An overview of FSU applied research tools for tropical cyclones

1. An overview of FSU applied research tools for tropical cyclones Robert Hart Andrew Murray, Ben Schenkel, Ryan TruchelutDept of MeteorologyFlorida State University http://moe.met.fsu.edu [Changes to moe.eoas.fsu.edu in late 2010] 32ND WMO HC Meeting Hamilton, Bermuda 11 March 2010

2. Motivation of Research • The work conducted by our group has always focused heavily on bridging the gap between research and application • It is founded in the immense respect for forecasters and the position they are in -- trying to develop tools that can help them, but also help explain the “whys” of the science. • We have always received, and wish to continue to receive, feedback on the products produced from both the research and application populations • Please email rhart@fsu.edu if you have any questions or requests. Forgive us if it takes more than a day or two to reply. • But always remember the work shown is not official --- always defer to official forecasts

3. Outline of tools discussed • Expanded landfall risk and preferred pathways • Short-term intensity change using hurricane core measurements • Structural analysis, forecasting, and predictability • A few newer developments if time

4. Part 1: Expanded climatological landfall risk • Many existing landfall products extend to five days • They are based upon passage near a point, given a current starting position • They also are only publically available when TCs exist, making their use for external R&D limited

5. Existing NHC real-time products

6. What would be additionally helpful • Climatological maps of landfall probability to highlight “pathways” of enhanced threat well more than 5 days in advance • “What is the most preferred pathway for a TC to make eventual landfall in Bermuda? • What latitude should a wave depart Africa to be at greatest risk for landfall in the Caribbean? • The climatological length of time to landfall • Real-time updates for current TC positions • Note: Funded by RPI/BIOS in 2009

7. Datasets • 6hourly global best-track datasets over the past century • Linearly interpolated to 10-minute timesteps to capture landfall on narrow landmasses • 0.0833 degree latitude & longitude (5-9km) land-sea mask from NCEP

8. Method • Define a “landfall or land-crossing” region (e.g. Florida) or “within a certain distance” of a location (e.g. Bermuda) • Use the 10-minute interpolated best-track database to determine every storm that crosses through that region, noting the time of first passage (and whether passage occurred at a required wind speed) • Repeat for all storms in the best-track database • Produce a gridded analysis of the percentage of the time a storm in a given location eventually makes landfall

9. Results: Landfall “anywhere”

10. Hurricane Landfall “anywhere”

11. Major hurricane Landfall within USA

12. New England Crossing

13. Time to New England Crossing

14. Hurricane crossing of Florida

15. Passing within 100km of Bermuda

16. Tropical Cyclone Crossing Haiti

17. Typhoon crossing Japan

18. Real-time web pagehttp://moe.met.fsu.edu/tcprob

19. Real-time web pagehttp://moe.met.fsu.edu/tcprob

20. Current & Future Additions • Probability swaths for multiple landfall occurrence (e.g., NC then New England or FL then LA) • Probability swaths for conditions: “If one landfall has already happened, what is the probability for another and where is it most likely?” • Using global reanalysis datasets (ERA40, MERRA, JRA) to extend TC tracks beyond best-track tracks, and using these extensions and gridded winds to extend landfall/crossing probabilities to Europe • Quantify uncertainties through yearly subset intercomparison

21. Use of landfall output • First and foremost remember that these are climatological averages. Any given storm will have probabilities well above or below those shown on the web page. This output provides a calibration but is NOT an official forecast. • Anticipating anomalous landfall risk associated with a developing or formed TC • Estimating timeframe for landfall if it were to occur using the most likely climatological track • Comparison of these landfall probability maps to the same from stochastic model sets for further calibration? • Subduing unrealistically premature forecasts of doom for TCs just exiting Africa?

22. Part 2: Using recon data to improve forecasting of intensity • Hurricane intensity forecasting has made far less progress in 20 years+ than track forecasting. It is counterintuitive that there is SO little improvement. • The benchmarks for hurricane intensity skill are statistical approaches (e.g. SHIPS) that focus largely on the environment • Wind shear, ocean temperature, time of year, etc. • Measurements of the core (the “eye”) are not sufficiently used, even though theory argues they should be important • Can we improve the existing benchmarks by incorporating storm core measurements from airplanes (vortex messages)?

23. A tale of two trends Images courtesy of NHC

24. Improved use of core data • Use airplane-reported core parameters in an attempt to predict the future • Eye structure (circular, concentric, elliptical, size) • Thermodynamics of the eye (temperature, moisture) • Thermodynamics just outside the eye (temperature) • Measures of balance and stability • Recent changes in all these fields, and many more

25. Example of a flight path Image courtesy of Google Earth and http://planalytics.files.wordpress.com/2009/09/recon-19z.png

26. Vortex Message Dataset

27. 18 Years of Vortex Message Reports with Eye Type Circular Eye Double Eye Elliptical Eye What is the average lifecycle of all these storms?

28. Gulf of Mexico Composite mean VDM evolution using first closed eye report as Time 0 “On average, what is the lifecycle of a TC once an eye forms?”

29. Graphical Display of Mean Lifecycle

30. Example case: Wilma (2005)

31. 18 Year Climatology of Atlantic Hurricane Eyes

32. 12hr Intensity Change Climatology 12-hour mean wind rate (hr-1) 12-hour SE of the mean Eye Diameter (nm) Eye Diameter (nm) Max. Sustained Wind (kt) Max. Sustained Wind (kt)

33. Need for a multi-parameter system • Two-predictor system leads to forecasts of strengthening for TCs < 90 kt and weakening for TCs > 90 kt => Hardly useful! • Prediction based solely on eye diameter and maximum wind speed is insufficient to accurately predict TC intensity changes • What predictors should be useful?

34. Predictor Examples • Example raw VDM predictors • Wind speed and surface pressure • Eye temperature and dewpoint • Example derived predictors • Temperature change across eyewall • Area of eye • Equivalent potential temperature • Inertial stability of the eye • Dewpoint depression of eye * Area of eye • Eyewall tilt

35. Optimal Predictors Chosen in Forecast Scheme Predictors (temperature, moisture, size of eye, etc) Current Intensity (kt)

36. Forecast Performance

37. Important Caveats The apparent improvement over SHIPS is not an apples to apples comparison SHIPS was developed using TCs over the entire basin, while this study (necessarily) only used TCs flown by recon Further, SHIPS has evolved over time as the science and observations have evolved A more apples to apples study would recalculate SHIPS style using the subset of storms used here and then intercompare

38. Part 2: Summary and Future Work • Independent testing showed that new technique is comparable to or surpasses the skill of SHIPS for short-term forecasts for the subset of storms flown by recon. • Predictability of future TC intensity is strongly a function of initial intensity and is not linear. “Regimes” of decreased predictability do exist. • Coming this summer: Real-time implementation via web page

39. Part 2: Summary and Future Work • Questions: • How do these climatologies compare to the same produced by forecast models? [HWRF, GFDL, etc] • Can the climatology of core structure just shown be used to improve reinsurance stochastic model representation of lifecycle? • Future: Examine satellite proxies for recon data to determine if the approach and skill can be extended to non-recon basins [NASA GRIP] • If not, potentially argues for more frequent Hurricane Hunter recon missions in the Atlantic basin and expanding recon flights to other basins.

40. Part 3: Structural guidance • Cyclones are typically classified as tropical or extratropical • In reality, most cyclones are shades of gray than one extreme or the other, for example: • Tropical cyclones interacting with troughs • Extratropical cyclones interacting with Gulf Stream and producing convection • Acknowledging these shades of gray can lead to improvement in analysis and forecasting • How do we determine what shade of gray for a given cyclone?

41. Images courtesy NCDC

42. Hurricane Gloria (1985) Hurricane Michael (2000)

43. Synoptic analysis of 1938 New England Hurricane (Pierce 1939) Surface analysis 21 September 1938 NY MA PA ? VA 940hPa (C. Pierce, Mon. Wea. Rev. 1939) MSLP

44. Example of nonclassic structure 21 December 1994 22 December 1994 23 December 1994 24 December 1994

45. Christmas 1994 Hybrid New England Storm • Gulf of Mexico extratropical cyclone that acquired partial tropical characteristics • A partial eye was observed when the cyclone was just east of Long Island • Gusts of 50-100mph across S. New England • Largest U.S. power outage (350,000) since Andrew in 1992 • Forecast 6hr earlier: Light rain, winds of 10mph. • Illustrates the remarkably complex relationship between cyclone track, interaction, intensity and structure See Beven (1997) for Case Study

46. The structure or “phase” of a cyclone important • Predictability is a function of structure • Model interpretation/trust is a function of structure • It is often not at first apparent what the model is forecasting, or the nature of cyclone development • Provides insight into the nature of NWP cyclone development that may otherwise be subtle or even ambiguous • Intensity envelope is a function of structure

47. The danger of relying on geographyor time of year.

48. The danger of relying on geographyor time of year.

49. A more flexible approach to cyclone characterization • To describe the basic structure of tropical, extratropical, and hybrid cyclones simultaneously using a cyclone phase space. What parameters? Lets compare the classic structures to begin. Parameter B Phase Space Parameter C Parameter A

50. Classic warm-core cyclone: Example: TC • Intensifies through: sustained convection, surface fluxes. • Cyclone strength greatest near the top of the boundary layer - + Cold Stratosphere Z Troposphere Wa rm L Height anomaly