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10 results for “Global daily seamless”
GlobalHighPM₂.₅: Global Daily Seamless 1 km Ground-Level PM₂.₅ Dataset over Land (2017–Present)
<p>GlobalHighPM<sub>2.5</sub> is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>This dataset contains input data, analysis codes, and generated dataset used for the following article. If you use the GlobalHighPM<sub>2.5</sub> dataset in your scientific research, please cite the following reference (Wei et al., NC, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. <a href="https://weijing-rs.github.io/publications/Wei_et_al-NC-2023.pdf" target="_blank" rel="noopener">First close insight into global daily gapless 1 km PM<sub>2.5</sub> pollution, variability, and health impact</a>. <em>Nature Communications</em>, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3</p> </li> </ul> <p><strong>Input Data</strong></p> <p>Relevant raw data for each figure (compiled into a single sheet within an Excel document) in the manuscript.</p> <p><strong>Code</strong></p> <p>Relevant Python scripts for replicating and ploting the analysis results in the manuscript, as well as codes for converting data formats.</p> <p><strong>Generated Dataset</strong></p> <p>Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM<sub>2.5</sub> dataset over land from 2017 to the present. This dataset exhibits high quality, with cross-validation coefficients of determination (CV-R<sup>2</sup>) of 0.91, 0.97, and 0.98, and root-mean-square errors (RMSEs) of 9.20, 4.15, and 2.77 µg m<sup>-3</sup> on the daily, monthly, and annual bases, respectively.</p> <p><strong>Due to data volume limitations, </strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2022 </strong>is accessible at: <strong><a href="../records/10795661">GlobalHighPM2.5 (2022)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2021 </strong>is accessible at: <strong><a href="../records/10398385">GlobalHighPM2.5 (2021)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2020 </strong>is accessible at: <strong><a href="../records/10402639">GlobalHighPM2.5 (2020)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2019 </strong>is accessible at: <strong><a href="../records/10402723">GlobalHighPM2.5 (2019)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2018 </strong>is accessible at: <strong><a href="../records/10402824">GlobalHighPM2.5 (2018)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2017 </strong>is accessible at: <strong><a href="../records/10403497">GlobalHighPM2.5 (2017)</a></strong></p> <p> continuously updated...</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
SGD-SM: Generating Seamless Global Daily AMSR2 Soil Moisture Long-term Products (2013-2019)
<p><strong>If you used our dataset, please cite our reference:</strong></p> <p><strong>Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021.</strong></p> <p><strong>Description:</strong></p> <ul> <li>A <strong>seamless global daily</strong> (<strong>SGD</strong>) AMSR2 soil moisture long-term (2013-2019) dataset is generated through the proposed model. This daily products include <strong>2553</strong> global soil moisture NetCDF4 files, starting from Jan 01, 2013 to Dec 31, 2019 (about <strong>20GB</strong> memory after uncompressing this zip file).</li> <li>To further validate the effectiveness of these products, three verification ways are employed as follow: 1) In-situ validation; 2) Time-series validation; And 3) simulated missing regions validation. More validation results can be viewed at <strong><a href="https://qzhang95.github.io/Projects/Global-Daily-Seamless-AMSR2">SGD-SM</a></strong>.</li> <li>An example Python code of extracting this dataset is also available at <strong><a href="https://github.com/qzhang95/SGD-SM">https://github.com/qzhang95/SGD-SM</a></strong>.</li> <li>Official LPRM AMSR2 Descending L3 soil moisture products indeed only have 28 daily files in May 2013 (missing data files in date May 11, May 12, and May 13).</li> <li>This soil moisture dataset is comprised of netCDF4 (*.nc) files. Therefore, users need to install <strong>netCDF4</strong> toolkit before reading the data: <pre><code class="language-python">pip install netCDF4 pip install numpy</code></pre> <p> </p> </li> <li>It should be noted that the original and reconstructed soil moisture data are both recorded in one NC file. User can read the original data, reconstructed data, and mask data as follows:</li> <li> <pre><code class="language-python">Data = nc.Dataset(NC_file_position) Ori_data = Data.variables['original_sm_c1'] Rec_data = Data.variables['reconstructed_sm_c1'] Ori = Ori_data[0:720, 0:1440] Rec = Rec_data[0:720, 0:1440] Mask_ori = np.ma.getmask(Ori)</code></pre> <p> </p> </li> </ul>
A global spatiotemporally seamless daily mean land surface temperature from 2003 to 2019
<p>The global daily mean land surface temperature product (GADTC product) was generated based on the improved ADTC-based framework (termed IADTC framework) which basically combines the annual temperature cycle and diurnal temperature cycle model. </p> <p>The GADTC product is organized by year and each .tif image contains the global spatiotemporally seamless daily mean land surface temperature (LST) for each day with the unit of Kelvin. </p> <p>The demo code of the IADTC framework is available at https://github.com/faluhong/IADTC-framework. </p>
GlobalHighCO: Global Daily Seamless 1 km Ground-Level CO Dataset over Land (2018–Present)
<p>GlobalHighCO is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived gapless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level CO dataset over land <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.93 and a root-mean-square error (RMSE) of 0.21 mg m<sup>-3</sup> on a daily basis.</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
GlobalHighO₃: Global Daily Seamless 10 km Ground-Level O₃ Dataset over Land (2000–Present)
<p>GlobalHighO<sub>3</sub> is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the first big data-derived gapless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) global ground-level maximum daily 8-hour average (MDA8) O<sub>3</sub> dataset over land <strong>from 2000 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.86 and a root-mean-square error (RMSE) of 6.25 ppb on a daily basis.</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
A global daily seamless 9-km Vegetation Optical Depth (VOD) product from 2010 to 2021
<p> </p> <p><strong>(I) DESCRIPTION</strong>:</p> <p>· A <strong>global daily seamless 9-km Vegetation Optical Depth (VOD)</strong> product is generated through gap-filling and spatiotemporal fusion model. This daily products start <strong>from Jan 01, 2010 to Jul 31, 2021 </strong>(about 20GB memory after uncompressing all zip files).</p> <p>· To further validate the effectiveness of these products, three verification ways are employed as follow: 1) Time series validation; 2) Simulated missing-region validation; And 3) Data comparison validation.</p> <p>· It is important to note that the original data contain missing dates, and these corresponding gaps are also present in our dataset.</p> <p><strong>(II) DATA FORMATTING AND FILE NAMES </strong></p> <p>For the convenience of our readers, we have two formats of data available for download.</p> <p><strong>1) MAT file (Version v1)</strong></p> <p>Data from 2010 to 2021 are stored separately into folders for the corresponding years, with each folder containing daily `.mat` files. The naming convention for the data is “YYYYXXZZ,” where YYYY is the 4-digit year, XX is the 2-digit month, and ZZ is the 2-digit date. The geographic scope is global and the grid size is 4000*2000.</p> <p>MATFILES (.mat): The folders with matfiles contain individual files for:</p> <p>1. Vegetation Optical Depth: VOD_seamless_9km_ YYYYXXZZ.mat</p> <p>2. Latitude/Longitude: VOD_9km_Coordinates.mat</p> <p><strong>2) NetCDF file (Version v2)</strong></p> <p>The year-by-year daily data from 2010 to 2021 are stored in the ‘.nc’ files for the corresponding years. The daily data within each year into one NetCDF file. <span>The variable names are named as VOD_xxxxyydd, where xxxx represents the year, yy represents the month, and dd represents the day. The longitude variable is named “lon” with a dimension of 4000×1, and the latitude variable is named “lat” with a dimension of 2000×1. </span></p> <p><span>It should be noted that these NetCDF files are saved using the netCDF4 library in Python, with the dimension order being (lat, lon). When reading these NetCDF files in MATLAB, the default data dimension order is (lon, lat). Therefore, it is necessary to transpose the variables to match the correct dimension order.</span></p>
A Reconstructed Global Daily Seamless TROPOMI SIF dataset at a 0.05-degree Resolution (SDSIF) using the ML approach
<p>To enhance the spatial and temporal resolutions and continuities of the TROPOMI SIF, a global daily seamless SIF product at 0.05-degree resolution (namely, SDSIF) from May 2018 to December 2020 was generated based on the ML approach using TROPOMI SIF, MODIS reflectance, and ERA5 reanalysis datasets. This dataset has been validated with the original TROPOMI SIF and the long-term tower-based SIF from five flux sites, which verified the reliability of SDSIF and the advantages over original TROPOMI SIF.</p>
Global long-term (2010-2020) daily seamless fused XCO2 and XCH4 from GOSAT, OCO-2, and CAMS-EGG4
<p>A novel spatiotemporally self-supervised fusion method is proposed to establish long-term daily seamless XCO<sub>2</sub> and XCH<sub>4</sub> products from 2010 to 2020 over the globe at grids of 0.25°. More details are provided in https://doi.org/10.5194/essd-2023-28.</p>
GlobalHighNO₂: Global Daily Seamless 1 km Ground-Level NO₂ Dataset over Land (2018–Present)
<p>GlobalHighNO<sub>2</sub> is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived gapless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level NO<sub>2</sub> dataset over land <strong>from 2018 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.92 and a root-mean-square error (RMSE) of 4.76 µg m<sup>-3</sup> on a daily basis.</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
SGD-SST: Seamless Global Daily Sea Surface Temperature Products
<p><strong>Seamless Global Daily SNPP-VIIRS Sea Surface Temperature </strong><strong>Products from 2020 to 2024</strong></p>
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