以IMERG多源融合降水产品为背景，融合了FY-3C、3D和3E卫星高质量微波降水产品后的降水融合产品；采用机器学习的方法反演FY-3C、3D、3E和3F卫星的降水产品，并将FY-3降水产品与IMERG中其他微波观测拼接。对拼接后的所有被动微波产品采用云运动矢量外推，并通过卡尔曼滤波与GOES、MetSat和Himawari 等静止卫星的红外降水产品融合。FY-3系列卫星的高质量微波降水产品可以有效补充IMERG中因微波观测缺口导致的误差，为全球降水研究提供更可靠的数据集。", "ds_source": "
以IMERG多源融合降水产品为背景，融合了FY-3C、3D和3E卫星高质量微波降水产品后的降水融合产品。", "ds_process_way": "
采用机器学习的方法反演FY-3C、3D、3E和3F卫星的降水产品，并将FY-3降水产品与IMERG中其他微波观测拼接。对拼接后的所有被动微波产品采用云运动矢量外推，并通过卡尔曼滤波与GOES、MetSat和Himawari 等静止卫星的红外降水产品融合。", "ds_quality": "
风云三号C、D、E星的均方根误差分别比IMERG产品低约9.9%、18%和19.5%。基于FY-3卫星的高质量PMW降水产品可填补IMERG星座中PMW观测的空白。融合FY-3与IMERG数据后，高质量PMW数据占总数据的比例显著提升，日均PMW数据比例稳定维持在50%左右。基于中国、欧洲和美国约4400个气象站全年小时观测数据的均方根误差（RMSE），全球降水数据精度提升16%。IMERG产品降水量高估率降低32%，强度高估率降低63%。此外，IMERG数据在09:00-12:00时段的降水频率低估问题改善了67%。降水检测精度和误报率也显著提升。", "ds_acq_start_time": "2022-01-01 00:00:00", "ds_acq_end_time": "2025-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -90.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 0, "ds_files_count": 1, "ds_format": "HDF", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "032435ff-0538-437f-afd1-0d30c366eeb4.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e", "ds_serv_man": "敏玉芳", "ds_serv_phone": "09314967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2026-01-31 15:34:51", "last_updated": "2026-01-31 15:34:51", "protected": false, "protected_to": null, "lang": "zh", "cstr": null, "i18n": { "en": { "title": "Global multi-source precipitation fusion dataset（2022-2025）", "ds_format": "HDF", "ds_source": "
Based on the IMERG multi-source fusion precipitation product, this precipitation fusion product integrates high-quality microwave precipitation products from FY-3C, 3D, and 3E satellites.", "ds_quality": "
The root mean square errors of Fengyun-3 C, D, and E satellites are about 9.9%, 18%, and 19.5% lower than IMERG products, respectively. High quality PMW precipitation products based on FY-3 satellites can fill the gap in PMW observations in the IMERG constellation. After integrating FY-3 and IMERG data, the proportion of high-quality PMW data in the total data significantly increased, and the daily PMW data proportion remained stable at around 50%. Based on the root mean square error (RMSE) of annual hourly observation data from approximately 4400 meteorological stations in China, Europe, and the United States, the accuracy of global precipitation data has improved by 16%. The overestimation rate of precipitation for IMERG products has decreased by 32%, and the overestimation rate of intensity has decreased by 63%. In addition, the underestimation of precipitation frequency in IMERG data during the period of 09:00-12:00 has improved by 67%. The accuracy and false alarm rate of precipitation detection have also significantly improved.", "ds_ref_way": "", "ds_abstract": "
Based on the IMERG multi-source fusion precipitation product, this precipitation fusion product integrates high-quality microwave precipitation products from FY-3C, 3D, and 3E satellites; Using machine learning methods to invert precipitation products from FY-3C, 3D, 3E, and 3F satellites, and concatenating FY-3 precipitation products with other microwave observations in IMERG. All passive microwave products after splicing are extrapolated using cloud motion vectors and fused with infrared precipitation products from stationary satellites such as GOES, MetSat, and Himawari through Kalman filtering. The high-quality microwave precipitation products of FY-3 series satellites can effectively supplement the errors caused by microwave observation gaps in IMERG, providing a more reliable dataset for global precipitation research.
", "ds_time_res": "", "ds_acq_place": "global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Using machine learning methods to invert precipitation products from FY-3C, 3D, 3E, and 3F satellites, and concatenating FY-3 precipitation products with other microwave observations in IMERG. All passive microwave products after splicing are extrapolated using cloud motion vectors and fused with infrared precipitation products from stationary satellites such as GOES, MetSat, and Himawari through Kalman filtering.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "气象卫星", "降水", "风云三号" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2022, 2023, 2024, 2025 ], "ds_contributors": [ { "true_name": "王开存", "email": "kcwang@bnu.edu.cn", "work_for": "北京大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "王开存", "email": "kcwang@bnu.edu.cn", "work_for": "北京大学", "country": "中国" } ], "ds_managers": [ { "true_name": "王开存", "email": "kcwang@bnu.edu.cn", "work_for": "北京大学", "country": "中国" } ], "category": "气象" }