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Dataset results
111 results for “Satellite Observations”
Surface Ozone–NOx–VOCs Chemistry in China: New Insights from GEMS Satellite Hourly Observations
<p>GEMS satellite data and ground level ozone fitting codes</p>
Dataset for "Increased variability in Greenland Ice Sheet runoff from satellite observations"
<p>##################################</p> <p>This archive contains source data for Slater et al. 2021 - Increased variability in Greenland Ice Sheet runoff from satellite observations, published in Nature Communications:</p> <p><a href="https://doi.org/10.1038/s41467-021-26229-4">https://doi.org/10.1038/s41467-021-26229-4</a></p> <p><br> Centre for Polar Observation and Modelling, School of Earth and Environment, University of Leeds<br> Corresponding author: t.slater1@leeds.ac.uk</p> <p>##################################</p> <p><br> The interannual elevation change maps, seasonal elevation change and runoff data are contained in the following data files:</p> <p><br> --------------------------------------------------------------<br> gris_ablation_zone_dh_cryosat.csv<br> --------------------------------------------------------------<br> Surface height change time series for the Greenland Ice Sheet ablation zone (in m) derived from CryoSat-2 satellite radar altimetry, and its estimated 1 sigma uncertainty between 2011 and 2020<br> --------------------------------------------------------------<br> gris_ablation_zone_dh_may_aug_cryosat.csv<br> --------------------------------------------------------------<br> Seasonal height changes between May and August (in m) for the Greenland Ice Sheet ablation zone within the 8 principal Zwally drainage basins derived from CryoSat-2 satellite radar altimetry between 2011 and 2020</p> <p>--------------------------------------------------------------<br> gris_ablation_zone_dh_may_aug_cryosat.csv<br> --------------------------------------------------------------<br> Seasonal height changes between September and April (in m) for the Greenland Ice Sheet ablation zone within the 8 principal Zwally drainage basins derived from CryoSat-2 satellite radar altimetry between 2011 and 2020</p> <p>--------------------------------------------------------------<br> gris_interannual_seasonal_dhdt_cryosat.nc<br> --------------------------------------------------------------<br> Interannual and seasonal elevation trends derived from CryoSat-2 satellite radar altimeter data acquired between 2011 and 2020. Posted on a 5 km grid in polar stereo graphic projection using EPSG:3413 - WGS 84 / NSIDC Sea Ice Polar Stereographic North </p> <p>--------------------------------------------------------------<br> gris_runoff_cryosat.csv<br> --------------------------------------------------------------<br> Annual Greenland Ice Sheet runoff estimates and their estimated uncertainty derived (in Gt/yr) derived from CryoSat-2 satellite radar altimetry between 2011 and 2020</p> <p>--------------------------------------------------------------<br> Acknowledgements:</p> <p>This work was supported by NERC through National Capability funding, undertaken by a partnership between the Centre for Polar Observation Modelling and the British Antarctic Survey, and by the European Space Agency’s Polar+ Earth Observation for Mass Balance study (4000132154/20/I-EF). M.M was supported by the Lancaster University-UKCEH Centre of Excellence in Environmental Data Science. A.L was supported by the NERC Meltwater Ice-sheet Interactions and the changing climate of Greenland research grant (MII Greenland NE/S011390/1). B.N was funded by NWO VENI grant VI.Veni.192.019. M.v.d.B and P.K.M acknowledge support from the Netherlands Earth System Science Centre (NESSC). Computational resources used to perform MAR simulations have been provided by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the F.R.S.FNRS under grant 2.5020.11 and the Tier-1 supercomputer (Zenobe) of the Fédération Wallonie Bruxelles infrastructure funded by the Walloon Region under grant agreement 1117545.</p> <p>Projects:</p> <p>European Space Agency’s Polar+ Earth Observation for Mass Balance study (4000132154/20/I-EF)</p> <p><a href="https://smb.eo4cryo.dk/">https://smb.eo4cryo.dk/</a></p>
Dataset for manuscript "Satellite Observations of Smoke-Cloud-Radiation Interactions Over the Amazon Rainforest"
<p>Dataset and scripts related to the manuscript "Satellite Observations of Smoke-Cloud-Radiation Interactions Over the Amazon Rainforest" by Ross Herbert and Philip Stier</p>
Data for "Satellite-observed strong subtropical ocean warming as an early signature of global warming"
<p>Data for “Satellite-observed strong subtropical ocean warming as an early signature of global warming”</p>
Three-dimensional Topside Ionospheric Model and Onboard GNSS Observations from LEO Satellites
<p>Here are three-dimensional topside ionospheric models based on machine learning methods and data from LEO satellites. </p>
Synergistic Observation of Geostationary Satellites FY-4A and FY-4B to Estimate Near-surface Pollutant Concentration dataset - CO
<p>High spatiotemporal resolution CO dataset of China's near-surface for summer and autumn 2022 based on the Geostationary satellite FY-4A and FY-4B</p>
PEATCLSM(Tb): A land surface data assimilation product for peatlands using PEATCLSM and brightness temperature (Tb) satellite observations (Northern Hemisphere output, Jan 2010 through Sep 2021)
<p>The dataset archived here includes an extended version of the analysis output shown in the paper, “Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework”, published in Remote Sensing of Environment Journal (Bechtold et al., 2020). The output was produced by combining peatland-specific land surface modeling (Bechtold et al., 2019b) embedded in the NASA Catchment Land Surface Model (CLSM) with L-band brightness temperature (Tb) observations (SMOS), applying the data assimilation framework of the SMAP Level‐4 Soil Moisture product (Reichle et al., 2019). We provide a single NetCDF file of the analysis output (9-km resolution EASEv2 grid, period Jan 2010 through Sep 2021, and between 45°N and 70°N, NE Asia excluded):<br> • daily_images.nc: Daily land states and fluxes (Table 1), provided as netCDF image-chunked image stack</p> <p>The file content is described in the file PEATCLSM_Tb_Documentation_20230830.pdf</p> <p>Please contact Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.</p> <p>Data usage statement:<br> This work is licensed under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/<br> If you decide to work with this data, we kindly ask to be informed at the outset of the nature of this work. If the data are essential to the work, or if an important result or conclusion depends on the PEATCLSM(Tb) data product, we would appreciate that you discuss these findings with us to ensure correct use and interpretation of the PEATCLSM(Tb) product. Furthermore, we are continuously improving the data assimilation product, a discussion of your work at an early stage may (i) help us to improve our product, and (ii) allow us to provide you with a newer version. Thanks!</p> <p>References:</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., & Koster, R. D. (2019a). PEAT-CLSM simulation output (Northern Peatlands) version 1. https://doi.org/10.17605/OSF.IO/E58YM</p> <p>Bechtold, M. et al. (2019b). PEAT‐CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., Roose, D., Balliston, N., Burdun, I., Devito, K., Kurbatova, J., Strack, M., & Zarov, E. A. (2020). Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework. <em>Remote Sensing of Environment</em>. https://doi.org/10.1016/j.rse.2020.111805</p> <p>Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J. M., Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M., Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., & Walker, J. P. (2019). Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(10), 3106–3130. https://doi.org/10.1029/2019MS001729</p>
Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations (Data)
<p>README file for LBL-ERA5, LBL-GCM, and band datasets used in:<br> Raghuraman et al., 2023, Geophysical Research Letters,<br> "Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations "</p> <p>Point of Contact: Shiv Priyam Raghuraman, shivr@alumni.princeton.edu</p> <p> </p> <p>LBL-ERA5</p> <p>6 files (3 experiments with olr and olr_clr output separately):</p> <p>2003-2021.GFDL.all-2010-o3_*.nc - varying WMGHG,Ts,T,q,clouds, fixed o3</p> <p>2003-2021.GFDL.fo3_*.nc - varying WMGHG and fixed Ts,T,q,clouds,surface albedo,o3</p> <p>2003-2021.GFDL.ff_*.nc - fixed Ts,T,q,clouds,surface albedo, varying o3</p> <p> </p> <p>LBL-GCM (clear-sky only)</p> <p>4 AM4 files:</p> <p>AM4piclim-control.nc (for ERF)</p> <p>AM4piclim-4xCO2_1xCO2IRF.nc (for ERF)</p> <p>AM4piclim-control_STRAT.nc (for SARF)</p> <p>AM4piclim-4xCO2_1xCO2IRF_STRAT.nc (for SARF)</p> <p>4 CM3/AM3 files:</p> <p>CTLAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>EXPAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>CTLAM3CM3_P1_2003C.nc (for IRF)</p> <p>CTLAM3CM3_P1_2021C.nc (for IRF)</p> <p> </p> <p>Band-AM4/MERRA-Total: GFDL AM4 with prescribed SSTs and sea-ice (AMIP) and nudged with MERRA winds</p> <p>2 files:</p> <p>atmos.200001-202112.olr.nc - all-sky OLR</p> <p>atmos.200001-202112.olr_clr.nc - clear-sky OLR</p> <p> </p> <p>Observational and reanalysis files used in paper (AIRS, CERES EBAF and SSF, GISTEMP, ERA5 input data) can be downloaded from their respective websites (see paper's "Open Research" section).</p> <p> </p>
FIGURES 1‒3. 1. Satellite map Ecuador. 2 in Rediscovery and redescription of Oxycheila buestani Wiesner with the first female record and new methodology for observation of Oxycheila Dejean and Oxygonia Mannerheim (Coleoptera: Cicindelidae)
FIGURES 1‒3. 1. Satellite map Ecuador. 2. West sub Andean region north from the town of Cumandá-Bucay. 3. Study area with surveyed transects L1 (red line) and L2 (yellow line).
Data from: Using satellite AIS to improve our understanding of shipping and fill gaps in ocean observation data to support marine spatial planning
Open the record for dataset details and reuse information.
Data from: Global energy sector methane emissions estimated by using facility-level satellite observations
Open the record for dataset details and reuse information.
Dataset for: Fast retreat of Pope, Smith, and Kohler glaciers in West Antarctica observed by satellite interferometry
Open the record for dataset details and reuse information.
Observations of Earth Quasi-Satellite (469219) Kamo`oalewa
<p>Companion dataset to Sharkey et al. (2021).</p>
IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations
<p>IT-SNOW is a serially complete and multi-year snow reanalysis for Italy. The dataset includes daily maps of Snow Water Equivalent (SWE), snow depth (HS), bulk-snow density (RhoS), and liquid water content (Theta_W). </p> <p>Data are organized in monthly netCDF files, each providing time and lat/lon information for georeference. Units are as follows: HS is in cm, SWE is in mm w.e., RhoS is in kg/m3, and Theta_W is in %. Note that maps are instantaneous snapshots at 11AM UTC, here assumed as representative values for the day. </p> <p>As the output of an operational chain employed in real-world civil-protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel 2, MODIS, and H-SAF products, and maps of snow depth from the spazialization of 1000+ on-the-ground snow-depth sensors. Additional information are available in the following paper submitted to Earth System Science Data: </p> <p>"IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations (2009-2021)", Francesco Avanzi et al., 2022. </p> <p>The initial time span of data is September 1, 2010 to August 31, 2021, with future updates envisaged on an annual basis (see updates below).</p> <p><strong>UPDATES</strong></p> <ul> <li>September 29, 2025: released v5 with the complete 2025 water year (September 2024 - August 2025).</li> <li>November 12, 2024: released v4 with the complete 2024 water year (September 2023 - August 2024).</li> <li>September 02, 2024: released v3.1 with the complete 2023 water year (September 2022 - August 2023) AND all previous water years (which were inadvertently NOT carried over while creating v3).</li> <li>September 02, 2024: released v3 with the complete 2023 water year (September 2022 - August 2023).</li> <li>December 20, 2023: released v2 with the complete 2022 water year (September 2021 - August 2022).</li> </ul> <p>LICENSE INFORMATION</p> <p>IT-SNOW is distributed under a CC BY-NC 4.0 license. you are free to: </p> <p>1. Share — copy and redistribute the material in any medium or format; <br>2. Adapt — remix, transform, and build upon the material;</p> <p>under the following terms: </p> <p>a. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.<br>b. NonCommercial — You may not use the material for commercial purposes.</p> <p><br>DATA ARE PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THESE DATA, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</p> <p>For details about the CC BY-NC 4.0 license, see: https://creativecommons.org/licenses/by-nc/4.0/deed.en</p>
Inferring the Photolysis Rate of NO2 in the Stratosphere Based on Satellite Observations
<p>Dataset for the paper</p>
GHRSST Level 3U Global Subskin Sea Surface Temperature from the Advanced Scanning Microwave Radiometer - Earth Observing System (AMSR-E) on the NASA Aqua Satellite
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. Remote Sensing Systems (RSS, or REMSS) is the provider of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by "_rt_" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. "Final" data (currently identified by "v7" within the file name) are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.
The Lake Observations by Citizen Scientists & Satellites (LOCSS) Level 1 Version 1.0
This dataset contains data from the Lake Observations by Citizen Science and Satellites project, LOCSS which is a lake monitoring network. The data represent the location and main descriptors of the lake gauges and their readings. LOCSS project aims to collaborate with local citizens to monitor small and medium sized lakes (i.e., lakes with an average surface area less than 100 km2). At each location, a lake gauge is installed and provided with a cellphone number. Local citizens read the water level at each lake gauge and sent it in a text message. Data can also be manually collected and uploaded later from the website in remote places where cellphone signal is challenged. The readings are specified in cm, m, or ft, according to the local unit system. This version of the dataset has lakes located in seven (7) countries: Bangladesh, India, Canada, the United States, Pakistan, and Nepal. This product consists of two files in comma-separated values (csv) format : 1) the list of gauges whose attributes include gauge coordinates, installation dates, the height of the gauge, reading units, city, time zone, and installation notes; 2) list of readings by each gauge specified in the local time. To discover more details about LOCSS, please visit https://www.locss.org/.
GHRSST Level 2P Global Subskin Sea Surface Temperature from the Advanced Scanning Microwave Radiometer - Earth Observing System (AMSR-E) on the NASA Aqua Satellite
The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. Remote Sensing Systems (RSS, or REMSS) is the provider of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of AMSR-E instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by "_rt_" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. "Final" data (currently identified by "v7" within the file name) are processed when RSS receives the atmospheric model National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Operational Global Analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.
Generated datasets for Yue et al. (2020, Earth and Space Science): "Combining In-situ and Satellite Observations to Understand the Vertical Structure of Tropical Anvil Cloud Microphysical Properties During the TC4 Experiment"
<p>This archive contains the data sets generated from the research conducted by Yue et al. (2020) titled "Combining In-situ and Satellite Observations to Understand the Vertical Structure of Tropical Anvil Cloud Microphysical Properties During the TC4 Experiment" published in Earth and Space Science. The method to generated the following data sets is described in Yue et al. (2020) and stored as Matlab .mat files.</p> <p>CombiningTC4_Satellite_eof_cov_mat.mat contains the correlation matrix shown in Figure 1a.</p> <p>TC4_processed.mat contains the correlation matrix shown in Figure 1b.</p> <p>RO_processed.mat contains the correlation matrix shown in Figure 2a.</p> <p>RVOD_processed.mat contains the correlation matrix shown in Figure 2b.</p> <p>ICE_processed.mat contains the correlation matrix shown in Figure 2c.</p> <p> </p> <p> </p>
Aggregated WRF-Chem outputs in "Evaluation of cloud and precipitation response to aerosols in WRF-Chem with satellite observations"
<p>This dataset contains the aggregated output of WRF-Chem simulations by Zhoukun Liu. </p> <p>The "MOR" in file name denotes the data from simulation which used Morrison scheme and "LIN" denotes the data from simulation which used Lin scheme.</p> <p>In "MOR_110km_core.nc" and "LIN_110km_core.nc", the variables are averaged over the cloudy pixels that have the highest 10% of cloud optical thickness in each 110km scene.</p> <p>In "MOR_110km_incloud.nc" and "LIN_110km_incloud.nc", the variables are averaged over all the cloudy pixels in each 110km scene.</p> <p>"MOR_110km_R.nc" and "LIN_110km_R.nc" contains the precipitaiton characteristics from models for each 110km scene. </p> <p> </p>
ScienceDex guides
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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.