Development of a Probabilistic Tropical Cyclone Genesis Prediction Scheme Jason Dunion1, John Kaplan2, Andrea Schumacher3, Joshua Cossuth4, & Mark DeMaria5.

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Presentation transcript:

Development of a Probabilistic Tropical Cyclone Genesis Prediction Scheme Jason Dunion1, John Kaplan2, Andrea Schumacher3, Joshua Cossuth4, & Mark DeMaria5 1 University of Miami/CIMAS – NOAA/AOML/HRD – SUNY/Albany 2 NOAA/AOML/Hurricane Research Division 3 Colorado State University/CIRA 4 Florida State University 5 NOAA/NESDIS/STAR Acknowledgements Funding: This NOAA Joint Hurricane Testbed project was funded by the US Weather Research Program in NOAA/OAR's Office of Weather and Air Quality NHC Points of Contact: Robbie Berg, Dan Brown, John Cangialosi, & Chris Landsea

Discussion Outline Tropical Cyclone Genesis Index (TCGI*) Motivation Explore utility of an objective, disturbance-centric scheme for identifying the probability of TC genesis for the NATL; Credit NHC’s visiting scientist program & a quiet night in the tropical NATL NHC’s Tropical Weather Outlook (TWO) Tropical Cyclone Genesis Index (TCGI*) Year-1 efforts (completed) Year-2 efforts (ongoing and upcoming) Preliminary Results Conclusions & Future Work * 1st Runner-up: Genesis of Nascent Forming Storms and Hurricanes (GoNFSHn)

NHC’s Tropical Weather Outlook TCGI strategy: complete the TWO efforts going on at NHC Highlight areas of disturbed weather & the potential for TC genesis (0-48 hr); 0-120 hr currently produced “in house” (“public”: summer 2013); ”Middle ground” probabilities (~40-70%): most challenging; Semi-subjective process: limited objective tools for providing guidance;

Timeline: Year-1 (Completed Tasks) Feb 2012: March 2012: June 2012: Complete identification/development of genesis predictors into the TCGI database (CIRA TCFP/SHIPS/Rapid Intensity Index); 60 potential predictors for testing (0-48 hr and 0-120 hr); Begin to develop/incorporate the TPW predictor into the TCGI database; March 2012: Present year-1 results at IHC June 2012: Complete identification/development of TPW & Dvorak T-number/CI value TCGI predictors; Develop a complete, continuous “Invest Best track” from a 10-yr Dvorak dataset: TAFB Dvorak fixes/Interpolation/Special BAMM);

Timeline: Year-2 June-Nov 2012 (nearing completion): Begin sensitivity testing for optimal combination of TCGI predictors (0-48h & 0-120h); Utilize RI Index methodologies (Kaplan et al.) Dec 2012 (ongoing): Develop code for running TCGI in real-time (0-48 h and 0-120 h); Jun-Aug 2013 (upcoming): TCGI real-time tests (0-48 and 0-120 h); Utilize NESDIS computers at CIRA (output via ftp site) or JHT computers; Aug 2013 (upcoming): Final TCGI code will be made available; Possible installation on the IBM >> operational SHIPS/LGEM guidance suite (if project is accepted);

TC Genesis Index (TCGI) TAFB Dvorak Fixes: Pre-Genesis Locations

TC Genesis Index Invest Tracks (2001-2010) Dvorak Fixes Interpolation Best track (post-gen) Special BAMM 2001-2010 invest Dvorak positions, interpolations, and Special BAMM positions (out to 120 hr)

TC Genesis Index (TCGI) Predictor Development Methodology (Kaplan et al. 2010, RI Index) Examine potential TCGI predictors (60 total); Predictor Selection: significant at the 99.9% level; Magnitude of each predictor >> evaluated for (0-48h & 0-120h) for all 2001-2010 cases; Sensitivity tests >> determine which combination of predictors yields the most skillful genesis forecasts 0-48 h and 0-120 h; linear discriminant analysis; Nix #4 and clean up

TC Genesis Index (TCGI) 2001-2010 Genesis vs Non-Genesis if a case didn’t have all predictors at all 6-hr time periods in the TCGI forecast, it was thrown out Stdev of NG for a given year ~3x that of G TCGI (0-48 hr) TCGI (0-120 hr)

TC Genesis Index (TCGI) 2001-2010 Invest Genesis Probabilities

TC Genesis Index (TCGI) Predictors/Probabilities/FAR Andrea- deep shear” was there vortex to remove? John Predictors: Relative weights only, right? Is 0-120 hr HDIV more than 2x important compared to VSHD?...or does it not work like that? Predictors: From your RII work, are these weights impressive or is it really comparing apples to oranges? Quantiles: each bin has ~same # of gen cases: what do scalebgn, scalend, and scaleavg average mean descriptively? Are they some total of the different predictors (be they pos or neg)? So can TCGI not predict ~>75% chance at 0-48 and ~>97% at 120? >> no we did have 100% occurrence and we extrapolate to 100% using bins 4&5 and to 0 using bins 1&2 DV12: GFS 12-hr Vortex Tendency (t=+12hr – t=0hr; 0-500 km) HDIV: 850 hPa Divergence (0-500 km) VSHD: 200-850 hPa Vertical Wind Shear (0-500 km; Vortex Removed) TNUM: Dvorak T-Number (t=0hr) PCCD: GOES Cold Cloud (<-40 C) Pixel Coverage (R=0-500 km) MLRH: 600-mb RH (R=0-500 km)

TC Genesis Index (TCGI) Skill Relative to “Yes” and “No” Nix dependent? Note: if all tropical cloud clusters were used instead of invests: skill wrt “yes” goes up…skill wrt to “no” drops

TC Genesis Index (TCGI) Skill Relative to Climatology

TC Genesis Index (TCGI) Possible Output Format

Conclusions & Future Work TC Genesis Index (TCGI) Disturbance-centric/objective/probabilistic 0-48 hr and 0-120 hr forecasts 60 predictors were evaluated 6-predictor prototype scheme has been developed Skill (relative to climatology): ~25% (0-48 hr); ~42% (0-120 hr) Year-1 Efforts: completed Development of Dvorak “Invest Best track” & TCGI predictors Year-2 Efforts: on-going Testing optimal combination of TCGI predictors: nearing completion Real-time code development: beginning TCGI real-time tests (0-48 and 0-120 h): June-Aug 2013 Future Direction Microwave imagery (e.g. 37 & 85 GHz) Ensemble model information Automated scheme for identifying Invests Expand TCGI to other basins