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.

Hurricane Rita made landfall near the Texas-Louisiana border in September 2005, causing major damage and disruption. As Rita approached the Gulf Coast, uncertainties in the storm's track and intensity forecasts, combined with the aftermath of Hurricane Katrina, led to major evacuations along the Texas coast and significant traffic jams in the broader Houston area. This study investigates the societal impacts of Hurricane Rita and its forecasts through a face-to-face survey with 120 Texas Gulf Coast residents. The survey explored respondents' evacuation decisions prior to Hurricane Rita, their perceptions of hurricane risk, and their use of and opinions on Hurricane Rita forecasts. The vast majority of respondents evacuated from Hurricane Rita, and more than half stated that Hurricane Katrina affected their evacuation decision. Although some respondents said that their primary reason for evacuating was local officials' evacuation order, many reported using information about the hurricane to evaluate the risk it posed to them and their families. Despite the major traffic jams and the minor damage in many evacuated regions, most evacuees interviewed do not regret their decision to evacuate. The majority of respondents stated that they intend to evacuate for a future category 3 hurricane, but the majority would stay for a category 2 hurricane. Most respondents obtained forecasts from multiple sources and reported checking forecasts frequently. Despite the forecast uncertainties, the respondents had high confidence in and satisfaction with the forecasts of Rita provided by the National Hurricane Center.

This study evaluates the impact of assimilating high-resolution, inner-core reconnaissance observations on tropical cyclone initialization and prediction in the 2013 version of the operational Hurricane Weather Research and Forecasting (HWRF) Model. The 2013 HWRF data assimilation system is a GSI-based hybrid ensemble–variational system that, in this study, uses the Global Data Assimilation System ensemble to estimate flow-dependent background error covariance. Assimilation of inner-core observations improves track forecasts and reduces intensity error after 18–24 h. The positive impact on the intensity forecast is mainly found in weak storms, where inner-core assimilation produces more accurate tropical cyclone structures and reduces positive intensity bias. Despite such positive benefits, there is degradation in short-term intensity forecasts that is attributable to spindown of strong storms, which has also been seen in other studies. There are several reasons for the degradation of intense storms. First, a newly discovered interaction between model biases and the HWRF vortex initialization procedure causes the first-guess wind speed aloft to be too strong in the inner core. The problem worsens for the strongest storms, leading to a poor first-guess fit to observations. Though assimilation of reconnaissance observations results in analyses that better fit the observations, it also causes a negative intensity bias at the surface. In addition, the covariance provided by the NCEP global model is inaccurate for assimilating inner-core observations, and model physics biases result in a mismatch between simulated and observed structure. The model ultimately cannot maintain the analysis structure during the forecast, leading to spindown.

Using methods unique for tropical cyclone studies in peer-reviewed literature, this study examines the dynamics and predictability of a nondeveloping tropical disturbance in the Gulf of Mexico during the 2004 hurricane season. Short-range ensemble forecasts are performed with a mesoscale model at low resolution with parameterized moist convection and at high resolution with explicit convection. Taking advantage of discrepancies between ensemble members, statistical correlation is used to elucidate why some ensemble members strengthen the disturbance into a tropical cyclone or hurricane and others do not.It is found that the two most important factors in the initial conditions for genesis in this case are the presence of deep moisture and high CAPE. These factors combine to yield more active initial convection and a quick spinup during the first 6-12 h. Because these factors result in quicker genesis in some ensemble members than others, they are also the primary source for spread early in the ensemble. Discrepancies after 12 h are amplified by differences in convection that are related to fluxes of sensible and latent heat. Eventually the wind-induced surface heat exchange mechanism results in even larger ensemble spread.

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.