Assimilating airborne Doppler radar observations with an ensemble Kalman filter for convection-permitting hurricane initialization and prediction: Katrina (2005)

Yonghui Weng, Fuqing Zhang

Research output: Contribution to journalArticlepeer-review

95 Scopus citations

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 super observations 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.

Original languageEnglish (US)
Pages (from-to)841-859
Number of pages19
JournalMonthly Weather Review
Volume140
Issue number3
DOIs
StatePublished - Mar 1 2012

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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