The impact of Reconnaissance Data on Hurricane Intensity Forecasts with a Cycling WRF-EnKF system

Penn State University (PSU) WRF-EnKF hurricane analysis and prediction system had been operated in real-time for Atlantic storms for 5 years with airborne Doppler radar data assimilation and performed remarkably well for all landfalling hurricanes from 2008 through 2012: averaged over all 102 applicable airborne tailor Doppler radar (TDR) missions, errors in forecast intensity for lead times of 1 to 5 days were 15-43% less than the corresponding official forecasts issued by the National Hurricane Center. In 2013 we developed it to a cycling system to assimilate all conventional and field experimental observations and to breakthrough the limitation of the small TDR sample size. This study exams the impact of reconnaissance data on hurricane intensity prediction with the PSU cycling WRF-EnKF system.

To establish a baseline level of hurricane intensity prediction accuracy, a control run without any Reconnaissance data assimilation by the cycling WRF-EnKF system is designed (named as APCT). Also two other experiments are designed to exam the impacts on hurricane intensity prediction by assimilating: 1) flight-level and dropsonde observations (APRC), flight-level, dropsonde and TDR observations (APAR). The preliminary result shows, 1)The APRC has smaller position and intensity errors than the APCT, impressive improvement is on the minimal sea level pressure forecast; 2)Reducing the sample size to the storms only with TDR missions (which means only important storms are considered), the intensity errors of APCT, APRC and APAR are smaller than OFCL. APRC has smaller track and intensity forecast errors than APCT, which means the assimilation of flight-level and dropsonde observations has impact on hurricane track and intensity forecasts. But the assimilation of TDR observation doesn't show any improvement by comparing the forecast of APRC and APAR.

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