Understanding of the Effect of Climate Change on Tropical Cyclone Intensity: A Review

Wu, Liguang; Zhao, Huihui; Wang, Chao; Cao, Jian; Jia, Lü

doi:10.1007/s00376-021-1026-x

Cited by 47 publications

(19 citation statements)

References 199 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As consequences of global warming, sea level will continue to rise and more intense typhoons will be generated (Stowasser et al, 2007;Wu et al, 2014;Wu et al, 2022). Typhoon-induced storm surge superposed on SLR can bring severer hazards to coastal areas.…”

Section: Discussionmentioning

confidence: 99%

Model Simulation of Storm Surge in the Northwestern South China Sea Under the Impact of Sea Level Rise: A Case Study of Super Typhoon Rammasun (2014)

Zhou

Vetter

et al. 2022

Front. Mar. Sci.

View full text Add to dashboard Cite

Because of global warming, the sea level is expected to continue to rise, possibly having a significant impact on the intensities and spatial distribution characteristics of coastal storm surges. In this study, we took super typhoon Rammasun (2014) as a case study and applied the SCHISM (Semi-implicit Cross-scale Hydroscience Integrated System Model) to simulate storm surge in the northwestern South China Sea under future sea level rise (SLR) scenarios. To improve the accuracy of storm surge hindcast, we used reconstructed wind field to drive the model in which ERA5 reanalysis data were superposed on the wind field calculated from the Holland parametric cyclone model. The results show that the storm surge hindcast was significantly improved by using this reconstructed wind forcing. 2-D and 3-D model hindcast capabilities were compared; the 3-D model reproduced the storm surge better. The regional sea level projections in 2050, 2100, 2200, and 2300 for RCP 4.5 scenarios (provided by the IPCC AR6 dataset) were superposed on the original water depth as the predicted sea levels, then those depths were used in models of storm surge in the study area under a typhoon identical to Rammasun. Model results demonstrate that storm surge peaks in most sea areas decrease nearly linearly with SLR, especially in regions of high surges.

show abstract

Section: Discussionmentioning

confidence: 99%

Model Simulation of Storm Surge in the Northwestern South China Sea Under the Impact of Sea Level Rise: A Case Study of Super Typhoon Rammasun (2014)

Zhou

Vetter

et al. 2022

Front. Mar. Sci.

View full text Add to dashboard Cite

show abstract

“…They represent a major hazard for life and property in exposed regions of the world. There are still many unanswered questions on the number, intensity, duration, trajectory, and probability of landfall of tropical cyclones in a warming climate (Emanuel, 2005;Webster et al, 2005;Chan, 2006;Vecchi et al, 2019;IPCC, 2021;Wu et al, 2022). IPCC (2021) estimated that "it is likely that the global proportion of major (Category 3-5) tropical cyclone occurrence has increased over the last four decades", but "there is low confidence in long-term (multi-decadal to centennial) trends in the frequency of all-category tropical cyclones".…”

Section: Introductionmentioning

confidence: 99%

Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset

Gardoll

Boucher

2022

Geosci. Model Dev.

View full text Add to dashboard Cite

Abstract. Tropical cyclones (TCs) are one of the most devastating natural disasters, which justifies monitoring and prediction on short and long timescales in the context of a changing climate. In this study, we have adapted and tested a convolutional neural network (CNN) for the classification of reanalysis outputs according to the presence or absence of TCs. This study compares the performance and sensitivity of a CNN to the learning dataset. For this purpose, we chose two meteorological reanalysis, ERA5 and MERRA-2, and used a number of meteorological variables from them to form TC-containing and background images. The presence of TCs is labeled from the HURDAT2 dataset. Special attention was paid to the design of the background image set to make sure it samples similar locations and times to the TC-containing images. We have assessed the performance of the CNN using accuracy but also the more objective AUC and AUPRC metrics. Many failed classifications can be explained by the meteorological context, such as a situation with cyclonic activity but not yet classified as TCs by HURDAT2. We also tested the impact of spatial interpolation and of “mixing and matching” the training and test image sets on the performance of the CNN. We showed that applying an ERA5-trained CNN to MERRA-2 images works better than applying a MERRA-2-trained CNN to ERA5 images.

show abstract

“…It poses a serious threat to the socio-economic development of China (Lei et al, 2009;Duan, et al, 2012Duan, et al, , 2014. In particular, with the global warming, the probability and proportion of typhoon enhancement in the Northwest Pacific are on the rise (Emanuel, 2005;Webster et al, 2005;Wu et al, 2022). The increase of typhoon intensity and strong typhoon frequency and the low moving speed result in a longer impact time and stronger intensity of landing typhoons.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial intelligence technology in typhoon monitoring and forecasting

et al. 2022

View full text Add to dashboard Cite

In recent years, with the emergence of new artificial intelligence (AI) technology and more observational data from automatic meteorological stations, radars and satellites, the deep learning has very broad application scenarios in the context of meteorological big data. The deep learning has powerful data learning ability and feature capturing ability of complex structures, which has now occupied an important position in the meteorological field and also become a hot topic in meteorological research. Especially, AI has shown great potential advantages in image recognition, which can provide new ideas and new directions for typhoon monitoring and forecasting. In this study, the data used include the typhoon best track data set provided by the China Meteorological Administration and the Himawari-8 and FY4 satellite image data from 2005 to 2020. We use the deep learning model to conduct the typhoon vortex identification, the determination of typhoon location and intensity, and the detection of typhoon intensity mutation with AI techniques. The main research content includes a typhoon vortex identification model based on deep image target detection, an intelligent typhoon intensity determination model based on image classification and retrieval, and a typhoon rapid intensification identification model. Then, a typhoon intelligent monitoring and forecasting system is constructed. The results show that the system can correctly identify typhoon vortices above the strong tropical storm grade in a percentage of 88.6%. The mean absolute error (MAE) and Root mean square deviation (RMSE) of typhoon intensity estimation are 3.8 m/s and 5.05 m/s, respectively, and the comprehensive accuracy of rapid intensification estimation of annual independent samples reaches 92.0%. The system is capable of performing the automatic identification, location and intensity determination, and intelligent tracking of tropical cyclones in real time by using high spatial and temporal resolution satellite images. This study may help further improve the operational techniques for typhoon monitoring and forecasting.

show abstract

Understanding of the Effect of Climate Change on Tropical Cyclone Intensity: A Review

Cited by 47 publications

References 199 publications

Model Simulation of Storm Surge in the Northwestern South China Sea Under the Impact of Sea Level Rise: A Case Study of Super Typhoon Rammasun (2014)

Model Simulation of Storm Surge in the Northwestern South China Sea Under the Impact of Sea Level Rise: A Case Study of Super Typhoon Rammasun (2014)

Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset

Application of artificial intelligence technology in typhoon monitoring and forecasting

Contact Info

Product

Resources

About