A STATISTICAL DETERMINISTIC APPROACH TO HURRICANE RISK ASSESSMENT

By Kerry A. Emanuel, S. Ravela, E. Vivant, Camille Risi. 2006

597 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/792850e331ad4b63ff6e198832724d750a64d0b9

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Citations in the corpus

On hurricane parametric wind and applications in storm surge modeling (2012, background): “…(-field) parameters; the storm characteristics may be estimated from observations [e.g., Best Track and H*Wind data], from forecasts [e.g., Kurihara et al., 1998], or from statistical [Vickery et al., 2000] or statistical/deterministic [Emanuel et al., 2006, 2008] synthetic hurricane simulations.”
A global reanalysis of storm surges and extreme sea levels (2016, unknown): “Hence, synthetic resampling techniques are needed to extend the tropical cyclone record to a longer record 33,39,40 .”
Risk and Adaptation: Evidence from Global Hurricane Damages and Fatalities (2016, methodology): “A total of 68,000 simulated cyclone tracks generated by Kerry Emanuel are used to predict the frequencies by location around the world (Emanuel et al. 2006; Mendelsohn et al. 2012).”
Machine Learning for the Geosciences: Challenges and Opportunities (2017, background): “necessary for implementing policy decisions. Some advances have been made in downscaling future projections to high spatial resolutions, e.g., using physics-based Markov Chain and Random Field models [73], but much remains to be done. Further, the data is sparse, and the uncertainty distributions re”
Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century (2013, background): “(17), the winds are derived from synthetic time series of winds constrained to have the same monthly means as those produced by the global model, as well as the same monthly mean covariances among the wind components at two model levels, where the fluctuations are defined in terms of departures of d”