Daily Precipitation Prediction Based on Multimodal Representation

Short-term weather forecasting is of great significance to people's lives, especially in terms of transportation. Since daily precipitation can be regarded as a nonlinear and non-stationary time series, it is difficult to predict it. Based on the contrastive learning method, A multimodal representation method for forecasting daily precipitation is proposed. Specifically, an encoder is used to convert the original weather data into feature representation, then it is optimized through contrastive learning, and the optimized features are used for prediction.