Delineation of Rain Areas with TRMM Microwave Observations Based on PNN

Shiguang Xu,Alemu Gonsamo,Chaoyang Wu,Yan Shen

doi:10.3390/rs61212118

Abstract

False alarm and misdetected precipitation are prominent drawbacks of high-resolution satellite precipitation datasets, and they usually lead to serious uncertainty in hydrological and meteorological applications. In order to provide accurate rain area delineation for retrieving high-resolution precipitation datasets using satellite microwave observations, a probabilistic neural network (PNN)-based rain area delineation method was developed with rain gauge observations over the Yangtze River Basin and three parameters, including polarization corrected temperature at 85 GHz, difference of brightness temperature at vertically polarized 37 and 19 GHz channels (termed as TB37V and TB19V, respectively) and the sum of TB37V and TB19V derived from the observations of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The PNN method was validated with independent samples, and the performance of this method was compared with dynamic cluster K-means method, TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product and the threshold method used in the Scatter Index (SI), a widely used microwave-based precipitation retrieval algorithm. Independent validation indicated that the PNN method can provide more reasonable rain areas than the other three methods. Furthermore, the precipitation volumes estimated by the SI algorithm were significantly improved by substituting the PNN method for the threshold method in the traditional SI algorithm. This study suggests that PNN is a promising way to obtain reasonable rain areas with satellite observations, and the development of an accurate rain area delineation method deserves more attention for improving the accuracy of satellite precipitation datasets.

Highlights

Precipitation has great significance in the study of ecology, hydrology and meteorology [1,2], but it is still a great challenge to acquire the spatial and temporal distribution of precipitation over many developing countries and mountainous regions, due to the sparse rain gauge network
We proposed a new rain area delineation method based on probabilistic neural network (PNN)
Independent validation indicated that the false alarm precipitation of PNN is 30% and 55% less than that generated by the Tropical Rainfall Measuring Mission (TRMM) 2A12 satellite precipitation dataset and Kmean method, while the misdetected precipitation of PNN is 19% and 26% less than them

Summary

Introduction

Precipitation has great significance in the study of ecology, hydrology and meteorology [1,2], but it is still a great challenge to acquire the spatial and temporal distribution of precipitation over many developing countries and mountainous regions, due to the sparse rain gauge network. Tian et al [13] separated the bias of high-resolution satellite precipitation datasets into three independent components: misdetected bias (rain areas that were incorrectly determined as no rain areas by satellite precipitation datasets), false alarm bias (no rain areas that were incorrectly determined as rain areas by satellite precipitation datasets) and hit bias (the rain areas correctly determined by satellite precipitation datasets, but for which the precipitation volumes were inaccurately estimated). They indicated that the false alarm precipitation was a leading error source in many high-resolution satellite precipitation datasets. Gebregiorgis and Hossain [15] pointed out that the uncertainty in the Climate Prediction

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Discussion

Conclusion