improvements in the statistical prediction of tropical cyclone rapid intensification

1. improvements in the statistical prediction of tropical cyclone rapid intensification Christopher M. Rozoff CIMSS/University of Wisconsin-Madison chris.rozoff@ssec.wisc.edu J. Kossin (NOAA / NCDC, CIMSS), C. Velden (CIMSS), A. Wimmers (CIMSS), M. Kieper, J. Kaplan (NOAA / HRD), J. Knaff (NOAA / NESDIS / StAR), M. DeMaria (NOAA / NESDIS / StAR) 65th Interdepartmental Hurricane Conference 3 March 2011 Miami, FL

2. outline • an overview of new statistical RI forecasting techniques collaborators: C. Rozoff and J. Kossin funding: NOAA GIMPAP, NOAA/NCDC • an overview of new structural predictors from passive microwave imagery collaborators: C. Rozoff, C. Velden, A. Wimmers, M. Kieper, J. Kaplan, J. Knaff, M. DeMaria, and J. Kossin funding: NOAA GOES-RRR

3. improving rapid intensification forecasting • a top priority of NHC and a central focus of HFIP • NHC currently utilizes an RI forecast index derived from environmental and GOES-IR features in the Statistical Hurricane Intensity Prediction Scheme (SHIPS) developmental dataset (SHIPS-RII; Kaplan et al. 2010, WAF) • SHIPS-RII provides probabilities of a tropical cyclone intensifying by 25, 30, and 35 kt in 24 h • we will now overview two new additional statistical techniques and new structural features for use in any statistical technique

4. two new RI forecast schemes:from Rozoff and Kossin (2011; WAF) • logistic regression • naïve Bayes classifier here, the xL = (x1, x2, … , xN) represent certain environmental and GOES-IR features, bi represent the fitted coefficients obtained from a least-squares technique, and a represents the RI threshold of 25, 30, and 35-kt intensity change per 24 h

5. data • for the years 1995-2009, we use environmental and GOES-IR features from the SHIPS developmental dataset based on gridded operational global analysis data, available at 0000 and 1200 UTC each day prior to 2000 and at 0000, 0600, 1200, and 1800 UTC from 2000 to present • there are N = 2572 and 2614 data points for the Atlantic and East Pacific, respectively

6. optimal predictors • the procedure to find optimal predictors for the logistic regression and Bayesian models is further described in Rozoff and Kossin (2011; WAF) • the types of predictors that have proven optimal for the two schemes include features like ocean heat content, vertical wind shear, departure from MPI, relative humidity, static stability, upper-level divergence, persistence, and basic predictors developed from GOES-IR

7. Brier skill scores:leave-one-year-out cross validation note: where pais the model’s forecast, pc the climatological prob of RI, and do is 0 if there is no RI and 1 if there is RI observed.

8. reliability diagrams East Pacific Atlantic

9. forecast for Hurricane Wilma (2005) RI threshold of 25 kt / 24 h RI threshold of 35 kt / 24 h

10. ensemble-mean RI forecasts • we compare with the forecasts from SHIPS-RII (version trained on the same dataset for 1995-2009 and we use identical data screening) using dependent testing

11. passive microwave-based predictors:collaboration with C. Velden, A. Wimmers, M. Kieper, J. Kaplan, J. Knaff, M. DeMaria, and J. Kossin • we have assembled a database of MW imagery (Tb; Vertical, horizontal pol. and PCT) from multiple sensors (24-km resolution SSM/I, 8.8-10.2-km resolution TRMM-TMI, 12-km resolution AMSR-E, and 11-km resolution WINDSAT) for the period 1995-2008 • the value of the 37 GHz MW predictors in the probabilistic prediction of RI has been preliminarily assessed using the logistic regression model • the predictors are currently being tested for synoptic times at 0000, 0600, 1200, and 1800 UTC in both logistic regression, Bayesian, and SHIPS-RII models

12. passive microwave-based predictors:center finding • center finding is achieved using the “Automated Regional Center Hurricane Eye Retrieval” (ARCHER; Wimmers and Velden 2010), which uses a combination of 37-GHz Tb gradients associated with the TC’s spiral bands and the potential ring of higher Tb that often surrounds an existing or developing eye horizontally polarized Tb and objective ring [TMI; Danielle (2004)]

13. passive microwave-based predictors:predictors • common predictors examined include minimum, average, and maximum Tb for the vertically (V) and horizontally (H) polarized 37 GHz channel and polarization corrected temperatures (PCT) in regions depicting the eye and eyewall. The objective ring definition was used to define eyes and eyewalls • fixed geometry predictors were also tested • objective feature selection was used to find optimal MW predictors for the logistic scheme. The logistic scheme also used SHIPS predictors, as discussed earlier optimal Atlantic predictors

14. passive microwave-based predictors:skill evaluation – Atlantic(1995-2008; SSM/I, WINDSAT, TMI, AMSRE) tested using leave-one-year-out cross validation N = 3006 and 2302 for vmax >= 25 and 45 kt, respectively • similar results are seen for the Eastern Pacific skill evaluation • skill can be made considerably higher by only using hi-res sensors

15. conclusions:new RI models • we introduced two new RI statistical models based on logistic regression and Bayesian principles, both of which use SHIPS features • cross-validation demonstrates both models are skillful relative to climatology in both the Atlantic and eastern Pacific • a three-member ensemble-mean of the logistic, Bayesian, and SHIPS-RII models provides superior skill to any of the individual members: for a rapid intensification threshold of 25 kt per 24 h, the 3-member ensemble-mean improves the Brier skill scores relative to the current operational SHIPS-RII by 33% in the Atlantic and 52% in the eastern Pacific

16. Conclusions:new RI models – real-time testing • real-time testing will be carried out during the upcoming 2011/2012 seasons to account for the errors introduced in using forecast data for the predictors • operational transitions would be fairly straight-forward: • new models and ensemble-mean can add directly to SHIPS text output • uses the same data that SHIPS and SHIPS-RII use

17. conclusions:new microwave predictors • we have developed TC-structure related predictors exploiting 37-GHz passive microwave imagery from low-earth orbiting satellites • independent testing of the logistic RI model in both the Atlantic and eastern Pacific shows that adding certain microwave-based predictors improves both Brier skill score and probability of detection significantly • we are currently extending these techniques to 19- and 85-GHz microwave frequencies

18. conclusions:new microwave predictors -real-time testing • real-time MW imagery will be obtained and algorithms will be adapted to handle real-time testing in-house with ultimate goal to test in an operational setting. • would incorporate real-time streams of SSM/I, SSMI/S, AMSRE, AMSU-B (for its 89 GHz data), WINDSAT, and TRMM

20. optimal predictors Atlantic predictors East Pacific predictors

21. passive microwave-based predictors:using only high resolution AMSRE & TMI for2002 - 2008 • Inclusion of MW predictors can improve BSS of logistic regression model from 25.5 to 39.1% and POD from 26.7 to 56% in Atlantic (for RI threshold of 25 kt / 24 h and storms at least 45 kt in intensity) (N = 843) • BSS can improve from 28.0 to 38.3% and POD from 39.2 to 51.0% in the East Pacific (N = 475)