Improvements in Deterministic and Probabilistic
Download
1 / 25

Improvements in Deterministic and Probabilistic Tropical Cyclone Surface Wind Predictions Joint Hurricane Testbed P - PowerPoint PPT Presentation


  • 180 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Improvements in Deterministic and Probabilistic Tropical Cyclone Surface Wind Predictions Joint Hurricane Testbed P' - marlee


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Slide1 l.jpg
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 l.jpg
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


Decay ships and nhc intensity forecast skill 2001 2003 l.jpg
Decay-SHIPS and NHC Intensity Forecast Skill 2001-2003


Slide4 l.jpg

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 l.jpg
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 l.jpg

AF Recon Flight Level Winds for Hurricane Lili

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


Slide7 l.jpg

AF Recon Flight Level Winds for Hurricane Lili

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


Slide8 l.jpg

Variational Wind Analysis for Lili

10/02/02 0000-1200 UTC



Slide10 l.jpg

Isotach Analyses for Hurricane Lili

10/01 0000 UTC – 10/03 1200 UTC


Slide11 l.jpg

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

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


Comparison of best track and variational analysis maximum wind 1995 2002 cases l.jpg
Comparison of Best Track and Variational Analysis Maximum Wind(1995-2002 Cases)


Eof analysis l.jpg
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 l.jpg
Variance Explained by each EOF

Tang. Wind:

99% w\ 6 EOF

IR Brightness T:

99% w\ 4 EOF


Tangential wind and ir eofs l.jpg
Tangential Wind and IR EOFs

Tang. Wind 1-3

Tang. Wind 4-6

IR 1-4


Part 1 project schedule l.jpg
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


Slide17 l.jpg

Preliminary Neural Network ResultsDependent data test with 1989-2002 Sample


Slide18 l.jpg
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 l.jpg

Monte Carlo Probabilities

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 l.jpg
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


Slide21 l.jpg

Impact of Model Changes Probabilities

(Fabian 2003 Example)

Old

New



Slide23 l.jpg

N=500 N=1000 Probabilities

Sensitivity to

the number of

realizations

N=2000 N=500000


Slide24 l.jpg

R34 Probabilities

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 l.jpg
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


ad