A Realtime Weather-Adaptive Hybrid ensemble 3DVAR Analysis System with Automatic Storm Positioning and On-demand Capability

A real-time, weather-adaptive hybrid three-dimensional ensemble variational data assimilation (3DEnVAR) system has been developed recently for the NOAA supported Warn-on-Forecast project (WoF). This system incorporates available ensemble forecasts, radar, and traditional observations within an analysis domain that has potential for severe weather, including tornadoes, hail and strong damaging winds. The goal of this work is to provide physically-consistent gridded data produced by the system to forecasters to help make their warning decisions in a timely manner. In a hybrid system, the ensemble information is commonly provided by an ensemble Kalman filter (EnKF) data assimilation system. However, the 3DEnVAR system linked with EnKF is computationally very expensive, so in this study, we use the short-range ensemble forecasting (SREF) system from NCEP instead to provide ensemble information for the 3DEnVAR system. The SREF system is likely to provide valuable information about mesoscale environmental uncertainty or variability but its use should not significantly increase the the overall computational cost of the system relative to the original 3DVAR technique (Gao et al. 2013; Smith et al. 2014) because the necessary ensemble forecasts are already generated. This system retains the fine-scale features of original 3DVAR system. For example, it has the ability to automatically detect and analyze severe local hazardous weather events. Furthermore, the analysis can also be performed with on-demand capability in which end-users (e.g., forecasters or scientists) set up the location of the analysis domain in real time based on the current weather situation. The analysis product may help forecasters identify strong circulations imbedded in thunderstorms so that the accuracy of warnings for hazardous weather threats may be improved. This system will be tested in the 2015 Hazardous Weather Testbed (HWT) Spring Experiment. In addition to the ensemble approach, it differs from previous HWT 3DVAR analysis systems in that the ARPS model is used to produce 5-min short-term forecasts that provide background fields for each successive analysis, resulting in a cycled hybrid 3DVAR analysis system. The performance of the system during the Spring Experiment will be reported during the conference.

Gao, J., T. M. Smith, D. J. Stensrud, C. Fu, K. Calhoun, K. L. Manross, J. Brogden, V. Lakshmanan, Y. Wang, K. W. Thomas, K. Brewster, and M. Xue, 2013: A realtime weather-adaptive 3DVAR analysis system for severe weather detections and warnings with automatic storm positioning capability. Wea. Forecasting, 28, 727-745.

Smith, T. M., J. Gao, K. M. Calhoun, D. J. Stensrud, K. L. Manross, K. L. Ortega, C. Fu, D. M. Kingfield, K. L. Elmore, V. Lakshmanan, and C. Riedel, 2014: Performance of a real-time 3DVAR analysis system in the Hazardous Weather Testbed. Wea. Forecasting, 29, 63-77.