Precipitation Nowcasting Based on Convolutional LSTM with Spatio-Temporal Information Transformation Using Multi-Meteorological Factors

Abstract. Precipitation nowcasting is vital for protecting lives and economic activities, yet accurate forecasts based solely on past precipitation remain elusive. Conventional numerical weather prediction (NWP) models offer a solution but incur substantial computational costs. Moreover, due to the rapid pace of climate change, long-term time series data are often inadequate for accurately addressing precipitation forecasting for extreme weather events in a short period of time, as past meteorological time series data may not accurately reflect current atmospheric conditions. There is an urgent need to rely on short-term time series for prediction tasks. Existing studies have employed Spatio-Temporal Information Transformation(STI) equations with iterative solutions for short-term time series prediction. However, the solution process involves relatively simple nonlinear operations, which are prone to cumulative errors and can result in inaccurate forecasts. In response, the present work proposes a dual encoder-decoder training framework based on the STI equation and the idea of dual learning, which can map multidimensional spatial features to the temporal prediction of future precipitation variables. This architecture addresses the limitations of inaccurate predictions for short-term time series data. Additionally, an adaptive weighted gradient loss (ADGLoss) is proposed to mitigate the prediction ambiguity caused by the extension of prediction time and rectify systematic underestimation of high-intensity precipitation regions. Leveraging the U.S.-based SEVIR dataset, the proposed model integrates multiple meteorological variables to generate 1-hour precipitation forecasts. Experimental results demonstrate that the STI-driven framework achieves superior predictive accuracy and reduced error rates in multi-step forecasting compared to state-of-the-art deep learning benchmarks. The model effectively captures the spatio-temporal dependencies between heterogeneous meteorological variables and precipitation patterns, offering a novel pathway for advancing spatio-temporal prediction tasks in climate informatics.