Operational forecasts of intensity change in tropical cyclones (TCs) show no distinguishable improvement over the past two decades (Franklin and Cangialosi 2011). While there have been numerous thrusts in TC numerical modeling, statistical and consensus models continue to have the highest predictive skill for intensity. These top performers respectively use environmental parameters and bias-correction to forecast future TC intensity. Diagnosis of the TC core and structure is not well captured through these methods. Nevertheless, Piech (2007) has shown through aircraft reconnaissance measurements that there are specific regimes of hurricane core observations at different intensities. Murray (2009) has further demonstrated predictive skill using statistical analysis of such core observations.

However, a lack of regular in-situ observations remains the largest obstacle to an operational inner-core based intensity forecast. To overcome such a challenge, a climatology of TC satellite data (HURSAT; Knapp 2008) is analyzed as a proxy for reconnaissance flights. The use of satellites allows worldwide coverage and can provide information on TCs where reconnaissance aircraft cannot fly. In particular, structural patterns as diagnosed by microwave channels from SSMI are explored as representations of physical processes within the TC core. With the help of ARCHER software (Wimmers and Velden 2010), characteristic features of the TC are identified and compared to future intensity change. Ultimately, the goals of this research are 1) to better understand what factors determine inner core changes, such as eye size, convective structure and storm size and 2) to incorporate core measurements of TCs into statistical guidance and further improve the most skillful TC guidance.

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.