scholarly journals Initialization of Tropical Cyclones in Numerical Prediction Systems

Author(s):  
Eric A. ◽  
Melinda S.

Author(s):  
Irina Sandu ◽  
François Massonnet ◽  
Guillian Achter ◽  
Juan C Acosta Navarro ◽  
Gabriele Arduini ◽  
...  


MAUSAM ◽  
2021 ◽  
Vol 70 (2) ◽  
pp. 195-214
Author(s):  
DODLA VENKATA BHASKAR RAO


2021 ◽  
Vol 28 (2) ◽  
Author(s):  
Yuan‐Yuan Liu ◽  
Lei Li ◽  
Ye‐Sen Liu ◽  
Pak‐Wai Chan ◽  
Wen‐Hai Zhang ◽  
...  


Climate ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 174
Author(s):  
Kelvin S. Ng ◽  
Gregor C. Leckebusch ◽  
Qian Ye ◽  
Wenwen Ying ◽  
Haoran Zhao

Parametric typhoon insurances are an increasingly used financial tool to mitigate the enormous impact of tropical cyclones, as they can quickly distribute much-needed resources, e.g., for post-disaster recovery. In order to optimise the reliability and efficiency of parametric insurance, it is essential to have well-defined trigger points for any post-disaster payout. This requires a robust localised hazard assessment for a given region. However, due to the rarity of severe, landfalling tropical cyclones, it is difficult to obtain a robust hazard assessment based on historical observations. A recent approach makes use of unrealised, high impact tropical cyclones from state-of-the-art ensemble prediction systems to build a physically consistent event set, which would be equivalent to about 10,000 years of observations. In this study, we demonstrate that (1) alternative trigger points of parametric typhoon insurance can be constructed from a local perspective and the added value of such trigger points can be analysed by comparing with an experimental set-up informed by current practice; (2) the estimation of the occurrence of tropical cyclone-related losses on the provincial level can be improved. We further discuss the potential future development of a general tropical cyclone compound parametric insurance.



2021 ◽  
Author(s):  
Julia Lockwood ◽  
Nick Dunstone ◽  
Leon Hermanson ◽  
Adam Scaife ◽  
Doug Smith ◽  
...  

<p>North Atlantic tropical cyclones are the costliest natural hazard affecting the US, and are capable of causing hundreds of billions of dollars of insured losses in a single season.  Tropical cyclone activity has been observed to show considerable decadal variability, linked with variations in sea surface temperatures in regions of the North Atlantic such as the main hurricane development region (MDR) and sub-polar gyre (SPG).</p><p>In this presentation we show that a multi-model ensemble of decadal prediction systems can skilfully predict north Atlantic hurricane activity and consequent US insured losses on multi-annual timescales, with a correlation coefficient of greater than 0.7 for 5 year mean hurricane activity.  Rather than tracking tropical cyclones directly in the dynamical models, we make predictions using an index based on predicted temperatures over the north Atlantic.  The skill of the dynamical models outperforms persistence, and could aid decision making for the (re)insurance industry over the US.  As part of the Copernicus Climate Change Service, a publicly available probabilistic forecast of 5 year mean north Atlantic hurricane activity and US insured losses has been produced and will be presented here.</p>



1974 ◽  
Author(s):  
Sarah Lichtenstein ◽  
Timothy C. Earle ◽  
Paul Slovic






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