Assimilating Airborne Doppler Radar Observations with an Ensemble Kalman Filter for Convection-Permitting Hurricane Initialization and Prediction: Katrina (2005)

Abstract

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.

Exploring Water Level Sensitivity for Metropolitan New York during Sandy (2012) Using Ensemble Storm Surge Simulations

“…Munsell and Zhang [6] presented the details of the WRF-EnKF Sandy runs, extending the analysis of Weng and Zhang [5] and Zhang et al [15]. The EnKF system used an ensemble of WRF forecasts to estimate flow-dependent background error covariance for the data assimilation cycling.…”

“…Experimental real-time forecasts during Sandy, including the Pennsylvania State University (PSU) Weather Research and Forecasting (WRF)-Ensemble Kalman Filter (EnKF) system that assimilated airborne Doppler radar observations [5,6] were analyzed. At a forecast lead-time four to five days prior to landfall, nearly 18.0% of the PSU WRF-EnKF ensemble members did not predict United States landfall.…”

Current Efforts for Prediction and Assessment of Natural Disasters: Earthquakes, Tsunamis, Volcanic eruptions, Hurricanes, Tornados, and Floods

“…Landfalling hurricanes are among the costliest and deadliest natural hazards [116][117]. Dangers associated with hurricanes, such as strong winds, torrential rains, storm surges, and ooding, have huge negative impacts on people and property [118].…”

“…Zhang et al [116] combined Doppler radar radial velocity observations with ensemble Kalman lter (EKF). The use of EKF is also presented by Weng et al [129], Weng and Zhang [117], Poterjoy and Zhang [130], Zhang and Weng [131], and Lu et al [132]. The National Oceanic and Atmospheric Administration (NOAA) is also experimenting with ensemble techniques such as EKF [133].…”

Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: observing system simulation experiments

“…Chung et al, 2009;Montmerle and Faccani, 2009) and superobbing (e.g. Lindskog et al, 2004;Zhang et al, 2009;Kawabata et al, 2011;Weng and Zhang, 2012). The former selects certain range gates with proper intervals and discards the others; the advantage is that extreme values, including maxima and minima, may be preserved instead of being smoothed out so that the magnitude of convective-scale features could be better represented.…”