Strong convection weather duration forecasting method based on integrated learning

Abstract

The invention discloses a strong convection weather duration forecasting method based on integrated learning. The method comprises the steps of S1, selecting a data source, wherein ground meteorological station data of a forecast area and two sounding station data closest to the forecast area are selected; S2, carrying out preprocessing data, wherein errors and missing data are eliminated, the duration of each strong convection weather is selected as output according to the calculated relevant strong convection forecast parameters as input, and the time is considered as 0 when no strong convection weather occurs on the current day, and normalization processing is performed on the forecast parameters, namely the input; and S3, performing selection of machine learning algorithms, wherein a Knearest neighbor algorithm, a polynomial regression algorithm, a decision tree algorithm and a neural network algorithm are selected. According to the method disclosed by the invention, various meteorological elements of the current day of the strong convection weather are mainly used for speculation of the possible duration of the strong convection weather, and through a multi-machine learning algorithm comparison strategy, the target task is trained and tested, and the optimal learning algorithm is selected and used in an actual forecasting task.

Description

technical field [0001] The invention relates to the technical field of weather forecasting, in particular to a method for forecasting duration of severe convective weather based on integrated learning. Background technique [0002] Weather forecasting (measurement) or weather forecasting (measurement) is the use of modern science and technology to predict the state of the earth's atmosphere at a certain location in the future. Since prehistoric humans have already begun to predict the weather to arrange their work and life accordingly (such as agricultural production, military operations, etc.). Weather forecasting today primarily uses the collection of large amounts of data (temperature, humidity, wind direction and speed, air pressure, etc.) and then uses current understanding of atmospheric processes (meteorology) to determine future air changes. Due to the confusion of atmospheric processes and the incomplete understanding of atmospheric processes by science today, ther...

AI Technical Summary

Problems solved by technology

This single algorithm forecast model cannot reflect the dynamic change characteristics of the data, resulting in generally poor forecast stability

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