Variability of Microwave Scattering in a Stochastic Ensemble of Measured Rain Drops

Francisco Tapiador,Enrique Arias,Alfonso Jiménez,Diego Cazorla,Raúl Moreno,Andrés Navarro

doi:10.3390/rs10060960

Abstract

While it has been proved that multiple scattering in the microwave frequencies has to be accounted for in precipitation retrieval algorithms, the effects of the random arrangements of drops in space has seldom been investigated. The fact is, a single rain drop size distribution (RDSD) corresponds with many actual 3D distributions of those rain drops and each of those may a priori absorb and scatter radiation in a different way. Each spatial configuration is equivalent to any other in terms of the RDSD function, but not in terms of radiometric characteristics, both near and far from field, because of changes in the relative phases among the particles. Here, using the T-matrix formalism, we investigate the radiometric variability of two ensembles of 50 different 3D, stochastically-derived configurations from two consecutive measured RDSDs with 30 and 31 drops, respectively. The results show that the random distribution of drops in space has a measurable but apparently small effect in the scattering calculations with the exception of the asymmetry factor.

Highlights

The retrieval of precipitation with radars and radiometers is important for a variety of environmental applications and human activities [1]
They provide accurate precipitation estimates that are crucial for monitoring extreme climate events, such as droughts [2,3,4], floods [5,6,7], and hailstorms [8,9]
Due to its global coverage and direct measurement, radars have become an essential tool to estimate precipitation, especially in complex terrains [10,11,12] and in sparsely populated areas affected by poor rain gauge coverage [13,14,15]

Summary

Introduction

The retrieval of precipitation with radars and radiometers is important for a variety of environmental applications and human activities [1]. They provide accurate precipitation estimates that are crucial for monitoring extreme climate events, such as droughts [2,3,4], floods [5,6,7], and hailstorms [8,9]. Regardless of how powerful radars and radiometers are, they are not immune to retrieval biases One such bias is the effect of multiple scattering in high-frequency radars [16,17]. It has been proved that multiple scattering in the microwave frequencies has to be accounted for in precipitation retrieval algorithms [18,19,20,21,22,23,24,25,26,27,28]

Objectives

Methods

Conclusion