Andrea Schumacher schumacher@cira.colostate

1. HURRICANE RESEARCH IN THE ROCKIES:AN OVERVIEW OF RESEARCH TO OPERATIONS ACTIVITIES AT CIRA FOR 2008 / 2009 Andrea Schumacher schumacher@cira.colostate.edu

2. Outline • SHIPS and LGEM • 2008 performance • 2009 plans • Monte Carlo wind probabilties • Text product problem during Fay • Evaluation of GPCE test • 2009 plans • AMSU Wind Retrievals • 2008 Updates

3. Outline cont… • Multiplatform Surface Wind Analysis • New product running experimentally in 2009 • HDOBS • Vortex Tracker • Knaff & Zehr Wind-Pressure Relationship • Modifications for operations • TC Formation Probability Product • Overview • Verification • Planned updates • New ideas for improvement

4. SHIPS and LGEM

5. SHIPS and LGEM for 2008 • SHIPS/LGEM predictors same as 2007 • Satellite input same as 2007 • None in LGEM • Atlantic SHIPS: GOES predictors and OHC • East Pacific SHIPS: GOES predictors • 2007 cases added to developmental sample • Includes 1982-2007 for basic mode • 1995-2007 for satellite data correction

6. SHIPS/LGEM Performance in 2008 Atlantic: Arthur through LauraEast Pacific: Alma through Marie (partial) Atlantic East Pacific

7. LGEM/SHIPS Differences • Atlantic • LGEM can better recover when storm moves from water over land and back over water • Many forecasts of this type in 2008 • East Pacific • LGEM has large low bias when initial intensity less than 30 kt • LGEM has larger low bias than SHIPS for low shear but marginal SST environments

8. Example of LGEM Recovery After Track Over Land

9. Example of LGEM Recovery After Track Over Land

10. 2009 Plans for SHIPS/LGEM • Add OHC input to East Pacific SHIPS • Investigate causes of low bias for east Pacific LGEM • Test 2-predictor version of LGEM • Growth rate a function of shear and vertical instability parameter • Instability from entraining parcel model • Persistence included through assimilation of previous storm history up to the forecast time • Test impact of OHC through SST cooling algorithm on vertical instability parameter

11. Monte Carlo Wind Probabilities

12. 2008 MC Wind Probabilities • Track/Intensity error distributions created for most recent 5 years • 2002-2006 replaced with 2003-2007 • Code modified to calculate input to wind speed probability table • “Capping” function added to improve inland probability estimates • Intensity can not exceed maximum value observed as a function of distance inland • Experimental version with track error distribution a function of model spread being tested • Stratify errors by GPCE values

13. GPCE MC Wind Probabilities Upper GPCE Tercile Lower GPCE Tercile

14. Test Plan for GPCE Version of MC Model • TPC visit by M. DeMaria in July 2008 • Too late to set up parallel MC model on IBM • Alternate test plan • Run GPCE MC model and operational version at CIRA • Run Atlantic cases where initial position is 1000 km or less from U.S. coastline • Sample size = 138 through Ike • Post plots on CIRA web page for qualitative evaluation • Perform quantitative verification at U.S. breakpoints • Cases should be available by end of 2008 season

16. MC Model for 2009 • Update error distributions for 2004-2008 • Implement GPCE or standard version depending on 2008 performance and NHC evaluation • Also test East/West Pacific cases • Fix rare problem identified by R. Berg/C. Lauer during TS Fay at Daytona Beach • P(50 kt) = 99%, P(34kt) = 87% • Problem from sampling wind model in just 4 quadrants and then azimuthally interpolating for weak storms with very large RMW and intensity near probability threshold (50 or 65 kt)

17. CIRA/NCEP AMSU Updates

18. CIRA/NCEP AMSU Update • NOAA-16 Coefficients updated to account for the loss of channel 4 (15 August), fixes are questionable between March 2008 and that time because channel 4 was intermittent. • Plans continue to have fixes provided from METOP and Aqua for the 2009 hurricane season (May 2009) • Have the retrievals • Working with NCO and NHC to get the METOP and Aqua AMSU data out of the operational BUFR

19. Multi-platform Tropical Cyclone Surface Wind Analysis

20. Multi-platform Tropical Cyclone Surface Wind Analysis • NESDIS product • Satellite only input (QuikSCAT, ASCAT, AMSU 2-d, GOES high density winds, IR proxy winds (proxy for flight-level; Mueller et al. 2006) • Provides 6-hourly tropical cyclone structure estimates • Variational data fitting method • 900 km domain • 4km (radial) X 10 degree (azimuthal) resolution • Crude Quality control accomplished by a three step process that compares input data to the analyses using increasingly stringent thresholds • Will be run experimentally at NESDIS in 2009 • Providing 2-d analyses • ATCF fixes (RMW, R34, R50, R64, MSLP)

21. Example: Sep 12 2009 00ZMulti-platform TC Surf. Wind Analysis

22. Initiatives to Utilize High Density Observations (HDOBS)

23. Initiatives to Utilize HDOBS for Improved Surface Wind Analyses • Increase analysis resolution (1km x 5 deg) • Bring in surface and dropwindsondes (work with H*Wind developers) • Possible real-time vortex tracker

24. Real-time Vortex Tracker • Uses HDOBS, Operational best track and OFCI • Maximize the tangential wind during the period of HDOBS • Minimization accomplished by a downhill simplex algorithm that uses a quadratic representation of dx and dy • Constrained by a user supplied time increment • Produce a cubic spline for latitude and longitude as a function of time • Intervals of the spline are user determined

25. Example Dean 2007 HDOBS 17 Sept 22 UTC - 18 Sept 01 UTC

26. Modifying the Knaff and Zehr wind-pressure relationship

27. Modifying the Knaff & Zehr wind-pressure relationship for operations • Work with Joe Courtney (BOM) • Courtney and Knaff (2009) in preparation. • Environmental pressure, Penv = POCI +2 • Size (S) has been related to R34, where • V500=R34/9 -3, R34 is the average value of the nonzero 34kt wind radii estimates (i.e., NE, SE, SW, NW). • V500c (i.e., climatology) is given in Knaff and Zehr (2007) • S= V500/V500c , with a minimum value of 0.4 • New equation ΔP for latitudes equatorward of 18 degrees

28. Knaff & Zehr wind-pressure relationship cont… For use equatorward of 18 degrees latitude: For use poleward of 18 degrees latitude: Where S is size, Ф is latitude, Vsrm1 = Vmax -1.5c0.63 , and c is the translation speed in kt. Finally, MSLP=ΔP + Penv

29. Tropical Cyclone Formation Probability Product

30. Motivation and Approach • Motivation • The forecasting of TC formation and intensity change have been identified as high priority areas of need by NOAA • Goal: To develop an objective, probabilistic forecast guidance product for TC formation • Approach • Generalize the linear discriminant analysis (LDA) approach of Hennon and Hobgood (2003) and Perrone and Lowe (1986) • Instead of using LDA to discriminate between developing and non-developing tropical cloud clusters, combine both environmental and convective parameters into one algorithm • Combine deterministic LDA algorithm with occurrence frequencies from dependent dataset to create 24-hour TC formation probabilities

31. 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

32. 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 “A Needle in a Haystack” Ratio of TC formation to non-formation points ~ 1:2000 Maximum climatological formation probability ~1.8% (E. Pacific)

33. Data • NCEP Global Model Analyses • Reanalysis 1995-1999 (2.5o grid) • Operational Analyses 2000-2005 (1.0o grid) • Geostationary Satellite Water Vapor Imagery • GOES-E 1995-2005 • GOES-W 1998-2005 • GMS-5 / GOES-9 / MTSAT-1R 2000-2005 • NHC/DOD Best Tracks 1949-2005 • Atlantic, E. Pacific, Central Pacific & W. Pacific • Subtropical and extratropical cases excluded • Unnamed depressions included since 1989

34. Input Parameters

35. Algorithm Overview Input parameter values calculated over each 5° x 5° sub-region in domain Sub-regions for which TC formation is highly unlikely are screened out of dataset (eg. 100% over land, large vertical shear) Linear discriminant analysis is used to discriminate between TC formation and non-formation cases based on parameter values TC formation occurrence frequencies are used to translate LDA function values to probabilities

36. Linear Discriminant Analysis Simple schematic: 2 groups, 2 attributes LDA seeks to find the vector a (i.e.,direction) that maximizes the separation of the two means, in standard deviation units, when the data are projected onto a. a Mathematically: LDA maximizes by solving for where *Schematics from UCLA DOE, http://www.doe-mbi.ucla.edu/~parag/multivar/dawords.htm

37. Algorithm-Derived LDA Coefficients Largest contributors

38. Verification Reliability Diagrams ROC Skill Scores

39. 2008 Season PerformanceAt A Glance Tropical Atlantic Caribbean E. Pacific

40. Hurricane Dolly TS Eduouard Hurricane Kyle Hurricane Ike & TS Josephine

41. Future Work • 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) • Develop a disturbance-centric algorithm • TAFB invest locations and T-Numbers (D. Brown) • Include current TCFP predictors, SHIPS predictors, TPW Eg. Full-domain water vapor strip, 2 July 18 Z – 3 July 15Z 2008

42. SHIPS as predictor for TC formation - preliminary look

43. Comments and Questions?