Impact of Phased-Array Radar Observations over a Short Assimilation Period: Observing System Simulation Experiments Using an Ensemble Kalman Filter

Abstract

Abstract The conventional Weather Surveillance Radar-1988 Doppler (WSR-88D) scans a given weather phenomenon in approximately 5 min, and past results suggest that it takes 30–60 min to establish a storm into a model assimilating these data using an ensemble Kalman filter (EnKF) data assimilation technique. Severe-weather events, however, can develop and evolve very rapidly. Therefore, assimilating observations for a 30–60-min period prior to the availability of accurate analyses may not be feasible in an operational setting. A shorter assimilation period also is desired if forecasts are produced to increase the warning lead time. With the advent of the emerging phased-array radar (PAR) technology, it is now possible to scan the same weather phenomenon in less than 1 min. Therefore, it is of interest to see if the faster scanning rate of PAR can yield improvements in storm-scale analyses and forecasts from assimilating over a shorter period of time. Observing system simulation experiments are conducted to evaluate the ability to quickly initialize a storm into a numerical model using PAR data in place of WSR-88D data. Synthetic PAR and WSR-88D observations of a splitting supercell storm are created from a storm-scale model run using a realistic volume-averaging technique in native radar coordinates. These synthetic reflectivity and radial velocity observations are assimilated into the same storm-scale model over a 15-min period using an EnKF data assimilation technique followed by a 50-min ensemble forecast. Results indicate that assimilating PAR observations at 1-min intervals over a short 15-min period yields significantly better analyses and ensemble forecasts than those produced using WSR-88D observations. Additional experiments are conducted in which the adaptive scanning capability of PAR is utilized for thunderstorms that are either very close to or far away from the radar location. Results show that the adaptive scanning capability improves the analyses and forecasts when compared with the nonadaptive PAR data. These results highlight the potential for flexible rapid-scanning PAR observations to help to quickly and accurately initialize storms into numerical models yielding improved storm-scale analyses and very short range forecasts.