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Improvements in Deterministic and Probabilistic Tropical Cyclone Wind Predictions: A Joint Hurricane Testbed Project Up

Improvements in Deterministic and Probabilistic Tropical Cyclone Wind Predictions: A Joint Hurricane Testbed Project Update. Mark DeMaria and Ray Zehr NOAA/NESDIS/ORA, Fort Collins, CO John Knaff and Kimberly Mueller CIRA/CSU, Fort Collins, CO

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Improvements in Deterministic and Probabilistic Tropical Cyclone Wind Predictions: A Joint Hurricane Testbed Project Up

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  1. Improvements in Deterministic and Probabilistic Tropical Cyclone Wind Predictions: A Joint Hurricane Testbed Project Update Mark DeMaria and Ray Zehr NOAA/NESDIS/ORA, Fort Collins, COJohn Knaff and Kimberly Mueller CIRA/CSU, Fort Collins, CO Presented at The Interdepartmental Hurricane Conference March 2005 Jacksonville, FL

  2. Outline • Deterministic Intensity Prediction • GOES and Recon Intensity Prediction (GRIP) model • Predictors from aircraft recon and IR radial structure combined with SHIPS forecasts • Evaluate neural network techniques • Probabilistic Intensity Prediction • Monte Carlo wind probability model • Results from 2004 • 2005 Plans • Are Intensity Forecasts Improving?

  3. The 2004 SHIPS Model • Statistical-dynamical intensity model (12-120 hr) • Developed from 1982-2003 sample • Empirical decay for portion of track over land • Track from adjusted 6-hour old NHC forecast • Version with satellite input operational for 2004 • SHIPS Input • Climatological: Julian Day • Atmospheric Environment: Shear, T200, 200, 850 • Oceanic Environment: SST, Ocean Heat Content • Storm Properties: Vm, dVm/dt, motion, PSL, lat, GOES Cold Pixel Count, GOES TB Std Dev • Most storm property inputs are indirect measurements

  4. SHIPS Forecast Skill 2004 Atlantic Sample

  5. Aircraft Data in the GRIP Model • USAF Reserve and NOAA aircraft data • Highly utilized for intensity estimation • Under utilized for intensity prediction • Real time automated analysis system • Real time aircraft database set up on NCEP IBM (C. Sisko) • Move data to storm-relative coordinates • Automated quality control • Test for data coverage • Gross error check • Check deviations from pre-analysis • Variational objective analysis in cylindrical coordinates • Greater azimuthal than radial smoothing

  6. Sample Analysis for Hurricane Jeanne 2004 Input Data Wind Analysis Isotachs 358 Dependent Cases (1995-2003, 12 hour intervals) 124 Independent Cases (2004, 6 hour intervals) Input to GRIP Model: Azimuthally Averaged Tangential Wind

  7. GOES Data in the GRIP Model • SHIPS already includes cold pixel count and Tb standard deviation (area averages) • Examine radial structure of GOES data for predictive signal Azimuthal Average

  8. GRIP Model Statistical Development • GRIP Predictors • EOF Version • SHIPS Forecast • Amplitudes of first four EOFs of GOES and Recon profiles (principal components) • Physical Version • SHIPS Forecast • 10 physical parameters from GOES and recon profiles • Final GRIP Model • EOF Version • SHIPS forecast, 2 recon PCs, 1 GOES PC • Physical Version • SHIPS forecast, 3 recon variables, 1 GOES variable • Both versions tested on 124 cases from 2004 Atlantic season

  9. GRIP Model Results2004 Independent Cases

  10. 2005 GRIP Model • Add 2004 cases and re-derive the coefficients • ~20% increase in sample size • Consider combined EOF and physical variable version • Run in real time during 2005 season for further evaluation

  11. Neural Network Model(Short Version: It didn’t work) • NN Model Development • SHIPS dependent dataset used for training • Non-satellite version • Development by Prof. Chuck Anderson, CSU computer science department • 5 to10% reduction in mean absolute error in dependent sample (12-120 hr) • Independent tests • 2-5% degradation • NN Method appears to over-fit training data • One final try with more stringent fitting requirements • Restrict input to only those predictors selected by SHIPS

  12. Monte Carlo Wind Probability Model • Provides 5 day surface wind probabilities • 34, 50 and 64 kt • Historical NHC track, intensity and radii-CLIPER error distributions • Includes forecast interval time continuity and bias corrections • Run in real time on NCEP IBM during 2004 • Results displayed on password-protected CIRA web site • Atlantic, east, central and western N. Pacific sectors

  13. Sample 34 kt Wind Probabilities

  14. 2005 Monte Carlo Model • Move web page to TPC w\ N-AWIPS graphics • Add t=0 hour probabilities • Include radii adjustment • Convert max in quadrant to average in quadrant • Ratios based upon H*Wind analyses • Provide TPC with distribution calculation code • Text product under development • Training being developed • Verification system still needed • Verification system could be used for all TC probabilistic forecasts (ensemble based, etc)

  15. Are Intensity Forecasts Improving? • 20 Year Atlantic sample (1985-2004) • Verification with consistent set of rules • All cases except extra tropical • Official, Persistence, SHIFOR, SHIPS and GFDL • Consider only 48 hour forecasts

  16. 48 Hour Intensity Errors1985-2004

  17. 48 Hour Intensity Forecast ErrorsNormalized by Persistence Errors

  18. Summary • GRIP Model to be tested in real time during 2005 season • 2004 results are encouraging • Last chance for neural network model • Monte Carlo probability model development continuing in 2005 • Intensity forecasts are improving Ref: Further improvements to SHIPS, Weather and Forecasting, in press.

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