1. FY09 GIMPAP Project Proposal Title Page Revised: October 31, 2008

1. 1. FY09 GIMPAP Project Proposal Title PageRevised: October 31, 2008 • Title: Analysis and Application of GOES IR Imagery Toward Improving Hurricane Intensity Change Prediction • Project Type: 1 • Status: Renewal • Duration: 2 years • Lead: Chris Rozoff • Other Participants: • Jim Kossin • Matt Sitkowski (Ph. D. student)

2. 2. Project Summary GOES infrared imagery will be analyzed with the goal of deriving information that increases the skill of North Atlantic and Northern East Pacific hurricane intensity forecasts. Using SHIPS and GOES infrared imagery, our goal will be to construct intensification and rapid intensification prediction-indices based on Bayesian Classification techniques. 2

3. 3. Motivation/Justification Supports NOAA Mission Goals: Weather and Water, and Homeland Security. Improvements in hurricane intensity and rapid intensity forecasting is a major goal of NOAA. One of the greatest threats to coastal communities and marine interests is a poorly forecasted rapid intensification event in a hurricane, but our ability to accurately forecast such events is presently limited. This study has good potential to improve our operational ability to forecast rapid intensification events. 3

4. 4. Methodology Utilizing the new HURSAT record, we will form a new rapid intensification index using a Bayesian classification method. In this method, we use class-conditioned probabilities for the predictors and a priori probabilities that are based on climatology. The conditional probabilities of the predictors are determined from optimally-smoothed kernel density estimation. This type of analysis has potential to increase forecast skill beyond the LDA method. 4

5. 5. Expected Outcomes If the rapid intensity statistical forecast scheme is successful, we will be able to provide a tool to forecasters that will provide additional objective guidance in forecasting rapid hurricane intensification events. In this case, we will coordinate with John Kaplan at the NOAA Hurricane Research Division and Mark DeMaria at NESDIS/CIRA to implement our findings into the existing operational product. 5

6. 6. Progress in FY08 Milestones FY08 Combine extended GOES-based SHIPS predictors with the remaining SHIPS predictors: The extended predictors have been handed off to our CIRA/RAMM-team collaborators who have now developed the code needed to convert the new satellite dataset to the format required for SHIPS. Perform regression analyses and independent testing using storm-by-storm cross-validation These steps will be performed after our CIRA/RAMM-team collaborators complete the incorporation of the new data into SHIPS. Document the new SHIPS error distributions. If warranted, begin the process of updating SHIPS (in collaboration with CIRA/RAMM) and begin preparing a manuscript to broadly disseminate the results. These steps will be performed after our CIRA/RAMM-team collaborators complete the incorporation of the new data into SHIPS. Begin revisiting the present Rapid Intensification (RI) index of Kaplan and DeMaria using the extended GOES predictors from our HURSAT record. We have reproduced the results of Kaplan and DeMaria and found that additional information from the HURSAT record can increase the skill of rapid intensification forecasts. Begin exploring and formulating new methodology for the RI index. The lion’s share of our progress has been made here and is described on the following slide. 6

7. 6. Progress in FY08 Milestones (continued) We completed a “first version” of a new Rapid Intensification forecasting scheme that uses a Bayes probabilistic model and environmental predictors from SHIPS and GOES IR brightness temperatures for both the Atlantic and Eastern Pacific Ocean basins. We completed a rigorous cross validation of the model for the period 1989-2006. Our first series of tests used predictors that were used in Kaplan and DeMaria (2006), including sea-surface temperature, the difference between the current and empirically derived maximum potential intensity, the 850-200 hPa vertical shear, the 850-700 hPa relative humidity, the previous 12-h intensity change, and predictors derived from GOES infrared data, including the percentage of area from 50-200 km radius with brightness temperatures less than -30 C and the standard deviation of brightness temperatures from 100-300 km radius. Using “leave-one-out” cross validation, the Brier Skill Scores are 10 and 12.6 % for the Atlantic and East Pacific Ocean basins, respectively. The false alarm rates for each basin are 0.4 and 0.5 %, respectively. On the other hand, Pierce scores are only 6 and 9 %, respectively. 7

8. 6. Progress in FY08 Milestones (continued) As an example, we apply the Bayes rapid intensity forecast scheme to Hurricane Wilma (2005). Rapid intensification occurred during the period between the red dashed lines. Gaps indicate when Wilma was over land. We explored new GOES IR-based predictors in the Atlantic Ocean, including features derived from a principle component analysis of azimuthal mean radial profiles of brightness temperatures. Initial cross-validation indicates that the Bayes model is up to 5% more skillful (using the Brier Skill Score) when using the principle component analysis predictors. 8

9. 7. FY09 Milestones FY09 Complete the formulation and error analyses of the new Rapid Intensification index based on Bayesian classification methodology. Extend the IR brightness temperatures principle component analysis to the East Pacific and evaluate the impact of its derived predictors on the Bayesian forecast scheme. Include the years 2007 and 2008 in the training set for the Bayesian scheme. Experiment with more sophisticated pattern recognition techniques, including Fourier analysis, on data derived from available satellite platforms. If results warrant, complete a manuscript to disseminate the results, and begin the communication processes required to ultimately transition the results to operations. 9

10. 8. Funding Profile (K) Summary of leveraged funding For FY08, we can leverage residual funds from our $35 K FY07 GIMPAP funding. One goal for FY07 was the construction and subsequent addition of new predictors in SHIPS, but we found that the new predictors did not increase the skill of SHIPS. Consequently, a portion of the analyses proposed for FY07 is not warranted, and will apply $10K of our FY07 GIMPAP funding to our proposed FY08 work (bringing the FY08 budget to $50K). 10

11. 9. FY09 Expected Purchase Items • FY07 • (25K): STAR CIMSS Grant for 2 people (5%-time PI Kossin and 25%-time graduate student Chiou-Jiu Chen). • FY08 • (40 K + 10K from FY07): STAR CIMSS Grant for 2 people (15%-time PI Kossin and 25%-time Graduate Research Assistant). • Personnel support (including benefits, IT charges, overhead, etc): 45K • Contracts: N/A • Software charges: N/A • Equipment: N/A • Travel (two people - 2008 interdepartmental Hurricane Conference) - 3K • Publication Charges - 2K • FY09 • (30K): STAR CIMSS Grant for 1 person (37%-time for Chris Rozoff) • Personnel support (including benefits, IT charges, overhead, etc): 25K • Contracts: N/A • Software charges: N/A • Equipment: N/A • Travel (two people - 2009 interdepartmental Hurricane Conference) - 3K • Publication Charges - 2K