A Moment-Based Polarimetric Radar Forward Operator for Rain Microphysics

Abstract

There is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model–observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of >200 million realistic DSDs from one-dimensional bin microphysical rain-shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of “truth” and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the sixth and ninth) perform better than the lower-order moments (zeroth and third) typically predicted by many bulk microphysics schemes.

A Bayesian Approach for Statistical–Physical Bulk Parameterization of Rain Microphysics. Part II: Idealized Markov Chain Monte Carlo Experiments

“…We expect that a single rainshaft case will be insufficient to constrain BOSS, as not all microphysical processes are likely to exert a dominant and observable influence on evolution of the DSD in a single case (Kumjian and Prat 2014). To determine the number of cases sufficient to constrain BOSS, we use the criterion that independent choices of column model boundary and thermodynamic conditions should produce equivalent estimates of BOSS parameters.…”

“…A crucial goal of BOSS is that process-level understanding is gained via observational constraint-the statistical-physical approach we have chosen renders all BOSS parameters physically interpretable, and in the ideal limit of a perfect model and comprehensive observations, we would expect that the representation of microphysical processes within BOSS would approach the ''true'' process rates of nature. In this sense, constraint of BOSS can be viewed in the context of microphysical ''fingerprinting'' (Kumjian and Prat 2014;Moiseev et al 2015Moiseev et al , 2017 or microphysical process retrievals (Williams 2016;Tridon et al 2017), with the additional benefit of robust estimation of uncertainties arising from a priori assumptions and observational uncertainties.…”

Retrieval of lower-order moments of the drop size distribution using CSU-CHILL X-band polarimetric radar: a case study

“…Whereas RBb used measured DSDs and the polarimetric radar forward operator to derive the retrieval algorithms for M 3 and M 6 , there has been a reverse moment-based polarimetric forward operator (Kumjian et al, 2019). This reverse approach employs a very large database of measured and binresolved one-dimensional (1-D) model output DSDs to build a look-up table that maps the various moment pairs to the expected values of Z h , Z dr , and K dp along with their standard deviations.…”

Evaluating the Performance of a Convection-Permitting Model by Using Dual-Polarimetric Radar Parameters: Case Study of SoWMEX IOP8

“…However, the bin model microphysics scheme was too complex and thus the computational cost was immense. In another study [29], a moment-based warm rain observation operator was designed. The relationship between the moments and polarimetric parameters was established on the basis of more than 2 million disdrometer data collected globally.…”