Ground daily rainfall predicting method based on satellite remote sensing and regression Kriging

Abstract

The invention discloses a ground daily rainfall predicting method based on satellite remote sensing and regression Kriging. The method comprises the steps that firstly, data are fast obtained through satellite remote sensing, and a regression relation among ground-based observation values, TRMM, DEMs and geographic positions of rainfall capacities of all levels is established according to the classification of the rainfall to obtain regression estimated values and regression residual errors of all levels; secondly, the spatial agglomeration degrees of the regression residual errors of all levels are analyzed, the trend removing is carried out on the regression residual errors, and the Kriging interpolation of the regression residual errors is carried out to obtain the regression residual error spatial distribution characteristics of all levels per 1 km; thirdly, the regression estimated values of all levels and the regression residual errors of all levels are added to obtain the ground-based predicting values of rainfall of all levels per 1 km; lastly, the ground-based predicting values of the rainfall of all levels are merged to obtain a daily rainfall predicting value per 1 km. According to the ground daily rainfall predicting method, the spatial and temporal distribution characteristics of the ground-based rainfall can be accurately predicted, the predicting precision of the ground daily rainfall is improved, the predicted space resolution is improved, and the key problem that the water conservancy department predicts the ground rainfall is solved.

Description

technical field [0001] The invention relates to a ground rainfall prediction method, in particular to a ground daily rainfall prediction method based on satellite remote sensing and regression kriging. technical background [0002] Precipitation changes drastically in time and space, and accurate prediction of precipitation is of great significance for hydrology, meteorology, and disaster forecasting. Precipitation data commonly used at present are mainly obtained through methods such as ground observation stations and satellite rain measurement. As a conventional means of measuring precipitation, ground observation stations have the characteristics of wide application, high precision, and mature technology. However, the network density of ground observation stations meets the quality requirements of related research work, and the precipitation observed by ground observation stations only represents Precipitation conditions in a certain range around ground observation stati...

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As-Syakur et al. (As-Syakur, A.R., Tanaka, T., Prasetia, R., Swardika, I.K., and Kasa, I.W., 2011, Comparison of TRMM multisatellite precipitation analysis (TMPA) products and daily-monthly gauge data over Bali .International Journal of Remote Sensing, 32, pp.8969-8982.) By analyzing the relationship between the ground observation station data in Bali and the TRMM satellite data, it is found that in the dry season, the TRMM satellite data and the ground sensing data have a better relationship. , and this correlation is worse in the wet season

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