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4 results for “Gap free dataset”
Global monthly and 0.1° gap-free XCO2 dataset
<p>This dataset consists of the global continental continuous column-averaged dry-air mole fraction of carbon dioxide (XCO2, unit: parts per million i.e., ppm) covering the period from September 2014 to December 2020. </p> <p>The dataset is derived from the official OCO-2 XCO2 product and incorporates data from multiple sources, including CO2 concentration data, vegetation index data, and meteorological data. We utilized a machine learning approach to generate a monthly-scale gapless CO2 product with a spatial resolution of 0.1°, stored in GeoTiff format.</p> <p> </p> <p><strong>Please cite the following article when using the dataset:</strong></p> <p>L. Zhang, T. Li, J. Wu, H. Yang, Global estimates of gap-free and fine-scale CO2 concentrations during 2014–2020 from satellite and reanalysis data, Environment International (2023), doi: https://doi.org/10.1016/j.envint.2023.108057</p>
A temporally consistent 8-day 0.05° gap-free snow cover extent dataset over the Northern Hemisphere for the period 1981–2019
<p>Northern Hemisphere (NH) snow cover extent (SCE) is one of the most important indicator of climate change for its unique surface property. However, short temporal coverage, coarse spatial resolution, and different snow discrimination approach among published SCE products hampers its detailed studies. Using the Advanced Very High Resolution Radiometer Surface Reflectance (AVHRR-SR) Climate Data Record (CDR) and several ancillary datasets, this study generated a temporally consistent 8-day 0.05° gap-free NH terrestrial SCE product for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite. This process consistent of five steps. First, a decision tree algorithm with multiple threshold tests was applied to detect SCE from daily AVHRR-SR CDR. Second, we merge two existing daily SCE products to take advantage of their spatial coverage. Third, an aggregation process was used to detect the maximum SCE in each 8-day periods. Forth, the GLASS SCE was generated with the help of snow cover probability climatology. Fifth, the validation process was carried out to evaluate the quality of GLASS SCE. Validation results by using 562 Global Historical Climatology Network stations during 1981–2017 (r=0.61, p<0.05) and MOD10C2 during 2001–2019 (r=0.97, p<0.01) proved that the GLASS SCE product is credible in snow cover frequency monitoring. Moreover, cross-comparison between GLASS SCE and surface albedo during 1982–2018 further confirmed its values in climate changes studies.</p> <p>The GLASS SCE data set provides binary maps of snow cover for the Northern Hemisphere from September 1981 to the December 2019. The data are organized by year and provided in GeoTIFF formats. The gridcells were flagged as “0” if classified as "Non-snow", "1" if retrieved from AVHRR satellite observations, and "2" if filled by IMS snow climatology.</p> <p>Spatial Coverage: N: 90, S: 0, E: 180, W: -180<br> Spatial Resolution: 0.05 deg x 0.05 deg<br> Samples = 7200<br> Lines = 1800<br> Temporal Coverage: September 1981 to December 2019<br> Temporal Resolution: 8-day</p>
Gap-free 1km PM2.5 dataset in China (2000-present)
<p>This is the monthly PM2.5 estimates across China from 2000 to 2022. If you want daily dataset, please go to <a href="https://zenodo.org/record/7229348">10.5281/zenodo.7229348</a>. More datasets can be found on the right navigation panel of <a href="https://zenodo.org/record/4569557">10.5281/zenodo.4569557</a>. </p> <p><strong>We also estimate other atmospheric data:</strong></p> <p>For full-coverage, 1-km, AOD data in China, please go to <a href="https://dataverse.harvard.edu/dataverse/atmospheric_data_by_WHUT">harvard dataverse</a>. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.</p> <p>We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2020 in China using a random forest-based hindcast modeling method. <strong>Our modeling method focused on improving pre-2013 PM2.5 estimates because for those years no available PM2.5 measurements can be directly used for constructing the model and evaluating the model performance. </strong>In our proposed method, observed predictor information before 2013 was incoporated into the modeling for the first time. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The monthly average data during 2000-2022 are released here in CSV format (if you want GEOTIFF files, please go to <a href="https://zenodo.org/record/8347128">10.5281/zenodo.8347128</a>) and free for non-commercial use. <em><strong>If you want use our dataset, please cite the following publication. </strong></em></p> <p>The estimates in 2021-2022 are separately predicted using the same modeling method developed in the publication below and samples in the corresponding predictive year (sample-based 10-fold cross validation R2 [RMSE] values are 0.91 [8.84 ug/m3] for 2021 and 0.93 [7.42 ug/m3] for 2022, respectively. </p> <p><strong>-He, Q., Ye, T., Wang, W., Luo, M., Song, Y., & Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020. <em>Journal of Environmental Management</em>, <em>342</em>, 118145.[<a href="https://doi.org/10.1016/j.jenvman.2023.118145">url</a>]</strong></p> <p><strong>-He, Q., Wang, W., Song, Y., Zhang, M., & Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020. <em>Atmospheric Research</em>, <em>281</em>, 106481.[<a href="https://doi.org/10.1016/j.atmosres.2022.106481">url</a>]</strong></p> <p> </p> <p> </p>
Gap-free 1-km PM2.5 dataset in China (2000-present)
<p>This is the monthly PM2.5 estimates across China from 2000 to 2022. If you want daily dataset, please go to <a href="https://zenodo.org/record/7229348">10.5281/zenodo.7229348</a>. More datasets can be found on the right navigation panel of <a href="https://zenodo.org/record/4569557">10.5281/zenodo.4569557</a>. </p><p><strong>We also estimate other atmospheric data:</strong></p><p>For full-coverage, 1-km, AOD data in China, please go to <a href="https://dataverse.harvard.edu/dataverse/atmospheric_data_by_WHUT">harvard dataverse</a>. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.</p><p>We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2020 in China using a random forest-based hindcast modeling method. <strong>Our modeling method focused on improving pre-2013 PM2.5 estimates because for those years no available PM2.5 measurements can be directly used for constructing the model and evaluating the model performance. </strong>In our proposed method, observed predictor information before 2013 was incoporated into the modeling for the first time. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The monthly average data during 2000-2022 are released here GEOTIFF format (if you want CSV files, please go to <a href="https://zenodo.org/record/8084388">10.5281/zenodo.8084388</a>) and free for non-commercial use. <i><strong>If you want use our dataset, please cite the following publication. </strong></i></p><p>The estimates in 2021-2022 are separately predicted using the same modeling method developed in the publication below and samples in the corresponding predictive year (sample-based 10-fold cross validation R2 [RMSE] values are 0.91 [8.84 ug/m3] for 2021 and 0.93 [7.42 ug/m3] for 2022, respectively. </p><p><strong>-He, Q., Ye, T., Wang, W., Luo, M., Song, Y., & Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020. </strong><i><strong>Journal of Environmental Management</strong></i><strong>, </strong><i><strong>342</strong></i><strong>, 118145.[</strong><a href="https://doi.org/10.1016/j.jenvman.2023.118145"><strong>url</strong></a><strong>]</strong></p><p><strong>-He, Q., Wang, W., Song, Y., Zhang, M., & Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020. </strong><i><strong>Atmospheric Research</strong></i><strong>, </strong><i><strong>281</strong></i><strong>, 106481.[</strong><a href="https://doi.org/10.1016/j.atmosres.2022.106481"><strong>url</strong></a><strong>]</strong></p><p> </p><p>If you want other atmospheric data, e.g., CO2 dataset, please go to <a href="https://zenodo.org/records/10022905">10.5281/zenodo.10022904.</a></p>
ScienceDex guides
Understand access before you commit
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