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Improvements in Deterministic and Probabilistic Tropical Cyclone Surface Wind Predictions Joint Hurricane Testbed Project Status Report. Mark DeMaria NOAA/NESDIS/ORA, Fort Collins, CO John A. Knaff, Jack Dostalek and Kimberly J. Mueller CSU/CIRA, Fort Collins, CO

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Improvements in Deterministic and Probabilistic Tropical Cyclone Surface Wind PredictionsJoint Hurricane Testbed ProjectStatus Report

Mark DeMaria

NOAA/NESDIS/ORA, Fort Collins, COJohn A. Knaff, Jack Dostalek and Kimberly J. Mueller

CSU/CIRA, Fort Collins, CO

Collaborators: Jim Gross (TPC), Charles Anderson (CSU),

Buck Sampson (NRL),Miles Lawrence(TPC), Chris Sisko (TPC)

Presented at The Inter-Departmental Hurricane Conference

March 3, 2004 Charleston, SC

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  • Deterministic Intensity Forecast Improvements

    • Can inner core data from aircraft and satellite improve SHIPS forecasts?

      • Automated objective analysis and EOF analysis

    • Compare neural network and linear regression models

  • Probabilistic Surface Wind Forecast Improvements

    • Calculate operational track/intensity and wind radii-CLIPER error distributions

    • Randomly sample errors using Monte Carlo method

      • Generate probabilities of 34, 50, 64 and 100 kt winds

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Decay-SHIPS and NHC Intensity Forecast Skill 2001-2003

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5 Basic Radial Profiles (Samsury and Rappaport 1991)





  • Develop objective method for

  • extracting similar information

  • Supplement with inner-core

  • GOES data


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Variational Wind Analysis for Aircraft Data

  • Combine 12 hours of recon data in storm-relative coordinates

  • Perform automated quality control

    • Analyze data to determine if coverage is sufficient

      • Designed to measure at least azimuthal wavenumber 0 and 1

    • Compare data to “pre-analysis” to eliminate bad points

  • Perform “variational” analysis to provide u,v on radial, azimuthal grid

    • azimuthal smoothing >> radial smoothing

    • Based on Thacker and Long (1988)

  • Preliminary prediction based upon azimuthal average tangential wind

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AF Recon Flight Level Winds for Hurricane Lili

Earth-Relative 10/02/02 0000-1200 UTC

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AF Recon Flight Level Winds for Hurricane Lili

Storm-Relative 10/02/02 0000-1200 UTC

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Variational Wind Analysis for Lili

10/02/02 0000-1200 UTC

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Isotach Analyses for Hurricane Lili

10/01 0000 UTC – 10/03 1200 UTC

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Azimuthally Averaged Tangential Wind (r=0 to 200 km)

Hurricane Lili 10/01 00 UTC to 10/03 12 UTC

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Comparison of Best Track and Variational Analysis Maximum Wind(1995-2002 Cases)

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EOF Analysis

  • ~400 cases with recon and IR data (95-03)

  • 51 radial grid points, r = 4 km

  • How to relate 102 IR and wind values to intensity change?

    • Empirical Orthogonal Function (EOF) Analysis

    • Mathematical technique for extracting common patterns from datasets

    • Apply to tangential wind and IR radial profiles

    • Work with small set of patterns instead of the entire profiles

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Variance Explained by each EOF

Tang. Wind:

99% w\ 6 EOF

IR Brightness T:

99% w\ 4 EOF

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Tangential Wind and IR EOFs

Tang. Wind 1-3

Tang. Wind 4-6

IR 1-4

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Part 1 Project Schedule

  • Spring 2004: Develop statistical intensity model using EOF amplitudes

    • Provide adjusted SHIPS forecast based upon inner core information

  • Spring 2004: Compare neural network and regression techniques

    • Collaboration with Dr. Charles Anderson, CSU Computer Science Department (Expert in Machine Learning Techniques)

  • Summer 2004: Implement variational aircraft analysis at NHC/JHT

  • Summer/Fall 2004: Test results on real-time forecasts

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Preliminary Neural Network ResultsDependent data test with 1989-2002 Sample

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Monte Carlo Model for Tropical Cyclone Surface Wind Probabilities(Initial support from Insurance Friends of the National Hurricane Center)

  • Calculate NHC track and intensity errors (along track and cross track) from multi-year sample

  • Determine large set of tracks and intensities (realizations) centered around official forecast by randomly sampling from error distributions

  • Estimate wind radii distributions from errors of radii-CLIPER model

  • Calculate probabilities by number of times specified point comes with radii of specified wind speed relative to total number of realizations

  • Run in real-time in 2003 season (starting August)

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Monte Carlo Probabilities

Wind Probability



Hurricane Fabian

Aug 31 2003 18Z

Vmax=115 kt

R34 100 75 75 100

R50 30 30 30 30

R64 20 20 20 20

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Modifications based on 2003 Results Probabilities

  • Model Changes

    • Improved portable random number generator

    • Complete error field sampling (instead of 1-99th percentiles)

    • Modified for use in the Atlantic, East/Central Pacific, and western North Pacific basins (i.e., Longitude … 0-360)

    • Option for 100 kt radii added for JTWC

  • Error Components

    • Improved radii-CLIPER model

      • Inclusion of initial wind radii asymmetries

        • radii match observed at t=0 hr

      • R34 bias correction

    • Intensity errors account forecast intensity and distance to land

    • Distributions being updated with 2003 cases

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Impact of Model Changes Probabilities

(Fabian 2003 Example)



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N=500 N=1000 Probabilities

Sensitivity to

the number of


N=2000 N=500000

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R34 Probabilities




9/9/04 06 Z

Vmax=115 kt

R34 130 130 130 130

R50 50 50 50 50

R100 20 20 20 20




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Part 2 Project Schedule Probabilities

  • Spring 2004: Investigate variable grid options

    • Improve efficiency and for NDFD applications

  • Spring 2004: Finalize probability model for 2004 season

  • Summer/Fall 2004: Run at NHC in real-time for Atlantic and East Pacific cases

  • Summer/Fall 2004: Coordinate with JTWC for real-time tests (directly on their ATCF)

  • Winter 2004: Evaluate results from 2004 runs

  • 2004 “Freebie”: Provide NHC and JTWC with updated Radii-CLIPER models