Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Open Access 1,2 Discussions Sciences Natural Hazards Hazards Natural and Earth System System and Earth , and D. Roelvink 2 3252 3251 , A. van Dongeren ect on the simulated wave results, suggesting room 2 ff , M. van Ormondt 1,2 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. UNESCO-IHE Institute for Water Education,Deltares, Delft, Delft, the the Netherlands Netherlands the overall spectrum, results showby consistent the underestimation model, of which theNorth for swell component the Sea Dutch is Coast,boundary open will can to have mainly a the come significantfor from Atlantic e improvement the for Ocean. the North In swell where boundarytion the conditions the within on proposed the the model Dutch NorthCoSMoS Continental and system, can the Shelf the provide swell Model. reasonably propaga- swell Finally good we wave and show surge that prediction. in forecast mode, that the simulated wave parameters andagreement surge with elevation data, from with the CoSMoS averagethe are root surge in mean good elevation square and erroris over 0.24 a m the tendency year for of of thelower the 0.14 frequencies m significant wave (swell). for Additionally, model wave when height. to a underestimate wave It separation the is algorithm height is noted applied of that on northerly there waves with a generic operational modelalong the system Dutch applied Coast. The heretion CoSMoS on to application the is predict Dutch not stormcurrents coast, limited and impact to but coastal storm can on flooding, impact also in dunes up predic- be other of environments. applied the In to this CoSMoS other paper,deep system we coastal water and present hazards wave validation the buoy such of set- data asmaterial. the and rip wave The tidal and evaluation gauge surge is measurements modelparameters presented as within and ground as it truth observed monthly using validation error ones. measures Hindcast between results computed over the whole year of 2009 show Knowledge of the actual conditionis of hydrodynamics essential in for the nearshore coastalsystem and monitoring can coastal serve activities. area as To a this toolics end, along in the providing a recent coast. coastal and In up-to-date this operational state paper model of we apply the CoSMoS hydrodynam- (Coastal Storm Modeling System), Abstract Nat. Hazards Earth Syst. Sci.www.nat-hazards-earth-syst-sci-discuss.net/2/3251/2014/ Discuss., 2, 3251–3288, 2014 doi:10.5194/nhessd-2-3251-2014 © Author(s) 2014. CC Attribution 3.0 License. 1 2 Received: 12 March 2014 – Accepted: 22Correspondence April to: 2014 L. – Sembiring Published: ([email protected]) 9 MayPublished 2014 by Copernicus Publications on behalf of the European Geosciences Union. A validation of an operationalsurge wave prediction and system for theCoast Dutch L. Sembiring 5 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3254 3253 orts. In addition, meteorological data as input for ff cient way and with low logistical e Application of such model systems as a coastal monitoring tool has also been Application of such model systems for wave forecasting has been demonstrated pre- ffi by wind fields from HIRLAM. It is reported that during year of 2005–2007, the cor- terms in the modela nearshore equation, prediction which system is (Behrens and a Gunther, required 2009). furtherdemonstrated improvement previously. necessary Alvares-Elucaria (2010) for developedcurrent an forecasting operational system wave for and at the north-eastern beach forecast part conditions ofStokes including Mallorca model rip Island for mainly currents. the aimed TheyThis nearshore model make to solves use the generate of mild surfzonetra slope a equation, waves input 2DH and and from derives Navier- deep currents its water two prediction. wave dimensional prediction wave spec- model WAM cycle 4, which is in turn forced demonstrated that WAM model thatto covers the provide North forecast Sea 2 and daystory Baltic ahead Sea, results. of was winter As capable storm improvementsatmospheric Britta to and the model Kyrill model, (developed in they by 2006model suggest German with does further satisfac- Weather not development Service). take on In into the addition account depth to induced that, wave the breaking as one of the source particularly true for enclosedatmospheric basins model where is anBertotti, often underestimation 2004, of found 2006). wind (Ponce In speed dewithin by addition, Leon the the wave wind and models fields Soares,over, are Cerneva which 2008; very et act Cavaleri sensitive al. as to and of (2008) forcing small significant reported input variations wave an heights (Bertotti underestimation inswell-wind and wave the by conditions. Cavaleri, case the In 2009). of contrast, WAM More- thefor low Cycle the model wind 4 case shows energy of relatively model input high better energy performance and input. during For combined the North Sea area, Behrens and Gunther (2009) WATCH III performing slightly better thanmodelling, the some other two. studies For show semi-enclosedstorm that basin events scale wave (Mazarakis conditions et al., canCerneva 2012; be et Bertotti well et al., simulated al.,tially 2008). in 2011; when Bertotti However, serious the and the Cavaleri, quality wind 2009; of condition the shows wave strong model temporal decreases and substan- spatial gradients. This is Pacific Ocean, and found that in general, all three models show good skill, with WAVE- and WAVAD (Resio and Perrie, 1989). They performed multi-decadal hindcasts for the e CoSMoS can be obtained fromare well-established run meteorological in models, operational most mode, ofOceanic e.g. which and GFS Atmospheric (Global Administration, USA), Forecastmodel, HIRLAM System, Unden (High operated et resolution by al., limited National 2002), area forecast, and Janssen ECMWF et (European al., centre 1997). for medium-range weather viously. On a global scale, Hanson etferent al., regional 2009 wave presented models: a skill WAM assessment (Gunther, of 2002), three III WAVEWATCH dif- (Tolman, 2009), ing coastal inundation and erosioncoast during (Barnard winter et al., storms 2009). alongcoastal Nowadays, the many processes Southern models are California and alreadyet available circulation with al., which can 1994; Wei be etprovide simulated al., such 1995; coastal (Stelling, Roelvink models 1984; et with boundary al., Van 2009). information Dongeren Model from systems larger area like CoSMoS models can in an model system can serveabout as the a key hydrodynamic toola and in model morphodynamic system providing can recent state bepaper and valuable of we up-to-date for apply the coastal information CoSMoS stake (Coastal coast. holdersan Storm and Output Modeling operational decision System, from model makers. Van Ormondt In system such etDutch this used al., Coast. 2012), here The to CoSMoSto model predict storm system storm impact is impact prediction, genericrip on but and currents can dunes and its be flooding, along application used in the is other to environments. not assess It limited other was coastal originally developed hazards, for such simulat- as Knowledge of the actualsential condition point of in nearshore and coastaledge, coastal the risk risk hydrodynamics management of is and coastal anand hazards, monitoring es- such rip as activities. coastal currents Using inundation, this beach can and knowl- be dune erosion, predicted and mitigated. To this end, a coastal operational 1 Introduction 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 10 km, while the resolution of the × 3256 3255 20 km. The surge model is driven by meteo data × erent sources to provide a reliable forecast. They use the hy- ff erent models and data in a comprehensive way to provide forecast information. ff The next nested model is the Dutch Continental Shelf Model (DCSM) which com- For coastal monitoring purposes along the Dutch Coast, Baart et al. (2009) intro- In this paper, the CoSMoS application to the North Sea basin and validation will The application of model systems for storm surge and tide prediction has also been ping is modeled basedHasselman et on al. Westhuysen (1973), et and al. depth induced (2007), wave bottom breaking friction model is formula from is Battjes from and wave model is approximately 15km from HIRLAM and amplitudesigned and using phase the of tideis several model forced relevant TPX072 by tidal (Egbert wind constituents field andby from are Erofeeva, HIRLAM red 2002). as- and lines The the in SWAN swellsimultaneously, model Fig. boundary allowing conditions 2) for (area wave-current are indicated interactions. obtainedmodel The from within model CoSMoS the set is global up summarized of WW3settings in the Table model. used 1, DCSM and The in model models the version are and model the run are parameter presented in Table 2. For the wave model, whitecap- D wave spectra at particularconditions locations for as the output. nested These models, outputs as are indicated used in as Fig. boundary 1prises by the the wave arrows modellution SWAN and of the the surge surge model model Delft3D-FLOW. is The approximately spatial 7.5km reso- 2 CoSMoS set up and modelThe description CoSMoS system is setmodels up are for the integrated North with Seamondt local basin et where (high regional al., resolution) wave 2012). models andFor surge (Baart The the model et global al., and model,action 2009, domain Wave density Watch van structure balance III Or- of equation. (WW3 WW3 CoSMoS from is appears here forced in by on) GFS Fig. is meteo 1. used, data which and generates solves 2- the The emphasis of thepaper validation is organized will as be follows: Sect.models on 2 within the describes it. operational the Section CoSMoS monthlythe set 3 performance. paper. up describes In This and briefly Sect. description of 4, methodologiesconclusions results and will of data be the drawn that validation in are will Sect. used be 5. presented in and discussed. Finally, and surge elevation simulated by the model, focussing on the Continental Shelf Model. (2009) and Van Ormondt et al. (2012). The validation will be on the wave parameters duced a real time forecastingphological operational storm system impacts. in Using orderdemonstrated a to the predict similar potential and forecast of hindcast system, such mor- ing Van a Ormondt storms, system et and to al. forecast hazardousfor (2012) dune swimmer swimming and safety conditions applications, beach during supplying erosionobtain CoSMoS dur- calmer up-to-date with nearshore weather. data bathymetry Particularly from alsotionally shows beach (Van potential cameras Dongeren to to et be al., applied 2013). fully opera- be presented. The system itself is similar to the one that is described in Baart et al. combining data from di drodynamic model ofet Delft3D-FLOW to al., compute 1999) tides forheight bias the and of waves. surge 10 % Preliminary and and root results SWAN mean from (Booij square the error of model 3.7 cm system for give the water a levels. wave it using meteorologicalMoreover, model they HIRLAM, implemented an toquality update of provide forecast, to tide initially the implemented andproach Kalman by now Heemink storm filter has and configuration surge been Kloosterhuis (1990). to appliedFlood forecasts. This in improve Early ap- the Warning operational predictioning System, system di Werner Delft-FEWS (Delft- etSpecifically al., for 2013), coastal which forecasting,tional has De model flexibility Kleermaeker system in for et the integrat- al. Dutch (2012) Coast present under an the opera- framework of the FEWS system, period are 86 % and 78in %, Spain respectively. A for similar monitoring approach application has also of been beach implemented nourishments (Gonzalesdemonstrated. et al., For 2007). the Netherlands,namic model, Verlaan DCSM et (Dutch Continental al. Shelf Model, (2005) Gerritsen make et al., use 1995) and of forced the hydrody- relation between model prediction and measurements for the wave heights and mean 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | R (3) (5) (7) (4) (1) (2) cient is ffi respectively. are simulated y y and and x x are mean value of y 3258 3257 and ) are used. The expressions are as follows: x 1 2 norm  e 2 )
 ˆ y y ) ˆ − y − i i − y are the mean angular of simulated and observed parameter y ( i cient is determined by a linear function of three break points ˆ 2 y y 2 1 ffi i − y # y
2 is the length of the time series parameter, 1 )sin ) and x ˆ N i = )sin( x i X ˆ ˆ y N  x x − ) − ) ) is the plane component of the directional data (unit vector), and i i 1 − N i i − n x i i x m n , ( − x x  ( 2 i m 1 1 x ) (6) sin( N = P N = cos( i 1 sin sin( i R X  N = 1 1 1 ˆ N i y X N = 1 N 1 = P N P i i  N = − 1 i N X " ˆ s = x cient of the directional data by also making use of the mean angular measures. ), bias, and normalized error ( 1 = = N arctan( = ffi = The system is designed in a MATLAB platform, where the initiation and operational = rms = norm rms e ˆ e model with the observedquantify ones the as error ground between truth. simulated( and Statistical observed error data. measures Here, are root mean used square to error e Protocol, Cornillon et al., 2003). 3 Data and method The validation is carried out by comparing simulated parameters obtained from the run are performed everytimer 12 loops h, in and thedefines managed system the by that so starting dictate called the timethe operational timer system, and run. loop. and end First, There downloads timemodels. the necessary are The of main wind second two model loop, time and run, which loop airin is triggers pressure sequence, the the data model starting loop, to overall frommodels. in be initiation Downloaded which global forcing used of model data and by runs and the will simulation regionala results be local models from executed OPeNDAP the server followed models (OPeNDAP: by are Open-source stored Project higher-resolution on for a Network Data Access used, and wind drag coe and the corresponding windDutch speed Coast Model (Delft3D and FLOW theon Local User Model the Manual). will application In not of be this CoSMoS part for paper, of the The the North analysis Sea. as we will focus Janssen (1978). For the Delft3D FLOW model, uniform bed roughness coe In the Eqs. (4)–(7), respectively, ( is the mean resultant vector. CB x R coe Circular correlation (CC) andFisher, circular 1996): bias (CB) are defined as follows (Berens, 2009; CC measures. The circular bias is definedparameter by subtracting from the the mean mean angular angular of ofby the the transforming computed observed. the Here, directional the data mean into angularand two is vector computed then components taking with the magnitude of fourmean unity, quadrant angular inverse (Berens, tangent 2009). of the Circular resultant correlation of is the computed vectors by as defining the correlation In the Eqs. (1)–(3), and observed parameter respectively, For directional data, circular correlation and circular bias are used as statistical error bias 5 5 20 15 10 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | is (9) (15) (12) (13) (14) (11) D is the angle δ of 1.8 is used. α is directional spreading as σ is wind speed, and U 3260 3259 # is a constant, θ θ   α d d 2 f f  ) )d )d 2 is the running wave direction. Here we have to keep in f ) ( θ θ f , 0 , θ ( f θ f ( ( σ f − 2 θ θE θ )d θE d  f f erentiate between energy that belongs to wind sea and swell. The ( ) (8) 1 ff   f sin )d cos E − , ] θ ) Z θ , RR ( RR δ exp f = ( D " π · 1 (10) ) is the wave energy as a function of frequency and direction and θ E ) 2 d θ 1 cos( f = ) is mean direction as function of frequency, , f ( √ ZZ f f σ θ ( ( E is the critical frequency, )d 0 αU 2 π arctan s [ θ c E )d = = θ f , 4 f = ) ) ≤ π p f , 1 g f ( f = 2 θ θ , δ , ( E 0 = θ f Since the wave model SWAN returns output as two-dimensional wave spectra as As the ground truth, data from deep water directional wave buoys and tidal gauge = ≤ ( ( m D mean p c H et al., 1963; Wenneker and Smale, 2013). well, consistent parameter definitionsdata. The can integral be wave parameters used then for will be both calculated simulated as and follow: observed and directional bimodality is not significant over the period of the data (Longuet-Higgins For the directional spreading, a normal distribution function is used: D ZZ therefore, Z tion, and directional spreading as functionTherefore, quasi of 2-D frequency, rather wave energy than spectrum the is full constructed 2-D using spectra. following expressions: E Where directional spreading. Since: record are used,considered: of Eierlandse stations Gat (EIELSGT), located K13(EURPFM). near Platform For (K13APFM), water the and levels, Dutch five EuroIJmuiden, gauges Platform Coast. Huibertgat, are and Three used: K13 Euro wavebuoys platform are platform, buoys processed, (Table Hoek stored 3 are van and and Holland, retrievable as Fig. wave energy 2). density, Data mean wave obtained direc- from the where between wind sea and the wind.sea Energy spectrum content while above below this it line is will be swell. The counted as calibration wind parameter only consists of one systembetween of wind sea sea and and swell one is system defined of as: swell. Thef demarcation line 0 θ For analysis purposes, inponents addition to will bulk also wave bewave parameters, spectrum computed. wind to To sea di this and end,algorithm swell com- an will algorithm largely will followsimplification be Portilla we applied et make on al. an the (2009) assumption total and that Hanson the et total al. energy (2009). content Here, in for the spectrum T For the mean wave direction, formula from Kuik et al. (1988) is used: where function of frequency, and mind that the shape of the directional spreading function is assumed to be Gaussian 5 5 20 15 10 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 0.08 m is given by − for IJmuiden. ◦ 3262 3261 shore, which makes it less prone to the variability of ff for K13 platform until 8 ◦ erences between computed phase and observations for the most im- ff In addition, a tidal analysis was performed towards the computed water level where two-week period that iswave shown, height with a by slight the overestimation model. tendency Monthly of wave the parameter observed errors are given in Fig. 8 for all portant constituent M2 is 2 4.1.2 Wave validation In general, the wavethe model hindcast shows period. good Examples skillparameters of in for time K13 reproducing series platform the are plots presented waveof with in November. parameters simulated Fig. The for 7, and wave height, where observed a peakwave wave storm period model occurred and at mean (blue) the wave are direction end computed in by the good agreement with the observations (red dots) over the model (red) tends toK13 slightly platform. over Relative predict error the isilar from observations to 0.4 (black), % Fig. except for 5, IJmuiden for inwell until stations Fig. by 9 6 % the the for model, bar-plot K13O1. with of platform. Absolute tidal Sim- higher di phase error is tendency presented. appears Tidal in phase diurnal is predicted constituents K1 and wind driven surge. In addition,mainly the results during also winter show period, that whilesmaller stronger with during positive a bias calmer tendency is months of found the beingobservation absolute negative. over Overall, bias the the is year. surge relatively is in good agreement with the amplitude and theobservations. phase Figure of several 5 relevant presentsnant tidal tidal a constituents constituents are bar-plot at the compared of Dutch with the Coast. For tidal the amplitude most important for constituent, six the M2, most domi- seasonal trend is clearlya seen similar tendency in of Fig. lowererror 4, rms during error where winter during months. all An summer the exceptionrelatively months is constant with tidal station around relatively K13 gauges 0.05 higher platform, to considered rms where 0.1platform m the show which rms over the error is year. is relatively This is far due o to the location of K13 cides with the winter period where stormier and higher wind speeds are expected. This February with a bias ofstation 0.12 m, K13 while platform the for strongest the negative bias month of of June. The relatively higher surge rms error coin- presents water levels plotlast for week location of IJmuiden November duringwater 2009, the level where (blue) storm elevates the that from computedis occurred astronomical water clearly prediction in level (grey). seen the This (green) from waterthe and the level computed observed observed raising surge (red). surge Monthly level errormean (black) plots square which for the error in surge (left a levels panel)of appear good in September, vary agreement until Fig. from with 0.21 4. 0.09 m Root m for(right for station panel), Europlatform station for the K13 the platform highest month for positive of the March. value For month is the found bias at station Huibertgat for the month of Model performance for waterulated level and tidal surge signal is andmined analyzed the by by surge comparing subtracting with both astronomical thesimulated the prediction observations. sim- tide from The the and surge tidal surge levels signal. levels were Results are deter- show in that good agreement with observations. Figure 3 typical yearly wave conditions for theevent. North As Sea we without are any interested particularanalysis, in significant this operational storm particular daily performance year rathersignificant is than wave thus specific height a event varied representativeduring from one. Weekly 0.5 week m averaged 48 during observed (monthwave week height of 27 was November). (month 4.79 m. For of Thesethe the July) three wave up latter heights buoys considered. to are period, approximately 2.8 the m in the maximum same observed range4.1.1 for Water level and surge validation 4.1 Hindcast A hindcast was performed for thesystem calendar year is of 2009, provided where the by forcing for the the model analyzed wind fields. In general, the year 2009 exhibited 4 Results and discussion 5 5 25 20 10 15 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | er from ff . ◦ 0.14 m and − 10 ± bin of observed ◦ in both Fig. 9a and ◦ ect of such scenario. The result ff and 210–270 ◦ ers more underestimation compared ff 0.15 m to 0.15 m (Fig. 8a, middle panel). − 0.08 s for all buoys considered, suggesting − 3264 3263 ect on negating the swell boundary is twice less erent periods. Figure 11 presents time series of ff ff 1.05 s up to erent peak wave period, for station K13 platform during − ff with most of the months give bias approximately ◦ and 9 ◦ ect of swell boundary as model system component ff erent color indicating di When the wave separation algorithm is applied, the swell and wind sea component In Fig. 9, scatter plots of observed and computed wave height are presented, with ff In order to assessthe the future, capability a of forecast CoSMoS mode to validation is predict performed events where a the number model of system days is into forced significant, where the simulationto result default (red) setting su ofror the increases model (on system averageNovember over (Fig. (blue). 11, the The right buoys) normalized panel), bythan the root the e 34 %. month mean of In square June, contrast, with er- during 15 % the increase4.2 in month normalized of rms error. Validation in forecast mode as the main inputshows into that the model performance wave degrades modelferent after reaction to removal from see of the the thewave model e swell height for boundary with di and with without dif- the the month swell boundary of for November theJune (right month (Fig. of panel) June 11, for (left left station panel) Eierlandse and panel), Gat. the During degradation the after month ignoring of the swell boundary is fairly This suggests again thatunderestimation of the northerly wave waves model at the underestimate Dutch swells, Coast. which4.1.3 may result in E The CoSMoS system appliesboundary conditions the for the global DCSM WAVEWATCH modelformed III (red where model lines we in to Fig. ignore 2). derive the Here, the a swell simulation boundary swell is and per- use only wind forcing from HIRLAM results show fairly goodbias agreement of with swell height observations.row) component In are (upper presented. Fig. For row) 100.19 m the and monthly (middle wind wind error panel sea sea and bias lower component, height row that the component in remains bias Fig. (lower negative is 10), over between which the is whole in year contrast (middle with panel the upper swell row height in Fig. 10). b). This tendency is observed in all the buoys considered. northerly waves with peakFig. wave 9a top-left period panel, greater in Fig. thanlike 9b the 7 top-left s and North bottom-right (blue Sea, panel). the and Forswells semi wave red can enclosed climate be seas will scatter expected be dots to dominated beis in by present open wind that to waves mainly while the come occasional northernwaves from part with the peak North, of period the where greater the Atlantic30 shelf than Ocean. nautical 7 From degree), s the there and results, is coming it athe from is consistent wave the height. tendency shown In North of that contrast, the (between for waves wavethis 330 that model and come underestimation to from underestimate (direction West/South between West do 150–210 not su di relatively calm period (theNovember, Fig. month 9b). of Six June, panelsincoming scatter Fig. wave direction. plot 9a) The in results and (a) show stormy and that the (b) period wave indicate model (the 60 tends month to underestimate of (Fig. 8b, left panel). The biasmiddle for panel), the ranging peak from wave periodthat is the consistently model negative gives (Fig. 8b, acast consistent time. underestimation For of thewith peak mean the wave observations period wave (Fig. direction, over 8c). theand model The hind- Eierlandse circular results Gat correlation show during varies a from theshowing 0.7 month significant a at of high agreement Europlatform January, correlation up betweenbetween to –16 the 0.9 model for the result most and of the the observation. months, Bias varies panel) ranges from 0.16 mmonth (Eierlandse of Gat, November). month The of normalizedconsistently May) error below until for 0.3 0.35 with the m bias wave (Europlatform, in heightFor a (Fig. the range 8a, peak of right wave panel) periodbuoy is for (Fig. the 8b), month the of November, rms to error 2.1 s ranges at from Eierlandse 1 Gat s for at the month the of Europlatform September the buoys considered. Over the whole year, the root mean square error (Fig. 8a, left 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent regional and lo- ff erent HIRLAM forcings are ff 3266 3265 erences, especially with the 48 h HIRLAM forecasts, ff erent HIRLAM are shown in Fig. 15 for location K13 plat- ff erent HIRLAM inputs together with the observations for IJ- ff erences are relatively small between results using analyzed wind (blue) ff erent HIRLAM are shown in Fig. 13 for station IJmuiden. A strong seasonal ff In order to test the CoSMoS system in forecast mode, it has been forced by HIRLAM To summarize, we conclude that CoSMoS Duth Continental Shelf Model is a fit-for- For the wave model, time series of wave heights for di which are the topic of further research. and storm surge upas to the two forecast days horizon in increases. advance. Ation smaller However, than the error for performance increase the is wave does heights. foundby degrade For for 11 surge the % elevation, surge on (from eleva- average, analyzed thethe to rms other error 24 hand, h) increases for and40 the % 25 % wave for model, (from 24 and the analyzed 48 rms to h error 48 forecast, h) respectively. increases for onpurpose all average regional stations. by model On 20 % towe provide and can boundary model and conditions predict for coastal coastal inundation, beach models and with dune erosion, which and rip currents, derestimation is found forsupported northerly by waves the with wave relatively separationthe low swell algorithm. frequency, boundary This which conditions suggests is on room also the for North improvement of for the24 model h domain. and 48 h forecasts. The model system is capable of predicting high wave events using wave buoys andperformed tidal where the gauges model available systemResults is along show forced the by that atmospheric Dutch the modelwith Coast. HIRLAM observations surge analysis. with Hindcasts elevation rms are produced errortends ranging by from to the 0.09 m slightly model to overestimate 0.21the is m. wave surge in On model, average, levels, simulated good the wave especially model agreement parameterserror during agree of well the 14 with % winter observationsdefault until with months. relative 30 settings %. For of However, model the tends model to system underestimate (swell swell boundary height. Using is included), a consistent un- A CoSMoS model system coveringcast the wave North conditions Sea and is wateris built levels which designed for can the in hindcast area a and alongcal generic fore- the models. way Dutch to A Coast. accommodate The validation system and is integrate performed di of the Dutch Continental Shelf Model (DCSM), 5 Conclusions significant wave height for di form. The rms errorat tends to K13 increase platform as forwind, the the and forecast month further horizon increased of increases. toOn For November, 0.46 instance the m average, and stronger rms 0.56 error error mIn with increments was contrast, 24 are the 0.31 and m monthly-averaged more 48 bias with hzon prominent does winds, analyzed increases. not during respectively. On increase the average, strongly the winter asforecast, rms the period. respectively error forecast for hori- increases all by locations. 20 % and 40 % for 24 and 48 h presented in Fig. 14,form, for five when days the in highestresults the weekly-averaged last (red: wave week 24 heights h, ofdots). November were green: However, for there observed. 48 location h) are The clear K13 are di while forecast plat- also results using in analyzed good and 24 agreement h with HIRLAM look observations relatively (black similar. Monthly errors of and results using either the 24vation or for di 48 h forecasted wind. Monthlyfeature error is plots retained of surge where ele- mer a season relatively while lower stronger bias rmsincrease is is error seen 33 and during % bias summer (fromrms period. exhibited analyzed error The to during increases greatest 48 sum- by rms h) 11for error for % all the (from stations. month analyzed of to December. 24 h) On and average, 25 the % (from analyzed to 48 h) are considered: a 24surge h elevation forecast plots and for a di muiden 48 during h November forecast. 2009. Figure Results48 using h 12 the ahead shows HIRLAM (red an forecasts and both(black example green dots). for of line Di 24 h respectively) and retain good agreement with observations by HIRLAM forecast winds of the year 2009. Two types of forecast HIRLAM wind fields 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3268 3267 cient inverse modeling of barotropic ocean tides, J. Atmos. , 2011. ffi , 2010. This research has been carried out in the framework of project: Real- 10.5194/nhess-11-2965-2011 10.2112/08-1133.1 In the future, merging CoSMoS with more integrated operational forecast system like mer. Meth. Fl., 11, 1097–1112, 1990. of wind-wave growth and swellDeutsche decay during Hydrographische Zeitschrift, the Joint 8, North 1973. Sea Wave Project (JONSWAP), projects, SMC, Comput. Geosci., 33, 916–931, 2007. many, 2002. three numerical wave models, J. Atmos. Ocean Tech., 26, 1614–1633, 2009. integrated coastal modeling system for analyzing beach processes and beach restoration In Quantitative SkillAmerican Assesment Geophysical for Union, Washington Coastal DC, Ocean, 425–467, 1995. edited by: Lynch, D., and Davies, A., Ocean Tech., 19, 183–204, 2002. forecast system indoi: the Adriatic Sea, Nat. Hazards Earth Syst. Sci., 11, 2965–2979, cluding sediments, DELFT3D-FLOW User Manual, 2013. S227–S234, 2009. for the Netherlands, Hydro122012. – Taking care of the sea, 13–15 November 2012, Rotterdam, Open-File Report 2009–1073, 21 pp., 2009. 16th Int. Conf. Coast. Eng., ASCE, 569–587, 1978. rope, Nat. Hazards, 49, 387–399, 2009. Tellus A, 56, 167–175, 2004. resolution, Ocean Eng., 33, 553–565, 2006. of the WAMC4 wave model for the Black Sea, Coast.geneous Eng., environment, 55, Data 881–893, Science 2008. Journal, 2, 164–174, 2003. Collins, B. D., Guza,hazards R. model; T., a Adams, tool P. N., for and predicting Thomas, the J. impact T.: The of framework severe of storms, a US coastal Geological Survey 1. Model description and validation, J. Geophys. Res., 104, 7649–7666, 1999. Roelvink, J. A.: Real-timeNetherlands, forecasting J. of Coastal morphological Res., 56, storm 1617–1621, impacts: 2009. a case study in The A nearshore wave anddoi: current operational forecasting system, J. Coastal Res., 503–509, FEWS is recommended in ordersince to have introducing more data accurate assimilation prediction into on the storm system surge will level increase forecast quality. Heemink, A. W. and Kloosterhuis, H.: Data assimilation for non-linear tidal models, Int. J. Nu- Hasselman, K., Barnett, T. P., Bouws, E., Carlson, D. E., and Hasselmann, P.: Measurements Hanson, J. L., Tracy, B. A., Tolman, H. L., and Scott, R. D.: Pacific hindcast performance of Gunther, H.: WAM Cycle 4.5., Institute for Coastal Research, GKSS Research Centre, Ger- González, M., Medina, R., Gonzalez-Ondina, J., Osorio, A., Méndez, F. J., and García, E.: An Gerritsen, H., de Vries, J. W., and Philippart, M. E.: The Dutch continental shelf model, in: Fisher, N. I.: Statistical Analysis of Circular Data, Cambridge University Press, 1996. Egbert, G. D. and Erofeeva, S. Y.: E Bertotti, L., Canestrelli, P., Cavaleri, L., Pastore, F., and Zampato, L.: The Henetus wave Deltares: Simulation of multi-dimensional hydrodynamic flows and transport phenomena, in- Bertotti, L. and Cavaleri, L.: Wind and wave predictions in the Adriatic Sea, J. Marine Syst., 78, De Kleermaeker, S., Verlaan, M., Kroos, J., and Zijl, F.: A new coastal flood forecasting system Berens, P.: CircStat: a MATLAB toolbox for circular statistics, J. Stat. Softw., 31, 1–21, 2009. Cornillon, P., Gallagher, J., and Sgouros, T.: OPeNDAP: accessing data in a distributed, hetero- Behrens, A. and Günther, H.: Operational wave prediction of extreme storms in Northern Eu- Cavaleri, L. and Bertotti, L.: The improvement of modelled windCherneva, and Z., wave Andreeva, fields N., Pilar, with P.,Valchev, N., increasing Petrova, P.,and Guedes Soares, C.: Validation Battjes, J. A. and Janssen, J. P.F. M.: Energy loss and set-up due to breaking of random waves, Cavaleri, L. and Bertotti, L.: Accuracy of the modelled wind and wave fields in enclosed seas, Booij, N., Ris, R. C., and Holthuijsen, L. H.: A third-generation wave model for coastal regions Barnard, P., O’Reilly, B., van Ormondt, M., Elias, E., Ruggiero, P., Erikson, L., Hapke, C., Baart, F., van der Kaaij, T., van Ormondt, M., van Dongeren, A., van Koningsveld, M., and Alvarez-Ellacuria, A., Orfila, A., Olabarrieta, M., Medina, R., Vizoso, G., and Tintoré, J.: work. References phodynamics” (project code 1202362).valuable The discussions, authors thank also Ivo for Wenneker providing and buoy Alfons data Smale and for useful algorithms that are used in this Time Safety on Sandycode Coasts 2010.05), funded and by the the Deltares’ strategic Flood research Control program 2015 “Event-driven research Hydro- program and (project Mor- Acknowledgements. 5 5 30 25 20 30 15 25 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3270 3269 , 2012. 10.5194/nhess-12-3433-2012 Swinkels, C., Roelvink, D., anddata Van Thiel assimilation de Vries, on J.:deaux, Rip two France, current 2013. distinct predictions through beaches, model Coastal Dynamics, Bordeaux University, Bor- dissipation in SWAN for deep and shallow water, Coast. Eng., 54, 151–170, 2007. 2002. nearshore model, Coast. Eng., 2741–2754, 1994. Delft-FEWS flow forecasting system, Environ. Modell. Softw., 40, 65–77, 2013. Rijkswaterstaat communication no.35, The Hague, 1984. 2009. C., Garcia-Moya, J. A.,B., Jones, Nielsen, C., N. Lenderlink, W., G.,Savijarvi, Degaard, McDonald, V., H., Rodriguez, A., Schreur, E., McGrath, B. Rummukainen, R., W., M., Navascues, Sigg, Sattler, R., K., Sass, and B. The, H., H.: HIRLAM-5 Scientific Documentation, the Netherlands: developments in2005. the last decade, Philos. T. R.surface Soc. waves. A, Part 1. 363, Highly 1441–1453, nonlinear unsteady waves, J. FluidCoastal Mech., Dynamics, 294, France, 71–92, 2013. 1995. Modelling storm impacts1152, on 2009. beaches, dunes and barrier islands, Coast. Eng., 56, 1133– Swinkels, C.: Simulating storm impactsFlood and Risk coastal flooding 2012, along Rotterdam, the the Netherlands Netherlands, coast, 28–29, in: 2012. wind sea and swell, J. Atmos. Ocean Tech., 26, 107–122,Phys. 2009. Oceanogr., 19, 193–204, 1989. in the western Mediterranean Sea, Coast. Eng., 55, 920–929, 2008. pitch-and-roll Buoy wave data, J. Phys. Oceanogr., 18, 1020–1034, 1988. 1963. idation over thedoi: Greek maritime areas, Nat. Hazards Earth Syst. Sci., 12, 3433–3440, system against Buoy and altimeter data, Weather Forecast., 12, 763–784, 1997. Van Dongeren, A., Van Ormondt, M., Sembiring, L., Sasso, R., Austin, M., Briere, C., Westhuysen, A. J., Zijlema, M., and Battjes, J. A.: Nonlinear saturation-based whitecapping Van Dongeren, A., Sancho, F. E., Svendsen, I. A., and Putrevu, U.: SHORECIRC: a quasi 3-D Werner, M., Schellekens, J., Gijsbers, P., van Dijk, M., van den Akker, O., and Heynert, K.: The Wenneker, I. and Smale, A.: Measurement of 2D wave spectra during a storm in a tidal inlet, Tolman, H. L.: User manual andUnden, P.,Rontu, system L., Jarvinen, documentation H., Lynch, of P.,Calvo, J., WAVEWATCH-III Cats, version G., Cuxart, 3.14., J., Eerola, K., Fortelius, Wei, G., Kirby, J. T., Grilli, S. T., and Subramanya, R.: A fully nonlinear Boussinesq model for Stelling, G.: On the construction of computational methods for shallow water flow problems, Verlaan, M., Zijderveld, A., de Vries, H., and Kroos, J.: Operational storm surge forecasting in Roelvink, D., Reniers, A., van Dongeren, A., van Thiel de Vries, J., McCall, R., and Lescinski, J.: Van Ormondt, M., Van Dongeren, A., Briere, C., Sembiring, L., Winter, G., Lescinski, J., and Resio, D. and Perrie, W.: Implications of an f-4 equilibrium range for wind-generated waves, J. Portilla, J., Ocampo-Torres, F. J., and Monbaliu, J.: Spectral partitioning and identification of Ponce de León, S. and Guedes Soares, C.: Sensitivity of wave model predictions to wind fields Longuet-Higgins, M. S. and Stewart, R. W.:Mazarakis, A N., note Kotroni, V., on Lagouvardos, wave K., set-up, and J. Bertotti, Mar. L.: Res., High-resolution 21, wave model 4–10, val- Kuik, A. J., van Vledder, G. P., and Holthuijsen, L. H.: A method for the routine analysis of Janssen, P. A. E. M., Hansen, B., and Bidlot, J.-R.: Verification of the ECMWF wave forecasting 5 5 30 25 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2 10 km × 3 − 1 s 7.5km − 2 s ∼ 2 / 1 3272 3271 20 km × 15km cient 90 m ∼ SWAN Delft3D Flow ffi cient) 0.00005 cient) 1.0 ffi cient) 0.067 m ffi ffi cient A, B, C (see Delft3D User Manual) 0.00063, 0.00723, 0.00723 ffi (breaker parameter) 0.73 (dissipation coe Model version and parameter settings. Model set up for DCSM. (proportionality coe (threshold saturation level) 0.00175 (bottom friction coe 0 ds R Open boundaryMeteo inputSource term:Wind growthWhite capping III WAVEWATCH GlobalBottom frictionDepth induced breaking HIRLAM Battjes and Janssen (1978) Westhuysen et Westhuysen al. et (2007) al. (2007) JONSWAP (Hasselman Tide et model al., TPX072 1973) HIRLAM – Grid size SWAN, model version 4072 Source termWhitecapping (Westhuysen et al., 2007) B DELFT 3-D FLOW, model version 4.01 Bed roughness Chezy coe Value C Bottom friction (Hasselman et al.,C 1973) Wind drag coe Depth induced breaking (Battjes andGamma Janssen, 1978) Alpha Table 2. Table 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3274 3273 Stations used in validation. B1B2B3 Eierlandse GatT1 Euro platformT2 K13 platform EIELSGTT3 Euro platformT4 EURPFM Hoek vanT5 Holland IJmuiden K13APFM HvH EUR Huibergat K13 platform Directional IJM wave buoy HUI K13 Directional wave buoy Directional wave buoy Tidal Tidal gauge gauge Tidal gauge Tidal Tidal gauge gauge Station Name Abbreviation in this paper Type Model and domain structure within CoSMoS. Fig. 1. Table 3. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3276 3275 The North Sea (left), with the colour shading represents depth. Red lines indicate the Water elevation for IJmuiden during the storm in November 2009, blue: observed water Fig. 3. level, green: computed waternomical level, prediction. black: observed surge, red: computed surge, grey: astro- swell boundary for the wave model.Table 3). The Dutch Coast (right), and stations used in analysis (see Fig. 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3278 3277 Monthly root mean square error (left) and bias (right) of surge elevation. Tidal amplitude comparison for six tidal constituents, black: observation, red: computed. Fig. 5. Fig. 4. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3280 3279 Time series of wave parameters, blue: model results, red dots: observations. Wave Tidal phase comparison for six tidal constituents, black: observation, red: computed. Fig. 7. height (upper panel), peakplatform. period (middle), and mean wave direction (lower). Location: K13 Fig. 6. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | s H of ob- ◦ : mean wave (c) . Green: observed p T month of November. Loca- : peak period, (b) (b) vs. computed, for every 60 s H month of July, : wave height, erent peak wave period, (a) ff 3282 3281 (a) 9 s. > p T 9 s, blue: < p < T 7 s, red: 7 Scatter plot of observed significant wave height < Monthly errors of wave parameters, p T with served incoming mean wave direction, for di Fig. 8. tion: K13 platform Fig. 9. direction. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3284 3283 for Eierlandse Gat for the month of June (left) and November s H Monthly errors of swell height (first row) and wind sea component (second row). Significant wave height Fig. 11. (right), black: observed; blue: with swell boundary; red: without swell boundary. Fig. 10. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent HIRLAM for location IJmuiden. ff 3286 3285 Surge elevation for IJmuiden during the November 2009 storm using, blue: analysed Monthly errors of surge elevation for di Fig. 13. HIRLAM; red: 24 h HIRLAM; green: 48 h HIRLAM; black dots: observation. Fig. 12. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent HIRLAM input, location K13 ff for di s H 3288 3287 erent HIRLAM input, blue: analyzed; red: 24 h forecast; ff for di s H Monthly error of significant wave height Significant wave height Fig. 14. green: 48 h forecast; black dots: buoy. Location: K13 platform. platform. Fig. 15.