The Statistical Representation of Tropical Cyclone In-Storm Sea Surface Temperature Cooling Using a Large Buoy Database

This study uses fixed buoy time series to determine the most important variables for tropical cyclone (TC) inner-core sea surface temperature (SST) cooling and to build a predictive equation for SST cooling that can be used for statistical modeling. To build predictive equations, SST cooling is first related to single variable predictors such as the SST before storm arrival, ocean heat content (OHC), mixed layer depth, sea surface salinity and stratification, storm intensity, storm translation speed, and latitude. Of all the single variable predictors, initial SST before storm arrival explains the greatest amount of variance for the change in SST during storm passage. Using a combination of predictors, we created nonlinear predictive equations for SST cooling. In general, the best predictive equations have four predictors and are built with knowledge about the prestorm ocean structure (e.g., OHC), storm intensity (e.g., minimum sea level pressure), initial SST values before storm arrival, and latitude.

The best-performing SST cooling equations are broken up into two ocean regimes: when the ocean heat content is less than 60 kJ cm⁻² (greater spread in SST cooling values) and when the ocean heat content is greater than 60 kJ cm⁻² (SST cooling is always less than 2°C), which demonstrates the importance of the prestorm oceanic thermal structure on the in-storm SST value. The new equations improve what is currently used in the SHIPS model which tends to under-predict SST cooling when the actual value is large. Implications of the new equation for intensity forecasting in SHIPS, or other statistical-dynamical model will be discussed.