Impact of Assimilating Airborne Reconnaissance Observations on Hurricane Intensity Prediction with the PSU Cycling WRF-EnKF system and its Real-time Forecast Performance during 2013 in support of NOAA HFIP and NASA HS3

This study evaluate the impacts of assimilating aircraft reconnaissance observations on the hurricane intensity prediction through a real-time hurricane prediction system based on cloud-permitting Weather Research and Forecasting (WRF) model an advanced data assimilation technique known as the ensemble Kalman filter (EnKF) developed at Pennsylvania State University (PSU). In comparison to the non-reconnaissance experiment that assimilates only conventional observations, as well as to the WRF forecasts directly initialized with the GFS operational analysis, hindcasts initialized with the assimilation of aircraft flight-level and dropsonde observations (“recon”) can significantly reduce both the track and intensity forecast errors at 1-5-day lead times averaged over 23 Atlantic storms during 2008-2012 with a total of 600+ initialization times.

The PSU WRF-EnKF hurricane analysis and forecast system was running in realtime during the 2013 Atlantic hurricane season, as part of the NOAA HFIP stream-1.5 experimental runs (stream 1 is operational runs and stream 2 is developmental runs while stream 1.5 is in between). This realtime hurricane prediction system is also used as part of the numerical guidance for the NASA Hurricane and Severe Storm Sentinel (HS3) field campaign. Preliminary analysis shows that the PSU WRF-EnKF realtime forecasts performed exceptionally well, especially for the cases with the assimilation of aircraft reconnaissance observations, including those HS3 experimental data from the NASA Global Hawk unmanned aircraft in realtime. Ongoing research is devoted to utilizing these realtime forecasts to understand the dynamics and predictability of the 2013 Atlantic tropical cyclones, most of which are apparently affected by strong vertical wind shear and a relatively dry environment.