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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 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


Outline
Outline Cyclone Wind Predictions:

  • 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?


The 2004 ships model
The 2004 SHIPS Model Cyclone Wind Predictions:

  • 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


Ships forecast skill 2004 atlantic sample
SHIPS Forecast Skill Cyclone Wind Predictions: 2004 Atlantic Sample


Aircraft data in the grip model
Aircraft Data in the GRIP Model Cyclone Wind Predictions:

  • 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


Sample analysis for hurricane jeanne 2004
Sample Analysis for Hurricane Jeanne 2004 Cyclone Wind Predictions:

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


Goes data in the grip model
GOES Data in the GRIP Model Cyclone Wind Predictions:

  • SHIPS already includes cold pixel count and Tb standard deviation (area averages)

  • Examine radial structure of GOES data for predictive signal

Azimuthal

Average


Grip model statistical development
GRIP Model Statistical Development Cyclone Wind Predictions:

  • 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


Grip model results 2004 independent cases
GRIP Model Results Cyclone Wind Predictions: 2004 Independent Cases


2005 grip model
2005 GRIP Model Cyclone Wind Predictions:

  • 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


Neural network model short version it didn t work
Neural Network Model Cyclone Wind Predictions: (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


Monte carlo wind probability model
Monte Carlo Wind Probability Model Cyclone Wind Predictions:

  • 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


Sample 34 kt wind probabilities
Sample 34 kt Wind Probabilities Cyclone Wind Predictions:


2005 monte carlo model
2005 Monte Carlo Model Cyclone Wind Predictions:

  • 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)


Are intensity forecasts improving
Are Intensity Forecasts Improving? Cyclone Wind Predictions:

  • 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


48 hour intensity errors 1985 2004
48 Hour Intensity Errors Cyclone Wind Predictions: 1985-2004


48 hour intensity forecast errors normalized by persistence errors
48 Hour Intensity Forecast Errors Cyclone Wind Predictions: Normalized by Persistence Errors


Summary
Summary Cyclone Wind Predictions:

  • 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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