Cloud-Resolving Hurricane Initialization and Prediction through Assimilation of Doppler Radar Observations with an Ensemble Kalman Filter

Abstract

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.

Ensemble Data Assimilation and Predictability of Tropical Cyclones

“…(2) Improve tropical cyclone track and intensity prediction through further development of the regional-scale, cloud-resolving ensemble-based data assimilation and prediction system capable of efficiently assimilating ground-based and airborne Doppler observations and satellite-derived products (Zhang et al 2009a(Zhang et al , 2009b. We examined the performance of the ensemble-based data assimilation and prediction system for several high impact historical cases with Doppler radar observations that includes Hurricanes Katrina (2005) In all these cases, it is found that the ensemble analysis and forecast system with Doppler observations assimilated can significantly improve the track/intensity prediction while reveal significant uncertainty in the forecast.…”

The Near-Real-Time SCALE-LETKF System: A Case of the September 2015 Kanto-Tohoku Heavy Rainfall

“…A regional NWP system can be used to increase the model resolution of an area of interest at a relatively low cost. Therefore, regional EnKF systems have also been widely developed and tested (Zhang et al 2006;Meng and Zhang 2007;Torn and Hakim 2008;Anderson et al 2009;Miyoshi and Aranami 2006;Miyoshi and Kunii 2012;Kunii 2014), and they have shown promising results in analyzing and predicting a variety of mesoscale phenomena, such as tropical cyclones (e.g., Zhang et al 2009;Zhang and Weng 2015;Torn 2010) and convective rainstorms (e.g., Yussouf et al 2013;Kunii 2014;Jones et al 2015).…”

Scale-dependent background-error covariance localisation

“…The approach known as successive covariance localisation (Zhang et al, 2009) is applied to an EnKF to assimilate a subset of the observations with relatively weak localisation to estimate the large scales and another subset with more severe localisation to estimate the small scales. However, since each observation may contain useful information on the error at all spatial scales, this approach ignores potentially useful information that would be available if all observations could be assimilated to estimate all scales simultaneously.…”