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,

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)

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

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)

Datasets  NHC/DOD Best Tracks  Atlantic, E. Pacific, Central Pacific & W. Pacific  Subtropical/Extratropical cases excluded  Unnamed depressions included since 1989  NCEP Global Model Analyses  Reanalysis (2.5 o grid)  Operational Analyses (1.0 o grid)  Geostationary Satellite Water Vapor Imagery (16 km Re- mapped Mercator projection)  GOES-E  GOES-W  GMS-5 / GOES-9 / MTSAT-1R

Parameters ABBREVParameter DescriptionSource LATLatitude ( ° )ATCF PLAND% land coverageLand covrge DSTRMDistance to nearest TC (km)ATCF CSSTClimatological sea surface temp ( ° C)Levitus VSHEAR 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

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

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)

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

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

Parameter Contributions By Basin ABBREVATLCEPACWPAC LAT PLAND DSTRM CSST VSHEAR CIRC THDEV HDIV PCCOLD BTWARM CPROB

Product Example: WP

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

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.

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 BSS All BSS > 0  skill over climatology RSS All RSS > 0  skill over climatology

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

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)