1. THE NESDIS TROPICAL CYCLONE FORMATION PROBABILITY PRODUCT: PAST PERFORMANCE AND FUTURE PLANS Andrea B. Schumacher, CIRA Mark DeMaria, NESDIS/StAR John A. Knaff, NESDIS/StAR Daniel Brown, NOAA/NWS TPC 61 st Interdepartmental Hurricane Conference, 3.6.08

2. Motivation  Need for objective, real-time TC formation guidance  NHC needs objective guidance to help make Tropical Cyclone formation forecasts  Numerical models provide objective guidance As resolution improves, forecast models gets better at handling TC formation However, still bias towards over-prediction (Beven 1999)

3. Approach  Compute 24-hour probability of TC formation over all 5º x 5º lat/lon grid boxes in domain by analyzing environmental & convective conditions

4. Approach  Compute 24-hour probability of TC formation over all 5º x 5º lat/lon grid boxes in domain by analyzing environmental & convective conditions “A Needle in a Haystack” Ratio of TC formation to non-formation points ~ 1:2000 Maximum climatological formation probability ~1.8% (E. Pacific)

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 (2.5 o grid)  Operational Analyses 2000-2005 (1.0 o 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 ABBREVParameter DescriptionSource LATLatitude ( ° )ATCF PLAND% land coverageLand covrge DSTRMDistance to nearest TC (km)ATCF CSSTClimatological sea surface temp ( ° C)Levitus VSHEAR850-200 hPa vertical shear (kt)NCEP GFS CIRC850 hPa circulation (kt)NCEP GFS THDEVVertical instability ( ° C)NCEP GFS HDIV850 hPa horizontal divergence (m/s)NCEP GFS PCCOLD% coverage by pixels colder than 40 CSat WV BTWARMAvg. cloud-cleared brightness temp ( ° C)Sat WV CPROBClimatological formation probabilityBest Track

7. Product Algorithm  Screening Step:  Remove all points where TC formation is highly unlikely  < 5% TCG points removed  75-80% non-TCG points removed

8. Product Algorithm  Linear Discriminant Analysis Step:  Uses developmental dataset parameter values  Solves for coefficients a 0,..,a n so that the distance between parameter means for each group (TCG and non-TCG) are maximized in standard deviation units  For each independent data point with parameter values x 1,…,x n, the LDF determines group membership, where LDF = a 0 + a 1 x 1 + … + a n x n  Deterministic (yes/no) *Perrone & Lowe (1986), Hennon & Hobgood (2003), Knaff et al. (2008)

9. Product Algorithm  Probability Determination Step:  Use dependent dataset to develop a relationship between LDF and TC formation probability  Coefficients and LDF/probability relationship defines the TCFP product

10. 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 replace operational product by Summer 2008

11. Parameter Contributions By Basin ABBREVATLCEPACWPAC LAT PLAND-0.21-0.27-0.20 DSTRM0.800.681.15 CSST VSHEAR-0.37-0.22-0.17 CIRC1.331.411.59 THDEV HDIV-0.22-0.39-0.33 PCCOLD0.630.290.56 BTWARM CPROB1.471.670.88

12. Product Example: WP11 2006

13. 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

14. Verification – Relative Operating Characteristic and Brier Scores  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/BS ref  ROC Skill Score: RSS = ROC/ROC ref - 1 Brier Score, BS = 1/N Σ i=1,n (p i -o i ) 2, where p i and o i 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.

15. Skill Scores & Interpretation  BSS positive, but small  RSS for E. Pacific much smaller than others  hard to beat climatology in that basin Dependent YearsIndependent Years Basin: ATLCEPACWPACATLCEPACWPAC BSS0.0120.0240.0250.0100.0320.021 All BSS > 0  skill over climatology RSS0.210.010.22.11.01.17 All RSS > 0  skill over climatology

16. Summary  The current TCFP has run operationally since 2004 and has shown skill with respect to climatology (not shown)  The TCFP has been successfully updated and extended to cover the Central and Western N. Pacific  2007 verification of the updated TCFP demonstrates it has skill above climatology alone in all 3 basins  Extended TCFP product to be implemented operationally by summer 2008

17. 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)