Wave forecasting system for operational use and its validation at coastal Puducherry, east coast of India

Ocean Engineering 80 (2014) 64–72

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Wave forecasting system for operational use and its validation at coastal Puducherry, east coast of India K.G. Sandhya a, T.M. Balakrishnan Nair a, Prasad K. Bhaskaran b,n, L. Sabique a, N. Arun a, K. Jeykumar a a b

Information Services and Ocean Sciences Group, Indian National Centre for Ocean Information Services, Ministry of Earth Sciences, Hyderabad, India Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur, Kharagpur-721 302, West Bengal, India

art ic l e i nf o

a b s t r a c t

Article history: Received 23 July 2013 Accepted 18 January 2014 Available online 16 February 2014

An incredible demand for coastal sea-state forecast in recent years has led to development and implementation of wave forecasting system in operational centers, having wide practical applications relevant to marine industry. The wave forecasting system takes advantage of parametric techniques, by nesting global ocean wave models to coastal and near-shore high-resolution wave models. The Indian National Centre for Ocean Information Services (INCOIS) at Hyderabad has a mandate for operational marine weather forecast services that envisages integration and coupling state-of-the-art weather models for operational oceanographic needs. In the present study, two state-of-art wave models viz; WAVEWATCH III (WW3) and Simulating WAves Nearshore (SWAN) are nested and forced with French Research Institute for Exploitation of the Sea/Laboratory of Oceanography From Space (IFREMER/CERSAT) blended surface winds. The objective is to investigate wave evolution at a coastal location off Puducherry in the east coast of India. To evaluate model performance, a detailed validation study is performed by comparing model-simulated wave parameters and wave spectra with corresponding in-situ wave rider buoy observations for four prominent seasons viz; northeast monsoon, southwest monsoon, pre- and post-monsoon. The study signifies applicability of nested wave model for operational use during normal weather condition at coastal Puducherry. & 2014 Elsevier Ltd. All rights reserved.

Keywords: WAVEWATCH III SWAN Nesting Wave rider buoy Validation

1. Introduction Weather over the Bay of Bengal can be classified into three broad categories viz; fair weather, southwest and northeast monsoon. During fair weather season, the sea-state is usually calm and distant swells occasionally overridden by locally generated wind waves dominate over coastal waters, whereas the monsoon seasons experience occasional extreme events such as cyclones accorded with enhanced wave activity in open and nearshore waters. The wave spectra during such extreme events are typically double or multi-peaked that comprises of both wind-sea and swell components co-existing together. In a swell dominated system, waves normally lose energy from high frequencies when the local wind-waves decay, wherein the spectral peak at low frequencies represents swell field. In a wind-dominated system having multi-peaked spectra, the total energy in the system is a combination of both wind-seas and swells co-existing together. In a typical swell dominated environment, the secondary peak

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Corresponding author. Tel.: +91 3222 283772; fax: +91 3222 255303. E-mail addresses: [email protected], [email protected] (P.K. Bhaskaran). 0029-8018/$ - see front matter & 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.oceaneng.2014.01.009

usually represents the energy from local wind-waves. The stateof-art third generation wave models such as WW3 (Tolman, 1997) and SWAN (Booij et al., 1999) represents better transition between wind-seas and swells compared to the former generation wave models (Durrant and Greenslade, 2011). The third generation wave models are based on sophisticated physics pertaining to wave generation, propagation and decay mechanisms. Hence, their applicability in the operational oceanography has gained popularity in the framework of practical marine related applications. In the North Indian Ocean, west coast of India experiences rough seas during the southwest monsoon season, which is relatively calm during rest of the year. On the contrary, the east coast experiences higher wave activity both during the southwest and northeast monsoon periods. In addition, the frequency of extreme events such as tropical cyclones is relatively higher in the Bay of Bengal compared to the Arabian Sea (Dube et al., 1997). With the increasing demand for modernization, initiation of new ports and harbors, and shipping activities along the Indian coast, there is equally an increasing demand to forecast ocean waves in open sea and coastal areas to aid marine applications. Routine ocean state forecast is very essential for optimum ship routing, and for loading/unloading operations in ports and harbors. Many users and scientific community depend on now-cast and ocean state

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forecast for marine related operations (Balakrishnan Nair et al., 2013). With the recent advances in computer and information technology, the need and importance of operational oceanography as a subject is well recognized and efforts are made to provide reliable forecasts to the user community. In addition to wave modeling efforts, the observational platforms from remote sensing systems such as altimeter, scatterometer and Synthetic Aperture Radar (SAR) also provide information about the ocean surface in basin scales. The in-situ observational platforms such as wave rider buoys and Automatic Weather Stations (AWS) provide location specific observations on real-time basis. These observational platforms are very essential for model initialization and validation studies; however, they require aid from a numerical model for forecasting applications. The period during the mid-80 was quite remarkable, in context to research and development on model physics and numerics relevant to the evolution of surface gravity waves. The knowledge acquired from these past works led to the development of present state-of-the-art third generation wave models, such as WAve Modeling (WAM, WAMDI Group, 1988), WW3 and SWAN. Each of these wave models had undergone stringent sensitivity checks by research groups worldwide, thereby constantly upgrading these models with time. This clearly reveals the fact that model deficiency still exists, and there is ample scope for further improvement. The published work of Cavaleri (2009) well articulates on the limitation of wave model physics dealing with physical processes using the spectral approach. The capability of wave models and their limitations to reproduce conditions during, and at peak of severe and extreme storms is an area to be fully explored (Cavaleri, 2009). It can be in terms of improved model physics; incorporation of fine-tuned physical parameterizations highly localized in nature; incorporation of short- and long-term environmental variables into wave models, improvement in numerical schemes etc. All these factors have a direct bearing on wave propagation and its evolution. Hence, it is a challenge to wave modelers to develop a robust modeling system that performs best qualitatively. With the wealth of knowledge in this subject area, operational oceanography has gained global importance. In a historical perspective, an inter-comparison exercise of various second-generation wave models is available in the report of Sea Wave Model Inter-comparison Project (SWAMP, SWAMP, 1985). Bidlot et al. (2002), Bidlot and Holt (2006) report on the performance study based on inter-comparison of operational wave forecasting system using monthly wave model data from different forecasting centers. In addition, one can find a very comprehensive recent review on the state-of-the-art developments in wave modeling in the published work of Cavaleri et al. (2007). In the Indian context, some earlier works on routine wave prediction using third generation wave models were reported by Prasad Kumar et al. (2000, 2003, 2004), Prasad Kumar and Stone (2007), Vethamony et al. (2006) and Remya et al. (2012). The use of wave models for specific practical applications using short-time wave data are described in the studies of Stone et al. (2005), Padhy et al. (2008), Prasad Kumar (2010), Chitra and Bhaskaran. (2013), Nayak et al. (2012) and Nayak and Prasad Kumar (2013a). Recent studies on wave-current-tide-surge interactions are reported by Rao et al. (2009), Chitra et al. (2010), Chitra and Bhaskaran (2012) and Bhaskaran et al. (2013). Relevant studies on improving physical parameterization in wave models for Indian seas are described in the work of Rajesh Kumar et al. (2008, 2009). A long-term validation of model computed wave parameters with observations for the Indian Ocean region is however missing. In an operational scenario, to verify the efficacy of model computed parameters, systematic validation studies with long term observational data is an essential pre-requisite. In this context, INCOIS plays a key role by providing ocean data, information and advisory

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services to the society, industry, government and scientific community through sustained ocean observational programmes, and modeling studies. At present, there is no Operational agency in India or abroad providing coastal ocean state forecast at Puducherry, and INCOIS has taken up initiative in setting up a modeling system for this purpose. The Israel Marine Data Center (ISRAMAR) is another operational agency that provides forecast for Mediterranean Sea and its sub-basins in a nested fashion. The model setup at INCOIS uses the multi-grid WW3 grid resolution of 0.051
0.051 that is far superior to forecasts from other Operational Centers. The framework of Operational modeling system at INCOIS provides forecast with optimum computational resources and time. The novelty of this work is the integration of two modeling systems viz; WW3 and SWAN to provide a very high-resolution forecast for Puducherry region, unlike the forecasts provided by other Operational agencies. This model configuration gives reasonably good results with low RMS error and high correlation coefficient. Specifically, the RMS error during the post- and north-east monsoon seasons is negligible, while the correlation coefficient is above 0.87. The statistics for the other seasons is also encouraging. INCOIS has a strong community user base at Puducherry, including the Pondicherry Multi-purpose Social Service Society (PMSSS). The forecast for this area being disseminated to these NGOs, who in turn performs secondary dissemination through media such as FM Radio and Village Information Centers. In this context, the infusion of this modeling system into the existing services will be advantageous to the user community. With the wealth of ocean data already available at INCOIS, the present study deals with a wave forecasting system for coastal Puducherry, its subsequent validation with in-situ wave-rider buoy data highlighting its applicability for operational use.

2. Study area The area of interest in the present study is the coastal location off Puducherry situated in the Coromandel Coast, Bay of Bengal and shown in Fig. 1. The region encompasses the geographical coordinates between 11145' and 12103' latitudes and 79137' and 79153' longitudes with an area of 293 km2. The northeast monsoon plays a major role in the local weather of coastal Puducherry, whereas the southwest monsoon plays a minor role in the same region. The bottom sediment strata off Puducherry coast is mainly composed of sand with mixture of silt and clay. The sand fraction ranges between 64% and 87%, followed by silt (10–29%) and clay (3–18%) as reported by Satheeshkumar and Khan (2009). The study region is generally flat with an average elevation of 15 m above the mean sea level. In a physiographic sense, the region comprises of coastal plains, alluvial plains and uplands. In addition, the coastal plain has a gentle slope with chain of sand dunes in major areas along the coast. The tidal range at Puducherry is low and the expected maximum range during spring tide is around 0.8 m.

3. Composition of the modeling system The state-of-the-art third generation wave-prediction models evolved initially with the development of WAM model followed by WW3, and coastal wave model SWAN. However, the modeling system architecture employed here is comprised of WW3 and SWAN. These two wave models are nested together to obtain the wave conditions off Puducherry for operational use. It solves the spectral action-density balance equation for wave number-direction space comprising non-linear physics to represent the evolution of directional wave spectra, to derive the wave field parameters. Both these models are used in many operational centers worldwide for routine

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Fig. 1. Study region with location of Puducherry and the nested domain of SWAN.

sea-state forecasts. The National Oceanic and Atmospheric Administration/National Centres for Environmental Prediction (NOAA/NCEP), USA developed the WW3 model, whereas SWAN model was developed at TuDelft University, Netherlands. A detailed description of WW3 model containing physical parameterization of model physics is available in the work of Tolman (1989, 1991), Tolman and Chalikov (1996). Recent developments and model up-gradation of numerical engine in 2007 enables WW3 to run in mosaic of grids providing a two-way exchange of information between overlapping grids (Tolman, 2008). It takes the advantage of full two-way interaction procedure for an arbitrary number of grids with arbitrary range of resolution. Under this model setup, each grid domain is considered as a separate wave model, and two-way interaction between all grids is a continuous process transforming the mosaic of individual grids into a single model. The similar parameterization of WW3 that is operational at NCEP (Chawla et al., 2007) is used in the present study. The nested domain for SWAN model encompasses the coastal area off Puducherry. The two-dimensional time varying energy density spectra at the boundaries of SWAN grid are obtained from the global WW3 and given to SWAN as boundary condition. This facilitates the best possible information exchange to the neighboring grids in a study domain suitable for operational needs.

on impact of wind forcing on WW3. Their results suggest that wave model output is critically sensitive to the choice of wind field, the higher resolution wind fields lead to improved statistics for wave model outputs. Therefore, to obtain realistic wave forecasts in the tropical Indian Ocean, it is necessary to have study area extended until Southern Ocean, the generation area for low frequency swells. These low frequency swells travel thousands of kilometer crossing the hemisphere, and finally reach various coastal destinations of Indian sub-continent. Sabique et al. (2012) studied the contribution of Southern Indian Ocean swells to the wave climate over the North Indian Ocean, and reported that southern swells is an important factor that determine waves over the Northern Indian Ocean with an increased effect during southwest monsoon season. The recent study by Nayak et al. (2013b) postulates that swells generated from Southern Ocean essentially modulate the local wind generated waves in the Bay of Bengal. In the present study, IFREMER/CERSAT blended winds (Bentamy et al., 2006, 2009) is used as input to force both WW3 and SWAN wave models. The blended winds are obtained from retrievals of remotely sensed QuikScat (Ebuchi et al., 2002) merged with the operational ECMWF analyzed winds for the global oceans. The framework of ECMWF uses the Integrated Forecasting System (IFS) using a four-dimensional (4D) variational assimilation scheme that assimilates several meteorological observations with ERS-1 and ERS-2 scatterometer winds (Rabier et al., 2000; Isaksen and Janssen, 2004). The IFREMER/CERSAT wind used for the present study is a quality-checked product, and its skill assessment with buoy winds is reported in the study of Bentamy et al. (2007). The blended wind data with remote wind observations, within 3 h and 0.251 from the analysis estimates, compare well over the global basin as well as over the sub-basins. The correlation coefficients exceed 0.95 while the rms difference values are less than 0.30 m s  1. Using measurements from moored buoys, the highresolution wind fields are found to have similar accuracy as satellite wind retrievals. Blended wind estimates exhibit better comparisons with buoy moored in open sea than near shore (Bentamy et al., 2006). This wind field is a regular gridded data having spatial resolution of 0.251
0.251 and available at temporal scale of every 6‐hour interval. The global wave model WW3 used in this study has six mosaic grids with four different grid resolutions that include the global, multiple-regional and coastal grids shown in Fig. 2. As seen from this figure, the grids have different horizontal resolutions viz; global grids (11
11), Indian Ocean basin (0.51
0.51), Bay of Bengal and Arabian

4. Data and methodology Wave models are very sensitive to input winds, hence highresolution and high quality winds are required to simulate realistically the wave parameters. Janssen et al. (1998) reports on the sensitivity of wave models to wind quality and advocate that error in input wind forcing can affect forecasted wave heights for a longer duration. Feng et al. (2006) performed an assessment study

Fig. 2. Multi-scale mosaic grids with different resolutions for WW3.

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Sea (0.251
0.251), and coastal grids (0.051
0.051). The timesplitting scheme in WW3 with four different time steps solves the physical processes to save computational time. The global time step used in WW3 is the maximum time step for source term integration, and used by the input winds to propagate the solution to neighboring grids. The propagation along spatial dimension is solved using a third-order accurate scheme, wherein the time step can be smaller or equal to the global time step. The intra-spectral propagation also uses the third order accurate scheme. The numerical integration of source terms uses a modified version of semi-implicit scheme. Global time step for this study is set to 3600 s, wherein the entire solution propagates to the neighboring grids. The minimum time step for source term integration is 30 s, and fixed based on the CFL criterion. To achieve computational efficiency, the maximum propagation time step is set to 900 s for longest wave components in the spectrum, as well for the refraction time step. The SWAN is a state-of-the-art wave model specifically developed to simulate realistic wave estimates for coastal and shallow waters. Fig. 1 shows the nested boundary of SWAN wave model with WW3. The SWAN uses an implicit integration scheme that makes it more robust and efficient for simulations in coastal waters. The governing equation is the spectral action balance equation balanced with different source and sink mechanisms. The SWAN setup at Puducherry in the present work is a small region having spatial dimensions of 1.1331
1.1331 and grid resolution of 250 m
250 m. The number of bins in the frequency and directional space are 33 and 36, respectively, which essentially takes care of frequency space in the surface gravity wave spectrum. The frequency used in SWAN ranges between 0.04 Hz to 0.58 Hz with  4 as the power of high frequency tail. The source term used for wind input is the formulation proposed by Komen et al. (1984), whereas the default settings apply to other source and sink terms. Model simulations were performed in a nonstationary mode with computational time step of 30 min. The finest grid of WW3 with a spatial resolution of 5 km was nested to SWAN enabling the free propagation of low frequency swells into the study region, as well as the region of interest. The measured waves off Puducherry (11.932671N, 79.854921E) are at 15 m water depth, using the moored Datawell Directional Waverider buoy DWR-MkIII (Barstow and Kollstad, 1991). This buoy measures heave motion in the range of 20 to þ20 m and periods between 1.6 s and 30 s, with a resolution of 1 cm in heave. The cross sensitivity of the heave is less than 3%. The wave direction measurement using DWR-MkIII is in the range of 0–3601 with a directional resolution of 1.51 and accuracy of 0.51 with reference to the magnetic north. The buoy data records were taken at a frequency of 1.28 Hz for 17 min every half an hour. Data for the period from 1 June 2007 until 31 July 2009 is used in the present study. The measured time series data is quality checked

Fig. 3. Validation of Significant wave height (in m) at coastal Puducherry for the period 2007–2009.

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for standard errors such as spikes, steepness, and constant signals (Haver, 1980). The wave data used in this study is similar to that reported in Glejin et al. (2013), Sanil Kumar et al. (2013). The buoy wave spectra were obtained using Fast Fourier Transform (FFT), wherein an FFT of eight series, each consisting of 256 measured vertical elevations, from buoy is added to obtain the spectra with high frequency cut off at 0.58 Hz. Parameters such as significant wave height and peak wave period are derived from the wave spectrum. The period corresponding to the maximum spectral energy is estimated as the peak wave period from the wave spectrum.

5. Results and discussion For operational wave-forecast, an important element is the verification of model computed wave parameters with observational data. This is quite vital to arrive at a deterministic level of confidence to ensure that model performs well for operational applications. In this process, the analyzed and forecasted parameters such as significant wave height and period necessarily require routine verification with in-situ buoy observations. In the present study, model simulations were carried out using the high performance computing (HPC) system available at INCOIS utilizing 256 processors. Further, the validation with in-situ wave-rider buoy off Puducherry was performed for the period from June 2007 until July 2009. Fig. 3 shows the comparison of significant wave heights between model and buoy observations. It is evident from both model and observation that mean significant wave height off Puducherry is below 1 m most of the time, and the wave activity increases during the northeast monsoon months (November and December). The overall trend in wave heights shows a reasonable match with buoy observations. The monthly variability in significant wave heights for all three years during the pre- and postmonsoon periods from both model and buoy records show a very good correlation. The significant wave heights during the southwest monsoon period exceed 1 m and are below 1.5 m. According to the best track archive from Joint Typhoon Warning Center (JTWC), two tropical storms occurred in the North Indian Ocean during the winter month of November 2008. The high waves in the order of about 4.5 m seen in buoy observation result from one of these extreme events viz; cyclone Nisha. During this event, the maximum significant wave height computed by the model was 2.8 m, when compared to the buoy observation of 4.5 m. This inconsistency is due to the poor data quality of input wind field used to force wave models during extreme events. The model computed wave heights are under-predicted during December 2008 (Fig. 3). During October 2007, a depression that formed southeast off Chennai resulted in high waves off Puducherry, especially due to its closer proximity. Similarly, during the period of mid-November 2007 the extreme waves recorded off Puducherry resulted from a Category-4 very severe cyclonic storm ‘Sidr’ that struck the head Bay of Bengal region. The period of ‘Sidr’ cyclone was from 11 to 16 November 2007 with recorded minimum central pressure of 944 mb. The ‘Sidr’ track was in the central Bay of Bengal, and very far away from the Puducherry location. Therefore, high waves off Puducherry resulted from intense swells that reached the coast during the intensification process of ‘Sidr’ cyclone. The model computed as well as the measured significant wave height off Puducherry was about 1.7 m during this period. The ‘Sidr’ cyclone had no significant effect at Puducherry, as seen from Fig. 3. During the high-wave activity period of mid-December, the model computed wave heights are  3.5 m whereas the buoy measured wave heights of  3.0 m. This is not due to any extreme event, but due to a local weather disturbance. Fig. 5 shows very low peak periods (Tp) during this period. Fig. 4 depicts a one-week representative comparison of significant wave heights

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Fig. 4. Comparison of significant wave height (in m) at coastal Puducherry during (a) January, (b) April, (c) July, and (d) October for a period of one week between nested wave model and buoy observation.

Fig. 5. Validation of peak wave period (in s) at coastal Puducherry for the period 2007–2009.

with buoy observation during the months of January, April, July and October. Fig. 4 is a derivative of Fig. 3, however shown in a magnified view. The comparative study signifies that the nested model performs reasonably well to simulate realistically the wave conditions off Puducherry during normal conditions. The comparison of peak period (in s) between model and buoy observations are shown in Fig. 5. It clearly reveals that the wave field in coastal Puducherry is dominated by low frequency waves during all seasons, except during the northeast monsoon season. A very recent study by Nayak et al. (2013b) pointed out that local wind-waves off Kalpakkam is modulated by the influence of distant swells that arrive from the Southern Ocean. The remote forcing effects from distant swells generated in the Southern Ocean travel over the hemisphere, and by virtue of non-linear

wave–wave interaction modify the local sea-state (Nayak et al., 2013b). In addition, the remote forcing effects from Southern Ocean swells are quite high in the Bay of Bengal compared to the Arabian Sea. The location of Kalpakkam in their study (Nayak et al., 2013b) is about 200 km north of Puducherry. The swell waves arriving in the North Indian Ocean has a typical time period of 12 s and higher. Hence, it confirms from waverider buoy observation that coastal Puducherry is under strong influence by distant swell activity. The swell waves are active for about 6 months in a year from June to November, as evidenced from the peak wave periods during 2007– 2009 (Fig. 5). Peak wave periods in excess of 12 s is very common during the months from June to November. The transition period from January to March has a relatively less swell activity when compared to those of other months. The higher swell activity off Puducherry as seen in Fig. 5 synchronizes with strong synoptic scale disturbances that develop in the Southern Ocean. The comparative study of model computed peak period with buoy observations show a good match for peak periods below 10 s. The model is unable to capture the higher peak periods above 10 s. This requires significant tuning of a multi-scale WW3 model nested to the intermediate grid of SWAN specifically covering the Bay of Bengal region to represent effectively the swell wave field arriving from Southern Ocean. This is not within the scope of the present study, and will be treated as a separate study. In general, the model is able to reproduce well the peak wave periods less than 10 s. Fig. 6 shows the overall seasonal scale performance of model computed significant wave heights with buoy observation represented as a scatter diagram. Various statistical measures such as correlation coefficient (R), bias (mean error), root mean square error (RMSE), scatter index (SI), and standard deviation (SD) between buoy observations and model computed wave heights

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Fig. 6. Scatter plot of significant wave height (in m) between model and buoy observations during (a) pre-monsoon, (b) Southwest monsoon, (c) post-monsoon and (d) Northeast monsoon using ECMWF blended winds. Validation omitting tropical cyclone cases for (e) pre-monsoon, and (f) post-monsoon are also given.

Table 1 Validation statistics between model and buoy observations for different seasons Season

Bias (m)

RMSE (m)

SI (%)

R

Buoy average (m)

Model average (m)

SD of buoy (m)

SD of model (m)

No. of points

Pre-monsoon SWM Post-monsoon NEM Pre-excluded Post-excluded

0.15 0.03  0.05  0.003 0.1 0.009

0.18 0.17 0.24 0.15 0.18 0.15

35.25 22.5 29.08 18.83 27.12 18.17

0.79 0.68 0.87 0.93 0.7 0.89

0.52 0.76 0.83 0.79 0.68 0.85

0.68 0.78 0.78 0.79 0.78 0.82

0.14 0.2 0.24 0.29 0.21 0.35

0.13 0.12 0.24 0.26 0.17 0.32

903 1971 727 1785 791 602

are examined to evaluate the model performance for operational use (shown in Table 1). The linear correlation coefficient measures the strength and direction of relationship between two variables, also referred as Pearson product moment correlation coefficient. The relationship can be either positive or negative depending on the directionality of the estimate. The analysis from Fig. 6 shows a strong positive correlation between buoy observations and model results.

A strong positive correlation (R40.8) is noticed (Fig. 6c and d, Table 1) especially during the winter (post- and northeast monsoon) months. The correlation coefficient for the other two seasons (preand southwest monsoon) is close to 0.8, and therefore reasonably good. The bias is a statistical quantity that signifies the average difference between model output and actual measured data. Positive bias of þ0.15 and þ0.03 noticed for the pre-monsoon and southwest

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Fig. 7. Comparison of one-dimensional wave energy spectra (m2/Hz) between model and buoy observation during (a) 02 January 2008 (21Z) (b) 03 January 2008 (03Z), (c) 01 April 2009 (18Z), (d) 03 April 2009 (12Z), (e) 20 July 2007 (03Z), (f) 25 July 2007 (12Z), (g) 01 October 2007 (15Z), and (h) 13 October 2007 (00Z).

monsoon periods (Fig. 6a and b, Table 1) respectively signify that model computed significant wave heights are slightly higher compared to the buoy observations. The wave heights during postmonsoon and northeast monsoon periods (Fig. 6c and d) have negative bias of  0.05 and  0.003 respectively (Table 1) . The negative bias values especially during winter months are negligible,

and in addition have a higher value for R. The RMSE is a generalized form of standard deviation. The information about the spread of data and their inter-relationships deduced from RMSE signify the overall residual variation. Hence, the RMSE is an absolute measure of the fit between model data and buoy measurement. The lower value of RMSE indicates a better fit of data between model and observations.

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Fig. 8. Comparison of one-dimensional wave energy spectra (m2/Hz) between model and buoy observation during 02 January 2008 (21Z) (a) with nesting in WW3, and (b) without nesting.

The comparison statistics during pre-, post-, northeast and southwest monsoon periods (Table 1) between model and buoy observation show that the RMSE varied from 0.15 m to 0.24 m, that signify a good fit. The scatter index (SI) expressed in percentage is another measure to express the goodness of fit between model and measurements. Lower values of SI indicate a better fit, and as seen, the absolute value of SI varied from 0.18 to 0.35 indicating a good fit between model and measured data. As discussed earlier, there are some extreme events in the pre- and post-monsoon periods. The statistics was calculated separately for these seasons excluding the periods of extreme events (Fig. 6e and f, Table 1). A reduction in errors is noticed when extreme events are excluded from overall analysis. It also signifies the inaccuracy of input wind forcing to model during extreme events. The overall evaluation based on statistical measures during normal conditions shows that model performs well during all seasons, and the performance rated as best during the northeast monsoon season. During the southwest monsoon period, wind system is from land to ocean resulting in smaller fetch at the buoy location, whereas the fetch is unlimited during the northeast monsoon season. The accuracy of model result is therefore fetch dependent and hence better wave conditions simulated for large fetch. The directional wave spectra describe the complex and chaotic phenomena of wind-generated waves in terms of the distribution of wave energy over different frequencies and directions. It provides a combined description of the sea-state containing energy levels in the surface gravity wave regime. Spectral representation of sea-state is useful as it provides a clear picture of the energy levels from windwaves and swells. In this context, the buoy spectra will be very useful for validation study against model derived wave spectra. The wave spectra from the model are compared with those from the buoy observation in Fig. 7 (a–h). The figures represent the comparison of representative spectra for the months of January (northeast monsoon), April (pre-monsoon), July (southwest monsoon) and October (postmonsoon). It is evident from the wave spectra that wave field at coastal Puducherry comprises both wind-seas and swells. It clearly show instances of bi-modal (Fig. 7c) and multi-modal (Fig. 7f) peaks associated with distant swells reaching the coastal waters off Puducherry. In general, the model computed wave spectra closely resemble the buoy wave spectra, and in general follow the energy level trend. The nested model has deficiency to reproduce the very low frequency waves 410 s (Fig. 7f–h) which points to the inaccuracies in the boundary conditions prescribed from WW3. It is noticed that the peak frequencies are highest during the NEM period (Fig. 7a and b), where wind-sea domination is clearly noticed in wave spectra. In addition, the wave spectrum during this period is broad and mostly single peaked. The results are quite satisfactory during NEM when wind-sea dominates along with consistent long fetch. The boundary forcing of

two-dimensional wave spectra from WW3 model to the nested SWAN model has a significant impact in simulating the wave field realistically, with the exception of very low frequency waves. To evaluate the importance of nesting on model computed wave parameters, a comparison of wave spectra between model and buoy observation was done with and without nesting for the month of January. The result is shown in Fig. 8a and b. In Fig. 8a, SWAN uses the boundary wave spectra from WW3, whereas in Fig. 8b the result of SWAN run in a standalone mode is shown. Based on the validation of wave parameters and wave spectra carried out for the period from 2007 to 2009, it can be summarized that the model computed wave parameters and spectra match reasonably well with observations, and hence its applicability for operational use is justified.

6. Summary and conclusions The study reports the application of a nested wave model to simulate the wave parameters off coastal Puducherry located in the east coast of India. Remote forcing effects from distant swells that originate in the Southern Ocean have a substantial role, and direct bearing to modulate the local wind-waves off Puducherry. Therefore, the resulting wave field in the study domain is a complex mixture of both wind-seas and swells. The wave spectra derived from the wave-rider buoy off Puducherry clearly show this evidence. The global WW3 model runs on a mosaic grid that provides time varying two-dimensional energy spectra at the boundaries of SWAN model. The blended wind input from IFREMER/CERSAT is used to force both WW3 and SWAN models. Model simulations are performed for the period from 2007 to 2009, and essential wave parameters along with wave spectra are derived at a point location off Puducherry where in-situ wave rider buoy data was available for validation. The study investigates overall performance of nested wave model for operational use at Puducherry by grouping the analysis into four prominent seasons. Statistical measures such as R, bias, RMSE and SI were calculated to assess the skill level of model computation against the buoy observations. The inter-comparison exercise with the buoy observations shows that negative biases of  0.05 m and 0.003 m are seen along with high correlation coefficient for the winter months. In addition, the RMSE varied from 0.15 m to 0.24 m for the entire analysis period that signifies a good fit. The performance of the models during the northeast monsoon is the best when compared to other seasons due to long consistent fetch as well as dominance of wind-sea. The results signify that model spectra closely follow the buoy spectra, however with exception to reproduce well the very low frequency waves. Based on this detailed validation study,

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it can be safely summarized that the overall performance of nested wave model is quite satisfactory for operational use. Acknowledgements The encouragement and facilities provided by the Director, INCOIS are thankfully acknowledged. We sincerely thank Dr. Shailesh Nayak, Secretary, MoES for the constant encouragement. We also thank Dr. Sanilkumar and Mr. Jai Singh from NIO, Goa for the help in buoy data collection. One of the authors take this opportunity to thank Dr. Girishkumar M S and Dr. Remya P G of INCOIS for their valuable support. Thanks are also due to Dr. Marcel Zijlema of Delft University of Technology, Netherlands for the valuable technical support and guidance extended during the initial phase of SWAN installation in the HPC at INCOIS. Finally, the financial support given by the Earth System Science Organisation, Ministry of Earth Sciences, Government of India is gratefully acknowledged. This is INCOIS contribution 175. References Balakrishnan Nair, T.M., Sirisha, P., Sandhya, K.G., Srinivas, K., Sanil Kumar, V., Sabique, L., Nherakkol, Arun, Prasad, Krishna, Rakhi Kumari, B., Jeyakumar, C., Kaviyazhahu, K., Ramesh Kumar, M., Harikumar, R., Shenoi, S.S.C., Shailesh, Nayak, 2013. Performance of the Ocean State Forecast system at Indian National Centre for Ocean Information Services. Curr. Sci. 105 (2), 175–181. Barstow, S.F., Kollstad, T., 1991. Field trials of the directional waverider. In: Proceedings of the First International Offshore and Polar Engineering Conference, 19991, III, pp. 55–63. Bentamy, A., Ayina, H.L., Queffeulou, P., Croize-Fillon, D., 2006. Improved near real time surface wind resolution over the Mediterranean Sea. Ocean Sci. Discuss 3, 435–470. Bentamy, A., Ayina, H.L., Queffeulou, P., Croize-Fillon, D., Kerbaol, V., 2007. Improved near real time surface wind resolution over the Mediterranean Sea. Ocean Sci. Discuss 3, 259–271. Bentamy, A., Croize-Fillon, D., Queffeulou, P., Liu, C., Roquet, H., 2009. Evaluation of high resolution surface wind products at global and regional scales. J. Oper. Oceanogr 2 (2), 15–27. Bhaskaran, P.K., Nayak, S., Subba Reddy, B., Murty, P.L.N., Sen, D., 2013. Performance and validation of a coupled parallel ADCIRC-SWAN model for THANE cyclone in the Bay of Bengal. Environ. Fluid Mech., 601–623, http://dx.doi.org/10.1007/ s10652-013-9284-5 Bidlot, J.R., Holmes, D.J., Wittmann, P.A., Lalbeharry, R., Chen, H.S., 2002. Intercomparison of the performance of operational ocean wave forecasting systems with buoy data. Weather Forecasting 17, 287–310. Bidlot, J.R., Holt, M.W., 2006. Verification of Operational Global and Regional Wave Forecasting Systems Against Measurements from Moored Buoys. JCOMM Technical Report. 30, WMO/TDNo, 1333 Booij, N., Ris, R.C., Holthuijsen, L.H., 1999. A third generation wave model for coastal regions, Part 1: Model description and validation. J. Geophys. Res 104 (C4), 7649–7666. Cavaleri, et al., 2007. Wave modeling ‐ The state of the art. Prog. Oceanogr., 603–674, http://dx.doi.org/10.1016/j.pocean.2007.05.005 Cavaleri, L., 2009. Wave Modeling‐Missing the Peaks. J. Phys. Oceanogr 39, 2757–2778. Chawla, A., Cao, D., Gerald, V., Spindler, T., Tolman, H., 2007. Operational Implementation of a Multi-grid Wave Forecasting System. 10th International Workshop on Wave Hindcasting and Forecasting, Oahu, Hawaii, 12 Chitra, A., Prasad Kumar, B., Jain, I., Bhar, A., Nayarana, A.C., 2010. Bottom boundary layer characteristics in the Hooghly estuary under combined wave-current action. Mar. Geod. 33, 261–281. Chitra, A., Bhaskaran, P.K., 2012. Parameterization of bottom friction under combined wave-tide action in the Hooghly estuary, India. Ocean Eng. 43, 43–55. Chitra, A., Bhaskaran., Prasad K., 2013. Numerical modeling of suspended sediment concentration and its validation for the Hooghly estuary, India. Coastal Eng. J. 55 (2), 1–23. Dube, S.K., Rao, A.D., Sinha, P.C., Murty, T.S., Bahulayan, N., 1997. Storm surge in the Bay of Bengal and the Arabian Sea: The problem and its prediction. Mausam 48 (2), 283–304. Durrant, T., Greenslade, D., 2011. Evaluation and Implementation of AUSWAVE. CAWCR Technical. Report no. 41, pp.52. Ebuchi, N., Graber, H.C., Caruso, M.J., 2002. Evaluation of Wind Vectors Observed by QuikSCAT/SeaWinds Using Ocean Buoy Data. J. Atmos. Oceanic Technol 19, 2049–2061. Glejin, J., Sanil Kumar, V., Balakrishnan Nair, T.M., Singh, J., Mehra, P., 2013. Observational evidence of summer Shamal swells along the west coast of India. J. Atmos. Oceanic Technol 30, 379–388.

Haver, S., 1980. Analysis of Uncertainties Related to the stochastic modeling of Ocean Waves. Division of Marine Structures. 80–09. Nor. Institute of Technology, Rep. UR p. 187 Feng, H., Vandemark, D., Quilfen, Y., Chapron, B., Beckley, B., 2006. Assessment of wind-forcing impact on a global wind-wave model using the TOPEX altimeter. Ocean Eng. 33 (11–12), 1431–1461. Isaksen, L., Janssen, P.A.E.M., 2004. Impact of ERS scatterometer winds in ECMWF's assimilation system. Q. J. R. Meteorolog. Soc 130, 1793–1814. Janssen, P.A.E.M., Wallbrink, H., Calkoen, C.J., van Halsema, D., Oost, W.A., Snoeij, P., 1998. VIERS-1 scatterometer model. J. Geophys. Res. 103, 7807–7831. Komen, G.J., Hasselmann, S., Hasselmann, K., 1984. On the existence of a fully developed wind-sea spectrum. J. Phys. Oceanogr. 14, 1271–1285. Nayak, S., Bhaskaran, P.K., Venkatesan, R., 2012. Near-shore wave induced setup along Kalpakkam coast during an extreme cyclone event in the Bay of Bengal. Ocean Eng. 55, 52–61. Nayak, S, Prasad Kumar, B. 2013a. Coastal vulnerability due to extreme waves at Kalpakkam based on historical tropical cyclones in the Bay of Bengal. Int. J. Climatol., 10.1002/joc.3776. Nayak, S., Bhaskaran, P.K., Venkatesan, R., Dasgupta, S., 2013b. Modulation of local wind waves at Kalpakkam from remote forcing effects of Southern Ocean swells. Ocean Eng. 64, 23–35. Padhy, C.P., Sen, D., Bhaskaran, P.K., 2008. Application of wave model for weather routing of ships in the North Indian Ocean. Nat. Hazard. 44, 373–385. Prasad Kumar, B., Kalra, R., Dube, S.K., Sinha, P.C., Rao, A.D., Kumar, R., Sarkar, A., 2000. Extreme wave conditions over the Bay of Bengal during severe cyclone— simulation experiment with two spectral wave models. Mar. Geod. 23, 91–102. Prasad Kumar, B., Pang, I.C., Rao, A.D., Kim, T.H., Nam, J.C., Hong, C.S., 2003. Sea state hindcast for the Korean seas with a spectral wave model and validation with buoy observation during January 1997. J. Korean Earth Sci. Soc 24 (1), 7–21. Prasad Kumar, B., Kalra, R., Dube, S.K., Sinha, P.C., Rao, A.D., 2004. Sea state hindcast with ECMWF data using a spectral wave model for typical monsoon months. Nat. Hazard. 31, 537–548. Prasad Kumar, B., Stone, G.W., 2007. Numerical simulation of typhoon wind forcing in the Korean seas using a spectral wave model. J. Coastal Res. 23 (2), 362–373. Prasad Kumar, B., 2010. Reliability based design method for coastal structures in shallow seas. Indian J. Geo-Mar. Sci. 39 (4), 605–615. Rabier, F., Jarvinen, H., Klinker, E., Mahfouf, J.F., Simmons, A., 2000. The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Q. J. R. Meteorolog. Soc 126, 1143–1170. Rajesh Kumar, R., Prasad Kumar, B., Satyanarayana, A.N.V., Subrahamanyam, D.B., Rao, A.D., Dube, S.K., 2008. Effect of varied atmospheric stability on sea surface drag in shallow seas and its impact on wind-wave growth. Nat. Hazard. 49, 213–224. Rajesh Kumar, R., Prasad Kumar, B., Subrahamanyam, D.B., 2009. Parameterization of rain induced surface roughness and its validation study using a third generation wave model. Ocean Sci. J. 44 (3), 125–143. Rao, A.D., Indu, J., Murthy, M.V.R., Murty, T.S., Dube, S.K., 2009. Impact of cyclonic wind field on interaction of surge-wave computations using finite element and finite difference models. Nat. Hazard. 49, 225–239. Remya, P.G., Kumar, R., Basu, S., Sarkar, A., 2012. Wave hindcast experiments in the Indian Ocean using MIKE 21 SW model. J. Earth Syst. Sci. 121 (2), 385–392. Sabique, L., Annapurnaiah, K., Balakrishnan Nair, T.M., 2012. Contribution of Southern Indian Ocean swells on the wave heights in the Northern Indian Ocean‐A modeling study. Ocean Eng 43, 113–120. Sanil Kumar, V., Dubhashi, K.K., Balakrishnan Nair, T.M., Singh, J., 2013. Wave power potential at a few shallow water locations around Indian coast. Curr. Sci 104 (9), 1219–1224. Satheeshkumar, P., Khan, B.A., 2009. Seasonal variations in physico-chemical parameters of water and sediment characteristics of Puducherry mangroves. Afr. J. Basic Appl. Sci 1, 36–43. Stone, G.W., Prasad Kumar, B., Sheremet, A., Watzke, D., 2005. Complex morphohydrodynamic response of estuaries and bays to winter storms: North-Central Gulf of Mexico, USA. In: FitzGerald, Duncan M, Knight, Jasper, (Eds.), High Resolution Morphodynamics and Sedimentary Evolution of Estuaries, pp. 364. SWAMP Group, 1985. Ocean Wave Modelling. Plenum, New York p. 256 Tolman, H.L., Chalikov, D., 1996. Source terms in a third-generation wind-wave model. J. Phys. Oceanogr 26, 2497–2518. Tolman, H.L., 1989. The Numerical Model WAVEWATCH: A Third Generation Model for the Hindcasting of Wind Waves on Tides in Shelf Seas. Communications on Hydraulic and Geotechnical Engineering, Delft University of Technology. Report no. 89-2, pp. 72. Tolman, H.L., 1991. A third-generation model for wind waves on slowly varying, unsteady and inhomogeneous depths and currents. J. Phys. Oceanogr 21, 782–797. Tolman, H.L., 2008. A mosaic approach to wind wave modeling. Ocean Model. 25, 35–47. Tolman, H.L., 1997. User Manual and system documentation of WAVEWATCH-III Version 1.15. NOAA/NWS/NCEP/OMB Technical Note 151, 97. Vethamony, P., Sudheesh, K., Rupali, S.P., Babu, M.T., Jayakumar, S., Saran, A.K., Basu, S.K., Kumar, R., Sarkar, A., 2006. Wave modelling for the North Indian Ocean using MSMR analysed winds. Int. J. Remote Sens. 27 (18), 3767–3780. WAMDI Group, 1988. The WAM model-a third generation ocean wave prediction model. J. Phys. Oceanogr 18, 1775–1809.