The Annals of Applied Statistics

A spatio-temporal modeling framework for weather radar image data in tropical Southeast Asia

Xiao Liu, Vikneswaran Gopal, and Jayant Kalagnanam

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Tropical storms are known to be highly chaotic and extremely difficult to predict. In tropical countries such as Singapore, the official lead time for the warnings of heavy storms is usually between 15 and 45 minutes because weather systems develop quickly and are of very short lifespan. A single thunderstorm cell, for example, typically lives for less than an hour. Weather radar echoes, correlated in both space and time, provide a rich source of information for short-term precipitation nowcasting. Based on a large dataset of 276 tropical storms events, this paper investigates a spatio-temporal modeling approach for two-dimensional radar reflectivity (echo) fields. Under a Lagrangian integration scheme, we model the radar reflectivity field by a spatio-temporal conditional autoregressive process with two components. The first component is the dynamic velocity field which determines the motion of the storm, and the second component governs the growth or decay of the returned radar echoes. The proposed method is demonstrated and compared with existing methods using real radar image data collected from a number of 276 tropical storm events from 2010 to 2011 in Singapore. The numerical comparison results show the advantage of the proposed method, in terms of the mean-squared-error, in modeling small-scale localized convective weather systems based on the 77 inter-monsoon season thunderstorm events.

Article information

Ann. Appl. Stat. Volume 12, Number 1 (2018), 378-407.

Received: August 2016
Revised: May 2017
First available in Project Euclid: 9 March 2018

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Spatial-temporal modeling precipitation forecast space-time conditional autoregressive model radar image analysis


Liu, Xiao; Gopal, Vikneswaran; Kalagnanam, Jayant. A spatio-temporal modeling framework for weather radar image data in tropical Southeast Asia. Ann. Appl. Stat. 12 (2018), no. 1, 378--407. doi:10.1214/17-AOAS1064.

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