A realtime weather-adaptive 3DVAR analysis system with automatic storm positioning and on-demand capability

Radar is a fundamental tool for severe storm monitoring and nowcasting activities. Forecasters examine real-time WSR-88D observations from multiple radars, other remote sensing tools, severe weather detection algorithms, and use their considerable experience and situational awareness to issue severe storm warnings that help protect the public from hazardous weather events. Escalating data flow rates from new sensors and applications, however, will make it challenging for forecasters to make the best use of all the available data in warning operations. In this study, we investigate the possibility of identifying supercells with a real-time, dynamically-adaptive three-dimensional variational data assimilation (3DVAR) system that incorporates all available radar observations. A storm positioning program is implemented in the 3DVAR system based on the National Severe Storm Laboratory (NSSL) WDSS-II two-dimensional composite reflectivity product. The system has the ability to automatically detect and analyze severe local hazardous weather by identifying mesocyclones at high spatial resolution (1km horizontal resolution) and high time frequency (every 5 minutes) using data primarily from the national WSR-88D radar network and NCEP's North American Mesoscale (NAM) model product. The analysis can also be performed with on-demand capability in which end-users (or forecasters) set up the location of the analysis domain in real time based on the current weather situation. Although still in the early development stage, this system performed very well during the spring of 2010. Many severe weather events, such as the Mississippi tornadoes on April 24th, Arkansas tornadoes on April 26th, and Oklahoma/Kansas tornadoes and hailstorms on May 10th, May 16th, May 19th, May 25th were all successfully detected and analyzed.

The objectivity of the procedure ensures that (i) all available information, including all nearby WSR-88Ds and NAM high resolution analysis and forecast products, are used, (ii) physically-consistent gridded data are provided to forecasters to help make their warning decisions in a timely manner, and (iii) the problem of subjectivity, inherent to some arbitrary criteria in some severe weather detection algorithms, is avoided. Furthermore, the analysis system can be also run offline, and this enables, for example, the study of a specific area in greater detail or the investigation of the evolution and lifetime of certain kinds of severe weather. The performance of the system will be accessed and discussed in the conference.