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Gregory R. Foltz
,
Karthik Balaguru
, and
Samson Hagos

ABSTRACT

Sea surface temperature (SST) is one of the most important parameters for tropical cyclone (TC) intensification. Here, it is shown that the relationship between SST and TC intensification varies considerably from basin to basin, with SST explaining less than 4% of the variance in TC intensification rates in the Atlantic, 12% in the western North Pacific, and 23% in the eastern Pacific. Several factors are shown to be responsible for these interbasin differences. First, variability of SST along TCs’ tracks is lower in the Atlantic. This is due to smaller horizontal SST gradients in the Atlantic, compared to the Pacific, and stronger damping of prestorm SST’s contribution to TC intensification by the storm-induced cold SST wake in the Atlantic. The damping occurs because SST tends to vary in phase with TC-induced SST cooling: in the Gulf of Mexico and northwestern Atlantic, where SSTs are highest, TCs tend to be strongest and their translations slowest, resulting in the strongest storm-induced cooling. The tendency for TCs to be more intense over the warmest SST in the Atlantic also limits the usefulness of SST as a predictor since stronger storms are less likely to experience intensification. Finally, SST tends to vary out of phase with vertical wind shear and outflow temperature in the western Pacific. This strengthens the relationship between SST and TC intensification more in the western Pacific than in the eastern Pacific or Atlantic. Combined, these factors explain why prestorm SST is such a poor predictor of TC intensification in the Atlantic, compared to the eastern and western North Pacific.

Full access
Karthik Balaguru
,
Gregory R. Foltz
,
L. Ruby Leung
,
John Kaplan
,
Wenwei Xu
,
Nicolas Reul
, and
Bertrand Chapron

Abstract

Tropical cyclone (TC) rapid intensification (RI) is difficult to predict and poses a formidable threat to coastal populations. A warm upper ocean is well known to favor RI, but the role of ocean salinity is less clear. This study shows a strong inverse relationship between salinity and TC RI in the eastern Caribbean and western tropical Atlantic due to near-surface freshening from the Amazon–Orinoco River system. In this region, rapidly intensifying TCs induce a much stronger surface enthalpy flux compared to more weakly intensifying storms, in part due to a reduction in SST cooling caused by salinity stratification. This reduction has a noticeable positive impact on TCs undergoing RI, but the impact of salinity on more weakly intensifying storms is insignificant. These statistical results are confirmed through experiments with an ocean mixed layer model, which show that the salinity-induced reduction in SST cold wakes increases significantly as the storm’s intensification rate increases. Currently, operational statistical–dynamical RI models do not use salinity as a predictor. Through experiments with a statistical RI prediction scheme, it is found that the inclusion of surface salinity significantly improves the RI detection skill, offering promise for improved operational RI prediction. Satellite surface salinity may be valuable for this purpose, given its global coverage and availability in near–real time.

Free access
Wenwei Xu
,
Karthik Balaguru
,
Andrew August
,
Nicholas Lalo
,
Nathan Hodas
,
Mark DeMaria
, and
David Judi

Abstract

Reducing tropical cyclone (TC) intensity forecast errors is a challenging task that has interested the operational forecasting and research community for decades. To address this, we developed a deep learning (DL)-based multilayer perceptron (MLP) TC intensity prediction model. The model was trained using the global Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors to forecast the change in TC maximum wind speed for the Atlantic basin. In the first experiment, a 24-h forecast period was considered. To overcome sample size limitations, we adopted a leave one year out (LOYO) testing scheme, where a model is trained using data from all years except one and then evaluated on the year that is left out. When tested on 2010–18 operational data using the LOYO scheme, the MLP outperformed other statistical–dynamical models by 9%–20%. Additional independent tests in 2019 and 2020 were conducted to simulate real-time operational forecasts, where the MLP model again outperformed the statistical–dynamical models by 5%–22% and achieved comparable results as HWFI. The MLP model also correctly predicted more rapid intensification events than all the four operational TC intensity models compared. In the second experiment, we developed a lightweight MLP for 6-h intensity predictions. When coupled with a synthetic TC track model, the lightweight MLP generated realistic TC intensity distribution in the Atlantic basin. Therefore, the MLP-based approach has the potential to improve operational TC intensity forecasts, and will also be a viable option for generating synthetic TCs for climate studies.

Open access
Karthik Balaguru
,
Gregory R. Foltz
,
L. Ruby Leung
,
John Kaplan
,
Wenwei Xu
,
Nicolas Reul
, and
Bertrand Chapron
Full access
Karthik Balaguru
,
Gregory R. Foltz
,
L. Ruby Leung
,
Samson M. Hagos
, and
David R. Judi

Abstract

Sea surface temperature (SST) and tropical cyclone heat potential (TCHP) are metrics used to incorporate the ocean’s influence on hurricane intensification into the National Hurricane Center’s Statistical Hurricane Intensity Prediction Scheme (SHIPS). While both SST and TCHP serve as useful measures of the upper-ocean heat content, they do not accurately represent ocean stratification effects. Here, it is shown that replacing SST within the SHIPS framework with a dynamic temperature T dy, which accounts for the oceanic negative feedback to the hurricane’s intensity arising from storm-induced vertical mixing and sea surface cooling, improves the model performance. While the model with SST and TCHP explains about 41% of the variance in 36-h intensity changes, replacing SST with T dy increases the variance explained to nearly 44%. These results suggest that representation of the oceanic feedback, even through relatively simple formulations such as T dy, may improve the performance of statistical hurricane intensity prediction models such as SHIPS.

Full access