Improvements in Deterministic and Probabilistic Tropical Cyclone Wind Predictions: A Joint Hurricane Testbed Project Update Mark DeMaria and Ray Zehr NOAA/NESDIS/ORA, Fort Collins, CO John Knaff and Kimberly Mueller CIRA/CSU, Fort Collins, CO Presented at The Interdepartmental Hurricane Conference March 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 Plans Are Intensity Forecasts Improving?

The 2004 SHIPS Model Statistical-dynamical intensity model ( hr) Developed from 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: V m, dV m/ dt, motion, PSL, lat, GOES Cold Pixel Count, GOES T B Std Dev Most storm property inputs are indirect measurements

SHIPS Forecast Skill 2004 Atlantic Sample

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 ( , 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 T b standard deviation (area averages) Examine radial structure of GOES data for predictive signal Azimuthal Average

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 Results 2004 Independent Cases

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 ( 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 ( ) 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 Errors

48 Hour Intensity Forecast Errors Normalized by Persistence Errors

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.