Cyclone Center. Using Citizen Science to Reconcile Global Tropical Cyclone Intensity. Chris Hennon University of North Carolina at Asheville, USA

Transcription

1 Cyclone Center Using Citizen Science to Reconcile Global Tropical Cyclone Intensity Chris Hennon University of North Carolina at Asheville, USA Ken Knapp, Carl Schreck, Scott Stevens, Jim Kossin, Peter Thorne, Michael Kruk, Paula Hennon, Jared Rennie

2 After 40+ years, regular TC reconnaissance in western North Pacific ends (Sept. 1987) /2/2014 IWTC-VIII Jeju South Korea

3 JMA establishes new Koba conversion table. Now heavily reliant on Dvorak, WNP experiences era of large uncertainty in TC intensity analysis /2/2014 IWTC-VIII Jeju South Korea

4 Fig. 4, Webster et al. (2005) Webster et al. (2005) and Emanuel (2005) use JTWC best-track data to demonstrate significant upward trends in intense WNP typhoon activity 12/2/2014 IWTC-VIII Jeju South Korea 2005

5 Fig. 3, Kamahori et al. (2006) Wu et al. (2006) and Kamahori et al. (2006) show clear differences between JTWC and JMA best-track data 12/2/2014 IWTC-VIII Jeju South Korea 2006

6 Nakazawa and Hoshino (2009) find significant differences in Data-T and CI numbers between JMA and JTWC ( ) 12/2/2014 IWTC-VIII Jeju South Korea 2009

7 Knapp and Kruk (2010) attempt to correct for interagency differences (wind speed averaging, CI to wind speed conversion) 12/2/2014 IWTC-VIII Jeju South Korea 2010

8 Barcikowska et al. (2012) examine WNP TC best tracks and find that disparate CI values drive interagency disagreements. 12/2/2014 IWTC-VIII Jeju South Korea 2012

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11 Cyclone Center (40 minutes) Philosophy Data and Methods Demo Early Results Current and Future Work Questions and Discussion (20 minutes) 12/2/2014 IWTC-VIII Jeju South Korea

12 Tenets of Cyclone Center Uniformity Simplicity Accuracy Power in Numbers

13 Uniformity study period using HURSAT B1 IR satellite data TC centered Inter-calibrated Uniform analysis applied to all user input 12/2/2014 IWTC-VIII Jeju South Korea

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15 Simplicity (Not) Accurate 12/2/2014 IWTC-VIII Jeju South Korea

16 Power in Numbers Crowd sourcing has proven to be an effective means of addressing large data sets HURSAT data contain nearly 3,000 storms, 300,000 images Require 10 classifications for each image Better skill, minimizes the impact of bad classifications Provides certainty information about each image Diminishing returns for more than 10 12/2/2014 IWTC-VIII Jeju South Korea

17 Demo

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39 Develop Consensus (Remove outliers, weight users) Determine DT (IR temperature from HURSAT) Determine MET and PT (User response to trend and pattern match) Determine FT, CI (Apply Dvorak constraints and rules) 12/2/2014 IWTC-VIII Jeju South Korea

40 Early Results

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42 Typhoon Ivan (1997) Typhoon Yvette (1992) 12/2/2014 IWTC-VIII Jeju South Korea

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45 Challenges and Concerns

46 Dvorak-type Concerns This is just a pattern matching exercise that ignores the physical underpinnings of the Dvorak technique You ignore VIS You are out to change best track data This isn t really the Dvorak technique 12/2/2014 IWTC-VIII Jeju South Korea

47 Overlapping Storms Typhoon Olaf Nancy 12/2/2014 IWTC-VIII Jeju South Korea

48 Data limitations View angle/resolution 12/2/2014 IWTC-VIII Jeju South Korea Cirrus obscuring eye

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50 Ongoing and Future Work

51 Science development Applying Dvorak DT, FT, CI guidelines to data Applying weighted consensus to DT Using ADT-HURSAT as a citizen scientist Final product: 32-year global TC intensity dataset User engagement TC community buy in, more experts the better Raise classification rate to complete data collection Site translations 12/2/2014 IWTC-VIII Jeju South Korea

52 Questions and Discussion

53 Discussion Points Science What do you want to see? Engagement Citizen scientists TC community we need more experts Uses Starting point for reanalysis Model for how a reanalysis could work? How can we help the forecasting community? Realtime crowd source classifications? Something else? 12/2/2014 IWTC-VIII Jeju South Korea

54 References Barcikowska, M., F. Feser, and H. Von Storch, 2012: Usability of best track data in climate statistics in the western North Pacific. Monthly Weather Review, 140, Emanuel, K., 2005: Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, Hennon, C.C., and coauthors, 2014: Cyclone Center: Can citizen scientists improve tropical cyclone intensity records? Bull. Amer. Meteor. Soc. Early Online Release [available online at Kamahori, H., N. Yamazaki, N. Mannoji, and K. Takahashi, 2006: Variability in intense tropical cyclone days in the western North Pacific. SOLA, 2, Knapp, K.R., and M.C. Kruk, 2010: Quantifying interagency differences in tropical cyclone best-track wind speed estimations. Monthly Weather Review, 138, Nakazawa, T., and S. Hoshino, 2009: Intercomparison of Dvorak parameters in the tropical cyclone datasets over the western North Pacific. Sci. Online Lett. Atmos., 5, Webster, P.J., G.J. Holland, J.A. Curry, and H.R. Chang, 2005: Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309, , doi: /science Wu, M.C., K.H. Yeung, and W.L. Chang, 2006: Trends in western North Pacific tropical cyclone intensity. Eos, Transactions of the American Geophysical Union, 87 (48), /2/2014 IWTC-VIII Jeju South Korea

55 Some Reanalysis/Homogenization Efforts Kossin et al. (2007, 2013) Global? Time Analyzed Method Yes Satellite objective (computer) Multiple assessments? No Landsea and colleagues Diamond et al. (2013) No Observations Yes No Observations? Kimberlain (NHC) Caroff (La Reunion) No Satellite objective (human) No? Satellite objective (human) Yes? 12/2/2014 IWTC-VIII Jeju South Korea