Abstract
With a high spatial resolution and wide coverage, satellite-based precipitation products have compensated for the shortcomings of traditional measuring methods based on rain gauge stations, such as the sparse and uneven distribution of rain gauge stations. However, the accuracy of satellite precipitation products is not high enough in some areas, and the causes of their errors are complicated. In order to better calibrate and apply the product’s data, relevant research on this kind of product is required. Accordingly, this study investigated the spatial error distribution and spatial influence factors of the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) post-process 3B42V7 (hereafter abbreviated as 3B42V7) data over mainland China. This study calculated accuracy indicators based on the 3B42V7 data and daily precipitation data from 797 rain gauge stations across mainland China over the time range of 1998–2012. Then, a clustering analysis was conducted based on the accuracy indicators. Moreover, the geographical detector (GD) was used to perform the error cause analysis of the 3B42V7. The main findings of this study are the following. (1) Within mainland China, the 3B42V7 data accuracy decreased gradually from the southeast coast to the northwest inland, and shows a similar distribution for precipitation. High values of systematic error (>1.0) is mainly concentrated in the southwest Tibetan Plateau, while high values of random error (>1.0) are mainly concentrated around the Tarim Basin. (2) Mainland China can be divided into three areas by the spectral clustering method. It is recommended that the 3B42V7 can be effectively used in Area I, while in Area III the product should be calibrated before use, and the product in Area II can be used after an applicability study. (3) The GD result shows that precipitation is the most important spatial factor among the seven factors influencing the spatial error distribution of the 3B42V7 data. The relationships between spatial factors are synergistic rather than individual when influencing the product’s accuracy.
Highlights
Precipitation data availability has been highlighted as a major constraint on the effective application of the hydrological model, and it has been argued that the quality of the precipitation data inputs to the model is often more important than the choice of model itself [1]
For a series of satellite-based precipitation products developed in recent years, this method effectively compensates for the deficiencies of the spatial continuity of the conventional measuring method based on rain gauge stations [4]
The normalized mean square error (NMSE) values in most of western China and parts of northeastern China are above 1.0, which indicates that the advisability of the product estimation is inferior to that of the rain gauge station observation and the products are not recommended for use in these regions [51]
Summary
Precipitation data availability has been highlighted as a major constraint on the effective application of the hydrological model, and it has been argued that the quality of the precipitation data inputs to the model is often more important than the choice of model itself [1]. Water 2019, 11, 1435 rain gauge stations This type of data plays an important part in documenting the characteristics of precipitation over global land areas, and the regional precipitation data are usually obtained by spatial interpolation. Due to the characteristic spatial uncertainty of precipitation and the sparse and uneven distribution of rain gauge stations on the land’s surface, it is generally difficult to satisfy the quality requirements of precipitation data for spatial distribution in some areas where precipitation data is obtained by this method [2,3]. The other way is the indirect measuring method based on sensor and data assimilation technology, such as precipitation radar and satellite-based remote sensing technology. For a series of satellite-based precipitation products developed in recent years, this method effectively compensates for the deficiencies of the spatial continuity of the conventional measuring method based on rain gauge stations [4]. With a larger coverage and higher spatiotemporal resolution, this method satisfies demands for the spatial distribution of precipitation data and could provide data references for some areas lacking rain gauge stations
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