Abstract
Passive microwave radiative transfer models are strongly influenced by the cloud and precipitation hydrometeor properties. Particularly, they can sensitively interact with frozen hydrometeors through multiple high-frequency channels. However, frozen hydrometeors are one of the most difficult parameters to comprehend due to the lack of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> data. Until recently, studies have attempted to describe more reasonable hydrometeor distributions using various microphysics parameterizations coupled with the weather research and forecasting (WRF) models. Herein, we aim to apply the proposed methods to passive microwave radiative transfer simulations. We implemented a passive microwave radiative transfer simulation that considers various microphysical assumptions by creating a new Mie scattering lookup table. Furthermore, we evaluated the bulk microphysics parameterizations [WDM6, Morrison (MORR), Thompson (THOM), and P3 schemes] for the tropical cyclone Krosa (2019) that were observed by the global precipitation measurement microwave imager instrument, specifically concentrating on the rimed and aggregated ice categories (snow, graupel, and P3 ice). Based on the evaluation results, we concluded the following: WDM6 graupel and MORR snow afford excessive scattering signals at 37 GHz. However, at 166 GHz, none of the parameterizations produces sufficient scattering signals for comparison with the observations. The P3 ice affords significantly underestimated scattering signals at 89 GHz and above, despite its sophisticated assumptions. On the contrary, THOM snow affords scattering signals similar to the observations, despite a shape-related error. In summary, this study introduced a method for implementing a microphysical-consistent radiative transfer computation and successfully showed how various microphysical assumptions of clouds can change the passive microwave radiative signatures.
Highlights
N UMERICAL weather prediction (NWP) models play an important role in the perception of the precipitation structures and processes, especially in areas where in-situ observation data are rare
The bulk schemes have been improved by adjusting the microphysical assumptions and adding other prognostic variables
This study aims to evaluate various microphysical assumptions, such as single-moment scheme, double-moment scheme, and unfixed density, using the microphysical-consistent passive microwave radiative transfer models (RTMs)
Summary
N UMERICAL weather prediction (NWP) models play an important role in the perception of the precipitation structures and processes, especially in areas where in-situ observation data are rare. Precipitation processes on a grid scale using microphysics parameterization, which can be classified into two different approaches: spectral (bin) microphysics and bulk microphysics scheme (hereinafter, bulk scheme). The former solves explicit microphysical equations to calculate the number concentration of hydrometeors on a finite-difference diameter bin, whereas the latter approximates the particle size distributions (PSDs) as a function of exponential, gamma, or lognormal distributions. The bulk schemes have been improved by adjusting the microphysical assumptions (e.g., shape, density, and PSD) and adding other prognostic variables. These approaches have been numerically validated in various ways over the years, bulk schemes are still one of the most uncertain physical processes in NWP models. We evaluate the microphysical assumptions of various bulk schemes using microwave remote sensing observations and a signal-based evaluation
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