Abstract
The utilization of polarimetric weather radars for optimizing cloud models is a next frontier of research. It is widely understood that inadequacies in microphysical parameterization schemes in numerical weather prediction (NWP) models is a primary cause of forecast uncertainties. Due to its ability to distinguish between hydrometeors with different microphysical habits and to identify “polarimetric fingerprints” of various microphysical processes, polarimetric radar emerges as a primary source of needed information. There are two approaches to leverage this information for NWP models: (1) radar microphysical and thermodynamic retrievals and (2) forward radar operators for converting the model outputs into the fields of polarimetric radar variables. In this paper, we will provide an overview of both. Polarimetric measurements can be combined with cloud models of varying complexity, including ones with bulk and spectral bin microphysics, as well as simplified Lagrangian models focused on a particular microphysical process. Combining polarimetric measurements with cloud modeling can reveal the impact of important microphysical agents such as aerosols or supercooled cloud water invisible to the radar on cloud and precipitation formation. Some pertinent results obtained from models with spectral bin microphysics, including the Hebrew University cloud model (HUCM) and 1D models of melting hail and snow coupled with the NSSL forward radar operator, are illustrated in the paper.
Highlights
Doppler polarimetric radars have become the standard for operational weather radars around the world
Used with permission from American Meteorological Society. Another example of how Polarimetric radar forward operators (PRFOs) helped improve the microphysical parameterization in the Hebrew University cloud model (HUCM) was the implementation of spontaneous raindrop breakup to supplement collision-induced breakup, which resulted in a reduction of the number of extremely large raindrops and an associated decreaseofinthe simulated
While the exact values of heating simulated in the lookup table shown in Figure 18b are expected to be somewhat sensitive to the model parameters used in the simulation, the correspondence between the latent-heating rate and ZH (in grey) and color-shaded (ZDR) column height overall appears robust and is in agreement with the results presented in Figure 6, as the latent-heating rate and w are inherently related
Summary
Doppler polarimetric radars have become the standard for operational weather radars around the world. For definitions and the physical meanings of these polarimetric variables, a reader can consult basic monographs [1,2,3] Using such multi-parameter measurements, one can discriminate between hydrometeors with different shapes, orientations, and phase compositions; reduce uncertainties in the retrievals of size distributions of atmospheric particles that are generally characterized by at least three parameters; and identify polarimetric signatures attributed to different microphysical processes. The Zhang et al paper focuses on a unified statistical synthesizing observation-based retrievals and model analyses Such an approach requires knowledge of the background error covariance and observation error covariance stipulated by the theory of optimal estimation.
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