Remote sensing rainfall error correction method and system based on nonlinear classification regression analysis

Abstract

The invention provides a remote sensing rainfall error correction method and system based on nonlinear classification regression analysis. The remote sensing rainfall error correction method comprises the following steps: establishing a target drainage basin rainfall geographic space-time information database; dividing a target drainage basin rainfall station, determining a training set and a test set, and calculating an initial error correction field at the target drainage basin rainfall station; determining a correction domain by taking observation field geographic information corresponding to the training set as a benchmark, and constructing a nonlinear classification regression model set in the correction domain on the basis of a support vector machine classification regression theory period by period; automatically screening a correction domain and nonlinear classification regression model parameters according to the geographic information of the test set, estimating a rainfall error field, and performing error correction and precision evaluation on a background field of the test set; utilizing a nonlinear classification regression model to construct rainfall error fields with different spatial resolutions to realize downscaling processing, and carrying out grid-by-grid and period-by-period error correction on a remote sensing rainfall product. According to the embodiment of the invention, the remote sensing rainfall data set with higher precision and higher resolution can be generated as required.

Description

technical field [0001] The present invention relates to the fields of hydrological and meteorological data analysis and rainfall forecast research, and more specifically, to a remote sensing rainfall error correction method and system based on nonlinear classification regression analysis. Background technique [0002] Among the elements of weather change, precipitation is one of the important physical processes driving the hydrological cycle of a watershed. Its spatial distribution profoundly affects the spatial pattern of related variables such as surface runoff, flood, and soil moisture content, and drives the temporal and spatial changes of water resources in a watershed. Therefore, timely and accurate estimation of precipitation is of great significance to the national economy, social life, and the safety of people's lives and property. [0003] Today's precipitation estimation methods mainly include surface rainfall station observation, weather radar and meteorological ...

AI Technical Summary

Problems solved by technology

However, due to the extremely complex spatiotemporal variability of rainfall in the basin and the many influencing factors, although the existing remote sensing rainfall correction methods can improve the accuracy gain to a certain extent, it is still difficult to match the runoff and flood in the changing environment due to the differences in explanatory variables and the performance of the method. Analytical accuracy requirements for simulation, water resource evolution prediction, etc.

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