The development of wind damage bands for buildings

Unanwa, Christian O.; McDonald, James R.; Mehta, Kishor C.; Smith, Douglas A.

doi:10.1016/s0167-6105(99)00047-1

Cited by 87 publications

(29 citation statements)

References 12 publications

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“…The "Generalized Extreme Value" (GEV) distribution (von Mises, 1954) is a family of continuous probability distributions developed in the extreme value theory to combine the Gumbel, Fréchet, and Weibull families, also known as Fisher-Tippett Type I, II, and III extreme value distributions Embrechts et al, 2003). The cumulative distribution function is defined as…”

Section: Calculation Of Extreme Eventsmentioning

confidence: 99%

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Winter storm risk of residential structures – model development and application to the German state of Baden-Württemberg

Heneka

Hofherr

Ruck

et al. 2006

Nat. Hazards Earth Syst. Sci.

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Abstract. The derivation of probabilities of high wind speeds and the establishment of risk curves for storm damage is of prime importance in natural hazard risk analysis. Risk curves allow the assessment of damage being exceeded at a given level of probability.In this paper, a method for the assessment of winter storm damage risk is described in detail and applied to the German state of Baden-Württemberg. Based on meteorological observations of the years 1971-2000 and on damage information of 4 severe storm events, storm hazard and damage risk of residential buildings is calculated on the level of communities. For this purpose, highly resolved simulations of storm wind fields with the Karlsruher Atmospheric Mesoscale Model (KAMM) are performed and a storm damage model is developed.Risk curves including the quantification of the uncertainties are calculated for every community. Local differences of hazard and risk are presented in state-wide maps. An average annual winter storm damage to residential buildings of minimum 15 million Euro (reference year 2000) for BadenWürttemberg is expected.

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Section: Calculation Of Extreme Eventsmentioning

confidence: 99%

“…In contrast to empirical approaches, it was tried to construct deterministic models (e.g. Unanwa et al, 2000;Pinelli et al, 2004). However, these projects only refer to the US and need large amounts of specific building information that is not available on a large scale in Germany.…”

Section: Introductionmentioning

confidence: 99%

Winter storm risk of residential structures – model development and application to the German state of Baden-Württemberg

Heneka

Hofherr

Ruck

et al. 2006

Nat. Hazards Earth Syst. Sci.

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“…Losses related to storm events can be assessed through empirical formulas that take into account variables describing the characteristics of a windstorm event, such as mean or maximum wind speeds (Dorland et al 1999;Unanwa et al 2000;Huang et al 2001) or storm duration (Schraft et al 1993). The most important parameter remains wind speed at the standard anemometer height of 10 m (maximum wind gusts) that is often the dominant factor leading to damage (Heneka and Ruck 2004).…”

Section: Introductionmentioning

confidence: 99%

Spatial Predictions of Extreme Wind Speeds over Switzerland Using Generalized Additive Models

Etienne

Lehmann

Goyette

et al. 2010

Journal of Applied Meteorology and Climatology

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The purpose of this work is to present a methodology aimed at predicting extreme wind speeds over Switzerland. Generalized additive models are used to regionalize wind statistics for Swiss weather stations using a number of variables that describe the main physiographical features of the country. This procedure enables one to present the results for Switzerland in the form of a map that provides the 98th percentiles of daily maximum wind speeds (W98) at a 10-m anemometer height for cells with a 50-m grid interval. This investigation comprises three major steps. First, meteorological data recorded by the weather stations was gathered to build local wind statistics at each station. Then, data describing the topographic and landscape characteristics of the country were prepared using geographic information systems (GIS). Third, appropriate regression models were selected to make spatially explicit predictions of extreme wind speeds in Switzerland. The predictions undertaken in this study provide realistic values of the W98. The effects of topography on the results are particularly conspicuous. Wind speeds increase with altitude and are greatest on mountain peaks in the Alps, as would be intuitively expected. Relative errors between observations and model results calculated for the meteorological stations do not exceed 30%, and only 12 out of 70 stations exhibit errors that exceed 20%. The combination of GIS techniques and statistical models used to predict a highly uncertain variable, such as extreme wind speed, yields interesting results that can be extended to other fields, such as the assessment of storm damage on infrastructures.

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“…Note that the property values are unitless. The fractional damage (Unanwa et al 2000) depends upon two threshold wind speeds; the lower threshold, S 0 , is the wind speed at which damage to property first occurs, while the second, S 1 , is the wind speed at which complete destruction occurs. Between these two threshold values, we model the increase in damage using a cosine curve,…”

Section: (B) Damage Cost Functionmentioning

confidence: 99%

A 4D‐Var study on the potential of weather control and exigent weather forecasting

Henderson

Hoffman

Leidner

et al. 2005

Quart J Royal Meteoro Soc

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SUMMARYFour-dimensional variational data assimilation is a well-established operational technique whereby a background estimate of the atmosphere is optimally blended with observations, subject to the constraints of the model dynamics and the uncertainties of the information presented to the system. We extend the usual approach by applying a modified version of the Penn State/NCAR fifth-generation mesoscale model (MM5) 4D-Var to find the smallest temperature increments required to minimize the wind damage over southern Florida during hurricane Andrew of 1992. The increments calculated by 4D-Var in this experiment created imbalances and asymmetries. As the storm resymmetrizes, at the end of the 4D-Var interval the model storm is largely weakened in situ. The amount of energy required to effect these changes is large. An alternate objective measure of the size of the increments could be formulated in terms of the likelihood of occurrence with respect to the estimated error characteristics of the model background field and the observations. A possible operational technique is presented whereby the likelihood of weather events of consequence is estimated-both subjectively and objectively.

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The development of wind damage bands for buildings

Cited by 87 publications

References 12 publications

Winter storm risk of residential structures – model development and application to the German state of Baden-Württemberg

Winter storm risk of residential structures – model development and application to the German state of Baden-Württemberg

Spatial Predictions of Extreme Wind Speeds over Switzerland Using Generalized Additive Models

A 4D‐Var study on the potential of weather control and exigent weather forecasting

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