Abstract
In an effort to improve radar data assimilation configurations for potential operational implementation, GSI EnKF data assimilation experiments based on the operational system employed by the Center for Analysis and Prediction of Storms (CAPS) real-time Spring Forecast Experiments are performed. These experiments are followed by 6-h forecasts for an MCS on 28–29 May 2017. Configurations examined include data thinning, covariance localization radii and inflation, observation error settings, and data assimilation frequency for radar observations. The results show experiments that assimilate radar observations more frequently (i.e., 5–10 min) are initially better at suppressing spurious convection. However, assimilating observations every 5 min causes spurious convection to become more widespread with time, and modestly degrades forecast skill through the remainder of the forecast window. Ensembles that assimilate more observations with less thinning of data or use a larger horizontal covariance localization radius for radar data predict fewer spurious storms and better predict the location of observed storms. Optimized data thinning and horizontal covariance localization radii have positive impacts on forecast skill during the first forecast hour that are quickly lost due to the growth of forecast error. Forecast skill is less sensitive to the ensemble spread inflation factors and observation errors tested during this study. These results provide guidance toward optimizing the configuration of the GSI EnKF system. Among the DA configurations tested, the one employed by the CAPS Spring Forecast Experiment produces the most skilled forecasts while remaining computationally efficient for real-time use.
Significance Statement
High-resolution ensemble forecasts that can skillfully predict thunderstorms provide an opportunity for warning severe weather further in advance. However, forecast accuracy is dependent upon many factors including the data assimilation system used to create the forecasts. This study optimizes the design of a real-time ensemble forecast and data assimilation system for a severe weather event where a line of thunderstorms produced hail, wind, and tornadoes in the southern United States. The 0–6-h forecasts predict the thunderstorms with moderate skill. Forecast accuracy is most sensitive to how frequently the data assimilation system assimilates radar observations, the degree to which radar observations are thinned, and the covariance localization radius. Results of this study can be used to design future real-time forecast systems for severe weather events.
Current affiliation: National Severe Storms Laboratory, Norman, Oklahoma.
Currrent affiliation: Office of Science and Technology Integration, Silver Spring, Maryland.
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