Delineation of Rain Areas with TRMM Microwave Observations Based on PNN

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. OPEN ACCESSRemote Sens. 2014, 6 12119 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.

Intelligent processing of electromagnetic data using detrended and identification

“…The PNN is a kind of neural network commonly used for pattern identification [35,36]. The PNN is a neural network model based on statistical principle.…”

Classificação de estratos florestais utilizando redes neurais artificiais e dados de sensoriamento remoto

“…Utilizou-se neste estudo a rede neural probabilística "probabilistic neural network" (PNN), proposta por Specht (1990), adequada para problemas de classificação. Ela é estruturada em três camadas: a camada de entrada, a camada de base radial e a camada competitiva (Xu et al, 2014). A arquitetura da rede neural probabilística é apresentada na figura 3, onde: X, H e Y representam, respectivamente, as variáveis de entrada, os neurônios da camada oculta e as variáveis de saída da rede PNN e k, m e n representam, respectivamente, o número de entradas, neurônios da camada oculta e saídas.…”