AI-based intelligent virtual image meteorological services

Abstract

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. Therefore, this article aims to designing 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 values of the actual daily average water vapor pressure in the 10 days of San Jose are 30.2 and 28.1 Pa, respectively, 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, and the maximum and minimum values of the actual daily average vapor pressure over 10 days of Santa Clara are 30.5 and 28.4 Pa, respectively, which can prove the effectiveness of AI-based intelligent virtual imaging meteorological services.