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Improvements in Real-Time Tropical Cyclone Products for the 2007 Season

Improvements in Real-Time Tropical Cyclone Products for the 2007 Season. Mark DeMaria, John Knaff and Ray Zehr NESDIS/StAR/RAMM Branch Andrea Schumacher and Robert DeMaria CIRA/CSU. Presented at The Tropical Prediction Center August 9, 2007. Outline.

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Improvements in Real-Time Tropical Cyclone Products for the 2007 Season

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  1. Improvements in Real-Time Tropical Cyclone Products for the 2007 Season Mark DeMaria, John Knaff and Ray Zehr NESDIS/StAR/RAMM Branch Andrea Schumacher and Robert DeMaria CIRA/CSU Presented at The Tropical Prediction Center August 9, 2007

  2. Outline • RAMMB Experimental Tropical Cyclone Web Page • Experimental Extended/Updated NESDIS Tropical Cyclone Formation Probability Product • Annular Hurricane Index • Improvements in the Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the 2007 Hurricane Season • Monte Carlo Wind Probability program

  3. The RAMMB Experimental Tropical Cyclone Web Pagehttp://rammb.cira.colostate.edu/products/tc_realtime/

  4. Purpose To display real-time tropical cyclone products created and/or developed by RAMMB and CIRA scientists and products not available elsewhere. Serve these data to the public via a web integrated database designed to accommodate future product development.

  5. Information Content Globally occurring tropical cyclones • Track history and current forecasts from NOAA (NHC or CPHC) or DOD/JTWC • Earth relative IR loops (4-km Mercator) • Storm relative LEO IR and Vis (1-km Mercator) • AMSU-derived radius vs. height cross sections (T and Vgrad) and intensity estimate time series • Earth relative oceanic heat content + forecast track • Multi-platform satellite only tropical cyclone surface wind analysis • Digital Dvorak intensity estimate time series • Storm relative TPW and water vapor imagery

  6. Experimental Product Examples (TC wind analysis)

  7. Experimental Product Example (oceanic heat content) Data provided by G. Goni at NOAA/AOML http://www.aoml.noaa.gov/phod/cyclone/data/ kJ/cm^2

  8. Operational Product Example (AMSU intensity) Available for all active tropical cyclones at ftp://ftpprd.ncep.noaa.gov/pub/data1/amsu

  9. Example: Subtropical Storm Andrea

  10. Digital Dvorak Intensity Estimates

  11. Example Water Vapor Products Storm Relative Total Precipitable Water Storm Relative Water Vapor Imagery Blending of AMSU and SSM/I TPW is explained in Kidder and Jones (2007, JAOT)

  12. RAMMB Experimental TC Web Page - Future • Add New products • STIPS and SHIPS forecast time series • 1-km forecast track relative visible imagery • Kinetic energy time series derived from the surface wind analyses • Transition the experimental products • Multi-platform surface wind analysis • Ocean heat content • Use in STIPS based consensus • WMO website (maybe) • Respond to user requests

  13. Extension of the NESDIS Tropical Cyclone Formation Probability Product to the Central and Western Pacific

  14. CURRENT TCFP PRODUCT - OVERVIEW • Determines the 24-hr probability of TC formation within each 5° x 5° lat/lon grid box (0° to 45° N, 140° to 350 W°) • Uses the following data sources: • NCEP GFS operational analyses • GOES-East water vapor imagery • NHC Best tracks • Running operationally at SSD since 2004 hurricane season. • Shows plots of domain-wide formation probabilities, predictor values, and sub-basin time series. Updates every 6 hours. • Available at : http://www.ssd.noaa.gov/PS/TROP/genesis.htm

  15. CURRENT TCFP PRODUCT – OVERVIEW (CONT…) CLIMATOLOGICAL FORMATION PROBABILITY (1949 – 2003) PERCENT LAND DISTANCE TO PREXISTING STORM CLIMATOLOGICAL SST VERTICAL SHEAR (800 – 250 hPa) 850 hPa CIRCULATION VERTICAL INSTABILITY GOES COLD PIXEL COUNT CLOUD-CLEARED WATER VAPOR BRIGHTNESS TEMP.

  16. CURRENT TCFP PRODUCT EXAMPLE – FLORENCE 2006 - 18 hr - 12 hr - 6 hr - 0 hr = Time of TC Genesis

  17. CURRENT PRODUCT - VERIFICATION Dependent probabilities are skillful by two measures • Brier Skill Score • Relative Operating Characteristic (ROC) score Interannual Variability Expected Yearly # TCs = ∫∫∫ P(x,y,t)dxdydt

  18. NEW TCFP PRODUCT – EXPANDED DOMAIN Expanded domain longitude eastward to 100° E (covers Central & Western N. Pacific Basins) Redefined basins based on satellite coverage Performed separate screening steps and discriminant analysis on each of the 3 basins.

  19. NEW TCFP PRODUCT - MODIFICATIONS • Added 2004 & 2005 to GOES-E sample set • Defined screening values and discriminant weights on each of the 3 basins, separately • Used a Finer Screening Process • Updated screening eliminates 5% of genesis cases and 75% of non-genesis cases (increase in eliminated non-genesis cases of ~5%  improve discriminant analysis) • Added of 850 hPa Horizontal Divergence as a Predictor

  20. NEW TCFP PRODUCT – WEB PAGE Updated/extended TCFP product currently running at CIRA, available at http://rammb.cira.colostate.edu/projects/gparm/ Main Page: points out areas of interest for TC genesis formation, links to web page for each basin Eg. Main Page graphic from 7/8/07 18Z, 18h prior to the formation of WP04 at 11.2N, 140.2 E

  21. Contour Changes Operational Product New Experimental Product

  22. UPDATED TCFP PRODUCT – FUTURE The updated NESDIS TCFP product is running locally at CIRA in 2007 Extended product will be evaluated/verified at end of 2007 season. After evaluation, the extended product will likely be transitioned to NESDIS operations Use GFS forecast information and time series analysis to extend product beyond 24 hr

  23. Annular Hurricane Index

  24. Annular Hurricane Characteristics • ENVIRONMENTAL CONDITIONS: • Weak Easterly/Southeasterly Wind Shear • Weak Relative Eddy Flux Convergence • 200 hPa Easterlies • SSTs in a range 25.4 to 28.6 °C steady or decreasing. OR • Weak Easterly shear, under an upper ridge, over SST <28.6 °C ALSO • Intensity > 85 kt (Knaff et al. 2003) STRUCTURE: • Distinctly axisymmetric • Large circular eyes • Greatly reduced rainband activity • Lasts at least 3 hours • Rare, occur ~4% of the time Isabel (2003)

  25. Annular Hurricane Intensity Characteristics Do not weaken rapidly after max intensity Intensity is very close to 85% MPI wrt SST Have large intensity biases & larger than normal intensity errors

  26. Determining Annular Structure Yellow = Structure Blue = Environment

  27. Annular Hurricane Index (AHI)

  28. AHI Step 1: Screening +/- 3 standard deviations from means of AH’s (1995-2003) 976 (54 AH) cases (6h) > 84 kt intensity  241 cases after screening (53 AH) Hit Rate = 100%, False Alarm Rate = 19%

  29. AHI Step 2: Linear Discriminant Analysis • Input: Predictors with sig. different means for AHI and no-AHI cases: • SST • U200 – 200 hPa zonal winds • σc– azimuthal standard deviation of BTs at Rc • VAR –variance of azimuthally-averaged BTs from TC center to 600 km • ΔT eye- max difference between Rc and any azimuthally-averaged BT at smaller radius • Output: Linear discriminant value (large is close to AHI, small is close to no-AHI)

  30. Verification STEP 1: Screening Reduced 941 (54) cases to 241 (53) FAR = 19% STEP 2: LDA “N” LDA “Y” Hit Rate ~ 87 % FA Rate ~ 6 % NAH AH STEP 1: Screening Reduced 387 (7) cases to 82 (7) FAR = 19% STEP 2: LDA “N” LDA “Y” Hit Rate ~ 100 % FA Rate ~ 4 % NAH AH Dependent Years (1995-2003) Independent Years (2004-2006)

  31. AHI Output & Interpretation If case doesn’t pass screening, AHI set to 0. If screening is passed, LDA function value is linearly scaled to obtain the Annular Hurricane Index, which ranges between 1 & 100. AHI is displayed at the end of the SHIPS model output file. AHI = 0  No annular structure AHI = 1  Worst match to annular structure AHI = 100  Best match to annular structure

  32. Example: Daniel 2006 7/20/06 0Z, vmax=95 kt AHI = 0 7/22/06 0Z vmax = 130 kt AHI = 100 7/21/06 0Z, vmax=120 kt AHI = 50

  33. Sample AHI Output on SHIPS text file ## ANNULAR HURRICANE INDEX (AHI) EP062007 COSME 07/16/07 00 UTC ## ## STORM NOT ANNULAR, SCREENING STEP FAILED, NPASS=4 NFAIL=3 ## ## AHI= 0 (AHI OF 100 IS BEST FIT TO ANN. STRUC., 1 IS MARGINAL, 0 IS NOT ANNULAR) ## ## ANNULAR INDEX RAN NORMALLY

  34. Annular Hurricane Index - Future • For the period 1995-2006, the AHI algorithm had a hit rate of 96% and a false alarm rate of 4% • AHI being run in real time during 2007 • Future work may add secondary eyewall formation index • Opposite extreme to annular hurricanes

  35. SHIPS Changes for 2007 • New coefficients with 2006 cases added • Refinement of method for calculation of vertical wind shear • GFS vortex removed • 0-500 km instead of 200 to 800 km • Add new vortex predictor from GFS • 0-600 km average 850 mb tangential wind • New code installed on the NCEP IBMs (Mist/Dew) on May 9th

  36. Example of GFS Vortex Removal Total Wind Symmetric Flow Residual 850 hPa 200 hPa

  37. LGE Model and SHIPS-1 • Prediction Equations • SHIPS: for t=6,12, …, 120 and xn are averaged from 0 to t • LGEM: numerically integrates dV/dt = V - (V/Vmpi)nV from t=0 to 120 hr, with Vmpi from SST, n=2.5, -1 =24 hr and growth rate  estimated from linear regression relationships • Predictors • SHIPS: 18 predictors, most averaged over entire forecast interval • LGEM: 14  predictors, most averaged over previous 24 hr • Treatment of land • SHIPS: Post processing step after linear prediction with KD model • Introduces potential term bias in ocean-land-ocean cases • LGEM: Prediction equation modified for land portion with KD model

  38. LGE Model and SHIPS-2 • Developmental Data • SHIPS:1982-2006, for cases over water for entire forecast interval • LGEM: 1982-2006, for cases over water for previous 24 hr • Satellite Data • SHIPS: Adjustment to prediction from OHC and GOES • LGEM: No satellite input • Advantages • SHIPS • Simple interpretation of contributions from predictors • Satellite data can have smaller sample size • Longer range forecasts constrained by time average • LGEM: • Solution constrained between 0 and Vmpi • More responsive to SST and environmental changes along track • More accurate treatment for storms that move from land to water • More room for future improvements

  39. 2007 SHIPS and LGEM Compared to Operational SHIPS(Re-runs of 2004-2006 seasons) Atlantic East Pacific

  40. Future Outlook for SHIPS and LGEM • SHIPS: • Maintenance mode, re-derive coefficients each year, test new predictors • Test TPW in HRD/NRL project • LGEM • Use adjoint LGE model to minimize V error instead of  error • Use past history of current storm for model optimization • Use Lagrangian parcel model to combine thermodynamic inputs • 2 predictor version (shear and convective potential) • Run from satellite derived soundings

  41. Test of Adjoint LGE Model • Assume  is linear function of shear  = a + bS • Find 4 constants a,b,,n to minimize error of single forecast for an entire storm • Hurricane Frances (2004) • 11 day forecast (46 six hour periods) • V=25 kt at t=0 (Aug 24 00 Z) • (11 N, 34 W) to Florida landfall • Minimization converged in ~500 iterations -1 = 21 hr, n=2.2, a-1 = 44 hr, b-1 = 2.7 hr/kt MAE = 3.2 kt

  42. Additional Cases • Name Length (hr) (hr-1) n a b MAE(kt) • Frances 04 270 .047 2.2 .023 -.009 3.2 • Claudette 03 162 .078 1.4 .022 -.004 5.8 • Daniel 06 140 .001 1.6 .005 -.013 12.1 • Debby 00 114 .057 2.3 .006 -.021 4.4 • Isabel 03 306 .038 1.5 .029 -.010 10.5 • Katrina 05 138 .051 6.0 .014 -.001 5.4 • Maria 05 210 .053 3.7 .008 -.007 3.1 • Ophelia 05 276 .087 1.7 .023 -.003 4.1

  43. Monte Carlo Wind Probabilities • Code Optimized (6 times faster) • Probability distributions updated • 2002-2006 • New JHT project to make probability distributions a function of GPSE • Study with M. Mainelli on relationship with Watches and Warnings • Begin writing journal article

  44. Summary • CIRA/RAMMB TC web page ready for 2007 • Satellite-only TC surface wind analysis • New TC formation product running in parallel at CIRA • Operational version on NESDIS/SAB web page • Annular hurricane index included with SHIPS • Updated SHIPS and LGEM operational • Improvements still possible for LGEM • MC probabilities with GPSE to be tested in 2008

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