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

• DOI: 10.1175/2011mwr3602.1
• Publication Date: 2011-09-27T11:46:49Z
• Authors: Yonghui Weng; Fuqing Zhang
• Keywords: Dropsonde; Initialization; Nowcasting

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