Rainstorm forecasting method based on PP theory and AF model

Abstract

The invention provides a rainstorm forecasting method based on a PP theory and an AF model. The rainstorm forecasting method comprises the following specific steps: 1, collecting and sorting data; 2, preprocessing a numerical mode forecasting product; 3, traversing historical similar weather situations; 4, qualitatively forecasting the rainstorm; 5, traversing historical similar weather situations of the rainstorm; and 6, quantitatively forecasting the rainstorm. According to the rainstorm forecasting method based on the PP theory and the AF model, historical reanalysis data and current numerical mode forecasting data are comprehensively utilized, and the rainstorm in a target area can be accurately forecasted by traversing a series of historical weathers similar to the current weather.

Description

technical field [0001] The invention belongs to the field of statistical forecasting, and mainly relates to an objective forecasting method of rainstorm volume and rainstorm probability based on Perfect Prog (PP for short) theory and Analog Forecast (AF for short) model, especially related to the method based on PP theory and AF model rainstorm forecasting method. Background technique [0002] my country is a country with frequent torrential rain (daily rainfall ≥ 50 mm). Heavy rain is one of the most important disastrous weathers in our country. It often induces floods, mudslides, landslides and other natural disasters, which can cause huge harm to the national economy and cause serious losses to people's lives and property. For example, on July 21-22, 2012, Beijing encountered the strongest rainstorm since the founding of the People's Republic of China (the city's average rainfall exceeded 150 mm, and the local maximum rainfall reached 460 mm) and the induced flood disast...

AI Technical Summary

Problems solved by technology

At present, although the forecasting ability of numerical models for large-scale systematic precipitation has been significantly improved, due to the suddenness, locality, and complexity of rainstorms, the forecast level of rainstorms is far lower than that of light rain and moderate rain. The accuracy of rainstorm forecast is still far from the actual needs of disaster prevention and mitigation

This bottleneck is mainly caused by the low temporal and spatial resolution of the numerical model and the imperfect physical process of the model, which makes it impossible to achieve fixed-point, timing, and quantitative fine-grained rainstorm forecasting.

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