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| Abstract Submission No. | ABS-2022-07-0381 |
| Title of Abstract | Analysis of Wave Climate in Northern Bay of Bengal using Deep Ocean Wave Measurements |
| Authors | K Jossia Joseph*, M Kalyani, R Venkatesan, G. Latha |
| Organisation | NIOT |
| Address | National Institute of Ocean Technology Chennai, Tamil Nadu, India Pincode: 600100 Mobile: 919444388553 E-mail: jossia@gmail.com |
| Country | India |
| Presentation | Oral |
| Abstract | The characteristics and variability of wave parameters remains major input in coastal and offshore developmental activities and a decisive factor in shoreline management. The waves play a significant role on the air-sea flux exchange and mixed layer dynamics as well. The climate change induced variability necessitates detailed analysis of wave characteristics in both coastal and open ocean. However, most of the wave records available are from coastal areas and lacks related information on oceanographic and meteorological parameters. The Bay of Bengal with its limited northward extent and significant seasonal variability demands accurate information on wave characteristics that are necessary for those who ventures into the sea. The presence of long swells along with the increase in intensity and frequency of cyclones in recent past further increases the complexity in wave characteristics. The satellite derived or model simulated wave parameters mostly fail during cyclone events. The analysis of simulated wave parameters in Northern Indian Ocean exhibits significant bias in estimating swell waves. The moored buoy network established by National Institute of Ocean Technology (NIOT) under Ministry of Earth Sciences provided the filled these gaps with systematic measurement of wave parameters both coastal and deep ocean along with a suit of met-ocean parameters. The moored buoy wave measurements in northern Bay of Bengal during the period 2011 to 2019 is utilized in this study to analyse the wave climate in detail. The analysis of wave parameters revealed significant seasonal variability in-line with the march of seasons. It is observed that rough sea conditions prevail during the southwest monsoon season and calm sea state in general for the remaining period except for the cyclone passage. The significant wave height is in general ~1m during calm seasons, which reaches more than 4m during southwest monsoon season. The dominance of local winds is indicated by high waves coincident with short wave period during southwest monsoon season. The seasonality is well reflected in the mean wave direction which exhibits south south westerly waves during most of the year and a transition period followed by north easterly waves by the end of the year, which extends till next February. The study area recorded many cyclone passages, which leads to extreme sea conditions. The moored buoy recorded maximum significant wave height of 6.3m during the cyclone Phailin in 2013 at this location. It is worthwhile to note that the moored buoy remained in-tact and continued the measurement even during these extreme sea conditions. The measurements exhibit presence of swell waves throughout the year at this location. However the contribution of swell waves are dominant during the calm period. The analysis shows the presence of long swells generated in the southern Indian Ocean and young swells generated within BoB with travel time of a few days to more than 10 days. These swell waves interact with the locally generated sea waves and modify the local sea state. The unprecedented change in sea state without any significant change in local wind and its impact while breaking in the coastal areas necessitates advance warning. However the analysis of simulated wave parameters of WW3 exhibit significant bias in magnitude and direction of swell waves. The magnitude is over estimated during calm sea conditions and under estimated during rough sea conditions. The realtime wave data from deep ocean moored buoys can provide significant information which can be readily utilised for various applications. The assimilation of these information can greatly improve the model performance which can result in better predicting the coastal sea state particularly the arrival of swell waves. |