Cloud block characteristic change-based precipitation estimation method of geostationary meteorological satellite

Abstract

The invention belongs to the technical field of precipitation estimation, and discloses a cloud block characteristic change-based precipitation estimation method of a geostationary meteorological satellite. The method includes: acquiring a precipitation simulation parameter characteristic set of the satellite to depict an occurrence and development process of cloud precipitation, and selecting and using an extremum normalization method to normalize different-dimensional cloud image characteristic parameters; and constructing a three-layer forward-type backpropagation neural-network-based precipitation estimation model, which is used for precipitation estimation of a region, of the satellite, and adopting a multi-index system to analyze the precipitation simulation precision of the model. According to the method, the deficiency of conventional meteorological observation can be largely compensated by precipitation distribution derived by utilizing remote sensing data of the meteorological satellite, and richer precipitation information can be provided; and short-term near forecasting is facilitated, monitoring flood disasters is facilitated, early warning for geological disasters is facilitated, and the method has important significance for accuracy rate increasing of weather forecasting and disaster prevention and mitigation.

Description

technical field [0001] The invention belongs to the technical field of precipitation estimation, in particular to a method for estimating precipitation of static meteorological satellites based on changes in cloud block characteristics. Background technique [0002] With the development of meteorological satellites, the application of satellite data to estimate the distribution and intensity of precipitation has also developed as an important aspect of satellite data applications. The ground observation and high-altitude detection stations are sparse, and due to the influence of surface thermal differences, the local differences in precipitation are relatively large. [0003] Image segmentation is a classic image processing problem. Its purpose is to divide an image into several meaningful regions, each of which has similar characteristics and meanings, but because it deals with the complexity of objects and the diversity of targets, It has always been a difficult and hot i...

AI Technical Summary

Problems solved by technology

[0006] To sum up, the problems existing in the existing technology are: the current ground observation and high-altitude detection stations are sparse, affected by the difference in surface thermal force, the local differences in precipitation are relatively large; the data of precipitation estimation still have large deviations; the existing In the image segmentation method, efficient segmentation cannot be performed in the presence of noise, which reduces the complexity of the process of solving parameters; and cannot improve the robustness of image segmentation and improve the quality of image segmentation

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