![]() ![]() It was first proposed by Evensen (1994) and has been adopted for data assimilation of many disciplines in the geosciences and beyond ( Evensen 2003 Hamill 2006). The ensemble Kalman filter (EnKF) is a state-estimation technique that uses short-term ensemble forecasts to estimate flow-dependent background error covariance or other probabilistic aspects of the background forecast. 2001), though its effectiveness in spinning up a full hurricane vortex for cloud-resolving hurricane prediction remains to be fully explored. Physical (diabatic) initializations using rainfall, radar, and/or satellite observations are a promising approach ( Krishnamurti et al. In addition, operational models generally have insufficient model resolution to effectively incorporate high-resolution convective-scale observations (such as those from radars) for cloud-resolving hurricane prediction. The mostly balanced, isotropic, flow-independent background statistics derived from long-term averages of past short-term forecast error ( Parrish and Derber 1992) are ill-suited for the highly flow-dependent background error covariances associated with tropical cyclones. Another part of the difficulty comes from the deficiency of the current generation of operational data assimilation systems, which use static background error covariance. Part of the difficulty of hurricane initialization comes from the lack of routine four-dimensional observations with sufficient spatial and temporal resolution to represent the initial hurricane structure and intensity. Numerical weather prediction models also have known difficulties in their “spinup” of a tropical cyclone or hurricane vortex with appropriate moisture, diabatic, and divergence structures at the initial time. 2008).ĭespite improvements in using advanced data assimilation methods with or without initial vortex bogussing, our ability to initialize a tropical cyclone with dynamically consistent structure and intensity remains limited, even with the assimilation of radar observations (e.g., Zou and Xiao 2000 Pu and Braun 2001 Xiao et al. High-resolution cloud-resolving mesoscale models, along with better initialization of the initial vortex, may be necessary to faithfully represent the internal dynamics that is crucial for hurricane intensity forecasts ( Houze et al. We thus have very limited skill in predicting tropical cyclone formation, rapid intensification, fluctuation, or decay ( Elsberry et al. However, there is virtually no improvement in our ability to predict hurricane intensity in terms of minimum sea level pressure, maximum wind speed, or amount of precipitation ( Houze et al. The current-day average 48-h forecast position is as accurate as a 24-h track forecast was 10 yr ago ( Franklin 2004). Over the past decade, significant progress has been made in short-range track forecasts of tropical cyclones. Landfalling hurricanes are among the deadliest and costliest natural hazards. 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. Moreover, nearly all members from the ensemble forecasts initialized with EnKF analysis perturbations predict rapid formation and intensification of the storm. These forecasts are also superior to simulations without radar data assimilation or with a three-dimensional variational scheme assimilating the same radar observations. ![]() 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. 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. The storm caused extensive damage along the southeast Texas coast but was poorly predicted by operational models and forecasters. 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. 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).
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