A Spatio-Temporal Modeling Approach for Weather Radar Reflectivity Data and Its Applications in Tropical Southeast Asia

TL;DR

This paper introduces a novel spatio-temporal autoregressive model for radar reflectivity data, capturing dynamic weather system motion and reflectivity changes, to improve short-term precipitation forecasting in tropical Southeast Asia.

Contribution

It develops a new spatio-temporal conditional autoregressive model driven by hidden processes for dynamic weather system modeling and short-term precipitation prediction.

Findings

Model effectively captures radar reflectivity dynamics.

Demonstrates improved short-term prediction accuracy.

Validated with real tropical Southeast Asia radar data.

Abstract

Weather radar echoes, correlated in both space and time, are the most important input data for short-term precipitation forecast. Motivated by real datasets, this paper is concerned with the spatio-temporal modeling of two-dimensional radar reflectivity fields from a sequence of radar images. Under a Lagrangian integration scheme, we model the radar reflectivity data by a spatio-temporal conditional autoregressive process which is driven by two hidden sub-processes. The first sub-process is the dynamic velocity field which determines the motion of the weather system, while the second sub-process governs the growth or decay of the strength of radar reflectivity. The proposed method is demonstrated, and compared with existing methods, using the real radar data collected from the tropical southeast Asia. Note that, since the tropical storms are known to be highly chaotic and extremely difficult to be predicted, we only focus on the modeling of reflectivity data within a short-period of time and consider the short-term prediction problem based on the proposed model. This is often referred to as the nowcasting issue in the meteorology society.