HINDCASTING OF WIND AND WAVE CLIMATE OF SEAS AROUND RUSSIA

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1 Leonid J. Lopatoukhin, Alexande V. Boukhanovsky, Ecateina S. Chenysheva, Segey V. Ivanov HINDCASTING OF WIND AND WAVE CLIMATE OF SEAS AROUND RUSSIA St. Petesbug State Univesity, Institute fo High Pefomance Computing and Infomation Systems, St. Petesbug, Russia

2 What is the Wave Climate? The ensemble of sea states, including synoptic, annual and inte-annual vaiability WAVE CLIMATE INVESTIGATIONS BEFORE? NOW!

3 BEFORE INPUT Wind measuements in a coastal zone INPUT Wind Ship measuements obsevations in a of coastal sea waves zone Empiical elation h=f(v) Evaluation of the statistics OUTPUT Long-tem distibution with the pobability the same as fo wind OUTPUT Long-tem distibution and exteme statistics PUBLICATIONS Handbook of Registe, Atlas fo Maines, Hogben & Lamb THEREFORE: Wave climate is long-tem distibution

4 NOW! (1) Advent of eanalysis (2) Numeical models of sea waves (3) High Pefomance Computing INPUT Pessue and wind fields (eanalysis) Data assimilation and veification RESULTS Wind measuements in a coastal zone OUTPUT CONTINUOUS HINDCASTING (30 and moe yeas) Set of diectional sea wave specta Climatic wave specta - Omnidiectional - Diectional Pesistence statistics Stom and calm duation Long-tem distibutions - Opeational - Exteme Univaiate Multivaiate THEREFORE: Wave climate is a set of diectional specta and descended fom them spatio-tempoal statistics

5 Wave Climate Hindcasting in Seas aound Russia Sea Yeas Lat., N Long.,E. Model?x Gid step?y Baents W- 60 N. WW-III (1.18) Okhotsk Caspian Azov nm Azov nm Baltic nm Noth W.- 10 E. WW-III (2.22) 15 nm Black SWAN C.III. V nm Ladoga lake SWAN C.III. V nm

6 Stages of wind and wave climate desciption Input Wind Field Data Pepaation: (composing of data souces, assimilation, intepolation to egula gid) Bathymety, Ice cove etc. Sea Waves Hindcasting by numeical models (WW, SWAN): Validation and compae with obsevations Statistical pocessing of climatic wave specta Statistical pocessing of fields of the paametes (wind speed, wave heights, peiods, diections etc.) Statistical simulations and estimation of T-yeas extemes

7 The poblem of wind data input: diffeences between climatic eanalysis and synoptic data Baents Sea Caspian Sea egion WINTER Okhotsk Sea SUMMER Caspian Sea Fig. 1. Compaison of wind speed value time seies. Solid line Measuements, Dot line Reanalysis. (?) Baents Sea (Sental Banken, 740.5N 310.0E), (b) Okhotsk Sea (Odoptu, N, E.), (?) Caspian Sea (Tuleny island, N, E) eanalysis obsevations

8 Caspian sea: diffeences between eanalysis and obsevations REGIONS of the Caspian sea Compaison of monthly mean wind speed and tenso of.m.s.

9 Pocedue of Data Assimilation and Intepolation + δ = Η ), ( 0 t V V k s ), ( ), ( ), ( ) ( ), ( 1, t t j t V t V R j j t ε + Σ = Ψ = - Stochastic model of eanalysis data (with noise ) - Equation of measuements (with noise ) δ ε ) ), ( )(, ( ), ( ), ( * s k k V k k V t V t t V t V + R = - Kalman filte equation

10 Validation of Assimilation Pocedue Kasnovodsk GMS (SE-Caspian), , sevee stoms only Tuleny Island GMS (N-Caspian), Mach-May 2001 (each 3 hous)

11 Validation of Hindcasting Pocedue with Assimilated Wind Data Pats of significant wave height time seies. Noth Caspian, point 44.10N, 48.49E (depth 24 m). 1 measuements. 2 - SWAN with step 1 hou. 3 SWAN with step 15 minutes. Q-Q plots of SWAN calculations fo N. Caspian (Febuay, 2002). (?) values fom table, (1-4 the same as in table), (b) diffeent time steps; 1-15 minutes, 2 1 hou.

12 Genetic classification as a tool fo wave specta modeling The techniques fo deceasing of data dimensionality Fomal othogonal expansions Clusteing Classification with leaning INITIAL DATA: Noth Sea Region Diectional wave specta (24 diections? 25 fequencies each) Continuous hindcasting: WW 2.22, 3 hous, Genetic systems of classes Classification of climatic specta (pobabilities fo point 2) I II III IV V

13 How to constuct the classification ules? Paameteization technique fo diectional specta Dataset of Initial Specta Smoothing Model of multipeaked spectum S ( ω, θ) = γ ps p ( ω, θ Ξ p) p Estimation of pevailed peak position ( ω 1, θ1 ) on the data sheet diectly Estimation of seconday peaks positions and othe paametes γ, p Ξ p by means of multistage Monte-Calo optimization Citeion of optimization: DI = i j S( ω, θ i * Sij j m ) S 00 * ij min γ ω k θ k k, ( ) ( ) m, max, n fields G( ω, θ) = C Appoximation S( ω, θ) S( ω) G( ω, θ) 2 S( ω) = αg ω m k exp ( ω)cos n [ Bω ] 2m( ω) γ ( θ /2) Contol of significance of the peaks has been obtained β( ω) Set of spectal paametes fo classification

14 Results: Occuence fo diffeent classes of the specta Computations fo all the months, Wind waves 2 Swell 3 Sea with fesh swell 4 Sea with old swell 5 Complicated sea

15 Sta epesentation of Makov pobability model of the vaiability of diectional specta SW-pat of the Noth Sea

16 Conditional Spatial Distibution of Sea Wave Specta Class I Class II (conditional occuence fo N-Pat)

17 Joint Spatial Distibution of Sea Wave Specta Class III Class IV (conditional occuence fo N-Pat)

18 Appoximation of ensemble of the climatic specta (statistical lineaization technique) Taget Point Notation of the diections - WW 2.22

19 Compaison with measued data: climatic specta and stoms in a Black Sea

20 How to compute the extemes in a point? Some methods fo exteme estimation IDM Appoach h T = F 1 ( p( T )) F max ( AMS Appoach x) = exp exp { { a [ x ] } N b N POT Appoach k F ( h) = pkg ( h) k

21 How to compute the extemes in a field? BOLIVAR appoach on the base of multiscale stochastic simulation of wave fields 100-yeas maxima waves in a Baents sea

22 RUSSIAN REGISTER OF SHIPPING. Wind and wave climate of Baents, Okhotsk and Caspian Seas. Handbook. Saint-Petesbug, 2003, 213pp. CONTENT Pat 1. Methods of wind and wave climate calculations. Pat 2. Refeence data of wind and wave climate of Baents, Okhotsk, and Caspian seas The following statistics is pesented in the pat 2: Exteme winds with etun peiods 1, 5, 10, 25, 50 and 100 yeas. (Omnidiectional and fo 8 diections ) Stom and weathe windows duations of wind speed. Wave heights, peiods, lengths (mean, significant, 3%, 1%, 0.1%) and wave cests with etun peiods1, 5, 10, 25, 50 and 100 yeas. Stom and weathe windows duations of wave heights. (Mean, ms, max fo months). Pobability of wave heights and diection fo months. Joint pobability of wave heights and mean peiods and egessions.

23 SUMMARY Hindcasting is the main tool fo wave climate investigations. Confidence of wind eanalysis is quite diffeent fo vaious basins and even thei pats. Ship data assimilation is a useful appoach fo impovement of wind data input. Continuous 30 yeas (and sometimes moe) hindcasting was pefomed fo a lot of seas nea Russia. Wave climate is a set two-dimensional specta and descended fom them spatiotempoal statistics. Genetic classification of two-dimensional climatic wave specta pefomed. Makov pobability model is used fo investigation of the vaiability of diectional climatic specta. Appoach to calculations of wave extemes on a point and field is elaboated. New Handbook of Wind and wave climate of Baents, Caspian and Okhotsk seas is based on hindcasting, and published by Russian Registe of shipping.