Assimilation of Polarimetric Radar Data to Improve Microphysics Parameterization and Short-Term Explicit Numerical Prediction of Deep Convective Storms

Dual-polarimetric weather radars send out radar pulses vertically and horizontally, providing a better assessment of precipitation size, type, and intensity. In the United States, the national radar system has been making dual-polarization measurements for over a decade which has resulted in better precipitation estimation and severe weather nowcasting. The goal of this research project is to determine how best to bring that data into numerical modeling systems, a process known as data assimilation. Successful implementation of this research would result in better weather forecasts, especially for severe and hazardous weather that is most impactful to society. The project would also provide training in advanced data methods to multiple early-career researchers. Polarimetric radar data is currently underutilized in numerical weather models due to unresolved challenges with the assimilation of this data. To advance this field of science, the research team proposes a three-pronged study of polarimetric data and how it is incorporated into numerical models. The first activity seeks to explore new information and improve the data quality of two radar quantities known as specific differential phase and differential backscattering phase. The second activity will test and modify various double moment microphysics schemes using polarimetric data as a constraint. Finally, the research team will incorporate the data assimilation research into the new Joint Effort for Data assimilation Integration (JEDI)-Model for Prediction Across Scales (MPAS) coupled modeling system. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.