Assimilation of Polarimetric Radar Data in Simulation of a Supercell Storm with a Variational Approach and the WRF Model

Abstract

Polarimetric radar data (PRD) have potential to be used in numerical weather prediction (NWP) models to improve convective-scale weather forecasts. However, thus far only a few studies have been undertaken in this research direction. To assimilate PRD in NWP models, a forward operator, also called a PRD simulator, is needed to establish the relation between model physics parameters and polarimetric radar variables. Such a forward operator needs to be accurate enough to make quantitative comparisons between radar observations and model output feasible, and to be computationally efficient so that these observations can be easily incorporated into a data assimilation (DA) scheme. To address this concern, a set of parameterized PRD simulators for the horizontal reflectivity, differential reflectivity, specific differential phase, and cross-correlation coefficient were developed. In this study, we have tested the performance of these new operators in a variational DA system. Firstly, the tangent linear and adjoint (TL/AD) models for these PRD simulators have been developed and checked for the validity. Then, both the forward operator and its adjoint model have been built into the three-dimensional variational (3DVAR) system. Finally, some preliminary DA experiments have been performed with an idealized supercell storm. It is found that the assimilation of PRD, including differential reflectivity and specific differential phase, in addition to radar radial velocity and horizontal reflectivity, can enhance the accuracy of both initial conditions for model hydrometer state variables and ensuing model forecasts. The usefulness of the cross-correlation coefficient is very limited in terms of improving convective-scale data analysis and NWP.

A New Melting Model and Its Implementation in Parameterized Forward Operators for Polarimetric Radar Data Simulation With Double Moment Microphysics Schemes

“…Given the pressing need for more efficient and compatible polarimetric observation operators, Zhang et al (2021) developed a set of parameterized polarimetric radar forward operators and tested them for both idealized and real supercell cases. Du et al (2021) assessed the performance of these parameterized forward operators in a variational assimilation system. In the case of an idealized supercell storm, the assimilation of PRD led to a reduction in the root mean square errors (RMSEs) of the model state variables at each analysis cycle.…”

“…Du et al. (2021) assessed the performance of these parameterized forward operators in a variational assimilation system. In the case of an idealized supercell storm, the assimilation of PRD led to a reduction in the root mean square errors (RMSEs) of the model state variables at each analysis cycle.…”

“…PRD offer an unprecedented opportunity to expand the knowledge of cloud microphysics processes to improve microphysics parameterization (Chen et al., 2021; Jung et al., 2012; Kumjian & Ryzhkov, 2008; Wu et al., 2021; Zhang et al., 2001, 2006; Zhou et al., 2022). Furthermore, PRD can be used to improve model initialization through data assimilation methods (Carlin et al., 2017; Ding et al., 2022; Du et al., 2021; Jung, Xue, et al., 2008; Jung, Zhang, & Xue, 2008; Putnam et al., 2019, 2021; Zhu et al., 2020).…”

Assimilation of 3D Polarimetric Microphysical Retrievals in a Convective-Scale NWP System

“…The indirect approach via retrievals circumvents these model deficiencies, but suffers from retrieval uncertainties. A few case studies from the USA, Japan, and China have already attempted the direct DA of 105 polarimetric observations with some success using the EnKF (e.g., Jung et al, 2008 Putnam et al, 2019;Zhu et al, 2020;Putnam et al, 2021) or the 3DVar method (e.g., Li et al, 2017;Du et al, 2021). Other studies have assimilated polarimetric observations indirectly via retrieved hydrometeor mixing ratios using the 4DVar approach (e.g., Wu et al, 2000), the 3DVar method (e.g., Mecikalski, 2010, 2012), or the EnKF method (e.g., 110 Yokota et al, 2016).…”