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The Impact of the Storm-Induced SST Cooling on hurricane intensity

Advances in Atmospheric Sciences 2006年第1期23卷 14-22页

作者： Tong ZHU Da-Lin ZHANG CIRA/CSU at NOAA/NESDIS/ORA Camp Springs MD 20746 USA Department of Meteorology University of Maryland College Park MD 20742 USA

The effects of storm-induced sea surface temperature （SST） cooling on hurricane intensity are investigated using a 5-day cloud-resolving simulation of hurricane Bonnie （1998）. Two sensitivity simulations are performed in which the storm-induced cooling is either ignored or shifted close to the modeled storm track. Results show marked sensitivity of the model-simulated storm intensity to the magnitude and relative position with respect to the hurricane track. It is shown that incorporation of the storm-induced cooling, with an average value of 1.3℃, causes a 25-hPa weakening of the hurricane, which is about 20 hPa per 1℃ change in SST. Shifting the SST cooling close to the storm track generates the weakest storm, accounting for about 47% reduction in the storm intensity. It is found that the storm intensity changes are well correlated with the air-sea temperature difference. The results have important implications for the use of coupled hurricane-ocean models for numerical prediction of tropical cyclones.

关键词： SST feedback hurricane intensity numerical modeling

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Dependence of hurricane intensity and Structures on Vertical Resolution and Time-Step Size

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Advances in Atmospheric Sciences 2003年第5期20卷 711-725页

作者： Da-Lin ZHANG and Xiaoxue WANGDepartment of Meteorology, University of Maryland, College Park, Maryland, 20742 USA Department of Meteorology University of Maryland College Park USA

In view of the growing interests in the explicit modeling of clouds and precipitation, the effects of varying vertical resolution and time-step sizes on the 72-h explicit simulation of hurricane Andrew (1992) are studied using the Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model (i.e., MM5) with the finest grid size of 6 km. It is shown that changing vertical resolution and time-step size has significant effects on hurricane intensity and inner-core cloud/precipitation, but little impact on the hurricane track. In general, increasing vertical resolution tends to produce a deeper storm with lower central pressure and stronger three-dimensional winds, and more precipitation. Similar effects, but to a less extent, occur when the time-step size is reduced. It is found that increasing the low-level vertical resolution is more efficient in intensifying a hurricane, whereas changing the upper-level vertical resolution has little impact on the hurricane intensity. Moreover, the use of a thicker surface layer tends to produce higher maximum surface winds. It is concluded that the use of higher vertical resolution, a thin surface layer, and smaller time-step sizes, along with higher horizontal resolution, is desirable to model more realistically the intensity and inner-core structures and evolution of tropical storms as well as the other convectively driven weather systems.

关键词： hurricane intensity vertical resolution numerical weather prediction

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A Wavelet-Based Deep Learning Framework for Predicting Peak intensity of hurricanes in the Atlantic Ocean

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Atmospheric and Climate Sciences 2023年第4期13卷 587-606页

作者： Jiahe Liu Xiaodi Wang The Mathematics Department Western Connecticut State University USA

Every year, hurricanes pose a serious threat to coastal communities, and forecasting their maximum intensities has been a crucial task for scientists. Computational methods have been used to forecast the intensities of hurricanes across varying time horizons. However, as climate change has increased the volatility of the intensities of recent hurricanes, newer and adaptable methods must be devised. In this study, a framework is proposed to estimate the maximum intensity of tropical cyclones (TCs) in the Atlantic Ocean using a multi-input convolutional neural network (CNN). From the Atlantic hurricane seasons of 2000 through 2021, over 100 TCs that reached hurricane-level wind speeds are used. Novel algorithms are used to collect and preprocess both satellite image data and non-image data for these TCs. Namely, Discrete Wavelet Transforms (DWTs) are used to decompose individual bands of satellite image data, eliminating noise and extracting hidden frequency details before training. Validation tests indicate that this framework can estimate the maximum wind speed of TCs with a root mean square error of 15 knots. This framework provides preliminary predictions that can supplement current computational methods that would otherwise not be able to account for climate change. Future work can be done by forecasting with time constraints, and to provide estimations for more metrics such as pressure and precipitation.

关键词： Tropical Cyclone (TC) hurricane intensity Convolutional Neural Network (CNN) Discrete Wavelet Transform (DWT)

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