Abstract
Abstract. Atlantic hurricane activity varies substantially from year to year
and so does the associated damage. Longer-term forecasting of hurricane risks
is a key element to reduce damage and societal vulnerabilities by enabling
targeted disaster preparedness and risk reduction measures. While the
immediate synoptic drivers of tropical cyclone formation and intensification
are increasingly well understood, precursors of hurricane activity on longer
time horizons are still not well established. Here we use a causal-network-based algorithm to identify physically interpretable late-spring
precursors of seasonal Atlantic hurricane activity. Based on these
precursors we construct statistical seasonal forecast models with
competitive skill compared to operational forecasts. In particular, we
present a skilful prediction model to forecast July to October tropical
cyclone activity at the beginning of April. Our approach highlights the
potential of applying causal effect network analysis to identify sources of
predictability on seasonal timescales.
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献