Spatiotemporal Information Transformation for Precipitation Nowcasting Using Multi-Meteorological Factors

Precipitation nowcasting, which entails high-resolution forecasting of precipitation events within 1–2 hours, is significant to daily life and professional activities. Nevertheless, accurate short-term precipitation forecasting remains a considerable challenge at present. Traditional numerical weather prediction, which relies on intricate physical equations to simulate the Earth's atmospheric state, necessitates substantial computational resources and frequently yields lower accuracy for small-scale forecasts, thereby failing to meet the demands of precipitation prediction in complex regions. Most deep learning methodologies concentrate exclusively on the spatiotemporal prediction of a singular precipitation variable, thereby neglecting the dynamic spatiotemporal relationships between precipitation and other meteorological data within the meteorological system. Moreover, due to the rapid pace of climate change, long-term time series data is often inadequate for accurately addressing precipitation forecasting for extreme weather events, since past meteorological time series data may not accurately reflect the current atmospheric conditions. There is an urgent need to rely on short-term time series for prediction tasks. However, most current methods that rely on short-term time series for prediction perform poorly in forecasting moderate to heavy precipitation events. Inspired by spatiotemporal information transformation schemes, we introduce a spatiotemporal information(STI) transformation equation from chaotic dynamics into the field of computer vision and develop a neural network model framework based on spatiotemporal information transformation. This framework maps high-dimensional spatial information to the temporal information of future precipitation information, thereby facilitating the integration of dynamic spatiotemporal relationships between various meteorological data and precipitation, and enabling the mutual transformation of spatiotemporal information for enhanced forecasting accuracy. Furthermore, we propose an adaptive gradient loss function designed to improve the model's sensitivity to learning moderate-intensity precipitation. This research utilizes the US SEVIR dataset for training and testing, which encompasses data such as satellite visible light, infrared temperature, humidity, and cloud precipitation while employing multiple meteorological data for precipitation forecasting over the subsequent hour. We selected the Structural Similarity Index, Peak Signal-to-Noise Ratio, False Alarm Rate, Critical Success Index, and Heidke Skill Score as both quantitative and qualitative evaluation metrics. Experimental results demonstrate that the STI framework reduces the model's error in moderate to heavy precipitation events, making the model more sensitive to severe rainfall events. Furthermore, when the STI framework is integrated into other deep learning models and retrained, it further enhances their precipitation prediction accuracy. This finding indicates that the STI framework effectively captures the dynamic spatiotemporal relationships between various meteorological and precipitation data.