天山是世界上距离海洋最远的山系，世界七大山系之一。该区域属于我国典型的高寒山区，被誉为“中亚水塔”，对于新疆乃至中亚地区均具有重要的战略意义。随着遥感技术的发展，卫星反演降水成为估算山区降水的重要手段，但是山区地形复杂且不均匀，致使山区降水反演产品精度不高。针对此问题，本研究开展天山山区多源降水融合数据集研制，以GSMaP卫星降水数据为初始场，同期区域1065个台站的实况降水数据，发展一种基于最优插值的星地降水产品融合方法，最终生成2000-2022年天山山区逐日降水产品集｡本数据集在研制过程中对实况数据进行了严格质控，对逐日融合降水数据进行了质量评估，可望为复杂地形区域水资源管理与高效利用提供数据支撑。
", "ds_source": "本数据集生产主要基于GSMaP卫星降水以及雨量站实况降水数据。
\n(1) GSMaP卫星降水：全球卫星降水图（GSMaP）提供了分辨率为0.1 x 0.1度的全球小时降雨量。GSMaP是全球降水测量任务的产物，该任务使用GPM核心卫星的多波段无源微波和红外辐射计，并在其他卫星星座的协助下估计得到每隔三小时全球降水观测。GSMaP 使用低轨卫星观测提供的微波数据集和地球同步卫星观测提供的可见光/红外数据集作为反演算法输入源，主要采用了云移动矢量法和卡尔曼滤波方法对源数据进行处理，生产了3种不同的遥感降水数据产品：GSMaP_NRT、GSMaP_MVK、GSMaP_Gauge，其中近实时数据产品 GSMaP_NRT 处理过程中采用的是前向云矢量运动方案，而标准产品 GSMaP_MVK 采用了双向（前向和后向）云矢量运动方案，GSMaP_Gauge 则是基于GSMaP_MVK与CPC（Climate Prediction Center）全球地面雨量站观测资料的校正版本。3套产品的时空分辨率均为 1 h 和 0.1°×0.1°。对比评估显示，GSMaP日降水在众多卫星降水产品中准确率最高，因此本研究选取经过雨量站校正的GSMaP_Gauge作为初始场开展融合降水数据集研制。
\n(2) 实况降水：选取天山山区104个固态降水站（其中包含57个国家站，并已剔除8个国际交换站）、961个区域自动站逐时降水数据，实况降水时间范围与GSMaP降水数据一致。该数据由新疆气象局信息中心整理并通过气候极值检验、单站极值检验和数据一致性检验等质量控制。需要指出的是，固态降水站安装的是称重式降水测量仪器，既可测量降雨也可测量降雪，而区域自动站安装的是翻斗式雨量计，只能测量降雨。由于961个区域自动站雨量计在冷季停止观测，因此本研究选取暖季的5-9月开展研究。按照10折交叉验证，将实况站点按不同海拔分为10组，每次选取每组中的9份，共计90%用于建模，剩余每组中的1份共计10%组成独立数据集进行融合产品的精度验证，以此保证训练样本及验证样本的代表性。
", "ds_process_way": "本研究开展天山山区多源降水融合数据集研制，以GSMaP卫星降水数据为初始场，同期区域1065个台站的实况降水数据，发展一种基于最优插值的星地降水产品融合方法，最终生成2000-2022年天山山区逐日降水产品集｡本研究中最优插值分析以GSMaP降水作为初估场，以站点实况降水为真值，每个格点上的最终降水分析值Ak等于该点的初估值Fk加上该格点上实况观测值与初估值的偏差，而这个偏差由一定范围内n个格点上已知的实况观测值Oi与初估值Fi的偏差加权估计得到。
", "ds_quality": "本数据集在研制过程中对实况数据进行了严格质控，对逐日融合降水数据进行了质量评估，可望为复杂地形区域水资源管理与高效利用提供数据支撑。选取天山山区 104 个固态降水站、以及 961 个区域自动站逐时降水数据，实况降水时间范围与 GSMaP降水数据一致，该数据由新疆气象局信息中心整理并通过气候极值检验、单站极值检验和数据一致性检验等质量控制。此外，基于本数据集的融合方法对比研究成果已通过同行专家评审，发表在业内权威期刊《Journal of Hydrology》，表明数据具有较高的可信度。
", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "新疆天山山区", "ds_acq_lon_east": 96.0, "ds_acq_lat_south": 38.5, "ds_acq_lon_west": 73.5, "ds_acq_lat_north": 45.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 1266589013, "ds_files_count": 25, "ds_format": "csv", "ds_space_res": "10km", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "69d74edb-16ff-4f80-961c-a1c77d1c2317.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2024-08-12 09:18:02", "last_updated": "2026-01-26 15:00:52", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.IDM.DB6553.2024", "i18n": { "en": { "title": "Multi-source Precipitation Fusion Dataset for the Tianshan Mountains, Xinjiang (2000-2022)", "ds_format": "csv", "ds_source": "This dataset was primarily generated using GSMaP satellite precipitation and ground-based rain gauge data.
\n(1) GSMaP Satellite Precipitation: The Global Satellite Mapping of Precipitation (GSMaP) provides global hourly rainfall estimates at a 0.1° × 0.1° resolution. As part of the Global Precipitation Measurement (GPM) mission, GSMaP integrates passive microwave and infrared radiometer data from the GPM Core Satellite and auxiliary satellite constellations to produce global precipitation observations every three hours. GSMaP uses microwave datasets from low-Earth-orbit satellites and visible/infrared datasets from geostationary satellites as inputs for its retrieval algorithms, employing cloud motion vector and Kalman filter methods. It generates three products: GSMaP_NRT (near-real-time, using forward cloud motion vectors), GSMaP_MVK (standard product with bidirectional cloud motion vectors), and GSMaP_Gauge (adjusted using CPC global rain gauge data). All three products share a temporal resolution of 1 hour and a spatial resolution of 0.1° × 0.1°. Comparative evaluations show GSMaP_Gauge (gauge-corrected) has the highest accuracy among satellite products, making it the initial field for this fusion dataset.\n(2) Ground Precipitation: We selected hourly precipitation data from 104 solid precipitation stations (including 57 national stations, excluding 8 international exchange stations) and 961 regional automatic stations in the Tianshan Mountains. Ground data matched the temporal coverage of GSMaP and underwent quality control (climate extreme checks, station-specific extreme checks, and consistency tests) by the Xinjiang Meteorological Information Center. Solid precipitation stations use weighing-type gauges (measuring both rain and snow), while regional stations use tipping-bucket rain gauges (rain-only). Due to seasonal operational limitations (regional stations cease observations in cold seasons), the study focused on the warm season (May–September). A 10-fold cross-validation approach was applied: stations were stratified by elevation into 10 groups, with 90% (9 groups) used for modeling and 10% (1 group) reserved for independent validation, ensuring representativeness.
", "ds_quality": "The dataset underwent strict quality control for ground data and comprehensive evaluation of daily fused precipitation. It is expected to enhance water resource management in complex terrains. Ground data from 104 solid precipitation stations and 961 regional automatic stations (aligned temporally with GSMaP) were quality-controlled by the Xinjiang Meteorological Information Center. Additionally, the fusion methodology was peer-reviewed and published in Journal of Hydrology, attesting to its credibility.
", "ds_ref_way": "", "ds_abstract": "The Tianshan Mountains, one of the world's seven major mountain systems, are the farthest mountain range from any ocean and represent a typical alpine region in China. Known as the \"Water Tower of Central Asia,\" this area holds strategic significance for Xinjiang and Central Asia. With advancements in remote sensing technology, satellite-derived precipitation has become a key tool for estimating mountainous precipitation. However, complex and heterogeneous terrain in mountainous regions leads to low accuracy in satellite precipitation retrieval products. To address this, this study developed a multi-source precipitation fusion dataset for the Tianshan Mountains. Using GSMaP satellite precipitation data as the initial field and incorporating ground-truth precipitation data from 1,065 regional stations, we developed an optimal interpolation-based fusion method for satellite-ground precipitation products, ultimately generating a daily precipitation dataset for the Tianshan Mountains (2000-2022). During development, strict quality control was applied to ground data, and daily fused precipitation data underwent rigorous evaluation. This dataset is expected to support water resource management and efficient utilization in complex terrain regions.
", "ds_time_res": "日", "ds_acq_place": "Tianshan Mountains, Xinjiang", "ds_space_res": "10km", "ds_projection": "", "ds_process_way": "In this study, we carry out the development of a multi-source precipitation fusion dataset for the Tianshan mountainous region, using the GSMaP satellite precipitation data as the initial field and the live precipitation data from 1065 stations in the region during the same period, to develop an optimal interpolation-based fusion method of the star-earth precipitation products, and ultimately to generate a day-by-day precipitation product set in the Tianshan mountainous region for the period of 2000-2022.In this study, the optimal interpolation analysis takes the GSMaP precipitation as the initial estimation field, and the station real precipitation as the true value, and the final precipitation analysis value Ak on each grid point is equal to the initial valuation Fk of the point plus the deviation of the real observation value on the grid point from the initial valuation, which is weighted and estimated by the deviation of the known real observation value Oi from the initial valuation value Fi on n grid points within a certain range.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "多源", "降水", "融合" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆天山地区" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "卢新玉", "email": "luxy@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" }, { "true_name": "伏晓慧", "email": "wanglt001@163.com", "work_for": "新疆乌鲁木齐市气象局", "country": "中国" }, { "true_name": "刘艳", "email": "liuyan@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" }, { "true_name": "王秀琴", "email": "104495920@qq.com", "work_for": "新疆气象信息中心", "country": "中国" }, { "true_name": "火红", "email": "huohong@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" }, { "true_name": "王敏仲", "email": "wangmz@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "刘艳", "email": "liuyan@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "卢新玉", "email": "luxy@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" } ], "category": "气象" }