Development of a wind gust model to estimate gust speeds and their return
                        periods

Seregina, Larisa; Haas, Rabea; Born, K.; Pinto, Joaquim G.

doi:10.3402/tellusa.v66.22905

Cited by 17 publications

(10 citation statements)

References 36 publications

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“…Recent studies that do address climate impacts include Cheng et al (2012aCheng et al ( , 2014, who employ statistical downscaling from a global circulation model (GCM) and a gust factor vs. wind speed characteristic, Seregina et al (2014) (discussed above), and Hewston and Dorling (2011), who use UK routine observation sites for historic gust climate variability and a regional climate model's daily maximum wind as a proxy for gust in projections to a future climate.…”

Section: New Developmentsmentioning

confidence: 99%

“…In this way the gust strength for different return periods was mapped over Germany at 1 km resolution using just 87 day-long simulations. Seregina et al (2014) used the gustiness analysis of Wieringa (1986) and exposure correction of Verkaik (2000) to standardise wind and gust measurements between sites in Germany. They then constructed relationships between Weibull distribution parameters for extremes of the (sustained) wind and those for extreme gusts, so that synthetic gusts could be obtained at further sites reporting only sustained wind, enabling a more comprehensive gust return period analysis using a 10-year dataset.…”

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Current gust forecasting techniques, developments and challenges

Sheridan

2018

Adv. Sci. Res.

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Abstract.Gusts represent the component of wind most likely to be associated with serious hazards and structural damage, representing short-lived extremes within the spectrum of wind variation. Of interest both for short range forecasting and for climatological and risk studies, this is also reflected in the variety of methods used to predict gusts based on various static and dynamical factors of the landscape and atmosphere. The evolution of Numerical Weather Prediction (NWP) models has delivered huge benefits from increasingly accurate forecasts of mean near-surface wind, with which gusts broadly scale. Techniques for forecasting gusts rely on parametrizations based on a physical understanding of boundary layer turbulence, applied to NWP model fields, or statistical models and machine learning approaches trained using observations, each of which brings advantages and disadvantages.Major shifts in the nature of the information available from NWP models are underway with the advent of ever-finer resolution and ensembles increasingly employed at the regional scale. Increases in the resolution of operational NWP models mean that phenomena traditionally posing a challenge for gust forecasting, such as convective cells, sting jets and mountain lee waves may now be at least partially represented in the model fields. This advance brings with it significant new questions and challenges, such as concerning: the ability of traditional gust prediction formulations to continue to perform as phenomena associated with gusty conditions become increasingly resolved; the extent to which differences in the behaviour of turbulence associated with each phenomenon need to be accommodated in future gust prediction methods. A similar challenge emerges from the increasing, but still partial resolution of terrain detail in NWP models; the speed-up of the mean wind over resolved hill tops may be realistic, but may have negative impacts on the performance of gust forecasting using current methods. The transition to probabilistic prediction using ensembles at the regional level means that considerations such as these must also be carried through to the aggregation and post-processing of ensemble members to produce the final forecast. These issues and their implications are discussed.Copyright statement. The works published in this journal are distributed under the Creative Commons Attribution 4.0 License. This license does not affect the Crown copyright work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 4.0 License and the OGL are interoperable and do not conflict with, reduce or limit each other.

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Section: New Developmentsmentioning

confidence: 99%

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confidence: 99%

Current gust forecasting techniques, developments and challenges

Sheridan

2018

Adv. Sci. Res.

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“…Such an approach has been adopted 80 widely for tropical cyclones, for both commercial and academic applications (e.g., 81 Vickery et al, 2000;James and Mason, 2005; Emanuel et al, 2006; Rumpf, et al, 82 2007; Hall and Jewson, 2007;Hall and Yonekura, 2013; Yonekura and Hall, 2014; 83 Bonazzi et al, 2014). Additionally, a statistical downscaling approach has been 84 utilized substantially in Europe for windstorms (Klawaand Ulbrich, 2003; Haas and 85 Pinto 2012; Born et al, 2012;Seregina et al, 2014), and to a lesser extent in North 86…”

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confidence: 99%

SynthETC: A Statistical Model for Severe Winter Storm Hazard on Eastern North America

Hall

Booth

2017

J. Climate

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13We develop, evaluate, and apply "SynthETC," a statistical-stochastic model of winter 14 extra-tropical cyclones (ETCs) over eastern North America. SynthETC simulates the 15 life cycle of ETCs from formation to termination, and it can be used to estimate the 16 probability of extreme ETC events beyond the historical record. Two modes of 17 climate variability are used as independent covariates: El Niño/Southern Oscillation 18 (ENSO) Niño3.4 and the monthly North Atlantic Oscillation (NAO). We use SynthETC 19 to estimate the annual occurrence rate over sites in eastern North America of 20 intense ETC passage in different ENSO and NAO states. Positive NAO is associated 21 with increased rates over the North Atlantic, while negative NAO is associated with 22 decreased rates over the North Atlantic and increased rates over northern Quebec. 23 Positive ENSO is associated with decreased rates over the North Atlantic, Ontario, 24 and the Canadian Maritime, while negative ENSO is associated with increased rates 25 over those regions, as well as the Great Lakes region. 26 27 28 3 Introduction 29 30Winter extra-tropical cyclones (ETCs) pose a major hazard to eastern North 31 America. These storms can cause damage through their precipitation, e.g., blizzards 32 and floods, and through their winds, e.g., extreme surface winds and storm surge. 33Heavy snowfall causes power outages, collapse of structures, and suspension of 34 travel and commerce. ETC winds sometimes reach hurricane force and drive 35 damaging coastal storm surge. Orton et al. (2016) found that in the New York City 36 area, ETCs are the dominant cause of surge events that have annual probability 37 greater than 1%. NOAA's NCDC estimated $40.9 billion in insured and uninsured 38 losses due to ETC events from 1980 to 2016 that caused more than one billion 39 dollars in damage each (www.ncdc.noaa.gov/billions/summary-stats; see also Smith 40 and Katz, 2013). 41 42ETCs are a dominant source of mid-latitude weather, and their varied formation and 43 evolution mechanisms have been studied using a wide range of observational, 44 theoretical, and modeling approaches (reviewed in Catto, 2016). The 45 characterization of ETC variability in the Northeast US has been documented for 46 some time (e.g. Miller 1946), and a climatology of northeast ETC tracks exists 47 (Hirsch et al. 2001). However, our understanding of the link between the storms' 48 variability, hazardous extreme events, and large-scale climate variability remains 49 incomplete (Colle et al. 2015). An examination of ETC tracks on seasonal timescales 50 suggests that there are a large set of forcing parameters, however the variance 51 4 explained by multiple predictors is less than 50% (DeGaetano 2008). 52Climatologically, ETC tracks have been used to identify a preferred track path for 53 storms that create wind hazards in the northeast (Booth et al. 2015). However, the 54 probabilistic analysis in that work is limited by the short time span of the reanalysis 55 utilized. A large body of w...

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“…The intensity of storms is not necessarily related to their impact and storm losses are better estimated from the strength of winds or wind gusts exceeding a certain threshold (Klawa and Ulbrich, 2003). Numerous studies are therefore dedicated to the estimation of the footprint of strong winds and gusts associated with winter storms as well as their return periods (DellaMarta et al, 2009;Hofherr and Kunz, 2010;Donat et al, 2011;Haas and Pinto, 2012;Seregina et al, 2014). These studies often require a combination of dynamical and statistical models to adequately represent the footprints.…”

Section: Introductionmentioning

confidence: 99%

Revisiting the synoptic-scale predictability of severe European winter storms using ECMWF ensemble reforecasts

Pantillon

Knippertz

Corsmeier

2017

Nat. Hazards Earth Syst. Sci.

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Abstract. New insights into the synoptic-scale predictability of 25 severe European winter storms of the 1995-2015 period are obtained using the homogeneous ensemble reforecast dataset from the European Centre for Medium-Range Weather Forecasts. The predictability of the storms is assessed with different metrics including (a) the track and intensity to investigate the storms' dynamics and (b) the Storm Severity Index to estimate the impact of the associated wind gusts. The storms are well predicted by the whole ensemble up to 2-4 days ahead. At longer lead times, the number of members predicting the observed storms decreases and the ensemble average is not clearly defined for the track and intensity. The Extreme Forecast Index and Shift of Tails are therefore computed from the deviation of the ensemble from the model climate. Based on these indices, the model has some skill in forecasting the area covered by extreme wind gusts up to 10 days, which indicates a clear potential for early warnings. However, large variability is found between the individual storms. The poor predictability of outliers appears related to their physical characteristics such as explosive intensification or small size. Longer datasets with more cases would be needed to further substantiate these points.

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Development of a wind gust model to estimate gust speeds and their return periods

Cited by 17 publications

References 36 publications

Current gust forecasting techniques, developments and challenges

Current gust forecasting techniques, developments and challenges

SynthETC: A Statistical Model for Severe Winter Storm Hazard on Eastern North America

Revisiting the synoptic-scale predictability of severe European winter storms using ECMWF ensemble reforecasts

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