A Re-examination of the Relationship between Empirical Maximum Potential Intensity of Tropical Cyclone and Sea Surface Temperature

Wednesday, 20 April 2016: 11:15 AM
Ponce de Leon C (The Condado Hilton Plaza)
Kelvin Sai-cheong Ng, The University of Hong Kong, Hong Kong, Hong Kong; and M. H. Lee and Y. Zong

Handout (7.9 MB)

The relationship between empirical maximum potential intensity (MPI) of tropical cyclone and sea surface temperature (SST) has been applied widely to tropical cyclone intensity forecast models in the literature and real time forecast. Several authors have attempted in the past to develop the relationship for different ocean basins. However, due to the use of different datasets and treatment of data, it is still uncertain whether there is a universal empirical equation that relates MPI to SST in all ocean basins. We re-examine this problem by using a uniform method of processing data from all 7 ocean basins, and evaluate the effects of the choice of different SST datasets on the MPI-SST relations. This study further assesses the uncertainty of maximum surface wind speed in SST bins where the number of observations is small and suggests ways for improvement.

This study shows that there is not a uniform relationship linking SST to MPI for the entire globe, and each ocean basin has its own MPI-SST relationship. There are two types of MPI-SST relation a) exponential, and b) (nearly) linear. The choice of the SST dataset and study period also affects the MPI-SST relation. The MPI-SST relations are relatively consistent between ERA-interim and JRA-55 reanalysis data, but the relationship derived using the World Ocean Atlas climatological SST is different. Furthermore, if the number of observations in an SST bin is limited (e.g. 10), the percentage error of the sampled maximum intensity is worse than 40%. Thus MPI is not a robust statistics, and similarly for the MPI-SST relations. An alternative 99th percentile intensity-SST relation is proposed, which is more robust and less sensitive to the choice of dataset and the number of observations.

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