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Josh Cossuth Robert Hart January 23, 2012 Collaborators: PowerPoint Presentation
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Josh Cossuth Robert Hart January 23, 2012 Collaborators:

Josh Cossuth Robert Hart January 23, 2012 Collaborators:

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Josh Cossuth Robert Hart January 23, 2012 Collaborators:

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  1. 1 Satellite-based identification and climatology of hurricane core features with respect to intensity change Josh Cossuth Robert Hart January 23, 2012 Collaborators: David Piech, Andrew Murray, Tony Wimmers, Chris Velden, Jeff Hawkins, Greg Gallina

  2. 2 Motivation • There is a marked trend of improvement for tropical cyclone (TC) track forecasts over the past few decades [left figure] • However, no such skill has been realized with TC intensity forecasts for the same time period [right figure]. Courtesy: Franklin and Cangialosi 2011

  3. 3 Current TC Intensity Forecasting • There are various methods used to forecast tropical cyclone intensity, including: • Numerical modeling • Statistical approaches • Consensus and bias-correction • However, these methods may not sufficiently incorporate observations of the TC core and structure. • Previous research (Piech 2007 and Murray 2009) have demonstrated that aircraft core observations by themselves skillfully predict short-term TC intensity.

  4. 4 Methodology • Data sources • Aircraft: reconnaissance vortex data messages from ATCF F-decks (1991-2010; mostly in the Atlantic, some east/central Pacific) • Satellite: HURSAT (Knapp 2008) TC-centered SSM/I observations (1987-2008; worldwide) • Aircraft data: • Reported eye diameter is compared to the prior National Hurricane Center operational intensity analysis (from ATCF A-decks) • Satellite data: • TC center location first guess by interpolated NHC/JTWC best track • ARCHER technique (Wimmers and Velden 2010) used to find satellite-based center and eye size • Eye size compared to interpolated best track intensity

  5. 5 Comparison of Data

  6. 6 ARCHER Technique Wimmers and Velden 2010 • Versions of ARCHER are used in real-time TC products (e.g. ADT, MIMIC) • Uses information in satellite image to determine TC center. • Spiral banding (left) • Eye scene (middle) • Final center (right) • Case used: Isabel 2003

  7. Satellite-derived hurricane eye size 7 Aircraft observed hurricane eye size

  8. 8 Frequency Distribution ofEye Size versus Intensity • First-time comparison of a TC structure climatology from aircraft and satellite data. • There appears to be favored regimes of intensity with eye size in TCs. Satellite-based, Global Aircraft-based, Atlantic

  9. 9 Climatological Intensity Change • Focusing on the Atlantic aircraft data, the rate of past/future intensity change can be examined using the current intensity and eye size. • Below: the 24 (left) and 12 (right) hours preceding an aircraft observation. • Hurricanes with medium to small eyes will, on average, intensify • Stronger hurricanes with larger eyes will generally weaken.

  10. 10 Climatological Intensity Change • Below, intensity change in the 12 and 36hours following the aircraft observation is examined. • There appears to be threshold based intensity, above which a TC will weaken (or below which a TC will intensify). • This threshold intensity goes down with lead time. Intensify Intensify Weaken Weaken

  11. 11 Eye Size as an Intensity Predictor • As just shown, there are preferred regimes of eye sizes and intensities for TCs, apparent in both the aircraft and satellite data. • Future intensity change varies within these regimes. • However, only looking at the eye size is too limited in scope to reliably determine the current and future intensity. • Satellite overpasses may be used as proxies of aircraft flights and can further examine storm-to-storm structure variability beyond the capabilities of recon flights.

  12. 12 TC Natural Coordinate System Hurricane Isabel – Infrared (10.7 μm)

  13. 13 Identification of TC Structure Hurricane Isabel – Microwave (85GHz) MISSING DATA (POLAR ORBITER)

  14. Features of TC structure that can be better identified in radial or polar coordinates may be important factors in forecasts of intensity. Similarly, it may be helpful to identify the evolution of the TC core with time (as shown on the left) and compare analyses with other similar storms. 14 Time Evolution of Radial Structure Azimuthal-mean satellite temperature change through time

  15. 15 Identifying Precursors toIntensity Changes • A polar coordinate system can help in interpreting structural signals in TCs, such as: • Size and symmetry of the eye and inner core • Outer cloud banding characteristics • Direct influence of environment (e.g. dry air) • Having such additional metrics and showing how they progress through time can help quantify intensity change and aid TC prediction.

  16. 16 Summary • Aircraft observations of tropical cyclone eye characteristics were gathered and combined with a newly created a satellite-based data set. • Both sets of measurements show preferred eye-size and intensity combinations, which can be used as a rudimentary intensity change metric. • Further details of TC structure from satellite observations can be derived and better compare TCs on a case-by-case basis.

  17. 17 Acknowledgments • References • Franklin, J. L. and J. Cangialosi, 2011: National Hurricane Center 2010 Forecast Verification. Proceedings, 65th Interdepartmental Hurricane Conference, Miami, FL • Knapp, K. R., 2008: Hurricane satellite (HURSAT) data sets: Low-earth orbit infrared and microwave data. Preprints, 28th Conf. on Hurricanes and Tropical Meteorology, Orlando, FL, Amer. Meteor. Soc., 4B.4. • Murray, D.A., 2009: Improved Short-Term Atlantic Hurricane Intensity Forecasts Using Reconnaissance-based Core Measurements. M.S. Thesis, Florida State University, 150 pp. • Piech, D., 2007: Atlantic Reconnaissance Vortex Message Climatology and Composites and Their Use in Characterizing Eyewall Cycles. M.S. Thesis, Florida State University, 139 pp. • Wimmers, Anthony J., Christopher S. Velden, 2010: Objectively Determining the Rotational Center of Tropical Cyclones in Passive Microwave Satellite Imagery. J. Appl. Meteor. Climatol., 49, 2013–2034. • Funding (supported in part by): • NASA GRIP Grant NNX09AC43G • AMS Graduate Fellowship (sponsored by SAIC/Advanced Science and Engineering Operation) • FSU Presidential Fellowship