本研究构建了全球陆表大气可降水量（PWV）数据集（2012至2020年），基于风云三号卫星系列（FY-3B、FY-3C和FY-3D）搭载的微波辐射成像仪（MWRI）观测。该数据集空间分辨率为0.25°×0.25°，通过融合卫星升轨和降轨观测，实现每日两次PWV记录。
\n经SuomiNet GPS和整合探空数据集（IGRA2）独立验证，本数据集RMSE分别为4.47 mm和3.89 mm，不同地表类型下RMSE介于2.90-5.49 mm。该数据集可有效捕捉PWV时空演变特征，支持极端天气事件引发的水汽局地突变过程精准解析。作为被动微波陆表PWV监测的重要进展，MWRI PWV数据集提供全天候高精度数据记录，有效填补了被动微波陆表PWV观测全球覆盖的空白，为大气科学研究、气候模式开发、水循环过程分析等提供重要数据支撑。", "ds_source": "
数据来源于Figshare网站（https://figshare.com/）。", "ds_process_way": "
数据集采用自动化机器学习（ML）模型构建，该模型整合了基于MWRI的地表特征参数，并以增强型全球定位系统（GPS）PWV数据集作为参考基准。", "ds_quality": "
模型训练集覆盖全球万余站点、超百万采样点，确保对全球PWV变化的稳健表征。", "ds_acq_start_time": "2012-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 6597650625, "ds_files_count": 2, "ds_format": "NetCDF", "ds_space_res": "0.25°*0.25°", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "da89842c-94a8-43af-8591-02f3666e955e.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15" ], "quality_level": 3, "publish_time": "2025-03-28 19:01:09", "last_updated": "2026-01-14 11:00:48", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB6812.2025", "i18n": { "en": { "title": "A Global Terrestrial Precipitable Water Vapor Dataset from 2012 to 2020 Based on Microwave Radiation Imager Measurements from Three Fengyun Satellites", "ds_format": "netcdf", "ds_source": "
Data sourced from Figshare website（ https://figshare.com/ ）.", "ds_quality": "
The model training set covers over 10000 sites and millions of sampling points worldwide, ensuring robust representation of global PWV changes.", "ds_ref_way": "", "ds_abstract": "
A global terrestrial precipitable water vapor (PWV) dataset has been developed using observations from the MicroWave Radiation Imager (MWRI) aboard the FY-3 satellite series (FY-3B, FY-3C and FY-3D) spanning 2012 to 2020. The dataset offers twice-daily PWV records at a spatial resolution of 0.25° × 0.25°, aligned with the ascending and descending orbits of the FY-3 satellites. The dataset was generated using an automated machine learning (ML) model that leverages MWRI-based features characterizing surface conditions and an enhanced Global Position System (GPS) PWV dataset as a reference.
\nTrained on over one million sampling points from more than ten thousand stations worldwide, the model ensures a robust representation of global PWV variations. Independent evaluations against SuomiNet GPS and Integrated Global Radiosonde Archive Version 2 (IGRA2) PWV products yielded root mean square error (RMSE) of 4.47 mm and 3.89 mm, respectively, with RMSE values ranging from 2.90 to 5.49 mm across various surface conditions. The dataset effectively captures both spatial and temporal PWV variations, allowing for precise examination of localized and abrupt changes in water vapor induced by extreme weather events. Representing a significant advancement in global terrestrial PWV monitoring, the MWRI PWV dataset provides an all-weather, high-precision data record that bridges gaps in global coverage of passive microwave-based terrestrial PWV observations. It is a valuable resource for atmospheric research, climate modeling, water cycle studies, and beyond.
", "ds_time_res": "年", "ds_acq_place": "Global", "ds_space_res": "0.25°*0.25°", "ds_projection": "", "ds_process_way": "The dataset is constructed using an automated machine learning (ML) model that integrates surface feature parameters based on MWRI and uses the Enhanced Global Positioning System (GPS) PWV dataset as a reference benchmark.", "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": [ "风云三号", "陆表大气可降水量", "卫星遥感", "机器学习" ], "ds_subject_tags": [ "大气科学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "夏祥鳌", "email": "xxa@mail.iap.ac.cn", "work_for": "中国科学院大气物理研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "夏祥鳌", "email": "xxa@mail.iap.ac.cn", "work_for": "中国科学院大气物理研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "夏祥鳌", "email": "xxa@mail.iap.ac.cn", "work_for": "中国科学院大气物理研究所", "country": "中国" } ], "category": "气象" }