Improvements in Deterministic and Probabilistic Tropical Cyclone Wind Predictions:

A Joint Hurricane Testbed Project Update

Mark DeMaria and Ray ZehrNOAA/NESDIS/ORA, Fort Collins, CO

 John Knaff and Kimberly Mueller

CIRA/CSU, Fort Collins, CO

Presented at The Interdepartmental Hurricane ConferenceMarch 2005 Jacksonville, FL

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?

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

SHIPS Forecast Skill 2004 Atlantic Sample

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12 24 36 48 60 72 84 96 108 120

Forecast Interval (hr)

Err

or

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lati

ve

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IFO

R5

Operational

Modified Decay Model

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

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

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

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Radius (km)

Bri

gh

tnes

s T

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(C

)

AzimuthalAverage

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

GRIP Model Results2004 Independent Cases

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12 24 36 48 60 72 84 96 108 120

Forecast Interval (hr)

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rce

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Physical Input

Principal Component Input

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

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

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

Sample 34 kt Wind Probabilities

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)

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

48 Hour Intensity Errors1985-2004

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PersistenceSHIFOROFCLSHIPSGFDL

48 Hour Intensity Forecast ErrorsNormalized by Persistence Errors

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1985

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2004

Year

Imp

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%)

OFCL

SHIPS

SHIFOR

Linear (OFCL)

Linear (SHIPS)

Linear (SHIFOR)

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.