BibTex format
@article{Lok:2021:10.1038/s43247-021-00259-8,
author = {Lok, CCF and Chan, JCL and Toumi, R},
doi = {10.1038/s43247-021-00259-8},
journal = {Communications Earth & Environment},
title = {Tropical cyclones near landfall can induce their own intensification through feedbacks on radiative forcing},
url = {http://dx.doi.org/10.1038/s43247-021-00259-8},
volume = {2},
year = {2021}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - Rapid intensification of near-landfall tropical cyclones is very difficult to predict, and yet has far-reaching consequences due to their disastrous impact to the coastal areas. The focus for improving predictions of rapid intensification has so far been on environmental conditions. Here we use the Coupled-Ocean-Atmosphere-Wave-Sediment Transport Modeling System to simulate tropical cyclones making landfall in South China: Nida (2016), Hato (2107) and Mangkhut (2018). Two smaller storms (Hato and Nida) undergo intensification, which is induced by the storms themselves through their extensive subsidence ahead of the storms, leading to clear skies and strong solar heating of the near-shore sea water over a shallow continental shelf. This heating provides latent heat to the storms, and subsequently intensification occurs. In contrast, such heating does not occur in the larger storm (Mangkhut) due to its widespread cloud cover. This results imply that to improve the prediction of tropical cyclone intensity changes prior to landfall, it is necessary to correctly simulate the short-term evolution of near-shore ocean conditions.
AU - Lok,CCF
AU - Chan,JCL
AU - Toumi,R
DO - 10.1038/s43247-021-00259-8
PY - 2021///
SN - 2662-4435
TI - Tropical cyclones near landfall can induce their own intensification through feedbacks on radiative forcing
T2 - Communications Earth & Environment
UR - http://dx.doi.org/10.1038/s43247-021-00259-8
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000694234900006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
VL - 2
ER -