EnKF Data Assimilation of Canadian Radar for a Lake-Effect Snowstorm in 2015

Zhan Li* and Yongsheng Chen, Iain Russell, Majid Fekri
York University, York University, The Weather Network, The Weather Network

A numerical study is conducted to implement the Ensemble Kalman Filter (EnKF) assimilation of Canadian radar data from the King City site (WKR). Using the Weather Research and Forecasting (WRF) model and the Data Assimilation Research Testbed (DART) system to conduct the EnKF data assimilation and numerical simulation at a 3-km resolution, a winter case of Ontario-Lake-effect snowstorm on January 26, 2015 is used to examine the impact of radar data assimilation on the analyses and short-term forecasts. Because this snowstorm was well captured both by the Canadian radar at King City site (WKR) and the U. S. Nexrad radar at Buffalo site (KBUF), we had a good opportunity to compare two experiments with either WKR or KBUF radar data assimilated.

These results demonstrate the positive impact of radar data assimilation using the EnKF method on the numerical analysis and short-term forecasts of the lake-effect snowstorm. As a result of assimilating both radar radial velocity and reflectivity from the WKR radar, the analyses and short-term forecasts (0-2 hour) well reproduce the occurrence, development and banded feature of the lake-effect snowstorm. Further comparison results reveal that the experiment of assimilating the Canadian WKR radar data outperforms the experiment of assimilating the U.S. KBUF radar data because the KBUF radar suffers the overshooting issue for the shallow convection of the lake-effect snowstorm. In addition, the sensitivity experiments demonstrate that 1) the implementation of the diabatic digital filter (DDFI) into the EnKF radar data assimilation benefits to the simulations of the lake-effect snowstorm; 2) the EnKF assimilation of the reflectivity data contributes much more to the simulation improvement than the assimilation of the radial velocity.

*email: zhanl@yorku.ca
*Preference: Oral