Abstract
This paper proposes an inverse model for raindrop size distribution (DSD) retrieval with polarimetric radar variables. In this method, a forward operator is first developed based on the simulations of monodisperse raindrops using a T-matrix method, and then approximated with a polynomial function to generate a pseudo training dataset by considering the maximum drop diameter in a truncated Gamma model for DSD. With the pseudo training data, a nearest-neighborhood method is optimized in terms of mass-weighted diameter and liquid water content. Finally, the inverse model is evaluated with simulated and real radar data, both of which yield better agreement with disdrometer observations compared to the existing Bayesian approach. In addition, the rainfall rate derived from the DSD by the inverse model is also improved when compared to the methods using the power-law relations.
Highlights
The raindrop size distribution (DSD) is one of the most important characteristics of a precipitating process, since the raindrop formation and its size evolution are related to the mechanism of cloud microphysics, kinetics, and thermodynamics
This study proposes a statistical approach to estimating the parameters of a truncated Gamma model for DSD with the polarimetric radar variables, called an inverse model
This study analyzed the polarimetric radar measurements collected by the Generation Weather Radar (NEXRAD) system located near Oklahoma City (35.333◦N, 97.278◦W), United States
Summary
The raindrop size distribution (DSD) is one of the most important characteristics of a precipitating process, since the raindrop formation and its size evolution are related to the mechanism of cloud microphysics, kinetics, and thermodynamics. This approach designs a forward operator to calculate the polarimetric variables for a given DSD by assuming the shape and orientation of raindrops, ambient temperature, and radar wavelength It approximates the forward operator with a polynomial function to generate a pseudo training dataset, which is inversely used to obtain the relation between the DSD and polarimetric variables. It adopts a non-parametric estimator based on a nearest-neighborhood method to retrieve the parameters within the truncated Gamma model.
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