>
Data for "Impact of Gaussian transformation on cloud cover data assimilation for historical weather reconstruction"
<p>This dataset contains the simulation results in "Impact of Gaussian transformation on cloud cover data assimilation for historical weather reconstruction".</p>
Canary Islands La Palma: cloud cover map (2015)
<p>A multi-temporal cloud cover map related to 10 Landsat 8 images in "La Palma" PA from August to December 2015.</p> <p>The 10 layers were related to the following dates: August 9<sup>th</sup>, August 25<sup>th</sup>, September 10<sup>th</sup>, September 26<sup>th</sup>, October 12<sup>nd</sup>, October 28<sup>th</sup>, November 13rd, November 29<sup>th</sup>, December 15<sup>th</sup>, December 31<sup>st</sup>.</p> <p>The maps was produced at 30 meters spatial resolution and projected in WGS84/UTM28N.</p> <p>The map has binary values where value 1 indicates cloud cover pixels whereas value 0 indicates No cloud cover pixels.</p>
Canary Islands La Palma: cloud cover map (2016)
<p>A multi-temporal cloud cover map related to 5 Sentinel-2A images in "La Palma" PA from December 2015to April 2016.</p> <p>The 5 layers representing the cloud cover (as OR among different temporal and spatial acquisitions in each month) were related to the following dates: December 2015, January 2016, February 2016, March 2016 and April 2016 .</p> <p>The maps was produced at 60 meters spatial resolution and projected in WGS84/UTM28N.</p> <p>The map has binary values where value 1 indicates cloud cover pixels whereas value 0 indicates No cloud cover pixels.</p>
MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000-2022)
<p>The Asia Water Tower region, with the Qinghai-Tibet Plateau at its core, is the most widespread region of snow cover on Earth, except for the North and South Poles. The topographic heterogeneity of the Asian Water Tower region is so great that the snow cover is thin and patchy, resulting in a highly time-varying snow cover in the region, and therefore daily-scale fractional snow cover data are urgently needed. This dataset is based on the MODIS surface reflectance product MO/YD09GA product, and the MODIS daily cloud-free fractional snow cover dataset for the Asian Water Tower region from 2000 to 2022 was produced using the MESMA-AGE algorithm and the MSTI algorithm. The high spatial resolution Landsat-8 image was taken as the "ground truth", the RMSE was 0.16, and the MAE was 0.10. This dataset has a time series from 26 February 2000 to 31 December 2022 with a spatial resolution of 0.005°, which can provide quantitative snow cover information on the spatial distribution of snow for mountain hydrological models, land surface models, numerical weather forecasts, etc.</p>
Data from: Effects of immune status on stopover departure decisions are subordinate to those of condition, cloud cover and tailwind in autumn-migrating common blackbirds (Turdus merula)
Open the record for dataset details and reuse information.
The diurnal data of the aerosol extinction coefficient of the Mount Qomolangma lidar, as well as precipitation, low cloud cover, relative humidity of three adjacent stations (Tingri, Lazi, Nyalam) of the Mount Qomolangma
<p><strong>The diurnal data of the the vertical average aerosol extinction coefficient of 0.15-2.5 km of the Mount Qomolangma lidar, as well as precipitation, low cloud cover, relative humidity of three adjacent stations (Tingri, Lazi, Nyalam) of the Mount Qomolangma in July 2018 and July 2019.</strong></p>
Converging findings of climate models and satellite observations on the positive impact of European forests on cloud cover
<p>Overview:<br>This repository hosts a comprehensive dataset resulting from a Space for Time (S4T) analysis (<em>Duveiller et al. 2018</em>). The dataset spans monthly data from 2004 to 2014, providing detailed insights into cloud cover dynamics and land cover characteristics. Leveraging observations from the Cloud CCI MODIS-Aqua dataset (<em>Stengel et al. 2017</em>) and RegCM5 (<em>Giorgi et al. 2023</em>) model outputs at 0.05 degrees resolution, it offers valuable resources for researchers studying atmospheric and terrestrial interactions.</p> <p>Contents:</p> <p>s4t_ESACCI.zip:<br>Output of the space-for-time algorithm applied to the Global MODIS-Aqua cloud cover data for low, medium, and high clouds.<br>s4t_RegCM5.zip:<br>Output of the space for time algorithm applied to the European RegCM5 cloud data for low, medium, and high clouds.<br>Variables:</p> <p>Cloud Area Fractions:<br>Includes low (cll), medium (clm), and high (clh) cloud area fractions, expressed as percentages.<br>Cloud layers are categorized based on cloud top pressure (CTP), following the convention of the International Satellite Cloud Climatology Project.</p>
Subdaily meteorological measurements of temperature, direction of the movement of the clouds, and cloud cover in the Late Maunder Minimum by Louis Morin in Paris
<p>Supplementary dataset of the publication: Pliemon, T., Foelsche, U., Rohr, C., and Pfister, C.: Subdaily meteorological measurements of temperature, direction of the movement of the clouds, and cloud cover in the Late Maunder Minimum by Louis Morin in Paris, Clim. Past, 2022</p> <p>For more details see the file.</p> <p> </p>
Timelapse of cloud cover during the 21 August 2017 solar eclipse
<p>Timelapse photos of cloud cover during the 21 August 2017 solar eclipse. Location: a small valley in the foothills of the Blue Ridge Mountains, near Charlottesville, VA. Photos were taken using a GoPro camera looking facing approximately SW every 5 seconds between 12:00 - 16:00 EDT (local time).</p>
Distinct Roles of Land Cover in Regulating Spatial Variabilities of Temperature Responses to Radiative Effects of Aerosols and Clouds
<p>Dataset of drawing figures in work 'Distinct Roles of Land Cover in Regulating Spatial Variabilities of Temperature Responses to Radiative Effects of Aerosols and Clouds'.</p> <p>Dataset File List </p> <p>1. Present_Day_Variables.nc : global spatial distributions of radiative forcing, climate sensitivity and temperature response </p> <p>2. Regional_Variables.xlsx : regional data of radiative forcing, climate sensitivity and temperature response</p> <p>3. Aerosol_Burdens.nc : monthly aerosol burdens (atmospheric column concentration) of ten simulation cases </p> <p>4. Aerosol_Burdens_README.txt : dimensional descriptions of Aerosol_Burdens.nc</p> <p>5. Monthly_Radiation.nc : monthly radiation variables of PD simulation for testing model performance</p> <p>6. Monthly_Radiation_README.txt : dimensional descriptions of Monthly_Radiation.nc</p> <p> </p>
A sample of the training data used in the paper "A Hybrid Physics-AI (HyPhAI) approach for probability fields advection: Application to cloud cover nowcasting"
<p>Copyright (2024) EUMETSAT</p>
Mapping scrub vegetation cover from photogrammetric point-clouds
<p>This dataset is derived from photogrammetric point cloud models of UAV imagery. It includes the Above ground models of vegetation as well as the isolated scrub vegetation.</p> <p>We illustrate the method with two case studies from the UK. The scrub cover at Daneway Banks, a calcareous grassland site in Gloucestershire was calculated at 21.8% of the site. The scrub cover at Flat Holm Island, a maritime grassland in the Severn Estuary was calculated at 7%. This approach enabled the scrub layer to be readily measured and if required, modelled to provide a visual guide of what a projected management objective would look like. This approach provides a new tool in reserve management, enabling habitat management strategies to be informed, and progress towards objectives monitored.</p>
Mapping scrub vegetation cover from photogrammetric point-clouds
Open the record for dataset details and reuse information.
ABoVE: High Resolution Cloud-Free Snow Cover Extent and Snow Depth, Alaska, 2001-2017
This dataset provides estimates of maximum snow cover extent (SCE) and snow depth for each 8-day composite period from 2001 to 2017 at 1 km resolution across Alaska. The study area covers the majority land area of Alaska except for areas covered by perennial ice/snow or open water. A downscaling scheme was used in which Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis 0.5 degree snow depth data were interpolated to a finer 1 km spatial grid. In the methods used, the downscaling scheme incorporated MODIS SCE (MOD10A2) to better account for the influence of local topography on the 1km snow distribution patterns. For MODIS cloud-contaminated pixels, persistent and patchy cloud cover conditions were improved by applying an elevation-based spatial filtering algorithm to predict snow occurrence. Cloud-free MODIS SCE data were then used to downscale MERRA-2 snow depth data. For each snow-covered 1 km pixel indicated by the MODIS data, the snow depth was estimated based on the snow depth of the neighboring MERRA-2 0.5 grid cell, with weights predicted using a spatial filter.
Raw datasets for publication: The new Mountain Observatory of the Project "Optimizing Cloud Seeding by Advanced Remote Sensing and Land Cover Modification (OCAL)" in the United Arab Emirates: First results on Convection Initiation" - Case studies only (5 and 6 Sept 2018)
<p>Here are zip files containing the raw data sets collected from the Halo Doppler lidar and the Mira Doppler cloud radar from the OCAL Observatory, UAE, on the 5th and 6th September 2018. All RHI and PPI scans at all angles and all time steps from these days are included.</p>
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OpenNeuro
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