This study explores the assimilation of Doppler radar radial velocity observations for cloud-resolving hurricane analysis, initialization, and prediction with an ensemble Kalman filter (EnKF). The case studied is Hurricane Humberto (2007), the first landfalling hurricane in the United States since the end of the 2005 hurricane season and the most rapidly intensifying near-landfall storm in U.S. history. The storm caused extensive damage along the southeast Texas coast but was poorly predicted by operational models and forecasters. It is found that the EnKF analysis, after assimilating radial velocity observations from three Weather Surveillance Radars-1988 Doppler (WSR-88Ds) along the Gulf coast, closely represents the best-track position and intensity of Humberto. Deterministic forecasts initialized from the EnKF analysis, despite displaying considerable variability with different lead times, are also capable of predicting the rapid formation and intensification of the hurricane. These forecasts are also superior to simulations without radar data assimilation or with a threedimensional variational scheme assimilating the same radar observations. Moreover, nearly all members from the ensemble forecasts initialized with EnKF analysis perturbations predict rapid formation and intensification of the storm. However, the large ensemble spread of peak intensity, which ranges from a tropical storm to a category 2 hurricane, echoes limited predictability in deterministic forecasts of the storm and the potential of using ensembles for probabilistic forecasts of hurricanes.

[1] This study examines a hurricane prediction system that uses an ensemble Kalman filter (EnKF) to assimilate highresolution airborne radar observations for convectionpermitting hurricane initialization and forecasting. This system demonstrated very promising performance, especially on hurricane intensity forecasts, through experiments over all 61 applicable NOAA P-3 airborne Doppler missions during the 2008-2010 Atlantic hurricane seasons. The mean absolute intensity forecast errors initialized with the EnKF-analysis of the airborne Doppler observations at the 24-to 120-h lead forecast times were 20-40% lower than the National Hurricane Center's official forecasts issued at similar times. This prototype system was first implemented in real-time for Hurricane Ike (2008). It represents the first time that airborne Doppler radar observations were successfully assimilated in real-time into a hurricane prediction model. It also represents the first time that the convection-permitting ensemble analyses and forecasts for hurricanes were performed in real-time. Also unprecedented was the on-demand usage of more than 23,000 computer cluster processors simultaneously in realtime.

Through a Weather Research and Forecasting model (WRF)-based ensemble Kalman filter (EnKF) data assimilation system, the impact of assimilating airborne radar observations for the convection-permitting analysis and prediction of Hurricane Katrina (2005) is examined in this study. A forecast initialized from EnKF analyses of airborne radar observations had substantially smaller hurricane track forecast errors than NOAA’s operational forecasts and a control forecast initialized from NCEP analysis data for lead times up to 120 h. Verifications against independent in situ and remotely sensed observations show that EnKF analyses successfully depict the inner-core structure of the hurricane vortex in terms of both dynamic (wind) and thermodynamic (temperature and moisture) fields. In addition to the improved analyses and deterministic forecast, an ensemble of forecasts initiated from the EnKF analyses also provided forecast uncertainty estimates for the hurricane track and intensity. Also documented here are the details of a series of data thinning and quality control procedures that were developed to generate superobservations from large volumes of airborne radial velocity measurements. These procedures have since been implemented operationally on the NOAA hurricane reconnaissance aircraft that allows for more efficient real-time transmission of airborne radar observations to the ground.

F i g . I . Evolution o f y e a rly m ean absolu te e rro rs a t d if fe r e n t fo recast lead tim e s by th e N a tio n a l H u rric a n e C e n te r ( N H C ) fo r (a ) tra c k (c e n te r position, in nautical m iles) and (b ) in te n s ity (m a x im u m 10-m w in d speed, in kn o ts ). AMERICAN METEOROLOGICAL SOCIETY JANUARY 2015 B A ltt | 25 The Life Cycles of Extratropical Cyclones is availa b le for $75 list/$55 members. To order, visit w w w.ametsoc.org/amsbookstore, or see the order form a t the back of this issue. AMERICAN METEOROLOGICAL SOCIETY JANUARY 2 0 15 BAlfr | 33

The dynamics and predictability of the intensification of Hurricane Edouard (2014) are explored through a 60-member convection-permitting ensemble initialized with an ensemble Kalman filter that assimilates dropsondes collected during NASA’s Hurricane and Severe Storm Sentinel (HS3) investigation. The 126-h forecasts are initialized when Edouard was designated as a tropical depression and include Edouard’s near–rapid intensification (RI) from a tropical storm to a strong category-2 hurricane. Although the deterministic forecast was very successful and many members correctly forecasted Edouard’s intensification, there was significant spread in the timing of intensification among the members of the ensemble. Utilizing composite groups created according to the near-RI-onset times of the members, it is shown that, for increasing magnitudes of deep-layer shear, RI onset is increasingly delayed; intensification will not occur once a critical shear threshold is exceeded. Although the timing of intensification varies by as much as 48 h, a decrease in shear is observed across the intensifying composite groups ~6–12 h prior to RI. This decrease in shear is accompanied by a reduction in vortex tilt, as the precession and subsequent alignment process begins ~24–48 h prior to RI. Sensitivity experiments reveal that some of the variation in RI timing can be attributed to differences in initial intensity, as the earliest-developing members have the strongest initial vortices regardless of their environment. Significant sensitivity and limited predictability exists for members with weaker initial vortices and/or that are embedded in less conducive environments, under which the randomness of moist convective processes and minute initial differences distant from the surface center can produce divergent forecasts.

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This study examines the prediction and predictability of the recent catastrophic rainfall and flooding event over Taiwan induced by Typhoon Morakot (2009) with a state-of-the-art numerical weather prediction model. A high-resolution convection-permitting mesoscale ensemble, initialized with analysis and flowdependent perturbations obtained from a real-time global ensemble data assimilation system, is found to be able to predict this record-breaking rainfall event, producing probability forecasts potentially valuable to the emergency management decision makers and the general public. Since all the advanced modeling and data assimilation techniques used here are readily available for real-time operational implementation provided sufficient computing resources are made available, this study demonstrates the potential and need of using ensemble-based analysis and forecasting, along with enhanced computing, in predicting extreme weather events like Typhoon Morakot at operational centers.

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