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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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slide1
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

outline
OUTLINE
  • 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
slide4
5 Basic Radial Profiles (Samsury and Rappaport 1991)

4

1

5

2

  • Develop objective method for
  • extracting similar information
  • Supplement with inner-core
  • GOES data

3

variational wind analysis for aircraft data
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
slide6
AF Recon Flight Level Winds for Hurricane Lili

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

slide7
AF Recon Flight Level Winds for Hurricane Lili

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

slide10
Isotach Analyses for Hurricane Lili

10/01 0000 UTC – 10/03 1200 UTC

slide11
Azimuthally Averaged Tangential Wind (r=0 to 200 km)

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

eof analysis
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
variance explained by each eof
Variance Explained by each EOF

Tang. Wind:

99% w\ 6 EOF

IR Brightness T:

99% w\ 4 EOF

tangential wind and ir eofs
Tangential Wind and IR EOFs

Tang. Wind 1-3

Tang. Wind 4-6

IR 1-4

part 1 project schedule
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
slide18
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)
slide19
Monte Carlo

Wind Probability

Model

Example:

Hurricane Fabian

Aug 31 2003 18Z

Vmax=115 kt

R34 100 75 75 100

R50 30 30 30 30

R64 20 20 20 20

modifications based on 2003 results
Modifications based on 2003 Results
  • 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
slide21
Impact of Model Changes

(Fabian 2003 Example)

Old

New

slide23
N=500 N=1000

Sensitivity to

the number of

realizations

N=2000 N=500000

slide24
R34

R50

Typhoon

Maemi

9/9/04 06 Z

Vmax=115 kt

R34 130 130 130 130

R50 50 50 50 50

R100 20 20 20 20

N=2000

R64

R100

part 2 project schedule
Part 2 Project Schedule
  • 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
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