Quantifying Model Uncertainty and Risk

Shome, Nilesh; Rahnama, Mohsen; Jewson, Stephen; Wilson, Paul

doi:10.1016/b978-0-12-804071-3.00001-x

Cited by 9 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CC BY 4.0 License. larger catalogues to achieve robust convergence of the ARI values, in line with other hurricane catastrophe models (Shome et al, 2018).…”

Section: Ari Wind Speed Verification 35supporting

confidence: 74%

See 1 more Smart Citation

A statistical-parametric model of tropical cyclones for hazard assessment

Arthur¹

2019

Preprint

View full text Add to dashboard Cite

We present the formulation of an open-source, statistical-parametric model of tropical cyclones (TCs) for use in hazard and risk assessment applications. The model derives statistical relations for TC behaviour (genesis rate and location, intensity, speed and direction of translation) from best-track datasets, then uses these relations to create a synthetic catalogue based on stochastic sampling, representing many thousands of years of activity. A parametric wind field, based on radial profiles and boundary layer models, is applied to each event in the catalogue that is then used to fit extreme value distributions 10 for evaluation of return period wind speeds. We demonstrate the capability of the model to replicate observed behaviour of TCs, including coastal landfall rates which is of significant importance for risk assessments.

show abstract

“…CC BY 4.0 License. larger catalogues to achieve robust convergence of the ARI values, in line with other hurricane catastrophe models (Shome et al, 2018).…”

Section: Ari Wind Speed Verification 35supporting

confidence: 74%

“…An important component of stochastic models is to check for convergence in solutions (Shome et al, 2018). For TCRM, this 35 can be checked by splitting the synthetic catalogue into two subsets, calculating ARI values from each and examining the range of values.…”

Section: Ari Wind Speed Verification 35mentioning

confidence: 99%

A statistical-parametric model of tropical cyclones for hazard assessment

Arthur¹

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…An important component of stochastic models is to check for convergence in solutions (Shome et al, 2018). For TCRM, this can be checked by splitting the synthetic catalogue into two subsets, calculating ARI values from each and examining the range of values.…”

Section: Ari Wind Speed Verificationmentioning

confidence: 99%

A statistical–parametric model of tropical cyclones for hazard assessment

Arthur

2021

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. We present the formulation of an open-source, statistical–parametric model of tropical cyclones (TCs) for use in hazard and risk assessment applications. The model derives statistical relations for TC behaviour (genesis rate and location, intensity, speed and direction of translation) from best-track datasets, then uses these relations to create a synthetic catalogue based on stochastic sampling, representing many thousands of years of activity. A parametric wind field, based on radial profiles and boundary layer models, is applied to each event in the catalogue that is then used to fit extreme-value distributions for evaluation of return period wind speeds. We demonstrate the capability of the model to replicate observed behaviour of TCs, including coastal landfall rates which are of significant importance for risk assessments.

show abstract

“…But 120 years of data do not provide a reliable understanding of the tail events and the shape of the distribution at those longer return periods. Shome et al (2018) cite this paucity of data as a reason for using quasi-physical simulation models, whereby modellers create "statistical storms" to expand and "fill in" the dataset. This process, however, relies on the (scant) historical evidence and so it cannot remove the problem of restricted evidence.…”

Section: Averaging and Its Limitationsmentioning

confidence: 99%

“…As the RMS ensemble is proprietary, some detective work is required here. We compared(Shome et al 2018;Jewson et al 2007;Sabbatelli and Waters 2015;Sabbatelli 2017).…”

mentioning

confidence: 99%

Making Confident Decisions with Model Ensembles

Roussos¹,

Bradley²,

Frigg

2021

Philos. of Sci.

View full text Add to dashboard Cite

Many policy decisions take input from collections of scientific models. Such decisions face significant and often poorly understood uncertainty. We rework the so-called confidence approach to tackle decision-making under severe uncertainty with multiple models, and we illustrate the approach with a case study: insurance pricing using hurricane models. The confidence approach has important consequences for this case and offers a powerful framework for a wide class of problems. We end by discussing different ways in which model ensembles can feed information into the approach, appropriate to different collections of models.

show abstract

Quantifying Model Uncertainty and Risk

Cited by 9 publications

References 32 publications

A statistical-parametric model of tropical cyclones for hazard assessment

A statistical-parametric model of tropical cyclones for hazard assessment

A statistical–parametric model of tropical cyclones for hazard assessment

Making Confident Decisions with Model Ensembles

Contact Info

Product

Resources

About