Disentangling Spatial‐Temporal Features for Controllable Factors Learning in Precipitation Nowcasting

Abstract

Precipitation nowcasting refers to the high‐resolution forecasting of rainfall and hydrometeors within 0–6 hr according to the official definition of the World Meteorological Organization, which has relied on numerical models for decades. Recently, artificial intelligence (AI) has shown promise in addressing precipitation nowcasting. However, three key scientific issues have not been studied: (a) the researcher does not know what semantic knowledge the AI model has learned in the high‐dimensional space that influences precipitation forecasts. (b) If the learned semantic knowledge can be quantified, which aspects of precipitation does each factor control? And (c) can the semantic knowledge contribute to improving the accuracy of precipitation nowcasting? Hence, this study proposes a human supervised spatial‐temporal disentanglement model (STNet) that perturbs high‐dimensional vectors in the latent space and enforces orthogonality constraints to disentangle the spatial‐temporal semantic knowledge learned by the model—referred to as disentangled latent factors (a set of orthogonal high‐dimensional vectors). Qualitative and quantitative experiments on the specialized nowcasting dataset (SEVIR) reveal that the disentangled latent factors primarily fall into two categories: temporal factors, which predominantly control the evolving precipitation pattern (dynamics), and spatial factors, which mainly regulate precipitation intensity (static). Specifically, the temporal factor constraint provides more significant improvements in precipitation area forecasting. The spatial factor constraint, on the other hand, focuses on optimizing precipitation intensity.