Research on Machine Learning-Based Multi-source Precipitation Data Fusion

Abstract

With the development of artificial intelligence (AI) in recent years, meteorological departments have also begun to improve algorithms and revise short-term forecasts via AI, expecting to timely capture meteorological clues in massive weather data, to "prevent meteorological disasters", and "calculate precipitation faster and more accurately". At present, AI has been initially applied to the meteorological field, especially to the analysis of massive meteorological data. For instance, the AI-based data analysis technology can rapidly judge the cloud type and the meteorological prototype in satellite images. The AI-based data fusion technology contributes to more three-dimensional and refined atmosphere data, which improves the temporal and spatial resolutions of precipitation data. If the big data in AI are used to analyze typhoons and identify the typhoon track and source, the errors resulting from the naked-eye observation of images by meteorologists can be avoided, thus considerably improving the scientificity and accuracy of weather forecasts. During data fusion, the severe convective weather characteristics reflected by massive historical precipitation data can be learned through machine learning methods to predict the evolution trend of disastrous weather within the future 1 to 2 h. Furthermore, precipitation data errors are corrected through AI data analysis, and a daily precipitation fusion dataset with a spatial resolution of 1 km is obtained.