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 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 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

5 Basic Radial Profiles (Samsury and Rappaport 1991) Develop objective method for extracting similar information Supplement with inner-core GOES 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

AF Recon Flight Level Winds for Hurricane Lili Earth-Relative 10/02/ UTC

AF Recon Flight Level Winds for Hurricane Lili Storm-Relative 10/02/ UTC

Variational Wind Analysis for Lili 10/02/ UTC

Isotachs (kt) from Variational Wind Analysis for Lili 10/02/ UTC

Isotach Analyses for Hurricane Lili 10/ UTC – 10/ UTC

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 ( Cases)

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 Tang. Wind: 99% w\ 6 EOF IR Brightness T: 99% w\ 4 EOF

Tangential Wind and IR EOFs Tang. Wind 1-3 IR 1-4 Tang. Wind 4-6

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

Preliminary Neural Network Results Dependent data test with Sample

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)

Monte Carlo Wind Probability Model Example: Hurricane Fabian Aug Z V max =115 kt R R R

Modifications based on 2003 Results Model Changes –Improved portable random number generator –Complete error field sampling (instead of 1-99 th 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

OldNew Impact of Model Changes (Fabian 2003 Example)

Effect of Number of Realizations on Probability Estimate

Sensitivity to the number of realizations N=500 N=1000 N=2000 N=500000

R34 R50 R64 R100 Typhoon Maemi 9/9/04 06 Z V max =115 kt R R R N=2000

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

Old vs. Improved Comparison Landfall of Isabel