School of Meteorology (Defense)

The Prediction and Assimilation of Polarimetric Radar Data Using Ensemble-Based Methods

Bryan Putnam

School of Meteorology

27 April 2016, 3:30 PM

National Weather Center, Room 4140
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK

Accurately representing the microphysical state of precipitation using bulk microphysics schemes, including the hydrometeor particle size distributions (PSDs), is an important part of improving convective-scale forecasts. In this talk, results will be presented that combine the use of dual-polarimetric (dual-pol) radar observations and ensemble forecast methods to evaluate and improve the model microphysical state. The dual-pol variables provide additional information on hydrometeor types and their PSDs compared to reflectivity (Z) alone.

First, probabilistic forecasts of simulated dual-pol variables are performed. Ensemble forecasts of a mesoscale convective system (MCS) from 9 May 2007 are initialized from ensemble Kalman filter (EnKF) analyses using both SM and DM microphysics schemes. Qualitative analysis of simulated differential reflectivity (ZDR) shows that the DM experiment better represents the PSDs of the convective and stratiform precipitation regions, while the specific differential phase (KDP) fields show that the SM experiment over-forecasts liquid water content in the convective areas. Quantitative ensemble forecast verification methods using dual-pol variables are considered for the first time. The DM experiment has improved skill scores and provides a sharper forecast compared to the SM experiment. Several challenges associated with evaluating dual-pol fields that have very fine-scale details will be discussed.

Second, dual-pol variables are assimilated using the EnKF and a DM microphysics scheme for two supercell cases: the 10 May 2010 central Oklahoma tornadic supercells and the 20 May 2013 Moore, Oklahoma tornadic supercell. ZDR and KDP are assimilated in separate experiments in addition to Z and radial velocity (Vr) and compared to a control experiment that assimilates only Z and Vr. Initial sensitivity experiments are performed to evaluate how different model error treatment methods, radar configurations, and observation filtering techniques can improve the analyses. These tests show that additive perturbations negatively impact the analyses when ZDR is assimilated, while the use of smoothed radar observations on the model grid improves the analyses, particularly for noisy ZDR observations. An evaluation of the results shows that the analyzed dual-pol fields when the dual-pol observations are assimilated better represent documented polarimetric signatures, such as the ZDR arc, compared to the control experiment. Additionally, comparisons of model microphysical state variables and mean mass diameter between the dual-pol and control experiments show that the assimilated dual-pol variables improve the estimate of the model microphysical state.

School of Meteorology (Defense) Seminar Series website