Estimation of Precipitation Area Using S-Band Dual-Polarization Radar Measurements

Abstract

Estimating precipitation area is important for weather forecasting as well as real-time application. This paper aims to develop an analytical framework for efficient precipitation area estimation using S-band dual-polarization radar measurements. Several types of factors, such as types of sensors, thresholds, and models, are considered and compared to form a data set. After building the appropriate data set, this paper yields a rigorous comparison of classification methods in statistical (logistic regression and linear discriminant analysis) and machine learning (decision tree, support vector machine, and random forest). To achieve better performance, spatial classification is considered by incorporating latitude and longitude of observation location into classification, compared with non-spatial classification. The data used in this study were collected by rain detector and present weather sensor in a network of automated weather systems (AWS), and an S-band dual-polarimetric weather radar during ten different rainfall events of varying lengths. The mean squared prediction error (MSPE) from leave-one-out cross validation (LOOCV) is computed to assess the performance of the methods. Of the methods, the decision tree and random forest methods result in the lowest MSPE, and spatial classification outperforms non-spatial classification. Particularly, machine-learning-based spatial classification methods accurately estimate the precipitation area in the northern areas of the study region.

Keywords

• classification
• machine learning
• statistical learning
• precipitation area estimation
• spatial classification