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Targeting strategies to improve hurricane track forecasts (JHT 03-05). PIs: Dr Sharanya J. Majumdar (University of Miami) Dr Sim D. Aberson (NOAA/AOML/HRD) Co-PIs: Dr Zoltan Toth (NOAA/NWS/NCEP/EMC), Ms Lacey D. Holland (SAIC at EMC), Dr Brian J. Etherton (UNC Charlotte),
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Targeting strategies to improve hurricane track forecasts (JHT 03-05) PIs: Dr Sharanya J. Majumdar (University of Miami) Dr Sim D. Aberson (NOAA/AOML/HRD) Co-PIs:Dr Zoltan Toth (NOAA/NWS/NCEP/EMC), Ms Lacey D. Holland (SAIC at EMC), Dr Brian J. Etherton (UNC Charlotte), Mr Paul Leighton (NOAA/AOML/HRD) (Acknowledgment: Mr Richard Wobus (EMC)) Interdepartmental Hurricane Conference, Charleston, SC. March 3rd 2004
Targeted Observations • Motivation • To improve tropical cyclone track and intensity forecasts by selective deployments of observations in the storm’s environment. • NOAA G-IV Synoptic Surveillance missions use targeted GPS dropwindsondes. • Present strategy: direct aircraft towards locations in which the “spread” (or standard deviation) of an ensemble forecast is large at the observation time.
Ensemble Spread Fcst for 2003091400: Isabel x x : Isabel’s Center
New Targeting Strategy • Ensemble Transform Kalman Filter (ETKF) • Has been used operationally by NWS for Winter Storm Reconnaissance since 2001 • Theory: Bishop et al. (MWR, 2001) • Targeted Observations: Majumdar et al. (QJRMS 2001, MWR 2002, QJRMS 2002) • Ensemble Initialization: Wang and Bishop (JAS, 2003) • Data Assimilation: Etherton and Bishop (MWR, 2004)
How the ETKF works Ensemble Initialization time Decision time Targeted Observing (analysis) time Verification time ti td ta tv t ETKF uses operational ensemble forecasts to rapidly predict signal variance = reduction in error variance for any deployment q of adaptive observations: Sq = PN – Pq = MPN(ta)HqT (HqPN(ta)HqT+R)-1HqPN(ta) MT = ZN(tv)TNCq Gq (Gq+I)-1 CqT TNTZNT(tv)
Summary Maps of Signal Variance ETKF predicts signal variance (reduction in forecast error variance) for all feasible deployments of targeted observations. Summarize these predictions in the form of a map or bar chart.
ACTUAL NCEP GFS SIGNAL ETKF PREDICTED SIGNAL VAR. ETKF predicts the variance of the “signal” : the impact of the targeted observations on the operational forecast. Impact on GFS analysis Impact on 1-day forecast Preliminary result from Hurricane Isabel ETKF uses 20-member 1-degree res NCEP GFS Ensemble, initialized 24-36h prior to targeted observing time. Impact on 2-day forecast
Ongoing Work • Examine 2003 targeting missions (5 Atlantic, 2 NW Pacific cyclones): Develop ETKF further, e.g. investigate vortex removal techniques and focus on storm asymmetries • Extend to “norms” of hurricane track and intensity (currently using simple norm of wind speed, which is used in WSR) • Synoptic assessment of ETKF summary maps and signal variance propagation • Quantitative tests of ETKF’s ability to predict signal variance and reduction in forecast error variance • Adapt scripts to run for GFDL and SHIPS models
Ongoing Work • ECMWF 1-deg ensembles are now available (Jan 04), and NCEP GFS 1-deg ensembles will soon be produced beyond 84h • ETKF software is being developed within the framework of Winter Storms: easy interface • Work in progress disseminated on Web site: http://orca.rsmas.miami.edu/~majumdar/tc/
Summer 2004 – Summer 2005 • Run improved ETKF for 1-deg ensembles for synoptic surveillance cases in upcoming 2004 hurricane season. • Regular communication with NHC once satisfactory version of ETKF is developed. • Communication with Taiwan (Dr Chun-Chieh Wu): Advising targeted observing locations for typhoon reconnaissance. • Combine ETKF software with flight track design software developed by Dr Sim Aberson under JHT funding. • Transition of ETKF software to operations.