Cyclones, windstorms and the IMILAST project

Hewson, Tim; Neu, Urs

doi:10.3402/tellusa.v67.27128

Cited by 91 publications

(171 citation statements)

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“…On the other hand, considerable discrepancies between tracking methods are found for shortlived, shallow, and slow moving systems, particularly over areas like the Mediterranean or over the continents (Neu et al, 2013;Lionello et al, 2016). More details on the intercomparison strategy, general results and proposed future directions of research are discussed in Hewson and Neu (2015).…”

Section: The Imilast Project Cyclone Track Data Setmentioning

confidence: 99%

“…Comparisons between the tracking methods are presented for example in Raible et al (2008), Neu et al (2013), Rudeva et al (2014) and Lionello et al (2016). Several case studies are discussed in Hewson and Neu (2015), including comparisons to observations. The colours of the method in Figs.…”

Section: The Imilast Project Cyclone Track Data Setmentioning

confidence: 99%

“…One of the objectives of the Intercomparison of Mid-Latitude Storm Diagnostics (IMILAST) project is to understand which cyclone statistics are robust to the choice of tracking algorithm (Neu et al, 2013). Such an assessment is necessary to be able to provide objective information to stakeholders regarding cyclone activity in general and windstorms in particular (Hewson and Neu, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Robustness of serial clustering of extratropical cyclones to the choice of tracking method

Pinto

Ulbrich

Economou

et al. 2016

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T Cyclone clusters are a frequent synoptic feature in the Euro-Atlantic area. Recent studies have shown that serial clustering of cyclones generally occurs on both flanks and downstream regions of the North Atlantic storm track, while cyclones tend to occur more regulary on the western side of the North Atlantic basin near Newfoundland. This study explores the sensitivity of serial clustering to the choice of cyclone tracking method using cyclone track data from 15 methods derived from ERA-Interim data (1979Á2010). Clustering is estimated by the dispersion (ratio of variance to mean) of winter [December Á February (DJF)] cyclone passages near each grid point over the Euro-Atlantic area. The mean number of cyclone counts and their variance are compared between methods, revealing considerable differences, particularly for the latter. Results show that all different tracking methods qualitatively capture similar large-scale spatial patterns of underdispersion and overdispersion over the study region. The quantitative differences can primarily be attributed to the differences in the variance of cyclone counts between the methods. Nevertheless, overdispersion is statistically significant for almost all methods over parts of the eastern North Atlantic and Western Europe, and is therefore considered as a robust feature. The influence of the North Atlantic Oscillation (NAO) on cyclone clustering displays a similar pattern for all tracking methods, with one maximum near Iceland and another between the Azores and Iberia. The differences in variance between methods are not related with different sensitivities to the NAO, which can account to over 50% of the clustering in some regions. We conclude that the general features of underdispersion and overdispersion of extratropical cyclones over the North Atlantic and Western Europe are robust to the choice of tracking method. The same is true for the influence of the NAO on cyclone dispersion.

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Section: The Imilast Project Cyclone Track Data Setmentioning

confidence: 99%

Section: The Imilast Project Cyclone Track Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robustness of serial clustering of extratropical cyclones to the choice of tracking method

Pinto

Ulbrich

Economou

et al. 2016

Tellus A: Dynamic Meteorology and Oceanography

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“…These atmospheric phenomena include gravity waves, such as mountain lee waves and associated rotors (Scorer, 1949;Lilly and Zipser, 1972;Durran, 1986;Shutts, 1997;Vosper, 2004;Doyle and Durran, 2002;Hertenstein and Kuettner, 2005;Mobbs et al, 2005;Doyle and Durran, 2007;Grubisic et al, 2008;Sheridan et al, 2017), sting jets (Martinez-Alvarado et al, 2012Baker et al, 2014;Hewson and Neu, 2015;Volonte et al, 2017;Hart et al, 2017), small convective cells such as those present in cold air outbreaks, that typically deliver showery rain accompanied by gusty winds, and the largest boundary layer eddies. Clearly this group straddles the categories of turbulence and mesoscale variability, albeit including examples of the latter associated with the local generation of increased winds and turbulence.…”

Section: Challengesmentioning

confidence: 99%

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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“…Although the general life cycle of extratropical cyclones has been known for almost one century, the intensification of storms and the generation of strong winds involve physical processes of different scales that are still not fully understood (Hewson and Neu, 2015). Recent advances have resulted from the attention drawn by devastating storms.…”

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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Cyclones, windstorms and the IMILAST project

Cited by 91 publications

References 69 publications

Robustness of serial clustering of extratropical cyclones to the choice of tracking method

Robustness of serial clustering of extratropical cyclones to the choice of tracking method

Current gust forecasting techniques, developments and challenges

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

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