Ice Water Content as a Precursor to Tropical Cyclone Rapid Intensification

Wu, S., B. Soden, and G. J. Alaka (2020), Ice Water Content as a Precursor to Tropical Cyclone Rapid Intensification, Geophys. Res. Lett., 47, e2020GL089669, doi:10.1029/2020GL089669.

This study examines how the structure and amount of cloud ice water content are related to rates of tropical cyclone (TC) intensification using CloudSat profiling radar measurements and simulations from the Hurricane Weather Research and Forecasting (HWRF) model. Observational studies have demonstrated the signal of TC intensification in the passive satellite measurements of frozen water concentration. However, the vertical and horizontal resolution of passive satellite observations are limited. CloudSat measurements and HWRF simulations provide high‐resolution data sets of ice water content to better understand its relationship with the rate of TC intensification. It is found that rapidly intensifying TCs have larger ice water content compared to TCs with slower intensification rates. Similar results are obtained even after accounting for the effect of initial TC intensity. Such precursors of rapidly intensifying TCs may be used to better understand and improve the prediction of TC intensification. Plain Language Summary It is difficult to accurately forecast future tropical cyclone intensity. Previous studies have analyzed historical tropical cyclones and built statistical hurricane forecast models to predict their intensity changes. These models are good at predicting whether a hurricane will strengthen or weaken. However, they have trouble predicting how fast a tropical cyclone is going to intensify. To better understand intensification rates for tropical cyclones, we analyzed the relationship between intensification rate and the amount of cloud ice from satellite observations and a forecast model. It was found that tropical cyclones with greater amounts of cloud ice are likely to intensify faster. This result suggested that observations of cloud ice may be used to improve the performance of hurricane forecast models.

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