Previous studies demonstrate that there is a relationship between a tropical cyclone's (TC) track and intensity (e.g. Velden and Leslie 1991, Emanuel et al. 2004) Increasing observations of environmental parameters and their assimilation as modeled variables during the past few decades have significantly enhanced the predictability of TC track, though there has been no concomitant increase in intensity prediction (Franklin and Cangialosi 2011). Despite this disconnect, statistical models that primarily use environmental conditions (e.g. DeMaria et al. 2005) are among the top performing methods to forecast TC intensity. Furthermore, Burpee et al. (1996) demonstrate that additional observations inside and around TCs reduce forecast error. The benefits of incorporating TC core observations to improve intensity forecasts are demonstrated in Murray (2009).

Toward this end, a climatology of TC inner-core structure is compiled and analyzed. Building upon the research of Piech (2007) and Murray (2009), which gathered and assessed aircraft reconnaissance measurements and compared them to TC intensity changes, satellite measurements from the HURSAT database (Knapp 2008) are used to diagnose TC structure and intensity. An analogue to reconnaissance observations is produced for all worldwide TCs from 1987-2009 using ARCHER software (Wimmers and Velden 2010). From this climatology, patterns of TC structure with respect to location and intensity can be used as an aid to TC prediction. As this climatology is expanded, validation of model performance may eventually reach beyond just track and intensity, but can include TC internal structure as well.

Burpee, R. W., J. L. Franklin, S. J. Lord, R. E. Tuleya, and S. D. Aberson, 1996: The impact of Omega dropwindsondes on operational hurricane track forecast models. Bull. Amer. Meteor. Soc., 77, 925–933.

DeMaria, Mark, Michelle Mainelli, Lynn K. Shay, John A. Knaff, John Kaplan, 2005: Further Improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS). Wea. Forecasting, 20, 531–543.

Emanuel, Kerry, Christopher DesAutels, Christopher Holloway, Robert Korty, 2004: Environmental Control of Tropical Cyclone Intensity. J. Atmos. Sci., 61, 843–858.

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.

Velden, C. S., and L. Leslie, 1991: The Basic Relationship between Tropical Cyclone Intensity and the Depth of the Environmental Steering Layer in the Australian Region. Wea. and Forecasting, 6, 244-253.

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.