61 st Interdepartmental Hurricane Conference, 3.6.08

1. The NESDIS Tropical cyclone formation probability product:Past performance and future plansAndrea B. Schumacher, CIRAMark DeMaria, NOAA/StARJohn A. Knaff, NOAA/StARDaniel Brown, NOAA/NWS TPC 61st Interdepartmental Hurricane Conference, 3.6.08

2. Motivation • Create a product that provides objective, real-time TC formation guidance • Current NHC TC formation guidance subjective • Working on developing probabilistic forecasts of TC formation (although still relatively subjective) • Benefit from objective guidance • What about models? • As resolution gets finer, forecast models gets better at handling TC formation • However, still bias towards over-prediction (Beven 1999)

3. Approach • Use what we already know about TC formation (i.e., environmental and convective parameters) • Use the statistical process of linear discriminant analysis (Perrone & Lowe 1986, Hennon & Hobgood 2003, Knaff et al. 2008) • Compute 24-hour probability of TC formation over all 5 x 5 lat/lon grid boxes in domain

4. Approach “A Needle in a Haystack” Ratio of TC formation to non-formation points ~ 1:2000 Maximum climatological formation probability ~1.8% (E. Pacific) Use what we already know about TC formation (i.e., environmental and convective parameters) Use the statistical process of linear discriminant analysis (Perrone & Lowe 1986, Hennon & Hobgood 2003, Knaff et al. 2008) Compute 24-hour probability of TC formation over all 5 x 5 lat/lon grid boxes in domain

5. Datasets • NHC/DOD Best Tracks 1949-2005 • Atlantic, E. Pacific, Central Pacific & W. Pacific • Subtropical/Extratropical cases excluded • Unnamed depressions included since 1989 • NCEP Global Model Analyses 1995-2005 • Reanalysis 1995-1999 (2.5o grid) • Operational Analyses 2000-2005 (2.0o grid) • Geostationary Satellite Water Vapor Imagery (16 km Re-mapped Mercator projection) • GOES-E 1995-2005 • GOES-W 1998-2005 • GMS-5 / GOES-9 / MTSAT-1R 2000-2005

6. Parameters

7. Product Algorithm Remove all points from dataset for which TC formation is highly unlikely (< 5 % of TCG points removed) Use dependent dataset to determine probabilities associated with each discriminant function value

8. Recent Product Updates • Added 2004 & 2005 to dependent dataset • Expanded to cover Central and Western N. Pacific basins • Uses GOES-E, GOES-W and MTSAT-1R water vapor imagery, divides domain into 3 basins (below) • Added HDIV (850 hPa horiz. divergence) parameter • Scheduled to be operational Summer 2008

9. Parameter Contributions By Basin

10. Product Performance

11. Reliability Diagrams • Both dependent years (orange) and independent years (green) show good agreement between predicted and observed TC formation probabilities • E. Pacific basin diagram suggests the actual TC formation probability may be greater than what the TCFP product predicts at higher probabilities – may need correction

12. Verification – Relative Operating Characteristic and Brier Scores • In this framework, TC formation is extremely rare (Ratio of TC form to non-form ~ 1:2000) • Typical metrics are not valid for such rare events. However, we can use metrics that compare TCFP product to climatology • Brier Skill Score: BSS = 1 - BS/BSref • ROC Skill Score: RSS = ROC/ROCref - 1 • Brier Score, BS = 1/N Σi=1,n (pi-oi)2, where pi and oi are the predicted and observed probability for case i, respectively • ROC is the area under the plot of hit rate vs. false alarm rate for various probability thesholds.

13. Skill Scores & Interpretation • BSS positive but small ??? • RSS for E. Pacific much smaller than others  hard to beat climatology in that basin

14. TCFP has been Summary

15. Future Plans & Improvements • Expand domain to include S. Hemisphere & Indian Ocean • Extend forecast period from 24 h to 48 h + • Use GFS forecast fields • Analyze global water vapor strip to identify upstream predictors, particularly convective signatures associated with tropical waves (Frank and Roundy 2006) • Identify new parameters to add to algorithm • TAFB invest locations and intensities (D. Brown)

17. Definition of Formation • Practical Definition of TC Formation: • The first point in the best track • Excludes extratropical, subtropical, wave, remnant low, disturbance designations TC Formation Locations 1949-2005

18. Conditions playing role in TCG • Gray (1968), Simpson (1971), Hebert (1978) • Gray (1975, 1979), DeMaria, Knaff and Connell (2001) • Parameter for the tropical Atlantic, combining satellite data and model analyses • Perrone and Lowe (1986), Hennon and Hobgood (2003) • Linear discriminant analysis in disturbance-relative environment • Approach: • Generalize PL86 and HH03. • Create real-time product that estimates the probability of TC formation within the next 24 hours in each 5o by 5o lat/lon area in domain.

19. Step 2: Screen Data • Purpose: Remove all points from dataset for which TC formation is highly unlikely • Approximately 5% of TC formation cases removed, while 75-80% of non-formation cases removed • Ratio of TCG to NTCG cases ~ 1 to 300-600

20. LDA cont… • CPROB, CIRC and DSTRM are largest contributors in all 3 basins • Ratio of TCG to NTCG cases ~ 1 to 7-17, but also reduced hit rates to 9-15%. • LDA produces a binary prediction scheme, whereas a probabilistic scheme is desired Standardized LDA coefficients for each parameter, each basin

21. Step 3: Linear Discriminant Analysis All dependent data points belong to one of two groups: TC formation or non-formation cases. The goal of linear discriminant analysis is to distinguish which group a data point belongs to, based on its environmental & convective parameter values x1,x2,…,xk. This is done by solving for coefficient values a1,a2,…,ak so that LDA determines a1,a2,…,ak so as to maximize the distance between the group means for each parameter in standard deviation units. DF=0 Graphical Interpretation of LDA for Case With 2 Predictors (x,y) and 2 Groups (From www.doe-mbi.ucla.edu)

22. TC Formation Parameters • Algorithm required a set of parameters believed important to TC formation:

23. The TCFP Algorithm • Step 1: Compute parameter values for each 5° x 5° sub-region in the analysis domain at each analysis time (0, 6, 12 and 18 UTC) • Due to temporal inconsistencies in satellite availability, algorithm was independently applied to each of the 3 basins (below).

24. Product Development • To derive a probabilistic scheme, the LDA function values are related to probabilities using the dependent cases • Example: Atlantic basin

25. Other Projects & Interests • Developing simple inner core SST cooling parameterization that uses translational speed, intensity and OHC • Update SHIPS param, which uses latitude and translational speed only • Societal impacts of landfalling hurricanes • Quick Response Grant, 2008 • Hope to improve understanding of the needs of pet care professionals during disasters

26. Additional Slides… If Needed

27. Product Verification – Annual Integrated Probability

28. Super Typhoon Sepat • http://rammb.cira.colostate.edu/products/tc_realtime/storm.asp?storm_identifier=WP092007