Analysis of the spatiotemporal velocity of annual precipitation based on random field

Chenlong Li and Yang Hu

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 4, 1232-1249

Abstract: Changes in precipitation directly impact river runoff volume, subsequently influencing food production, and the security of downstream urban areas. In this study, we introduce a random velocity field (RVF) capable of performing multi-step predictions while providing interpretable insights into precipitation variations. The RVF leverages the gradient of a Gaussian random field to learn spatiotemporal velocity patterns and employs a predictive process to reduce dimensionality and enable multi-step forecasting. Bayesian parameter estimation is obtained using the Markov Chain Monte Carlo (MCMC) method. Our analysis reveals a noticeable shifting trend in annual precipitation based on diverse real datasets. This trend serves as a valuable foundation for further exploration of urban flood control and agricultural development strategies.

Date: 2025
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DOI: 10.1080/03610926.2024.2330678

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