Simulation of Microwave Brightness Temperatures of an Evolving Hailstorm at SSM/I Frequencies

Abstract

A simulation of the appearance of an intense hailstorm in the passive microwave spectrum is conducted in order to characterize the vertical sources of radiation that contribute to the top-of-atmosphere microwave brightness temperatures (Te) which can be measured by satellite-borne radiometers. The study focuses on four frequencies corresponding to those used on the USAF Special Sensor Microwave Imager (SSM/I), a recently launched payload flown on the U.S. Air Force DMSP satellites. Computation of the microwave brightness temperatures is based on a vertically, angularly, and spectrally detailed radiative transfer scheme that has been applied to the highly resolved thermodynamical and microphysical output from the three-dimensional Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS). The RAMS model was used to carry out a 4-h simulation of an intense hailstorm that occurred on 11 July 1986 in the vicinity of Eldridge, Alabama. Initial conditions for the cloud model run were developed from the 1986-COHMEX data archive. Two types of vertically resolved radiative structure functions referred to as a "generalized weighting function" and an "emission source weighting function" are used to describe the process by which radiation originates and reaches the satellite radiometer. In addition, these weighting functions are subdivided into individual contributions by the various hydrometeor species generated by the cloud model. Along with the surface contribution and cosmic background radiation, these weighting functions provide a normalized description of where radiation originates and how it ultimately reaches the satellite. It is emphasized that this information provides an indepth understanding of how precipitation retrieval algorithms should be designed visa -vis the passive microwave problem.

The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies

“…Both infrared (IR) and microwave (MW) emissions are used for precipitation retrievals. While IR estimates of rainfall are only indirect because IR measurements are sensitive only to the uppermost layers of clouds, MW observations have the great advantage of providing a more direct measurement of the precipitation due to the ability of MW radiation to penetrate precipitating clouds and interact with its liquid and ice hydrometeors (e.g., Mugnai et al, 1990;Wilheit et al, 1994;Weng and Grody, 2000;Bennartz and Petty, 2001;Bauer et al, 2005). Passive microwave (PMW) techniques for the estimation of precipitation have seen great advances over the past years, due largely to the increased number of radiometers available, with improved sensing capabilities (i.e., higher spatial resolution, number of available channels useful for precipitation retrieval) and due to the several theoretical studies on microwave radiative transfer modeling through precipitating clouds (e.g., Mugnai et al, 1993;Wilheit et al, 1994;Smith et al, 1998Smith et al, , 2002Stephens and Kummerow, 2007;Skofronick-Jackson and Johnson, 2011).…”

Global Precipitation Map Using Satellite-Borne Microwave Radiometers by the GSMaP Project: Production and Validation

“…Using the numerical-model simulation, Mugnai et al [58] investigated vertical sources of radiation that contribute to the top-of-atmosphere Tbs in terms of the four frequencies corresponding to the SSM/I. They demonstrated that the 85-GHz signals were emissions from the upper level liquid and ice scattering in the upper reaches of tall precipitation clouds.…”

Radar, Passive Microwave, and Lightning Characteristics of Precipitating Systems in the Tropics

“…In any given storm, these values may not be coincident. However, based on theory (Wilheit et al 1982;Mugnai et al 1990;Adler et al 1991;Vivekanandan et al 1991) and observations (Spencer 1986;Hakkarinen and Adler 1988;Rutledge and MacGorman 1988;Fulton 1988, 1994;Fulton and Heymsfield 1991;Keighton et al 1991;Toracinta et al 1996;Mohr et al 1996;Petersen et al 1996Petersen et al , 1999Carey and Rutledge 2000), including detailed examination of hundreds of individual precipitation features from this dataset, we are making an implicit assumption that the relationships between them are sufficiently robust for describing and comparing the relative strengths of the strongest convective events within convective systems and the relationship to lightning. Table 1 shows that precipitation features occur with greater frequency in the tropical ocean regions than in the tropical land regions even when normalized by the respective region areas.…”