Value weather forecast and artificial intelligence (AI) coupling predication method for wind speed extremum of costal typhoon

Abstract

The invention discloses a value weather forecast and AI coupling predication method for wind speed extremum of a costal typhoon. According to the prediction method, a value weather forecast based model is established, a deep neural network is trained according to practically measured data as well as the value weather forecast based model to obtain a deep neural network model after training, a meso-scale meteorological value mode simulation method is used to carry out dowscaling calculation again, wind speed and friction wind speed in further 24h are predicted, and an optimal peak factor is combined to obtain the wind speed extremum in the further 24h. Analysis data in the meso-scale value mode and subjective geographical information serve as input, the complex physical process of taking regard of is taken into integrated consideration, the average wind speed , the root mean square wind speed and the like of a specific target position with the practical physical meanings are predicted in a simulated way by solving an atmospheric movement equation, and the disadvantage that statistical prediction and AR predication methods rely on mathematical statistical methods completely is overcome.

Description

technical field [0001] The invention belongs to the field of meteorological forecasting, and in particular relates to a numerical weather forecasting-artificial intelligence coupled forecasting method for coastal typhoon extreme wind speed. Background technique [0002] In coastal typhoon-prone areas, wind disasters are an important factor that threatens the safety of buildings. In order to reduce the losses caused by wind disasters, it is of great significance to predict the wind speed and prepare in advance before the arrival of strong winds. However, the wind speed is often very random, especially in the case of a typhoon, the wind speed and direction of the typhoon are uncertain, and the instability of the wind speed is particularly significant, which brings great challenges to the advanced prediction of the wind speed and its extreme value . [0003] At present, wind speed prediction methods can be roughly divided into the following four categories: (1) numerical weath...

AI Technical Summary

Problems solved by technology

However, the current wind speed prediction is often corrected for the average wind speed output by the numerical weather forecast. Therefore, it is impossible to effectively predict the occurrence of extreme wind speed in the future. For example, CA 106971032A discloses a combination of non-parametric kernel density estimation and numerical weather forecast The wind speed prediction method, combined with the Chebyshev neural network model to correct the predicted wind speed of the numerical weather forecast, can only make corresponding predictions for the future average wind speed

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