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Effect of Super Cyclone “GONU” on Sea Level Variation Along Iranian

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Effect of Super Cyclone “GONU” on Sea Level Variation Along Iranian Indian Journal of Geo Marine Sciences Vol.42 (4), August 2013, pp 470-475 Effect of super cyclone “GONU” on sea level variation along Iranian coastlines Mohammad Reza Khalilabadi1& Dariush Mansouri2 1Marine Science Faculty, Khorramshahr Marine Science and Technology University, Isfahanan, Tehran, Iran. 2Department of Marine Physics, Faculty of Marine Sciences, Tarbiat Modares University, P.o.Box: 14115-111, Tehran, Iran. [E-Mail: [email protected]] Received 30 October 2011; revised 9 August 2012 Gonu was the strongest tropical cyclone to hit the region since records began in 1945. In June 2007, cyclone Gonu made landfall in Oman, affecting more than 20000 people and killing 50, before reaching the Islamic Republic of Iran. In Iran, roads and electricity networks were destroyed by the winds and floods. A cargo ship sank in the coastal waters of Haghani jetty in Bandar-Abbass. Analysis of hourly sea level recorded by tide gauges of the National Cartographic Center of Iran (NCC) at Jask and Bandar-Abbas provides a record of non-tidal sea level during the period of 6–7 June 2007, when cyclone crossed the south coast of Iran. Analysis shows that historical NCC tide-gauge data would be useful for testing the numerical models that are now emerging as an important component of defense from storm surges in the coast. [Keywords: Super Cyclone, GONU, Sea Level, Iranian Coastlines, Persian Gulf] Introduction possible to do this with high accuracy because the The flooding that occurs in the coastal areas due to periods associated with tides are known accurately. the surges has been a major cause of loss of life and They are defined by the motion of the Earth–Moon– property. Understanding the dynamics underlying the Sun system. The water-level oscillation associated surges and developing the capability to predict them with a particular period is generally identified in is therefore highly desirable. terms of the tidal constituent for that period. Major The essence of the dynamics of storm surges is constituents, numbering about 60, is removed from well known. Storm surges are atmospherically-forced the raw data, the residue can be associated with the oscillations of the water level in a coastal region, with impact of other factors, including storms. A brief periods ranging from minutes to days. They are long outline of the method is given below. gravity waves and belong to the same class as tides. A For M possible harmonic constituents, the time number of numerical models have been formulated to series x() tn , n=1,…., N can be expanded as gain the capability to predict the storm surges that M occur in the Oman Sea. The effort has produced x() tn = x () t +∑ Cq cos(2 π fq t n − Q q ) + x r () t n encouraging results and further refinements are being q=1 sought through ongoing research. These positive … (1) aspects have also underlined a difficulty faced during In which x( t ) is mean value of the record, xr is modeling: observations to check the performance of the residual portion of the time series (which may the predictive models have been scarce, if at all contain other kinds of harmonic constituents), available. The reason is that the instrumentation ∆= tnt , and where Cq, f q and Qq are respectively designed to measure normal sea-level variation, such n as tides, breaks down under the onslaught of the constant amplitude, frequency and phase of the abnormally high sea level during major surges. q'th constituent that we have specified. In the present configuration, we assume that the specified Materials and Methods frequencies have the form fq = q N ∆ t so that Analysis of the hourly sea-level data involves the argument 2πfq t n = 2 π n N . Reformulation of removal of the signal associated with the tide. It is equation (1) as: REZA & MANSOURI et al.: EFFECT OF SUPER CYCLONE “GONU” ON SEA LEVEL VARIATION 471 x( tn )= x +∑ [ Aq cos(2 π fq t n ) + B q sin(2 π fq t n )] spaced. To simplify the summations (4), trigonometric identities are often used. This requires +xr() t n …(2) that the data be evenly spaced and that the matrix Yield a representation in terms of the unknown terms be calculated over segments of the time series coefficients Aq ,B q where with no gaps. The resultant matrix D is symmetric so 1 2 2 2 that only the upper triangle consisting of 2M+3M+1 C(AB)q= q + q (Frequency component amplitude) element needs to be stored during the computations. −1 Qq = tan ( Bq A q ) (Frequency component phase lag) We then seek solutions z through the matrix equation z= D−1 y For q=0,….., M. The mean value, x , should be −1 subtracted from the record prior to computation of the Where D is the inverse of the matrix Fourier coefficients. The objective of the Least- N C1 C 2 ...CM S 1 S 2 ...SM squares analysis is to minimize the variance, e2 , of C1 CC 11 CC 12 ...CC 11M CS 11 CS 12 ...CS1M C2 CC 21 CC 22 ...CC2M CS21 CS 22 ...CS2M the residual time series xr ( t n ) in equation 2, where ... ... ... ... ... ... ... ... ... D = Ck CC M1 CC M 2 ...CCMM CS M1 CS M 2 ...CSMM N N 2 2 2 S1 SC 11 SC 12 ... SC1M SS11 SS 12 ... SS1M e=∑ xr( t n ) = ∑ { x ( tn ) − [ x + ∑ M ( tn )]} …(3) S2 SC 21 SC 22 ...SC2M SS21 SS22 ... SS2M n=1 n=1 q=1 ... ... ... ... ... ... ... ... ... SMM SC11 SCM 2 ...SCMM SC M1 SS M 2 ...SSMM And where for convenience we define M as ∑ …(7) M M and y and z are column vectors ∑M() tn = ∑ [ Aq cos(2π fq t n ) + B q sin(2 π fq t n )] q q=1 ..(4) yc0 A0 yc1 A1 = [Aq cos(2π qn N ) + Bq sin(2 π qn N )] ∑ yc2 A2 ... ... Taking the partial derivatives (3) with respect to y= ycM , z= AM ys1 B1 the unknown coefficients Aq and Bq , and setting the ys2 B2 results to zero, yields 2M+1 simultaneous equations ... ... for the M+1 constituents ysM BM ∂e2 The elements of z yield the required coefficients = 0 ∂Aq K=0,…,M Aq ,B q for each specified harmonic constituent. To N k 2πqn find these solutions, we substitute the elements of D =2∑ xn − x + ∑ M − Cos ()N n=1 q= 1 for time tn = n ∆ t N N …(5) Ck = C os 2πkn, Sk = Sin 2 π kn ∑ ()N ∑ ()N n =1 n =1 ∂e2 …(8) = 0 N ∂Bq 2πkn 2πjn K=0,…, M CCkj= CC jk = ∑ Cos Cos N k ( NN) ( ) 2πqn n=1 = 2∑x() tn − x + ∑ M − Sin ()N n=1 q= 1 …(9) N 2πkn 2πjn …(6) SSkj= SS jk = ∑ Sin Sin …(10) ()NN( ) Derivation of the coefficients in (4) requires n=1 solution of a matrix equation of form Dz=y in which N 2πkn 2πjn is an (M+1)*(M+1) matrix involving sine and cosine CSkj= SC jk = ∑ Cos Sin …(11) ( NN) ( ) summation terms, y is a vector (column matrix) n=1 incorporating summations over the data series and z is where kn = k ∆tn ))(( , and the elements of y a column matrix containing the required coefficients N N∆t are given by Aq and Bq Gaps in the data are still permitted at this N stage since the observation times, tn , used in the yck= x n Cos 2πkn ∑ ( N ) …(12) least-squares method are not required to be evenly n=1 472 INDIAN J. MAR. SCI., VOL 42, NO. 4, AUGUST 2013 N provided dwellings stocked with supplies and ysk = xn Sin 2πkn ∑ ( N ) …(13) medicine. In Iran, 40000 were evacuated from coastal n=1 regions. The Iranian Red Crescent and National In our analysis, we used 62 constituents, i.e. Disaster Task Force were fully mobilized. Both M = 62. The analysis of data from a location consisted countries had the situation well under control. of using a year-long time-series of hourly values of During the onset phase of the monsoon, a low sea level as measured by a tide gauge at that location. pressure area developed over East-Central Arabian For discrimination of astronomical tide from recorded Sea on 31st May 2007. It concentrated into a sea level data we used low-pass filter. The Depression over the same area and lay centred at 1800 astronomical tide was then subtracted from the UTC of 1st June near Lat. 15.00 N and Long. 68.00 E. recorded data to determine the residue, i.e. the Initially, the system moved in a westerly direction and de-tided sea level. intensified into a Deep Depression at 0300 UTC of For performing the trace of cyclone we used air 2nd June and lay centred near lat 15.00 N and long. pressure data in the case study region. We interpolate 67.50 E. it further intensified into a cyclonic storm the air pressure data and then show the movement of “GONU” and lay centred at 0900 UTC near Lat. the cyclone. For interpolation of these data we used 15.00 N and Long. 67.00 E. Thereafter, it moved in Inverse Distance Weighted (IDW). IDW is a method a north-northwesterly direction, intensified into a of interpolation that estimates cell values by Severe Cyclonic Storm; lay centred at 0000 UTC of averaging the values of sample data points in the 3rd June near Lat.15.50 N and Long. 66.50 E. Moving neighborhood of each processing cell. The closer a in a northnorthwesterly direction, it intensified into a point is to the center of the cell being estimated, the Very Severe Cyclonic Storm and lay centred at more influence, or weight, it has in the averaging 1800 UTC of 3rd June, near Lat.
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