T. M. Balakrishnan Nair

Observational Evidence of Summer Shamal Swells along the West Coast of India

Wave data collected off Ratnagiri, which is on the west coast of India, in 2010 and 2011 are used to examine the presence of the summer shamal swells. This study also aims to understand variations in wave characteristics and associated modifications in wind sea propagation at Ratnagiri. Wind data collected using an autonomous weather station (AWS), along with Advanced Scatterometer (ASCAT) and NCEP data, are used to identify the presence of summer shamal winds along the west coast of the Indian subcontinent and on the Arabian Peninsula. NCEP and ASCAT data indicate the presence of summer shamal winds over the Arabian Peninsula and northwesterly winds at Ratnagiri. This study identifies the presence of swells from the northwest that originate from the summer shamal winds in the Persian Gulf and that reach Ratnagiri during 30% of the summer shamal period. AWS data show the presence of northwest winds during May and southwest winds during the strong southwest monsoon period (June‐August). Another important factor identified at Ratnagiri that is associated with the summer shamal events is the direction of wind sea waves. During the onset of the southwest monsoon (May), the sea direction is in the direction of swell propagation (northwest);however,during the southwestmonsoon (June‐August), a major part of the windsea direction is from the southwest. The average occurrence of summer shamal swells is approximately 22% during the southwest monsoon period. An increase in wave height is observed during June and July at Ratnagiri due to the strong summer shamal event.

Validation and Intercomparison of SARAL/AltiKa and PISTACH-Derived Coastal Wave Heights Using In-Situ Measurements

SARAL/AltiKa, the first Ka-band altimeter, now provides an opportunity to study wave characteristics in the worlds coastal ocean with improved accuracy. In the present work, AltiKa-derived significant wave heights (Hs) in the coastal ocean and inland water bodies have been analyzed using in-situ measurements. Analysis shows that AltiKa measured Hs agree well with the in-situ measurements with high correlation (0.98), low bias (6 cm), and low RMSE (19 cm) in the coastal ocean, and the performance is highly consistent across different coastal zones in the three tropical oceans. AltiKa performance is found to be very good (RMSE = 24 cm and correlation = 0.94) near to the coast (<;2 km). In addition to the evaluation of AltiKa Hs, another coastal altimetry product, Innovative Processing System Prototype for Coastal and Hydrology Applications (PISTACH)-derived Hs using Jason-2 altimeter, has also been validated with in-situ measurements. The OCE3 retracking algorithm provided in PISTACH is able to improve the Jason-2 Hs in the 100-25 km coastal zone. None of the retracking algorithms showed significant improvement of Hs in the 0-10 km coastal zone.

Comparison of Grid Averaged Altimeter and Buoy Significant Wave Heights in the Northern Indian Ocean

A quantitative comparison of the collocated inter-annual significant wave height (SWH) data collected between 2006 and 2009 from buoys and altimeters at nine buoy locations (total n = 2241) in the Northern Indian Ocean is attempted for assessing the validity of daily averaged gridded altimeter significant wave height (ASWH) provided by AVISO for operational use. ASWH is underestimated by 0.20 m, the root-mean-square error (RMSE) is less than 0.30 m, the Scatter Index is less than 20%, and the correlation coefficient is greater than 0.90. Further, at three locations, the examination of the above statistics showed that the bias and RMSE is high during the southwest monsoon season compared with the Northeast monsoon. Scatter Index showed only slight variation (14–18%) for different ranges of SWH. The response of the daily average gridded ASWH data during extreme conditions (cyclones) in the vicinity of the buoy locations is poor at all compared buoy locations. The gridded ASWH from different satellite missions provided by AVISO can be used for basin scale validation experiments of the wave model and for climatological studies in the Indian Ocean, except during cyclone conditions.

Neural-Network-Based Data Assimilation to Improve Numerical Ocean Wave Forecast

This paper demonstrates the skill level of a wavelet neural network in improving numerical ocean wave predictions of significant wave height (H8) and peak wave period (Tp) having practical applications in operational centers. The study uses data of H8 and Tp for a coastal region off Puducherry located in the east coast of India, and obtained from a high-resolution wave model resulting from nesting of the SWAN model with the WW3 model. A wave rider buoy located off Puducherry provided data for a period of 25 months during the period from June 2007 until July 2009 used in this study. The time series of error between numerical and corresponding measured values was first constructed, and using a wavelet neural network, the errors were predicted for future time steps. The predicted errors when incorporated into the model values provided the updated prediction of H8 and Tp. The study signifies that numerical estimations could be significantly improved using this procedure. The results provide quite satisfactory predictions with a lead time varying from 3 to 24 h. The study points out that adequate training of the neural network is an essential prerequisite to obtain good performance and skill levels. A comparison between the suggested prediction method with the standalone neural network model trained with measured data off Puducherry showed that the former approach is preferred over the latter in obtaining a sustained prediction performance.

Ship-Mounted Real-Time Surface Observational System on board Indian Vessels for Validation and Refinement of Model Forcing Fields

A network of ship-mounted real-time Automatic Weather Stations integrated with Indian geosynchronous satellites Indian National Satellites (INSATs)] 3A and 3C, named Indian National Centre for Ocean Information Services Real-Time Automatic Weather Stations (I-RAWS), is established. The purpose of I-RAWS is to measure the surface meteorological-ocean parameters and transmit the data in real time in order to validate and refine the forcing parameters (obtained from different meteorological agencies) of the Indian Ocean Forecasting System (INDOFOS). Preliminary validation and intercomparison of analyzed products obtained from the National Centre for Medium Range Weather Forecasting and the European Centre for Medium-Range Weather Forecasts using the data collected from I-RAWS were carried out. This I-RAWS was mounted on board oceanographic research vessel Sagar Nidhi during a cruise across three oceanic regimes, namely, the tropical Indian Ocean, the extratropical Indian Ocean, and the Southern Ocean. The results obtained from such a validation and intercomparison, and its implications with special reference to the usage of atmospheric model data for forcing ocean model, are discussed in detail. It is noticed that the performance of analysis products from both atmospheric models is similar and good; however, European Centre for Medium-Range Weather Forecasts air temperature over the extratropical Indian Ocean and wind speed in the Southern Ocean are marginally better.

Monsoon control on trace metal fluxes in the deep Arabian Sea

Particulate fluxes of aluminium, iron, magnesium and titanium were measured using six time-series sediment traps deployed in the eastern, central and western Arabian Sea. Annual Al fluxes at shallow and deep trap depths were 0.47 and 0.46 g m-2 in the western Arabian Sea, and 0.33 and 0.47 g m-2 in the eastern Arabian Sea. There is a difference of about 0.9–1.8 g m-2y-1 in the lithogenic fluxes determined analytically (residue remaining after leaching out all biogenic particles) and estimated from the Al fluxes in the western Arabian Sea. This arises due to higher fluxes of Mg (as dolomite) in the western Arabian Sea (6–11 times higher than the eastern Arabian Sea). The estimated dolomite fluxes at the western Arabian Sea site range from 0.9 to 1.35gm-2y-1. Fe fluxes in the Arabian Sea were less than that of the reported atmospheric fluxes without any evidence for the presence of labile fraction/excess of Fe in the settling particles. More than 75% of Al, Fe, Ti and Mg fluxes occurred during the southwest (SW) monsoon in the western Arabian Sea. In the eastern Arabian Sea, peak Al, Fe, Mg and Ti fluxes were recorded during both the northeast (NE) and SW monsoons. During the SW monsoon, there exists a time lag of around one month between the increases in lithogenic and dolomite fluxes. Total lithogenic fluxes increase when the southern branch of dust bearing northwesterlies is dragged by the SW monsoon winds to the trap locations. However, the dolomite fluxes increase only when the northern branch of the northwesterlies (which carries a huge amount of dolomite accounting 60% of the total dust load) is dragged, from further north, by SW monsoon winds. The potential for the use of Mg/Fe ratio as a paleo-monsoonal proxy is examined.

Numerical simulation and observations of very severe cyclone generated surface wave fields in the north Indian Ocean

Accurate wave forecast is most needed during tropical cyclones as it has adverse effects on the entire marine activities. The present work evaluates the performance of a wave forecasting system under very severe cyclonic conditions for the Indian Ocean. The wave model results are validated separately for the deep water and shallow water using in-situ observations. Satellite altimeter observations are also utilized for validation purpose. The results show that the model performance is accurate (SI < 26% and correlation > 0.9) and consistent during very severe cyclones (categories 4 and 5). The power of the cyclone waves which hit in the eastern Indian coastal region is also analysed and it reveals that the coastal region which lies on the right side of the cyclone track receives high amount wave energy throughout the cyclone period. The study also says that the abnormal waves mostly present on the right side of the track.

High Frequency Tail Characteristics in the Coastal Waters off Gopalpur, Northwest Bay of Bengal: A Nearshore Modelling Study

Over the years, continued uncertainty amid − 4 and − 5 frequency exponent representation observed in the slope of the high-frequency tail of a wind-wave frequency spectrum is a major concern. To comprehend the nature of the high-frequency tail an effort has been made to assess the slope of the high-frequency tail with measured data recorded for 3 years off Gopalpur. The study demonstrates that the high-frequency slope of the spectra varied seasonally in the range of n = − 2.13 to − 3.48. The swell and wind sea parameters calculated by separation frequency method, shows that 64.6% of waves were dominant by swell and the rest 34.9% by sea annually. Single, double and multi-peaked spectra occur 12.23, 71.80 and 15.37% annually. To simulate wave spectra, the nested WAM-SWAN model is forced with ERA-Interim winds and 1D wave spectra comparisons, when performed, proved to be encouraging. From the comparisons of measured and theoretical spectra it is concluded that JONSWAP model could not describe the high-frequency tail of measured spectrum, as indicated by the very high Scatter Index ranging from 0.24 to 1.44. Whether there exists a correct slope for the high-frequency tail is still a question. Moreover, the philosophy of a unique slope at any coastal location remains uncertain for the wave modelling community.

Modification of a linear regression-based multi-model super-ensemble technique and its application in forecasting of wave height during extreme weather conditions

ABSTRACT In this study, we focus on the improvement of wave forecast of the Indian coastal region using a multi-model ensemble technique. Generally, a number of wave forecast are available for the same region from different wave models. The main objective of this study is to merge the wave forecasts available at Indian National Centre for Ocean Information Services from different wave models to obtain an improved wave forecast using a multi-model super-ensemble method [Krishnamurti et al. 1999. Improved weather and seasonal climate forecasts from multi-model super-ensemble. Science. 285:1548–1550] during extreme weather conditions and to modify Krishnamurthy’s techniques and validate with observations for a better prediction. Here, Multi-grid WAVEWATCH III, Simulating WAves Nearshore and MIKE 21 Spectral Waves are used for the generation of wave forecast. We propose a modification of Krishnamurthy’s linear regression-based ensemble model. By using both of these ensemble techniques, we perform a multi-model ensemble forecasting of significant wave height up to 24-h lead time in the Indian Ocean for three different cyclones (Nilofar, Hudhud and Phailin) and during the southwest monsoon. A comparison of ensemble predictions and individual model predictions with the actual observations showed generally satisfactory performance of the chosen tools. At the time of severe cyclones such as Hudhud and Phailin, our modified technique shows significantly better prediction than the linear regression-based ensemble technique.

An assessment on oil spill trajectory prediction: Case study on oil spill off Ennore Port

A Liquefied Petroleum Gas (LPG) tanker and a chemical tanker collided two nautical miles off Ennore port on 28 January, 2017. Around 196.4 metric tons (MT) of Heavy Furnace Oil (HFO) was spilled and drifted towards the shore. Oil spill drift advisory and prediction was made by Indian National Centre for Ocean Information Services (INCOIS) using General National Oceanic and Atmospheric Administration (NOAA) Operational Modeling Environment (GNOME), an oil spill trajectory model. The trajectory model was forced with analysed and forecasted ocean currents from Global Ocean Data Assimilation System (GODAS) based on Modular Ocean Model 4p1 (GM4p1). It was found that spread of HFO obtained from oil spill trajectory model GNOME, has matched well with the observed spread from Sentinel-1A satellite dataset. However, the spread of the HFO was underestimated by the trajectory model, when forced with forecasted GM4p1 currents. Additional ground truth observation from Indian Coast Guard also corroborates this finding.