Polarimetric radar data (PRD) have the potential to be used in numerical weather prediction (NWP) models to improve convective-scale weather forecasts. However, studies in this area are limited. To assimilate PRD into NWP models, a forward operator is needed to establish the relationship between model physics parameters and polarimetric radar variables. Such forward operators must be accurate enough to make quantitative comparison between radar observations and model output feasible, and also computational efficiency to enable incorporation into a data assimilation scheme. To address this goal, a set of parameterized PRD simulators for horizontal reflectivity, differential reflectivity, specific difference phase, and correlation coefficient have been developed recently (Zhang et al. 2019). In this study, we have tested the performance of this forward operator in a variational data assimilation scheme (Gao et al. 2013). First, an adjoint model for these new PRD simulators was developed. Second, both the forward operator and adjoint model were built into the three-dimensional variational scheme. To explore the potential utility of assimilating these dual-polarization parameters, some preliminary data assimilation experiments have been done with an idealized supercell storm. In these experiments, the assimilation of differential reflectivity and specific difference phase, in addition to radar radial velocity and reflectivity, helped to improve the accuracy of initial conditions for model hydrometer variables and ensuing model forecasts. In contrast, the usefulness of correlation coefficient was very limited in terms of improving convective-scale data analysis and NWP.
Gao, J., et al., 2013: A realtime weather-adaptive 3DVAR analysis system for severe weather detections and warnings with automatic storm positioning capability. Wea. Forecasting, 28, 727-745. http://dx.doi.org/10.1175/WAF-D-12-00093.1.
Zhang G. et al. 2018: Current Status and Future Challenges of Weather Radar Polarimetry:
Bridging the Gap between Radar Meteorology/Hydrology/Engineering and Numerical Weather Prediction, Adv. Atmos. Sci. 36, 571-588. Doi: https://doi.org/10.1007/s00376-019-8172-4