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4 results for “Precipitation fusion”
Precipitation oxygen isoscape for mainland China from 1870 to 2017 generated based on data fusion and bias correction of iGCMs simulations
<p>The dataset includes the stable oxygen isotope of precipitation for the mainland of China over the 1870-2017 period, at a spatial resolution of 50-60 km and a monthly temporal resolution. In order to make full use of observations to integrate the advantages of various iGCMs, the combination of data fusion and bias correction methods are used. Some physical-based ancillary data are introduced in the fusion methods, including elevation and meteorological data, to enrich the climate and terrain information in the process of data fusion. Specifically,</p><p>(1) for the 1979-2001 period, nine simulations from six iGCMs (CAM2, GISS E, HadAM3, IsoGSM2, LMDZ4, and MIROC32) and ancillary data are fused with observations by using the CNN fusion method;</p><p>(2) for the 2002-2007 period, seven simulations from four iGCMs (GISS E, IsoGSM2, LMDZ4, and MIROC32) and ancillary data are fused by using the CNN fusion method;</p><p>(3) for the 1969-1978 period, four simulations from three iGCMs (CAM2, GISS E, and HadAM3) and ancillary data are fused by using the CNN fusion method;</p><p>(4) for the 1958-1968 and 2008-2017 periods, two iGCM simulations (CAM2 and HadAM3 for 1958-1968 and IsoGSM2 and LMDZ4 zoomed for 2008-2017) are corrected by using two BCMs, and ensemble mean (mean of four simulations) is then calculated;</p><p>(5) for the 1870-1957 period, one iGCM simulation (HadAM3) is corrected by using two BCMs, and the ensemble mean (mean of two simulations) is then calculated.</p><p>Compared with the existing iGCMs, the isoscape has high quality and stability for a large region in China at the monthly scale. However, it should be noted that the isoscape may be more reliable for the common periods of most iGCMs (1969-2007), but mediocre for other periods. </p>
Precipitation hydrogen isoscape for East China from 1969 to 2017 generated based on data fusion of iGCMs simulations
<p>The dataset includes the stable hydrogen isotope of precipitation for East China over the 1969-2017 period, at a spatial resolution of 50-60 km and a monthly temporal resolution. This dataset was built based on the Convolutional Neural Network (CNN) method, fusing observations and isotope-equipped general circulation models (iGCMs) simulations of hydrogen isotope composition. Some physical-based ancillary data are also introduced in the fusion methods, including elevation and meteorological data, to enrich the climate and terrain information in the process of data fusion.</p>
Multi-Source Precipitation Data Fusion Across Continental United States
<p><strong><span>Dataset Description</span></strong><span>: This dataset supports our research presented in the paper "<em>A deep learning-based framework for multi-source precipitation fusion</em>" by Gavahi et al., (2023) published in <em>Remote Sensing of Environment</em>. The study introduces a novel deep learning architecture for merging and downscaling multiple precipitation products, aiming to enhance quantitative precipitation estimation (QPE) accuracy. The developed model, the Precipitation Data Fusion Network (PDFN), integrates 3D-CNN and ConvLSTM layers to capture the inherent spatiotemporal dependencies of precipitation data. The results indicate significant improvements in error statistics.</span></p> <p><span>The dataset includes merged daily precipitation estimations using the PDFN model. The data cover the Continental United States (CONUS) and are provided at a spatial resolution of 0.05 degrees. Temporal coverage spans from January 1, 2015, to April 30, 2024. The coordinate reference system used is WGS1984.</span></p> <p><strong><span>Note</span></strong><span>: Since the PERSIANN-CDR dataset is only available until the end of 2023, in this dataset, we used PDIR-Now instead to ensure the dataset's continuity and completeness. In the original paper, we used PERSIANN-CDR, but here we used PDIR-Now to extend the dataset to cover the period until April 30, 2024.</span></p> <p><strong><span>Usage Notes</span></strong><span>: This dataset is intended for use in applications such as land surface modeling, flood forecasting, drought monitoring and prediction. Users are requested to cite the associated paper when utilizing the dataset for academic or research purposes. </span></p> <p><strong><span>Related Publications</span></strong><span>: For further details on the methodology and applications of this dataset, refer to the paper "Gavahi, K., E. Foroumandi, and H. Moradkhani (2023), A deep learning-based framework for multi-source precipitation fusion, Remote Sensing of Environment, doi:10.1016/j.rse.2023.113723"</span></p>
MGP: a new 1-hourly 0.25° global precipitation product (2000-2020) based on multi-source precipitation data fusion
<p>The multi-source merged global precipitation product (MGP; 0.25°/ hourly; 2000-2020; 60°N/S), which takes advantage of the complementary strengths of satellite, reanalysis, and gauge data to obtain reliable precipitation estimates over the global land surface, provides a new higher-quality precipitation dataset for data users to realize their respective research purposes and the social and economic activities. The data developers hope that MGP will play an important role in a variety of science communities (e.g., hydrology, meteorology, climatology, ecology, and agriculture).</p>
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