<p>Tianshan Mountain is the farthest mountain system from the ocean in the world and one of the seven major mountain systems in the world. This region belongs to my country's typical alpine mountainous area and is known as the "Central Asia Water Tower". It is of great strategic significance to Xinjiang and even Central Asia. With the development of remote sensing technology, satellite precipitation retrieval has become an important means to estimate precipitation in mountainous areas. However, the mountainous terrain is complex and uneven, resulting in low accuracy of precipitation retrieval products in mountainous areas. In response to this problem, this study carried out the development of a multi-source precipitation fusion data set in the Tianshan Mountains. Using GSMaP satellite precipitation data as the initial field and the live precipitation data from 1065 stations in the region during the same period, we developed a satellite-ground precipitation product fusion method based on optimal interpolation, and finally generated a daily precipitation product set in the Tianshan Mountains from 2000 to 2022. During the development process, this dataset has strictly controlled the actual data and evaluated the quality of daily integrated precipitation data. It is expected to provide data support for water resources management and efficient utilization in complex terrain areas. </p>
The production of this dataset is mainly based on GSMaP satellite precipitation and actual precipitation data from rainfall stations.
(1) GSMaP satellite precipitation: The Global Satellite Precipitation Map (GSMaP) provides global hourly rainfall with a resolution of 0.1 x 0.1 degrees. GSMaP is a product of the Global Precipitation Measurement Mission, which uses multi-band passive microwave and infrared radiometers from the GPM core satellite and estimates global precipitation observations every three hours with the assistance of other satellite constellations. GSMaP uses microwave data sets provided by low-orbit satellite observations and visible/infrared data sets provided by geosynchronous satellite observations as input sources of the inversion algorithm. It mainly uses cloud movement vector method and Kalman filtering method to process the source data, and produces 3 different remote sensing precipitation data products: GSMaP_NRT, GSMaP_MVK, and GSMaP_Gauge. Among them, the near-real-time data product GSMaP_NRT uses a forward cloud vector motion scheme in the processing process, while the standard product GSMaP_MVK uses a two-way (forward and backward) cloud vector motion scheme. GSMaP_Gauge is a corrected version based on GSMaP_MVK and CPC (Climate Prediction Center) global surface rainfall station observation data. The spatio-temporal resolutions of the three sets of products are all 1 h and 0.1°×0.1°[14]. Comparative evaluation shows that GSMaP daily precipitation has the highest accuracy among many satellite precipitation products [5]. Therefore, this study selects GSMaP_Gauge corrected by rainfall stations as the initial field to develop a fused precipitation data set.
(2) Live precipitation: Hour-by-hour precipitation data from 104 solid precipitation stations in the Tianshan Mountains (including 57 national stations and 8 international exchange stations have been excluded) and 961 regional automatic stations are selected. The distribution of stations is as shown in Figure 1. The live precipitation time range is consistent with the GSMaP precipitation data. The data was compiled by the Information Center of the Xinjiang Meteorological Administration and passed quality controls such as climate extreme value test, single station extreme value test and data consistency test. It should be pointed out that solid precipitation stations are equipped with weighing precipitation measuring instruments, which can measure both rainfall and snowfall, while regional automatic stations are equipped with tipping bucket rain gauges, which can only measure rainfall. Since 961 regional automatic stations stop observing rain gauges during the cold season, this study selects May to September during the warm season for research. According to the 10-fold cross-verification, the live stations are divided into 10 groups according to different altitudes. Nine samples from each group are selected each time, with a total of 90% used for modeling. The remaining one sample from each group totaling 10% constitutes an independent data set. Accuracy verification of fusion products to ensure the representativeness of training samples and verification samples.
This study carried out the development of a multi-source precipitation fusion data set in the Tianshan Mountains. Using GSMaP satellite precipitation data as the initial field and the live precipitation data from 1065 stations in the region during the same period, a satellite-ground precipitation product fusion method based on optimal interpolation was developed, and finally generated A daily precipitation product set in the Tianshan Mountains from 2000 to 2022. The optimal interpolation analysis in this study uses the GSMaP precipitation as the preliminary estimation field, and the actual precipitation at the station as the true value. The final precipitation analysis value Ak at each grid point is equal to the initial estimation Fk at that point plus the deviation between the actual observation value and the initial estimation at that grid point. This deviation is weighted estimated from the deviation between the known actual observation value Oi and the initial estimation Fi at n grid points within a certain range.
During the development process, this dataset has strictly controlled the actual data and evaluated the quality of daily integrated precipitation data. It is expected to provide data support for water resources management and efficient utilization in complex terrain areas. Hourly precipitation data from 104 solid precipitation stations and 961 regional automatic stations in the Tianshan Mountains are selected. The actual precipitation time range is consistent with the GSMaP precipitation data. The data was compiled by the Information Center of the Xinjiang Meteorological Administration and passed the climate extreme value test, single station extreme value test and data consistency test. In addition, the comparative research results of fusion methods based on this dataset have passed peer expert review and were published in the industry's authoritative journal "Journal of Hydrology", indicating that the data has high credibility.
| # | number | name | type |
| 1 | 2023D01A17 | Other | |
| 2 | 2023TSYCCX0079 | Other | |
| 3 | 2021kf06 | Other |
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CSTR:33110.11.ariddc.00013
10.12389/ariddc.00013