Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , 5 Sigma rey Open Access 3 ff . A compari- ft S Discussions Sciences , L. C. Sha . 4 erent degrees of model ff Natural Hazards Hazards Natural and Earth System System and Earth , and B. D. Youngman 1 ce.gov.uk) , K. I. Hodges 3 ffi erent storms have di 2011 2012 ff , H. E. Thornton 2 www.europeanwindstorms.org , L. C. Dawkins 2 , M. A. Stringer 3 , A. J. Champion 1 , which is a combination of storm area calculated from the storm footprint ft ). S ce Hadley Centre, Exeter, UK ffi 2013 , This discussion paper is/has beenSystem under Sciences review (NHESS). for Please the refer journal to Natural the Hazards corresponding and final Earth paper in NHESS if available. Met O Department of Meteorology, University of Reading,College Reading, of UK Engineering, Mathematics andNational Physical Centre Sciences, for University Earth of Observation, Exeter,National Exeter, University Centre UK of for Reading, Atmospheric Reading, Science, UK University of Reading, Reading, UK due to the modeltion not method was simulating developed strong toand enough estimate correct the pressure for true this gradients. distribution underestimation. Avariation of The which new gusts recalibration is at recalibra- model essential each allows given grid forbias. that storm-to-storm point The di catalogue is available at ample allowing users toand characterise catastrophe extreme models. Several European storm storms, severitycould indices and best were validate investigated represent to climate find aindex which was list of knownand high maximum 925 loss hPa wind (severe) speed fromincluded storms. the in storm The the track. All best catalogue, the and performing listedson the severe of storms remaining the are ones model were footprintwell selected to using represented, station although observations revealed for that some storms storms were generally the highest gusts were underestimated The XWS (eXtremegenerated WindStorms) maximum three-second cataloguewinter wind-gust consists windstorms footprints of to fora storm hit 50 valuable Europe tracks resource of over for and the 1979–2012. both model- most The academia extreme and catalogue industries is such intended as to (re)insurance, be for ex- Abstract Nat. Hazards Earth Syst. Sci.www.nat-hazards-earth-syst-sci-discuss.net/2/2011/2014/ Discuss., 2, 2011–2048, 2014 doi:10.5194/nhessd-2-2011-2014 © Author(s) 2014. CC Attribution 3.0 License. Anatol, Lothar and MartinBillion that USD (indexed struck to in 2012)2007 worth December of 1999 damage, inflicted and lead approximately to 13.5 over 150 fatalities ( 1 Introduction European windstorms aregusts extra-tropical that are cyclones capable of with producingto devastating very structural socioeconomic impacts. damage, strong They can power windsworks, lead outages resulting in or to severe disruption millions violent and of even loss people, of lives. and For example closed the transport windstorms net- D. B. Stephenson 1 2 3 4 5 Received: 13 January 2014 – Accepted: 12Correspondence February to: 2014 J. – F. Roberts Published: (julia.roberts@meto 7 MarchPublished 2014 by Copernicus Publications on behalf of the European Geosciences Union. The XWS open access catalogueextreme of European windstorms from 1979–2012 J. F. Roberts 5 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , , and Strachan , aims to ad- ce Integrated footprints ffi Landsea et al. Della-Marta et al. ), or are not publically describes the method 3 . 2002 6 ce Unified Model. , ffi evaluates the storm footprints 4 describes the data and methods . 5 2 Bessemoulin ), are widely used in the tropical cyclone www.europeanwindstorms.org describes the recalibration method. Conclu- 2014 2013 5 2010 , ). These catalogues are also widely used within ) and for evaluating climate models (e.g., 2012 , 2012 , ). Levinson et al. 1999 , ). This catalogue has not been digitised and is now long out-of-date. Ventrice et al. 1995 1991 , Manganello et al. ( ; Lamb 2013 Hodges Recalibrated maximum three-second gustData footprints Archive using System Met (MIDAS) weather O station observations. Maximum three-second gust footprintsreanalysis over dynamically a downscaled 72 h-period using the using Met the O ERA Interim Tracks of the 3 hourly locations ofimum the mean maximum T42 sea 850 level hPa pressures relativethe vorticity, (MSLP) min- ERA and Interim maximum reanalysis 925 hPa identified( wind by speed an from automated cyclone tracking algorithm What is the best method for defining extreme European windstorms? How well do the modelthe storm reasons footprints for compare any with biases? observations, and whatWhat are is the best way to recalibrate the footprints given the observations? , ), only pertain to a specific country (e.g., ) and IBTRACS ( – – – In order to create the catalogue, several scientific questions had to be addressed: Despite the utility of tropical cyclone catalogues, no comparable catalogue of Euro- This paper describes how the above questions were addressed to produce the XWS By cataloguing these events, the intensity, location and frequency of historical wind- Publically available historical storm catalogues, such as HURDAT ( 1. 2. 3. catalogue. The paper is structured as follows: Sect. storms can be studied.development This (such is crucial as to theand understanding North for the Atlantic evaluating factors and jet that improving stream influence the their or predictions of the weather North and Atlantic climate models. Oscillation), The details of the storm tracksdetails and of the the modelled recalibration footprints will will be be described discussed below. in The Sect. This section describes theextreme datasets European windstorms and in models the used XWS to catalogue, which produce consists the of: data for the 50 2 Data European winter windstorms. Themaps catalogue of consists maximum of three-secondriod tracks wind-gusts and for at model-generated each each model stormthree-second grid wind-gusts (hereafter as point the gusts). over maps a are 72 h referred pe- to as the storm sions and future research directions are discussed in Sect. dress this gap, by producing a publically available catalogue of the 50 most extreme used to select theusing 50 weather most station extreme data, storms. and Section Sect. used to generate the storm tracks and footprints, and Sect. More recent catalogues only contain2009 information on storm intensityavailable. ( The XWS catalogue, available at the insurance and reinsurance industrycyclones. to assess risks associated with intense tropical pean windstorms currently exists. One ofthat the of last major freely available catalogues was et al. cyclones, including observed trackscatalogues of are an storm essential position resourcederstand and for how the intensity. scientific climate Tropical community cyclone cyclones variability and (e.g., modulates are used the to development un- and activity of tropical community. These catalogues provide quantitative information about historical tropical 2004 5 5 20 10 15 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) ). . are N in 3 ◦ ◦ ). For 2002 2005 ( , 3.1 MetUM grid N to 70 ◦ ), over 33 ex- (equivalent to ◦ ◦ 2011 , ), reducing the inher- Davies et al. MetUM, lateral bound- . The 0.22 so that the grid spacing ◦ 1 ◦ 2002 ( Hoskins and Hodges is used for the MSLP. For the Dee et al. ◦ (true) latitude remains at approxi- E in longitude, 35 ◦ ◦ W to 25 . To drive the 0.22 and latitude 37.5 were tested but only the results for 3 ◦ ) initial conditions. This results in daily 24 h 1 ◦ ◦ 2016 2015 ) have used 6 hourly reanalysis data, but here 2006 Hoskins and Hodges , and 10 2011 ). These are linked together, initially using a nearest ) to a horizontal resolution of 0.22 ) tracking algorithm. This uses 850 hPa relative vor- ◦ ◦ , , 6 0.7 ◦ 1995 1999 MetUM is initialised each day using the 18Z reconfigured ( ECMWF ∼ , ). These constraints have been modified from those used ◦ ce Unified Model (MetUM) version 7.4 ( 1995 1999 ( , ffi Hodges Hodges et al. ) subject to adaptive constraints on the displacement distance and track Hodges Hodges 1995 , Using a non-rotated pole would give a grid resolution of 24 km at the equator but 16 km at latitude, but using the rotated pole the resolution at 50 1 ◦ The downscaled region covers western Europe and the eastern North Atlantic (here- The MSLP and maximum 925 hPa wind speed associated with the vorticity maxima Previous studies ( 24 km at the model’s equator). The atmospheric model used to perform the down- Hodges The dataset used to create thescaling windstorm ERA footprints is Interim generated by∼ (T255 dynamically down- scaling is the Met O MSLP value given is that at the vorticity centre. 2.2 Windstorm footprints 2.2.1 Dynamical downscaling the MSLP the location of the minimum is only given if it is a true minimum. If not, the Storms are tracked(ECMWF) in Interim the Reanalysis European (ERA Centre Interim) for data Medium set Range ( Weather Forecasts 2.1 Storm tracks garded due to spin up, allowingIntegrated the Forecast model System, to adjust from the ECMWF IFS (the EMCWF levels, with the model top being 80 km. after referred to as theuses “WEuro” a region), rotated and pole is atdoes shown a not in longitude vary Fig. substantially of over 177.5 ary the domain and initial conditions6 hourly for analyses. the The WEuro 0.22 ERA domain Interim are analyses generated and from a the 30 h ERA forecast Interim is performed. The first six hours are disre- The model’s non-hydrostatic dynamical equationsadvection are and solved semi-implicit using semi-Langrangian time stepping. There are 70 (irregularly spaced) vertical forecasts for the full period.data set By is combining created. the daily forecasts a new, higher resolution 50 mately 24 km, giving a more regular grid spacing.) background removed as described in smoothness ( a certain radius of925 the hPa vorticity wind maximum. speed, A radii radiusgiven of of as 3 6 this was found to be the best indicator of storm severity (see Sect. 3 hourly data are usedwindstorms to have very produce fast more propagation reliable speeds.Interim tracks The by 3 since hourly splicing some data the extreme are 3tification European obtained h for forecasts and ERA in tracking between progresses the 6 the hourly data analyses. Before are the smoothed iden- to T42 and the large scale are retained for furtherperiod analysis. in The a European algorithmlatitude. domain identified 50 defined 5730 of as these storms 15 storms over were the selected 33 for yr the catalogueare found as in described post-processing. in This Sect. is done by searching for a minimum/maximum within ent noisiness of the vorticity andfied making by tracking determining more reliable. the The vorticity cyclonesdata maxima are as by identi- described steepest ascent in maximisation inneighbour the search, filtered and then refined( by minimizing a cost function for track smoothness for 6 hourly data to be suitable for the 3 hourly data. Storms that last longer than 2 days tended winters (October–March 1979/80–2011/12).the The cyclones identification is performed and following trackingbased the on of approach the used in ticity to identify and track the cyclones. 5 5 20 10 15 10 25 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | N ◦ Cσ ). + is the is esti- 1977 radius of σ 10m ( 10m ◦ U E and 35 U ◦ = MetUM dataset gust ◦ U 1000 km. By exper- (where W to 25 ∼ within a 3 ◦ 2 /σ ) Panofsky et al. , are estimated by finding 10m ), and although lifetimes of U gust ), which could result in days U ). The 72 h period over which − 0.25, giving 2011 = 2.1 shows the footprints for the same , gust ) 2006 3 U , > C ) Haylock /σ ) 2018 2017 10m U Mailier et al. − ), and is commonly used in catastrophe models cur- gust U (( 2003 P , is the standard deviation of the horizontal wind), has a 25 % σ is estimated from the universal turbulence spectra, and C ). shows the footprints for windstorms with track IDs 4769, 4773, 4782, 4774, 1987 Klawa and Ulbrich 2 , of the track position at each 6 h timestep are assumed to be associated with 3 N, excluding Iceland. ◦ Extra-tropical cyclones typically have scales of several hundreds to Land on the ERA Interim grid in the European domain defined by 15 3 2 Footprints are therefore likely to include gusts from two or more windstorms. This Figure Footprints were created for each of the 5730 storms identified by the tracking algo- Maximum three-second gusts at a height of 10 m, which output every 6 h and give the Beljaars The European extratropical cyclones identifiedfrequent events. by Over the the tracking 33 algorithmevents extended pass winters are that through relatively have the beencyclones domain tracked, in exhibit on average any temporal 2.5 given clustering 72 ( h period. Furthermore, extra-tropical 2.2.3 Footprint contamination For this catalogue the footprint ofgust a at windstorm each is grid defined point as overmain. a the 72 maximum The h three-second three-second period gust during which hasdamage the been storm ( shown passes to through the have do- a robust relationship with storm 2.2.2 Creating the windstorm footprints footprints show that stormand 4769 the was Mediterranean Sea, in storms factmake 4773 much a and impact very 4774 on weak are land,January northern event and 2007, over the storms which southern dominant which is event did France the is not famous storm storm 4782, Kyrill centred (January on 2007). 15Z 18 centred on times 15Zand 17 January 18Z 2007, 19 06Zwhole January 18 domain 2007 January (the 2007, respectively, contaminated 15Zable, derived and 18 footprints). by are January dominated The taking by 2007 footprints onewindstorms, maximum large are but event. gusts created Figure almost using over the indistinguish- decontamination the method described above. The new is then derived bywindstorm. taking the maximum of these gusts within the 72 h period of the imenting with radii ofcapture 300 the to high 2000 gusts km,from other we associated systems. found with that the 1000 storms, km but was small generally enough large to enough avoid to contamination can create problemsattempt when to trying isolate the to footprintradius attribute to a damage particular to windstorm,that all a particular the particular windstorm gusts and within windstorm. all a other To 1000 km data is rejected. The “decontaminated” footprint with even more storms. Theperiod highest number is of 8, storms for passing the through period in starting a at single 00Z 72 on h 6 February 1985. the value at which the10 m normalised maximum wind gust, speed ( and exceedance probability, i.e. ( mated from the friction velocity using the similarity relationrithm of applied to ERA Interim (1979–2012; see Sect. windstorm can be longera than windstorm 72 h last longer it than is this. rare that themaximum damaging gust winds achieved associated over with theare preceding used 6 to h create period, the footprints. from In the the MetUM 0.22 the gusts, identified as having the maximumthe 925 track hPa centre. wind This speed was overthe done land storm to at ensure its that most the damaging 72 h phase. period of the footprint captured the maximum gusts were taken was centred on the time which the tracking algorithm to 70 rently used by theis insurance commonly industry. used The in the 72 h insurance industry period ( was chosen because again it 5 5 20 10 15 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) ). ). D 1991 2008 ( , ected by ff Lamb ): 1 − (area) and assuming ), defined as the max- ), the area a N ) to understanding how Leckebusch et al. max max U V ) which would be expected to 2009 , this gives an SSI calculated 1 , N ). The SSI used by were considered, both resulting in , ). Note that here severity is defined 3 ◦ 1991 ( 25) , p. 7). A similar SSI can be derived by 2008 − , i and 10 u Lamb ( 1991 ◦ , 25 2020 2019 > i u a). Della-Marta et al. X 4 Lamb land, Stephenson (intensity) and footprint index = ): ) it was noted that wind speeds of 38–44 knots (19.5– , Fig. N 3 max 3.2   U 3 2011 1991 ( , 25) ) uproot trees and cause severe damage to buildings. over continental European and Scandinavian land. A threshold can be combined to form a storm severity index (SSI). Numerous 1 − 1 − i ) and the overall duration of occurrence of damaging winds ( − N Lamb u ), although in the catalogue both the contaminated and uncontami- A ( 3 Haylock 25 . and > i u AD X was used as it is recognised as being the wind speed at which damage . max ) damage chimney pots and branches of trees and wind speeds of 45–52 1 U ) have been selected for the XWS catalogue. The challenge when select- 3 N land, max 1 ) was also considered, defined as the area of the (uncontaminated) footprint − V − 1 N N 2.1 3 max = U radius of the cyclone track. Radii of 6   ◦ = = Indices Alternatively the intensity of a storm can be approximated from the footprint rather The uncontaminated footprints are used for the calculation of the storm severity in- f ft Lamb of 25 ms starts to occur. In dices (see Sect. S S than the track, asropean the and mean Scandinavian land. offrom Combining the the with excess footprint index gust only: speed cubed at grid points over Eu- 22.6 ms knots (23.1–26.8 ms SSIs have been developedperiod with of their windstorms uses ranging over from Europe the ( estimation of the return Damaging winds were defined as those inS excess of 50 knots (25.7 ms The cube of the wind speedto is model a wind measure power of and thecombining damage advection the of ( kinetic track energy index andthat is the used duration of alltion storms of an is event 72 h ( in accordance with the insurance industry defini- windstorms will change under anthropogenic climateAn change SSI ( was used toSea, rank British the Isles storms and Northwest in Europe theis by catalogue based of on extreme storms the overdamaging greatest the winds North observed ( wind speed over land ( (Sect. ing these storms was definingin an many “extreme ways, storm”. for A examplesured storm in losses can terms i.e. be of a defined a severe as meteorological event extreme ( index, or extreme values of in- Fifty of the most extreme storms of the 5730 identified by the tracking algorithm man mortality, ecosystem damage, etc.meteorological The index aim that here selects was stormssevere. to Expert that find elicitation were an with both optimal individuals meteorologicallytion objective in extreme of the and 23 insurance severe industry storms in ledbe the included to period if the 1979–2012 considering identifica- (Table insuredis loss considered only. to The be most the successfulcategory one meteorological C that index storms ranks in most Sect. of these 23 severe as extreme (defined as a slightly poorer performance by thestorm index ( (fewer storms in categorythat C). exceeds The size 25 ms of the nated footprints are available. 3 How to select extreme windstorms in terms of total insurance loss, however other measures are possible such as hu- Meteorological indices from both theThese track indices and included footprint the of maximum the intensityimum storm of were 925 the hPa investigated. storm wind ( a speed 3 over continental European and Scandinavian land within 3.1 Possible meteorological indices 5 5 20 15 10 10 15 25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | N se- and f Klawa f98 S S , . The 23 ft selects an N ). The use S 2 ft , and , 10 are also S ft f N S ) the summand and S , ft S max 2003 U ( b). are broadly similar, con- 4 ft respectively, hence severe S e–h). 5 f98 S a–c). These are Atlantic storms and 6 has a stronger dependence on (Fig. , demonstrating that ). A similar trend exists within the N 7 ft ft N , S S in the footprint. A relative local 98th select events from 29, 26, 27, 30 and . Klawa and Ulbrich i max and U f98 f98 S S . In i max 2022 2021 U d). They both select “meteorologically extreme 27 identifies the number of storms in category and , however, maximises the number of storms in 6 ft − ). ft (not shown) and 7 S x S f results in more storms in category C than the fixed ) weather station data are used, but an equivalent S = , y ft f98 are related to one another. A positive association exists S S is the meteorological index used to select the 50 storms 2003 5 , (1) , ( ). Of the 27 category B storms selected by is the most successful index at identifying severe storms. ft 3 N S ft  (not shown) and , and 4 by both 1 S f . The index S f 2013 − N max , and between , S i U ft i S 98, N u ). This threshold implies that at any location storm damages are u , , 9 by  N i , ect the degree to which damage increases with growing wind speed in and 98, ff max 2003 ( U is the 98 % quantile of maximum gust speeds during the reference period >u max i max and the strongest extremal dependence exists between i is the maximum gust at grid point U u X U Klawa and Ulbrich selects storms located over the UK and Northern Europe and samples storms i ) is multiplied by population density to calculate a loss index, but here the aim 98, ft 1 u u Cavicchia et al. max land, S U give 15, 15, 17, 10 and 13 storms in category C respectively (Table = In summary the index Indices The location of the 50 storms selected by The catalogue will comprise of the 23 severe storms and 27 storms which are ex- f98 f98 S percentile threshold can be used as an alternative to the fixed threshold, as in where index over the full time periodthe of meteorologically the extreme XWS and catalogue, severeperiod. hence Atlantic For giving storms these a that reasons good occurredfor representation throughout the of XWS the catalogue. time series for allfewer 5 in indices, the period with 2000–2010 more (Fig. storms selected inIt the depends period 1985–1995 on and both the area and maximum wind speed intensity of the storm. The selected by almost even combination of large area and intense wind speed30 yr storms. out ofperiod the spanned 33 yr by period the 1979–2012 XWS respectively, catalogue hence well all (Fig. indices represent the which are strong andlect well very represented similarly in located storms theand (Fig. reanalysis not data. severe” Indices (categorygenerally B) weaker storms and that less arescale well located ( represented in by the the Mediterranean reanalysis which data are due to their small of the relative threshold index threshold equivalent, category C and therefore iscally the extreme and most severe successful storms. index at identifying both meteorologi- centrated around the UK and Northern Europe (Fig. and Ulbrich used. In assumed to occurexpected on to 2 a % ofexcess all of the days. threshold This value, adaptation hence to the wind normalised climate rather can than absolute also winds be are SSI can be calculatedthreshold from when the predicting footprint severity: to quantify the advantage of usingS a relative where (October–March, 1979–2012) at grid point treme in the optimal meteorological index.storms The in intercept the of the top numberC K of storms such the 23 and that severe 50 storms are selected for the catalogue (Fig. between than most severe storms areranked according in to thestorms top are 18 best %, characterised 7 by %, a 5 high %, value 10 of % and 16 % of storms when in Eq. ( is to find a purely meteorological index for storm3.2 severity. Results The indices presented in Fig. 5 5 25 20 15 10 10 15 25 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 8 ). ref h 2007 ect, by , ff ected by the ff was estimated ref line, meaning that h x ), which may further = y 2007 Howard and Clark , ; ective roughness. ff 2010 , for observed gust speeds of greater than ff 500 m (plotted in red). Donat et al. Howard and Clark 2024 2023 ∼ , rather than the e 0 could be subject to large errors. Nevertheless, ), so this bias should be corrected. z 5 ected region given the irregular locations of the a and d. The observational footprints are defined ref ff 8 h line. It was found that these points are mostly from , above which the wind speeds are una ective roughness parametrisation, which is needed to x ref ff ) a method was proposed to correct for this e h = y which are believed to be erroneous). line, flattening o 2007 1 ( x − = y ect of sub-grid scale orography on the synoptic scale flow, however it , showing that the model is underpredicting extreme gusts. In Fig. 1 ff − (for stations at altitudes less than 500 m, and removing gusts for which the c and f shows scatter plots of model maximum gusts against observed max- 1 25 km grid to a specific location without applying any corrections. For the 50 8 − cult to confirm the exact a 25 ms ∼ ffi For a number ofviate storms from the the plots∼ of model vs. observed gusts appears to de- For all storms therepopulation, is below the a morestations with dispersed altitudes population greater separate than from the general this can be seen forstorm the Jeanette. storm Kyrill, although the problem is not so severe for the Howard and Clark – – It would be desirable to apply this correction to all of the storms in the catalogue, In In this model only wind speeds on seven model levels were archived which means The first issue has been noted previously, and is a common issue with climate and However, two problems with the model are apparent from these scatter plots: The scatter plots show that the gusts are scattered about the Figure Example observational footprints for the storms Jeanette (October 2002) and Kyrill (January 2007) are shown in Fig. Observational data were extracted fromstorms, the MIDAS all database. stations For roughly each ofduring within the the the 72 selected WEuro h period domain weredata which used were recorded to a evaluate maximum the mixture gusts MetUM of windstorm 1, footprints. 3 The and gust 6 hourly maximum gusts. 4 Evaluation of MetUM windstorm footprints in the recalibration model (see Sect. from finer resolution data (asimprove was the done correction. in although the extraction of the archivedcostly data process on all and model cannot levels be is a done time at consuming present. and Instead, altitude is used as a covariate surface, and then assuming a log-profileusing to the interpolate local wind vegetative speeds roughness, back down to 10 m, the estimation of windapplying speeds the at correction to10 the m storm winds Kyrill for gaveat high a lower altitude clear altitudes locations, improvement remained although tofrom the the (plot orographic maximum data underestimation not at of shown). the extreme For resolution gust this of the calculation MetUM, but it is possible to estimate It is caused byestimate the the use of e ancauses e unrealistically slow wind (and hence gust) speeds atestimating 10 m. a reference height, numerical weather prediction models (e.g. from a storms in the catalogue,is the 5.7 ms mean root meanobservations square read (RMS) 0 ms error in the model gusts in general the model gustscially are impressive in when agreement considering with that the the observations. model This gusts result have is simply espe- been interpolated imum gusts for allMetUM maximum of gusts for the each stations specificinterpolation station between in location grid were the points. calculated using observational bilinear footprint for each storm. The observations. in the same way72 h as period, the but modelthe instead footprints, locations of of plotting a each the station. griddedand maximum A map observations quick gust they inspection agree over show of onit the the the the footprints same is maximum shows on di gust that the the recorded regions model at where the high gusts occur, although 5 5 20 25 15 10 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | g ≤ 8 ∂P/∂y and shows that 500 m which 8 ≤ at stations which ∂P/∂x 4 a shows the observed 10 is similar to that of gusts MetUM is only being driven 1 ◦ − in the estimated gust at a par- 1 erent parameterisation schemes − ff 25 ms are highlighted. This plot shows that > 1 − 2026 2025 25 ms ) show that di > , is 0.57 for stations which recorded gusts greater r 2012 ( b. cient, 10 ffi for the locations of the stations with altitude erences of up to 10–20 ms 5 ff Born et al. shows a scatter plot of the model error in the maximum gusts (which is approximately when the model begins to systematically 1 9 − c, the observed geostrophic winds were estimated by reconstructing the ob- . This strong relationship indicates that the underlying problem is with the line, and the behaviour of the gusts 1 c shows the maximum model geostrophic winds against maximum ob- 10 − x 10 = . The correlation coe 1 y − For Fig. For the observations the maximum 10 min mean winds are the maximum of the instan- 5 4 Figure To investigate whether the underprediction of gusts is due to the underestimation These plots show that Kyrill deepened earlier (further west) than the model predicted, Regarding point (i), To test this, Fig. in the usual way. Themated maximum by geostrophic taking winds the for maximum both of model the and 6 hourly observations instantaneous were geostrophic esti- winds. of strong pressure gradientsminimum MSLP for for some the storms storm (point Kyrill ii), were compared. the Figure observed and modelled passed through similar areas and have very similar observational footprints, yet Fig. Possible reasons forscribed the above underprediction include (i) of themodel high gust can parameterisation gusts reproduce scheme for used, thediction and low strong is (ii) altitude dependent pressure whether on the stations gradients. the It de- storms’ is locations unlikely because that the the storms underpre- Jeanette and Kyrill 4.1 Underprediction of high gusts for low altitude stations recorded gusts for thisof storm. the The stations geostrophic whichfor winds recorded the gusts corresponding locations to where strong the gusts locations were recorded, a higher proportion (85 %) of the When the MetUM is reinitialisedit then (every has 24 h) the initial with conditionsmore the to of storm develop a into already problem an within for extremebefore the event. rapidly This domain reinitialisation. moving is storms The expected which to observational be many can footprint travel of of quite the far Kyrill into strongest showngions the gusts in where domain the recorded Fig. model for failed towhere this reproduce the storm the model depth were pressure of just gradients the would central to be MSLP, i.e. the underestimated. in south regions served of geostrophic the winds re- served 6 hourly mean seaings. level The pressure instantaneous field geostrophic by winds bilinearly could interpolating then MSLP be station estimated record- from and so the depth oftimated. the A minimum possible MSLP reason over for Ireland, thistoo the is far UK that east and the to western Denmark capture boundary isthe the of underes- western early the boundary, WEuro stages when domain of it is thisat enters storm the the well. domain boundaries, If the the so 0.22 storm it develops may outside not simulate a low as extreme as in the reanalysis data. minimum MSLP (over the sameat 72 all h stations period where over datasame which was period the available. is maximum The shown gusts) minimum in recorded MSLP Fig. from the model over the can sometimes lead to di ticular site. If this is thefrom cause which of the the gusts underforecasting are one derived would expect not the to 10 show m this winds problem. taneous 10 min meanThe winds model which maximum are wind recordedwhich speeds are every are output 1, every the 3, 6be maximum h. or Since of comparable 6 the the h to model instantaneous depending the timestepof 10 on is 10 m both min 10 the min, wind the mean station. the speeds observations observedbetween model and wind error wind model speeds in speeds. should may maximum Thebe be gusts significant. true underestimated, and maximum but error wind given in the maximum speeds wind strong speeds correlation this does not appear to shows that the underforecasting in Kyrill is much more pronounced. against the model errorrecorded in both the these maximum measures forgreater 10 m than the 25 10 storm ms min Kyrill. mean Theunderpredict winds stations the gusts which for recorded this gusts on storm) the are highlighted in red.25 All ms points lie approximately than 25 ms 10 m winds rather than the gust parametrisation itself. 5 5 20 15 10 10 20 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . ). = 2 j σ , j 2004 are re- ) takes , 5 2 γ ) represent ects model s ff erences be- ff and 1 Browning γ , ) apparent in some 0 1 is a variance param- − γ ) and lat( ects, though this latter 2 , s ff 2 σ β , 25 ms 1 β > , and , s 0 ) is used to allow multiple wind β (where lon( , .  ) γ ) c 2000 Σ s Σ , , T )lat( ects capture unmodelled di , (where MVN means multi-variate normal s ff ,0) d shows the maximum model geostrophic k , and  } b b ) 10 Σ ... 2028 2027 Σ s , , ( j 2 T ),lon( (0, are covariance matrices. Maximum likelihood is σ X s ,  , , log observed gusts are normally distributed with c 2  ) is the observed maximum gust for storm j Σ 2 γ log are recalibrated. Where MetUM gusts do not exceed s , { ( σ ),lat( 1 1 j ) is the corresponding MetUM output, noting that only  (0,0,0) s MVN − Y γ ) s )),  , and ( k , ∼ s 0 j ), has the linear form Pinheiro and Bates j ( ) b γ T X z c ) , s Σ ),lon( ( 2 ), ,2 + MVN s j z s β ect with each storm. This model is based on an underlying ( k , c ∼ ), j ff 1 , γ s , and ( T X ( β ,1 ) ) s j , j s ,2 0 c X ( j , β T (log are modelled. Gusts are log-transformed. The random e b j ,0 z , ects model ( j 1 cients and elevation( ff (log ,1 m ( + − c j j ffi (  k b , = is a vector of known covariates for location m (67 %). For comparison Fig. , j N ) ) ,0 b 1 s , which is a function of MetUM gust, location and elevation, and variance j s ∼ ( , the recalibrated footprint uses the original MetUM output. By assuming that j − ( + 20 ms b T 1 ) , at location z m k − z s J MetUM output. The proposed method is based on polynomial regression between > ( , β j ◦ )  Y s ( Let We conclude that the underestimation of strong gusts ( A random e 0 ... 25 ms j 2 = k tween storms, one example being whether a storm has a sting jet ( ≤ geostrophic winds were underestimated compared to the locations which had gusts from the underestimation of the centralwould pressure not depth and make strong sense pressure to gradients.varies apply It a from “universal” storm correction to to all storm. storms, The since recalibration the method problem described below (Sect. winds against maximumKyrill observed the geostrophic model reproduces winds the for tight pressure Jeanette, gradients where and highstorms unlike geostrophic is for winds. due to the underprediction of the geostrophic component of gusts, resulting used to estimate standardised longitudes and latitudes with mean zero and unit variance, respectively), where ( distribution), gression coe where eter. This assumes thatmean for storm The mean, X 1, X then has the formulation log Not only does this allowout a observational specific data storm’s can footprint too, tofeature by is be integrating not recalibrated, out utilised but the storms here. random with- e 5.1 Statistical model specification The notation adopted is that a separate randompolynomial e relationship betweenstorm-specific observed relationships and deviate according MetUM-simulated tolocation-specific gusts, some covariates. from distributional The assumptions which random and e 20 ms the observations are representative ofan the estimate true of gusts, the the distribution regression of relationship true gives gusts givenstorm the MetUM’s footprints output. to be recalibrated simultaneously, which is achieved by associating This section introduces awhere recalibration statistical describes method estimating for0.22 the “recalibrating” true wind distribution of stormtransformed wind footprints, gust gusts, speeds: given the the the response explanatory variable variable represents the stationmain MetUM are observations used, output. and ranging All betweenavailability. storms Gusts station above from data 20 154 ms within to 1224 the stations, footprint’s depending do- on data into account storm-to-storm variation. 5 Footprint recalibration 5 5 20 15 10 10 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | line www. x = together erence in y 3 ff ce Unified Model ffi . Using an index with ft ), which identified 5730 ciently robust way. Var- S , which depends on both . ffi ft 1 S − 1999 , ) are shown in Table c Σ 1995 cient data, and the desire for parsi- ( 0,1,2. This formulation allows the ffi ect of the western domain boundary = threshold, non-exceedances are also ff and cient data to give a reliable statistical k 1 b ffi − . However, little appreciable di Σ 1 for − Hodges shows that the recalibrated gusts are more T 2030 2029 ) k 11 (solid lines) show that for the storm Kyrill, where the lon:lat, γ , 11 , so recalibration results in an increase in gust speed. shows the resulting recalibrated footprints for the storms k 1 − lat, 11 γ , k . The catalogue gives tracks, model generated maximum 25 ms lon, γ ∼ , k resolution. The MetUM footprints compare reasonably well to obser- ◦ elev, γ ( line and even shows a slight decrease for high MetUM gusts. This shows = x k was chosen here as it retained su γ = 1 y − The model used to generate the storm footprints is the Met O A new recalibration method was developed to correct for the underestimation of high The severe storms ranked highly (in the top 18 %) in all meteorological indices inves- The tracking algorithm used was that of The choice of threshold above which to recalibrate the MetUM’s gusts is arbitrary; Parameter estimates (excluding those of We havehave compiled hit a Europeeuropeanwindstorms.org catalogue over of thethree-second gust 50 period footprints and of recalibrated October–March footprints for the 1979–2012, each storm. available most at extreme winter storms to 6 Conclusions to pressure fields or coastalmony, proximity. these Due were to not insu tested here. so that mean relationship to varyious with combinations elevation and of location thepresented in included model a were covariates su found wererion. to tested, However, perform more though best complex those based relationships used on could in the be the Akaike captured Information with Crite- covariates related gusts. The method allows for storm-to-storm variation. This is necessary because not Kyrill (January 2007) thisgradients is of because the the MetUM storm,being underestimates close which to the is continental strong Europe. possiblyaltitudes pressure The an greater MetUM than footprints e have 500 m largeapplied errors due for for this, to gusts but the it at orographic has drag not scheme. been applied A in correction this can version be of the catalogue. are not the focus of this catalogue. tigated. The choice ofbe index biased is or sensitive incomplete. to Ifthe the loss comparison given data of list were the available of indices. for severe many storms, storms which this may may(MetUM) improve at 0.22 vations, although for some storms the highest gusts are underestimated. For the storm logical indices were investigated. It wasstorm found that area the and index intensity, washighlighted the by most the successful insurance industry. at23 The characterising severe 50 23 storms storms severe plus chosen storms a for the relative the top threshold catalogue 27 would are other result the storms in as more Mediterranean ranked storms by being selected, which storms in the catalogue period. To select the storms for the catalogue several meteoro- predictions was found for thresholds in the range 15–25 ms 20 ms model, while ensuring that gustsraw were and “extreme”. To recalibrated improve consistency footprintsused between at in the model the estimation, 20 but mstween downweighted MetUM-simulated exponentially gusts according to and the 20 ms deficit be- MetUM gusts were significantly underestimated,for the MetUM mean gusts increases of above For Jeanette the MetUM gusts comparedto better the to observations, so thethe mean importance lies close of including storm-to-storm variation when recalibrating footprints. tively large uncertainty (prediction intervals,mean column 1; relationships, columns for 4 and agust 5). plotted station The in example Column located 1 in of Fig. London, between MetUM and observed with standard errors. Figure Jeanette and Kyrill. Columnconsistent 1 with of the Fig. observationsin general than negatively originally biased (column simulated 3), by though the predictions are MetUM, accompanied which by are rela- 5 5 20 25 15 10 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 2012. ), and including tracks 2011 , , 2006. 10.3402/tellusa.v64i0.17471 ce’s global and regional modelling of the ffi MetUM data, and Joaquim Pinto for useful Compo et al. , 2013. ◦ 2032 2031 , 2011. ce, who would like to acknowledge the financial support ffi erent tracking algorithms and atmospheric models. Fur- ff 10.1007/s00382-011-1220-0 We wish to thank the Willis Research Network for funding BY, and Ange- er the same biases. The method gives an estimate of the true distribution ff 10.5194/nhess-11-2847-2011 , S. D., and Worley, S. J.: The Twentieth Century Reanalysis Project, Q. J. Roy. http://www.ecmwf.int/research/ifsdocs/CY31r1 ff We intend to update the catalogue yearly to include recent events. Possible future tracks, J. Atmos. Sci., 59, 1041–1061, 2002. 1999. reanalyses ERA Interim, NASA4906, MERRA, 2011. NCEP CFSR, and JRA-25, J. Climate, 24, 4888– able at: dynamically-downscaled global climate models,2857, doi: Nat. Hazards Earth Syst. Sci., 11, 2847– multi-model ensembles for storm loss estimations, Clim. Res., 44, 211–225, 2010. Dynam., 1–13, doi: Wood, N.: A newatmosphere, dynamical Q. J. core Roy. Meteor. for Soc., the 131, Met 1759–1782, 2005. O Balmaseda, M. A., Balsamo, G.,Bidlot, Bauer, J., P., Bechtold, Bormann, P., Beljaars, N.,Healy, A. S. Delsol, C. B., C., M., Hersbach, van Dragani, H.,McNally, de A. Hólm, R., Berg, E. P., Fuentes, Monge-Sanz, L., V., M., B. Isaksen,Tavolato, M., C., L., Geer, Morcrette, Kållberg, Thépaut, A. J. P., J. J., Köhler, J., M.,performance N., Haimberger, Park, of Matricardi, L., and B. the M., data Vitart, K., assimilation Peubey, F.: C., system, The de Q. ERA J. Rosnay, P., Roy. Interim Meteor.period Soc., reanalysis: of 137, wind configuration 553–597, storms 2011. and over Europe, Int. J. Climatol., 29, 437–459, 2009. cyclones, Q. J. Roy. Meteor. Soc., 130, 375–399, 2004. son, B. E.,Crouthamel, R. Vose, I., R. Grant,Marshall, A. G. S., J., N., Rutledge, Maugeri, Groisman,Woodru M., P. G., Y., Mok, Jones, Bessemoulin, H. P.Meteor. Y., D., Soc., P., Nordli, 137, Kruk, Brönnimann, O., 1–28, M. Ross, 2011. S., C., T. F., Kruger, Trigo, Brunet, A. R. C., M., M., Wang, X. L., towards a probabilistic view, Tellus A, 64, 17471, doi: Ocean. Tech., 4, 613–626, 1987. of gusts at each MetUM grid point, therefore also quantifyingplans the include uncertainty extending in the gusts. scaling catalogue to back the 20th in century time reanalysis dataset by ( performing tracking and down- all storms su Hoskins, B. J. and Hodges, K. I.: New perspectives on the northern hemisphere winter storm Hodges, K. I., Lee, R. W., and Bengtsson, L.: A comparison of extratropical cyclones in recent Hodges, K. I.: Adaptive constraints for feature tracking, Mon. Weather Rev., 127, 1362–1373, Hodges, K. I.: Feature tracking on the unit sphere, Mon. Weather Rev., 123, 3458–3465, 1995. Haylock, M. R.: European extra-tropical storm damage risk from a multi-model ensemble of ECMWF: IFS documentation – Cy31r1 operational implementation, Tech. rep., ECMWF, avail- Donat, M. G., Leckebusch, G. C., Wild, S., and Ulbrich, U.: Benefits and limitations of regional Della-Marta, P.M., Mathis, H., Frei, C., Liniger, M. A., Kleinn, J., and Appenzeller, C.: The return Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Cavicchia, L., von Storch, H., and Gualdi, S.: A long-term climatology of medicanes, Clim. Browning, K. A.: The sting at the end of the tail: damaging winds associated with extratropical Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Glea- Davies, T., Cullen, M. J. P., Malcolm, A. J., Mawson, M. H., Staniforth, A., White, A. A., and Born, K., Ludwig, P., and Pinto, J. G.: Wind gust estimation for Mid-European winter storms: Bessemoulin, P.: Les têmpetes en France, Ann. Mines, 9–14, 2002. Beljaars, A. C. M.: The influence of sampling and filtering on measured wind gusts, J. Atmos. discussions on historical windstormand event Climate sets. Solutions DBS at wishes theHET to U. were thank of supported Exter the by for Centrefrom partial the for the support Met Business Lighthill of O Risk hisResearch Network time Council for on this (Consortium this project. project. onmarking, LD JFR Learning Risk was and and supported in Elicitation by theNERC (CREDIBLE the TEMPEST Environment: project); Natural project Diagnostics, NE/J017043/1). Environment and Integration, LS AC was Bench- was funded funded by by the the National Centre for Atmospheric Science. References windstorms, using Gaussian processtude kriging data methods, as a and way using to high statisticallyAcknowledgements. downscale resolution the alti- footprints. lika Werner at Wilis ReStanden for for her enthusiastic her support help and and ideas. JFR advice would using like the to thank 0.22 Jessica and footprints derived from di ther improvements to the recalibration include recognition of spatial features of the 5 5 5 30 25 15 20 10 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2033 2034 ects Models in S and S-PLUS, Springer, 2000. ff , 2003. 10.5194/nhess-3-725-2003 Climate Extremes and Society,Press, edited chap. by: 1, Murnane, 2008. R. and Diaz, D., Cambridge University Tech. rep., Swiss Reinsurance Company, 2007. weather events in the US, Tech. rep., Swiss Reinsurance Company, 2013. ropical cyclones, Mon. Weather Rev., 134, 2224–2240, 2006. Adams, J. M.,Tropical Altshuler, cyclone E. climatology in L., aclimate Huang, 10-km modeling, global B., J. atmospheric Climate, Jin, GCM: 25, E. toward 3867–3893, weather-resolving 2012. K., Stan,turbulent C., velocity Towers, components P., in and theMeteorol., Wedi, 11, surface 355–361, layer N.: under 1977. convective conditions, Bound.-Lay. interaction between an Africantively Easterly coupled atmospheric Wave, diurnally Kelvin wave, varying Mon. convection, Weather Rev., and 140, a 1108–1124, convec- 2012. mogenous global tropical cyclone2010. best-track dataset, B. Am. Meteorol. Soc., 91, 377–380, tropical cyclone activity with a26, 133–152, hierarchy of 2013. AGCMs: the role of model resolution, J. Climate, database reanalysis project: documentationHURDAT Database, for in: 1851–1910 Hurricanes alterations and and Typhoons:nane, Past, additions R. Present J. to and and Future, the Liu, edited K.-B., by: Columbia Mur- University Press, 2004. severity measure for the Northeast Atlantic region, Meteorol. Z., 17, 575–587, 2008. University Press, 1991. cation of severedoi: winter storms in Germany, Nat. Hazards Earth Syst. Sci., 3, 725–732, Appl., 14, 105–116, 2007. Stephenson, D. B.: Definition, diagnosis, and origin of extreme weather and climate events, in: Sigma: No 2/2013: Natural catastrophes and man-made disasters in 2012: a year of extreme Sigma: No 2/2007: Natural catastrophes and man-made disasters in 2006: Low insured losses, Pinheiro, J. and Bates, D.: Mixed-E Manganello, J., Hodges, K. I., Kinter-III, J. L., Cash, B. A., Marx, L., Jung, T., Achuthavarier, D., Panofsky, H. A., Tennekes, H., Lenschow, D. H., and Wyngaard, J. C.: The characteristics of Mailier, P. J., Stephenson, D. B., Ferro, C. A. T., and Hodges, K. I.: Serial clustering of extrat- Ventrice, M. J., Thorncroft, C. D., and Janiga, M. A.: Atlantic tropical cyclogenesis: a three-way Strachan, J., Vidale, P.-L., Hodges, K., Roberts, M., and Demory, M.-E.: Investigating global Levinson, D. H., Diamond, H. J., Knapp, K. R., Kruk, M. C., and Gibney, E. J.: Toward a ho- Leckebusch, G., Renggli, D., and Ulbrich, U.: Development and application of an objective storm Landsea, C. W., Anderson, C., Charles, N., Clark, G., and Dunion, J.: The Atlantic hurricane Lamb, H. H.: Historic Storms of the North Sea, British Isles and Northwest Europe, Cambridge Klawa, M. and Ulbrich, U.: A model for the estimation of storm losses and the identifi- Howard, T. and Clark, P.: Correction and downscaling of NWP wind speed forecasts, Meteorol. 5 25 30 20 15 10 570 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) 1 − f98 S ) (ms 1 C 0 − f 15 15 17 10 13 23 n S B n A ) (ms 88 27 6 27 27 1 n 1310 27 27 − ft S . (best case) 0 27 3.1 (25 km N (worst case) 23 27 2036 2035 ) grid boxes) (ms 1 dependent − max U (ms independent are defined in Sect. f98 S and max f U S , ft S , max ft f f98 N N S S S Index and severity U Index Index and severity perfectly , The number of storms in categories A, B and C for each index, where category max The 23 severe storms highlighted by insurance experts in the period 1979–2012. The U Name87JAnatol DateDagmar-Patrick of 26Daria Dec 2011Emma 30.08Erwin 3 16 Dec Oct 1999 1987FannyGero 39.53 65 39.86 25 JanHerta 1990 29 Feb 2008Jeanette 37.92 8 622 JanKlaus 25.12 742 2005 1 769 4 600 Jan 1998Kyrill 11 39.22Lore Jan 881 38 424 516 2005 47 457 27 007 768 3 34.60 178 Oct FebLothar 2002 1990 65 338 39.13 48 102Martin 39.43 36.92 48 598 047 24 104.56 669 Jan 33.16 12Oratio 2009 169 94.62 633 297 53 18 068Stephen Jan 293 2007 37.23 7874 1497 36Ulli 077 28 572 72.57 Jan 26 437 1994 Dec 12 36.38 300 1999 569Vivian 78.00 40 914 27 17 Dec 552 75 1999 31.60 256Wiebke 367 472 36.72 239 6296 30 79.23 26 Oct 15 Dec 2000 23 936Xylia 032 658 1234 1998 37.18 91 060 15.80 Xynthia 12 733 39.53 38.45 219.21 24 438 356 9.96 380 496Yuma 59 432 26 000 49.40 3 415 Feb 26 Jan 469 1990 28 2012 Feb 1990 13 8756 140.22 317 645 818 18 494 818 35.16 478 32.24 36.32 28 27 21 328 Oct 164.46 Feb 10 371 1998 2010 4431 612 19 36 24 575 667 460 792 755 24 32.62 69.09 940 Dec 26.72 54.17 1997 751 397 132.26 36 18 071 846 39.92 10.16 56.39 40 864 666 295 068 25 163 19 891 019 179 56 775 205 15 3382 988 23 109 73.69 656 5 625 905 14.19 118.11 18 706 13 039 350 2680 138.80 4035 54.07 3.33 Table 2. A storms arecally severe extreme and and not not severe, meteorologically categorycategory C D extreme, storms storms category are are severe B not and severe meteorologically storms and extreme, and not are meteorologically meteorologi- extreme. Table 1. indices Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . 5 lon:lat,0 lon:lat,2 γ γ ◦ 9 in lati- lat,0 lat,2 γ γ 0.8135 0.2734 0.0832 0.0321 ◦ − − in latitude, with spacing ◦ lon,0 lon,2 γ γ to 16.89 ◦ to 16.89 elev,0 elev,2 ◦ γ γ , and in the rotated coordinate frame ◦ 17.65 − 2 β lon:lat,1 0.1266 0.0056 0.1382 0.1883 0.0004 0.0292 γ − − 2038 2037 1 lat,1 β and latitude 37.5 γ ◦ . in longitude, and -17.65 ◦ in longitude, and ◦ 0 ◦ lon,1 γ 1.3411 1.8340 0.1506 0.5199 , and in the rotated coordinate frame it extends − − ◦ to 29.58 to 29.58 σ β ◦ ◦ elev,1 1.6023 0.0030 γ − − 9.36 Footprints of storms 4769, 4773, 4782 and 4774 made by Domain of model used to generate the footprints (inner rect- − M.-E.: Investigating Global Tropical Cyclone Activity witherarchy a of Hi- AGCMs: The Role26, 133–152, of 2013. Model Resolution, J. Climate, ical Cyclogenesis: A Three-Way InteractionEasterly between an Wave, African Diurnally Varyingtively Convection, Coupled Atmospheric and Kelvin a140, Wave, 1108–1124, Convec- Mon. 2012. Weather Rev., Parameter estimates and standard errors for the mean function described in Sect. Fig. 2. taking the maximum gusts over the whole domain (contaminated). Strachan, J., Vidale, P.-L., Hodges, K., Roberts, M., and Demory, Ventrice, M. J., Thorncroft, C. D., and Janiga, M. A.: Atlantic Trop- Fig. 1. angle). The domain hasand a latitude rotated 37.5 pole withfrom a -9.36 longitude oftude, 177.5 with spacing 0.22 Domain of model used to generate the footprints (inner rectangle). The domain has . ◦ Standard error 0.0007 0.0007Standard error 0.0007 0.0006 0.0008 0.0007 0.0007 0.0007 0.0007 0.0007 0.0007 0.0007 0.0002 0.0008 0.0007 0.0007 ParameterEstimate log Parameter Estimate 805 810 it extends from Fig. 1. a rotated pole with a longitude of 177.5 0.22 Table 3. mate models, Nat.2011. Hazards Earth Syst. Sci., 11,Rev., 2847–2857, 123, 3458–3465, 1995. Weather Rev., 127, 1362–1373, 1999. tratropical Cyclones in Recent ReanalysesMERRA, ERA NCEP CFSR, Interim, and NASA JRA-25, J.2011. Climate, 24, 4888–4906, ern Hemisphere Winter Storm Tracks,1061, J. 2002. Atmos. Sci., 59, 1041– speed forecasts, Met. Apps., 14, 105–116, 2007. losses and the identification of severeNatural winter Hazards storms and in Earth Germany, System Sciences, 3, 725–732, 2003. Northwest Europe, Cambridge University Press, 1991. J.: The Atlantic Hurricanementation Database for 1851–1910 Reanalysis Alterations Project: and AdditionsDAT Docu- to Database, the in: HUR- Hurricanesfuture, and edited typhoons: by Past, Murnane, present R.versity Press, J. and 2004. and Liu, K.-B., Columbia Uni- plication of an objective storm severity measureAtlantic for region, the Meteor. Northeast Z., 17, 575–587, 2008. Gibney, E. J.: Toward aBest-Track Homogenous Dataset, Global Bull. Tropical Amer.2010. Cyclone Meteorol. Soc., 91, 377–380, Serial Clustering of Extratropical Cyclones,134, Mon. 2224–2240, Weather Rev., 2006. L., Jung, T., Achuthavarier, D.,Huang, B., Adams, Jin, J. E. M., K.,cal Altshuler, Stan, Cyclone E. C., Climatology L., Towers, in P., a andToward 10-km Wedi, Global Weather-Resolving N.: Atmospheric Tropi- Climate GCM: Modeling,3867–3893, J. 2012. Climate, 25, J. C.: The characteristics of turbulentsurface velocity layer components under in the convectiverol., conditions, 11, Bound.-Lay. 355–361, Meteo- 1977. Springer, 2000. 2006: Low insured losses,many, 2007. Tech. rep., Swiss Reinsurance Cop- 2012: A year ofSwiss extreme Reinsurance Copmany, weather 2013. events in the US, Tech.extreme rep., weather and climate events, in: Climateciety, extremes edited and by so- Murnane, R. andPress, Diaz, 2008. D., Cambridge University Hodges, K. I.: Feature Tracking on the Unit Sphere, Mon.Hodges, Weather K. I.: Adaptive Constraints forHodges, Feature K. I., Tracking, Lee, Mon. R. W., and Bengtsson, L.: A Comparison of Ex- Hoskins, B. J. and Hodges, K. I.: New Perspectives on the North- Howard, T. and Clark, P.: Correction and downscaling of NWP wind Klawa, M. and Ulbrich, U.: A model for the estimation ofLamb, storm H. H.: Historic Storms of theLandsea, North C. Sea, W., Anderson, British C., Isles Charles, and N., Clark, G., and Dunion, Leckebusch, G., Renggli, D., and Ulbrich, U.: Development and ap- Levinson, D. H., Diamond, H. J., Knapp, K. R., Kruk, M. C., and Mailier, P. J., Stephenson, D. B., Ferro, C. A. T., and Hodges, K. I.: Manganello, J., Hodges, K. I., Kinter-III, J. L., Cash, B. A., Marx, Panofsky, H. A., Tennekes, H., Lenschow, D. H., , and Wyngaard, Pinheiro, J. and Bates, D.: Mixed-Effects Models in S and S-PLUS, Sigma: No 2/2007: Natural catastrophes and man-made disasters in Sigma: No 2/2013: Natural catastrophes and man-made disasters in Stephenson, D. B.: Chapter 1: Definition, diagnosis, and origin of Roberts et al.: The XWS catalogue 745 750 755 760 765 770 775 780 785 790 795 800 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . 2.2.3 ◦ 9 in lati- ◦ are defined ) 1 − f98 S to 16.89 (ms ◦ and f98 f S ,S ft ) S 1 , − N , (ms f 2040 2039 max S U ) Roberts et al.: The XWS catalogue 1 . − in longitude, and -17.65 ◦ ◦ (ms , and in the rotated coordinate frame it extends ft ◦ S to 29.58 ◦ As Figure 2, but footprints were de-contaminated using the Footprints of storms 4769, 4773, 4782 and 4774 made by , but footprints were decontaminated using the method described in Sect. Domain of model used to generate the footprints (inner rect- 2 M.-E.: Investigating Global Tropical Cyclone Activity witherarchy a of Hi- AGCMs: The Role26, 133–152, of 2013. Model Resolution, J. Climate, ical Cyclogenesis: A Three-Way InteractionEasterly between an Wave, African Diurnally Varyingtively Convection, Coupled Atmospheric and Kelvin a140, Wave, 1108–1124, Convec- Mon. 2012. Weather Rev., Footprints of storms 4769, 4773, 4782 and 4774 made by taking the maximum gusts Fig. 3. method described in Section 2.2.3.plotted The to track show of the each relationship storm between is storm over- track and footprint Fig. 2. taking the maximum gusts over the whole domain (contaminated). Strachan, J., Vidale, P.-L., Hodges, K., Roberts, M., and Demory, Ventrice, M. J., Thorncroft, C. D., and Janiga, M. A.: Atlantic Trop- Fig. 1. angle). The domain hasand a latitude rotated 37.5 pole withfrom a -9.36 longitude of 177.5 tude, with spacing 0.22 As Fig. C 0 17 10 13 23 15 15 n 805 810 The track of each stormprint. is over-plotted to show the relationship between storm track and foot- Fig. 3. over the whole domain (contaminated). Fig. 2. (25 km grid boxes) B N n ) 1 A 6 27 8 27 − 1310 27 27 n (ms

max

U (best case) 0 27

max

(worst case) 23 27 U dependent 98 f ft N 8 27 f max S S S Index U independent 87J 16 Oct 1987 39.53 622 38424457 65338 104.56 Ulli 3 Jan 2012 36.32 397 19019179 15988 14.19 Lore 28 Jan 1994 31.60 438 13818494 4431 54.17 Gero 9 Jan 2005 39.13 293 17552256 23032 9.96 Herta 3 Feb 1990 33.16 437 15936658 12733 49.40 Daria 25 Jan 1990 37.92 881 48047669 53068 72.57 Xylia 30 Oct 1998 26.72 295 5625905 2680 54.07 Yuma 24 Dec 1997 39.92 205 13039350 4035 3.33 Klaus 24 Jan 2009 37.23 472 24356496 26469 140.22 Kyrill 18 Jan 2007 36.38 1234 59432000 8756 164.46 Name Date of Erwin 8 Jan 2005 39.22 598 36077572 40914 79.23 Fanny 4 Jan 1998 34.60 297 12300569 6296 15.80 Oratio 30 Oct 2000 38.45 645 36667755 18846 56.39 Emma 29 Feb 2008 25.12 768 12169633 7874 78.00 Vivian 25 Feb 1990 35.16 940 40864068 56775 73.69 Lothar 26 Dec 1999 36.72 380 18818478 10612 69.09 Martin 27 Dec 1999 37.18 415 21328371 24460 132.26 Anatol 3 Dec 1999 39.86 742 47007178 48102 94.62 Wiebke 28 Feb 1990 32.24 751 25163891 3382 118.11 Jeanette 27 Oct 2002 36.92 1497 75367239 91060 219.21 Stephen 26 Dec 1998Xynthia 39.53 27 Feb 2010 32.62 317 19575792 666 36071 10.16 23109656 18706 138.80 Dagmar-Patrick 25 Dec 2011 30.08 65 1769600 516 39.43 The 23 severe storms highlighted by insurance experts in the period 1979–2012. The indices The number of storms in categories A, B and C for each in- Index and severity mate models, Nat.2011. Hazards Earth Syst. Sci., 11,Rev., 2847–2857, 123, 3458–3465, 1995. Weather Rev., 127, 1362–1373, 1999. tratropical Cyclones in Recent ReanalysesMERRA, ERA NCEP CFSR, Interim, and NASA JRA-25, J.2011. Climate, 24, 4888–4906, ern Hemisphere Winter Storm Tracks,1061, J. 2002. Atmos. Sci., 59, 1041– speed forecasts, Met. Apps., 14, 105–116, 2007. losses and the identification of severeNatural winter Hazards storms and in Earth Germany, System Sciences, 3, 725–732, 2003. Northwest Europe, Cambridge University Press, 1991. J.: The Atlantic Hurricanementation Database for 1851–1910 Reanalysis Alterations Project: and AdditionsDAT Docu- to Database, the in: HUR- Hurricanesfuture, and edited typhoons: by Past, Murnane, present R.versity Press, J. and 2004. and Liu, K.-B., Columbia Uni- plication of an objective storm severity measureAtlantic for region, the Meteor. Northeast Z., 17, 575–587, 2008. Gibney, E. J.: Toward aBest-Track Homogenous Dataset, Global Bull. Tropical Amer.2010. Cyclone Meteorol. Soc., 91, 377–380, Serial Clustering of Extratropical Cyclones,134, Mon. 2224–2240, Weather Rev., 2006. L., Jung, T., Achuthavarier, D.,Huang, B., Adams, Jin, J. E. M., K.,cal Altshuler, Stan, Cyclone E. C., Climatology L., Towers, in P., a andToward 10-km Wedi, Global Weather-Resolving N.: Atmospheric Tropi- Climate GCM: Modeling,3867–3893, J. 2012. Climate, 25, J. C.: The characteristics of turbulentsurface velocity layer components under in the convectiverol., conditions, 11, Bound.-Lay. 355–361, Meteo- 1977. Springer, 2000. 2006: Low insured losses,many, 2007. Tech. rep., Swiss Reinsurance Cop- 2012: A year ofSwiss extreme Reinsurance Copmany, weather 2013. events in the US, Tech.extreme rep., weather and climate events, in: Climateciety, extremes edited and by so- Murnane, R. andPress, Diaz, 2008. D., Cambridge University Index and severity perfectly in Section 3.1. Table 2. dex, where category A stormsextreme, are category severe B and storms notsevere, are meteorologically category C meteorologically storms extreme are and severeand and not category meteorologically D extreme, storms aretreme. not severe and not meteorologically ex- 10 Table 1. Roberts et al.: The XWS catalogue Hodges, K. I.: Feature Tracking on the Unit Sphere, Mon.Hodges, Weather K. I.: Adaptive Constraints forHodges, Feature K. I., Tracking, Lee, Mon. R. W., and Bengtsson, L.: A Comparison of Ex- Hoskins, B. J. and Hodges, K. I.: New Perspectives on the North- Howard, T. and Clark, P.: Correction and downscaling of NWP wind Klawa, M. and Ulbrich, U.: A model for the estimation ofLamb, storm H. H.: Historic Storms of theLandsea, North C. Sea, W., Anderson, British C., Isles Charles, and N., Clark, G., and Dunion, Leckebusch, G., Renggli, D., and Ulbrich, U.: Development and ap- Levinson, D. H., Diamond, H. J., Knapp, K. R., Kruk, M. C., and Mailier, P. J., Stephenson, D. B., Ferro, C. A. T., and Hodges, K. I.: Manganello, J., Hodges, K. I., Kinter-III, J. L., Cash, B. A., Marx, Panofsky, H. A., Tennekes, H., Lenschow, D. H., , and Wyngaard, Pinheiro, J. and Bates, D.: Mixed-Effects Models in S and S-PLUS, Sigma: No 2/2007: Natural catastrophes and man-made disasters in Sigma: No 2/2013: Natural catastrophes and man-made disasters in Stephenson, D. B.: Chapter 1: Definition, diagnosis, and origin of 745 750 755 760 765 770 775 780 785 790 795 800 11

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Roberts et al.: The XWS catalogue 0 2 lat, lat, : : lon lon γ γ ft S 0 2 lat, lat, γ γ 0 2 The number of storms in f98 for the top 20 % of storms. S lon, lon, , γ γ (b) f98 storms, for index max S C U , n 0 2 50). N + = , and elev, elev, B and C γ γ n N n , . max ft + U 2 1 S B max β plot the rank of each storm in each index. and n U lat, : N + lon A γ ) for the top with n C (e–h) ft 2042 2041 of storms for a)-d) the logarithm of each n S 1 1 % β lat, γ , and N storms, for index 0 1 C for the top 20 The relationship between n ft lon, with S γ + B Fig. 5. 12 and index and e)-h) thepoints rank show of the 23 each most storm severe in storms each index. Solid black max n U 1 σ β log elev, γ 0.0007 0.0007 0.0007 0.00080.0006 0.0007 0.0007 0.0007 0.0007 0.0007 0.0007 0.0007 0.0002 0.0008 0.0007 0.0007 -1.6023 -1.3411 1.8340 -0.1266 0.0056-0.0030 0.1382 -0.1506 -0.8135 0.5199 -0.1883 0.2734 0.0004 0.0292 -0.0832 0.0321 ) for the top C n Conceptual diagram of meteorological extremity and severity. All 5730 storms can ). b) The number of storms in category C ( Estimate Estimate The relationship of Parameter Parameter plot the logarithm of each index, and Standard error Standard error = 50 C n (a–d) Solid black points show the 23 most severe storms. Fig. 4. (a) be classified intometeorologically 1 extreme of and 4 not(category categories: severe C) severe (category and and B), notstorms not severe in severe and meteorologically category and meteorologically A, not extreme B extreme meteorologically (category and extreme A), C (category must total D). 50 The ( number of Fig. 5. category C ( + Parameter estimates and standard errors for the mean function described in Section 5. B a) Conceptual diagram of meteorological extremity and severity. All 5730 storms can be classified into 1 of 4 categories: severe n + A n Fig. 4. and not meteorologically extreme (category(category A), meteorologically C) extreme and and not not( severe severe (category and B), not severe and meteorologically meteorologically extreme extreme (category D). The number of storms in category A, B and C must total 50 Table 3. Roberts et al.: The XWS catalogue Roberts et al.: The XWS catalogue 13 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , N (b) , max ) occurs for the 50 storms . U f98 max S U (a) (d) ft S (c) , N . (b) , f98 max S U (a) and d) ft S 2044 2043 , c) N , b) max U . f98 S d) . ft The number of events in each winter for the 50 storms se- f98 S ) occurs for the 50 storms selected by S , c) max (d) U N The location of the centre of 850 hPa relative vorticity when the maximum wind speed The number of events in each winter for the 50 storms selected using , b) Fig. 7. lected using a) 14 and ft max S U Fig. 7. (c) Fig. 6. over land ( The location of the centre of 850 hPa relative vorticity when the maximum wind speed over land ( Fig. 6. selected by a) Roberts et al.: The XWS catalogue Roberts et al.: The XWS catalogue

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 15 : (f) and (c) 500 m ≤ . 500 m are plotted in blue. The x ≤ = shows the low altitude y are highlighted (g) 1 . − x = y 25 ms > station gust) vs. error in model wind at each of − 2046 2045 are plotted in red. The solid line represents : corresponding model footprints for the same storms. overlain, with contours representing the density of points for easy 1 − (e) (f) and and : observational footprints for the storms Jeanette (October 2002) and Kyrill are plotted in red. (b) (c) 1 (d) . g) shows the low altitude data from plots c) and f) overlain, with contours representing the density of points for x − = and a) Minimum MSLP for the storm Kyrill at observational y 500 m are plotted in blue. The solid line represents Plot of error in model gust (model gust - station gust) vs error ≤ Plot of error in model gust (model gust a) and d): Observational footprints for the storms Jeanette (October 2002) and Kyrill (January 2007). b) and e): Corresponding model Fig. 9. the stations that recordedgusts both greater gusts than and 25 ms winds. Points representing stations which recorded plot of model gustfootprint. vs. Gusts from observational stations gust with altitudesaltitude for greater each than 500 of m are the plotted stations in red, plotted and in those with the observational Fig. 8. (a) (January 2007). data from plots comparison. in orange. d) As for c) but for the storm Jeanette. Fig. 10. stations. b) Asmodel for geostrophic a) wind but againstwind maximum minimum ’observed’ for model geostrophic the MSLP.geostrophic c) winds same at Maximum the storm. location ofwhich Each a recorded station gusts point with during altitude represents thiscations storm. of the Points stations which representing maximum recorded the gusts lo- Fig. 9. in model wind at eachwinds. of Points the representing stations thatthan stations 25 recorded which ms both recorded gusts and gusts greater 16 Fig. 8. footprints for the samefootprint. storms. Gusts c) from and stations f): with Plot altitudes of greater model than gust 500 versus m observational are gust plotted for in each red, of and the those stations with plotted altitude in the observational easy comparison. solid line represents Roberts et al.: The XWS catalogue Roberts et al.: The XWS catalogue

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 17 but (a) ) intervals based on a ··· 500 m As for ≤ (b) ) intervals based on are highlighted in or- ··· 1 − are highlighted 1 − 25 ms > line is plotted in grey. Column 2 x 25 ms = > y 2048 2047 500 m which recorded gusts during this storm. Points ≤ line is plotted in grey. Column 2 shows the mean recalibrated footprint, column 3 its x = y Maximum model geostrophic wind against maximum “observed” (c) are plotted in red. 1 but for the storm Jeanette. − a) Minimum MSLP for the storm Kyrill at observational (c) Plot of error in model gust (model gust - station gust) vs error Minimum MSLP for the storm Kyrill at observational stations. As for Recalibrated footprints for Jeanette (row 1) and Kyrill (row 2). Column 1 shows ob- (d) Fig. 10. stations. b) Asmodel for geostrophic a) wind but againstwind maximum minimum ’observed’ for model geostrophic the MSLP.geostrophic c) winds same at Maximum the storm. location of Each a station point with altitude represents the maximum Fig. 9. in model wind at eachwinds. of Points the representing stations thatthan stations 25 recorded which ms both recorded gusts and gusts greater which recorded gusts during thiscations storm. of Points stations which representing recorded the gusts in lo- orange. d) As for c) but for the storm Jeanette. 16 Recalibrated footprints for Jeanette (row 1) and Kyrill (row 2). Column 1 shows observed against raw MetUM maximum gusts for minimum model MSLP. geostrophic wind for theat same the storm. location Each of point a station represents with the altitude maximum geostrophic winds representing the locations of stations which recorded gusts Fig. 10. (a) ange. shows the mean recalibrated footprint,and column 5 3 the its 2.5 ratio % to and the 97.5 original % footprint prediction and bounds, columns respectively. 4 a station located in London are superimposed. The Fig. 11. served against raw MetUMrecalibrated maximum mean gusts (—), for 95 % stations confidence across (- Europe. As - an -) example, and the 95 % prediction ( stations across Europe. As an example, the recalibrated mean (—–), 95% confidence ( - - - ) and 95% prediction ( Fig. 11. station located in London areratio superimposed. The to the original footprint and columns 4 and 5 the 2.5% and 97.5% prediction bounds, respectively. Roberts et al.: The XWS catalogue