AI-based intelligent virtual image meteorological services

Abstract

Although modern meteorological service and prediction systems have achieved good applications in numerical models, these models are often influenced by multiple random factors and cannot adapt well to the meteorological service and prediction needs of complex climate regions. With the continuous development and maturity of artificial intelligence (AI) algorithms represented by neural networks, meteorological departments are attempting to replace or compensate for traditional numerical models with statistical methods. This article designed an AI intelligent meteorological service platform to simulate what happens when people use it. Based on commonly used AI technologies in meteorological research, the temporal data algorithm mentioned in this article is used for prediction. This article selected the actual daily average water vapor pressure and daily average relative humidity data of three meteorological stations over the past 10 days for analysis. The maximum and minimum of the actual daily average vapor pressure for 10 days are 30.2 and 28.1 Pa, respectively, the predicted maximum and minimum values are of 30 and 28 Pa, respectively, and the maximum and minimum values of the actual daily average vapor pressure over 10 days of Cupertino are 30.4 and 28.4 Pa, respectively, which can prove the effectiveness of AI-based intelligent virtual imaging meteorological services. This article simulated the AI intelligent meteorological service platform and used temporal data algorithms for prediction.