Jonathan Labriola;
Nathan Snook;
Youngsun Jung;
Ming Xue;
Bryan Putnam
Center for the Analysis and Prediction of Storms
The 8th EnKF Data Assimilation Workshop
Evaluation of a Data Assimilation and Forecast System for High-Resolution Explicit Hail Prediction
Talk:
EnKF_Labriola_2018.pptx
Hail causes over 1 billion dollars in damage annually in the United States. Explicit hail prediction using high-resolution (500 m grid spacing) models has the potential to increase severe weather warning lead times and mitigate hail damage. Few studies have evaluated how the skill of hail prediction varies between advanced ice microphysics schemes, which largely differ in treatment of rimed ice. In this study an ensemble Kalman filter (EnKF) is used to assimilate surface and radar observations for a severe hail event on 19 May 2013 into ensembles run using either a double moment (DM), triple moment (TM), or variable density rimed ice DM microphysics scheme. Hail particle size distributions and microphysical budgeting terms are analyzed to determine how the predicted hail category is modified during assimilation for different microphysics schemes. Ensemble based surface hail forecasts initialized from the analyses are verified against radar derived hail products. Results indicate the ensemble run using the variable density rimed ice DM scheme produces surface hail size forecasts with the most skill both in terms of the spatial extent and size of hail. The variable density rimed ice DM scheme has improved representation hail within the melting layer and uses prognostic density to improve the production of hail.
Hail causes over 1 billion dollars in damage annually in the United States. Explicit hail prediction using high-resolution (500 m grid spacing) models has the potential to increase severe weather warning lead times and mitigate hail damage. Few studies have evaluated how the skill of hail prediction varies between advanced ice microphysics schemes, which largely differ in treatment of rimed ice. In this study an ensemble Kalman filter (EnKF) is used to assimilate surface and radar observations for a severe hail event on 19 May 2013 into ensembles run using either a double moment (DM), triple moment (TM), or variable density rimed ice DM microphysics scheme. Hail particle size distributions and microphysical budgeting terms are analyzed to determine how the predicted hail category is modified during assimilation for different microphysics schemes. Ensemble based surface hail forecasts initialized from the analyses are verified against radar derived hail products. Results indicate the ensemble run using the variable density rimed ice DM scheme produces surface hail size forecasts with the most skill both in terms of the spatial extent and size of hail. The variable density rimed ice DM scheme has improved representation hail within the melting layer and uses prognostic density to improve the production of hail.
Thanks to McGill's Atmospheric and Oceanic Sciences department for hosting this web-site.