| The future development of hydro- disciplinary depends largely on the data which is required to improve the model availability and adequacy for calibration. Remote sensing technology can play a key role in this process. Remote sensing data can be obtained through a shared platform, which allows data shared and flowed. The current global remote sensing data can be shared except satellite pictures. There are a number of satellite-based remote sensing data products. In this paper, we compare the applicability of the satellite observations of precipitation in China, and discuss the differences of them. We look forward to providing data to support the no information region hydrological study.The main conclusions of this paper include the following aspects:Firstly, we analyze GPCP and CMAP precipitation data in the China's land area. Found that:GPCP and CMAP have a better correlation east of 100°E than the west of 100°E. Another result is that the correlation is worse in the winter than the other seasons. This is mainly because of satellite precipitation products for the principle of inversion of snow is not very clear. The spatial difference indicats that the Northeast area, the Huanghuai sea area, Yangtze River and south of the area, northwest and the southwest area's conformity showing a decreasing progressively tendency. Analysis for the areas, which has little station distribution of it, for example northwest and southwest etc, has a big difference between GPCP and CMAP. We think it is relate to different fusion method in the process of production between them. From the 40°N to the south of most regions of China, both are using of satellite data which based on the GPI algorithm. GPCP before using it after microwave data calibration, but CMAP data is not. This may be the internal causes of difference of Huang-huai-hai region and the Yangtze and south of the Yangtze River area. In the north of 40°N, the difference is small because of the data source is derived from the SSM/I. Results indicate that GPCP is more suitable for China's rainfall characteristics.Secondly, based on GPCP data and the rain gause, we analysis different time scales including the annual average,quarter average and average month, finding that the rainfall in less GPCP data and rain gause has a better correlation than the rainfall in more. This is mainly because the precipitation stations for local precipitation representative poor. GPCP and rain gause space station consistency show that:the 56 river basin which belong to the northeast area, south of the Yangtze river and yellow sea area. When the area is above 70000 km, GPCP and rain gause's relative error is below 12%. In the large areas of the northwest and southwest, relative error is too big. We analyze the reason that density of meteorological stations plays a very important role, and another reason is relevant to type of precipitation in each region.Thirdly, to compare of our four different areas GPCP data accuracy, the results indicate that:The highest accuracy of GPCP data is the Yangtze River and south of the Yangtze River area. In this area when the number of sites in above 4, and the area above 49000 km2, precision can reach more than 11%. The accuracy of the yellow sea area is less than the Yangtze River and south of the Yangtze River area, when the number of site above 5, area threshold for 67000 km2, precision can reach 12%. The northeast area also can achieve a high precision, a relative error less than 15%, a basin area more than 70000 km2, the number of sites more than 5. Because of the Northwest and southwest area GPCP data error poor, can be only applied in individual region.Finally, based on GPCP data using linear regression, residual mass curve, M-K inspection, and wavelet methods, this paper researches a typical river basin evolution of precipitation from 1980 year to 2006. And using PREC/L data verifies the conclusion. That year, spring, summer, autumn GPCP data can be a very good characterization of precipitation, but the result of the winter was poor. |
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