Kinetic Energy Budgets in Different Quadrants During Tropical Cyclone Recurvature: A Case Study

2022 ◽  
Author(s):  
Guanbo Zhou ◽  
Jie CAO ◽  
Longsheng Liu
2015 ◽  
Vol 71 (2) ◽  
pp. I_1513-I_1518 ◽  
Author(s):  
Yoko SHIBUTANI ◽  
Sota NAKAJO ◽  
Nobuhito MORI ◽  
Sooyoul KIM ◽  
Hajime MASE

2019 ◽  
Author(s):  
Bingchuan Nie ◽  
Qingyong Wuxi ◽  
Jiachun Li ◽  
Feng Xu

Abstract. A methodology for assessing the storm tide inundation under TCI (tropical cyclone intensification) and SLR (sea level rise) is proposed, which integrates the trend analysis, numerical analysis and GIS-based analysis. In the trend analysis, the potential TCI and SLR can be estimated based on the long-term historical data of TC (tropical cyclone) and MSL (mean sea level) considering the non-stationary and spatially non-uniform effect; the numerical simulation is relied on the ADCIRC+SWAN model, which is capable of taking into account the tide-surge-wave coupling effect to improve the precision of water elevation prediction; the water elevation is then analyzed on the GIS platform, the potential inundation regions can be identified. Based on this methodology, a case study for the Southeast China coast, one of the storm surge prone areas in China, is presented. The results show that the high water elevation tends to occur in the bays and around the estuaries, the maximal water elevations caused by the typhoon wind of 100-year recurrence period can reach as high as 6.06 m, 5.82 m and 5.67 m around Aojiang, Feiyunjiang and Oujiang river estuaries, respectively. Non-stationary TCI and SLR due to climate change can further deteriorate the situation and enhance the risk of inundation there, i.e. the potential inundation area would expand by 108 % to about 798 km2 compared with the situation without considering TCI and SLR. In addition, the remotely sensed maps and inundation durations of the hardest hit regions are provided, which will aid the prevention and mitigation of storm tide inundation hazard and future coastal management there.


2018 ◽  
Author(s):  
Sandy Hardian Susanto Herho ◽  
Dasapta Erwin Irawan

Sonic anemometer observation was performed on 29 - 30 September 2014 in Ledeng, Bandung to see diurnal variations of Turbulence Kinetic Energy (TKE) that occurred in this area. The measured sonic anemometer was the wind velocity components u, v, and w. From the observation result, it can be seen that the diurnal variation that happened was quite significant. The maximum TKE occurs during the daytime when atmospheric conditions tend to be unstable. TKE values were small at night when atmospheric conditions are more stable than during the daytime.


2014 ◽  
Vol 142 (12) ◽  
pp. 4646-4657 ◽  
Author(s):  
Michael E. Kozar ◽  
Vasubandhu Misra

Abstract Integrated kinetic energy (IKE) is a useful quantity that measures the size and strength of a tropical cyclone wind field. As a result, it is inherently related to the destructive potential of these powerful storms. In most current operational settings, there are limited resources designed to assess the IKE of a tropical cyclone because storm track and maximum intensity are typically prioritized. Therefore, to complement existing forecasting tools, a statistical scheme is created to project fluctuations of IKE in North Atlantic tropical cyclones for several forecast intervals out to 72 h. The resulting scheme, named Statistical Prediction of Integrated Kinetic Energy (SPIKE), utilizes multivariate normal regression models trained on environmental and storm-related predictors from all North Atlantic tropical cyclones occurring from 1990 to 2011. During this training interval, SPIKE outperforms persistence and is capable of explaining more than 80% of observed variance in total IKE values at a forecast interval of 12 h, trailing down to just below 60% explained variance at an interval of 72 h. The skill of the SPIKE model is evaluated further using bootstrapping exercises in order to gauge the predictive abilities of the statistical scheme. In addition, the performance of the SPIKE model is also evaluated for the 2012 Atlantic hurricane season, which notably falls outside of the training interval. Ultimately, the validation exercises return shared variance scores similar to those found in the training exercises, serving as a proof of concept that the SPIKE model can be used to project IKE values when given accurate predictor data.


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