Spatial--temporal mesoscale modeling of rainfall intensity using gage and radar data

TL;DR

This paper presents a framework combining radar and gage data using spatial logistic regression to accurately estimate high-resolution rainfall intensity, addressing uncertainties and biases in both data sources.

Contribution

It introduces a novel spatial-temporal modeling approach that integrates radar and gage data through a latent process to improve rainfall estimation accuracy.

Findings

Effective integration of radar and gage data improves rainfall estimates.

The model accounts for biases and errors in both data sources.

Enhanced rainfall prediction accuracy for weather and hydrological models.

Abstract

Gridded estimated rainfall intensity values at very high spatial and temporal resolution levels are needed as main inputs for weather prediction models to obtain accurate precipitation forecasts, and to verify the performance of precipitation forecast models. These gridded rainfall fields are also the main driver for hydrological models that forecast flash floods, and they are essential for disaster prediction associated with heavy rain. Rainfall information can be obtained from rain gages that provide relatively accurate estimates of the actual rainfall values at point-referenced locations, but they do not characterize well enough the spatial and temporal structure of the rainfall fields. Doppler radar data offer better spatial and temporal coverage, but Doppler radar measures effective radar reflectivity ($Ze$) rather than rainfall rate ($R$). Thus, rainfall estimates from radar data suffer from various uncertainties due to their measuring principle and the conversion from $Ze$ to $R$. We introduce a framework to combine radar reflectivity and gage data, by writing the different sources of rainfall information in terms of an underlying unobservable spatial temporal process with the true rainfall values. We use spatial logistic regression to model the probability of rain for both sources of data in terms of the latent true rainfall process. We characterize the different sources of bias and error in the gage and radar data and we estimate the true rainfall intensity with its posterior predictive distribution, conditioning on the observed data.