A Roadmap for Unified Ocean Modeling and Forecasting system for INCOIS

INCOIS, being the nodal organization to provide operational oceanographic services, is actively involved in the numerical modeling of ocean circulation, waves, tsunami and storm-surge as well as regional coupled ocean-atmosphere models for the prediction of track and intensity of tropical cyclones. In order to optimise the models used in INCOIS for these activities and to make a seamless prediction system from global to regional domains, it was decided to have a revisit on the ocean modeling efforts of INCOIS. Outcome of this review as well as a proposal to develop a seamless prediction system is documented in this report. It is envisaged that this document will be used as a guideline for the future ocean modeling efforts in INCOI

Enhancing observations data: A machine-learning approach to fill gaps in the moored buoy data

Moored buoy observations are crucial for understanding oceanographic processes, climate variability, and marine ecosystems and validating numerical models. Moored buoys provide high-quality data on wind, air and ocean temperature, ocean currents, waves, salinity, and other essential parameters. However, data gaps/missing data often compromise these datasets due to instrument failure, power loss, maintenance difficulties, biofouling, and environmental factors, leading to inaccurate analyses, biased conclusions, and reduced data utility. To address this issue, we employed machine learning on reanalysis products to predict and fill gaps in a 12-year moored buoy dataset. Our approach leverages the completeness and consistency of reanalysis data to enhance the accuracy and reliability of moored buoy records. By filling gaps, we improve data continuity, reduce uncertainty, and increase the value of moored buoy data for oceanographic and atmospheric research applications, such as studying ocean circulation, monitoring climate change, and predicting marine weather patterns. We trained and compared multiple machine learning models to predict missing values, which include linear regression, neural networks, random forests, and gradient boosting. Our results show that the ensemble approach with the least square boosting achieved the highest accuracy. The gap-filled dataset demonstrates improved completeness and accuracy, enhancing its utility for research and applications

Performance and validation of a coupled parallel ADCIRC-SWAN model for THANE cyclone in the Bay of Bengal

An accurate prediction of near-shore sea-state is imperative during extreme events such as cyclones required in an operational centre. The mutual interaction between physical processes such as tides, waves and currents determine the physical environment for any coastal region, and hence the need of a parallelized coupled wave and hydrodynamic model. The present study is an application of various state-of-art models such as WRF, WAM, SWAN and ADCIRC used to couple and simulate a severe cyclonic storm Thane that developed in the Bay of Bengal during December 2011. The coupled model (ADCIRC-SWAN) was run in a parallel mode on a flexible unstructured mesh. Thane had its landfall on 30 December, 2011 between Cuddalore and Pondicherry where in-situ observations were available to validate model performance. Comprehensive experiment on the impact of meteorological forcing parameters with two forecasted tracks derived from WRF model, and JTWC best track on the overall performance of coupled model was assessed. Further an extensive validation experiment was performed for significant wave heights and surface currents during Thane event. The significant wave heights measured along satellite tracks by three satellites viz; ENVISAT, JASON-1 and JASON-2, as well in-situ near-shore buoy observation off Pondicherry was used for comparison with model results. In addition, qualitative validation was performed for model computed currents with HF Radar Observation off Cuddalore during Thane event. The importance of WRF atmospheric model during cyclones and its robustness in the coupled model performance is highlighted. This study signifies the importance of coupled parallel ADCIRC-SWAN model for operational needs during extreme events in the North Indian Ocean

A numerical study of hypothetical storm surge and coastal inundation for AILA cyclone in the Bay of Bengal

The head Bay region bordering the Bay of Bengal is highly vulnerable to tropical cyclones. Catastrophic risks from storm surge and associated inundation are quite high due to high population density in coastal areas, socio-economic conditions, and shallow bathymetry. It features the world’s largest deltaic system comprising of ‘Sunderbans’ bordered by West Bengal and Bangladesh. In a geomorphologic sense, the head Bay region is a low-lying belt comprising several barrier islands and river drainage systems, numerous tidal creeks, and mud flats having a high risk for widespread inundation. In addition, the high tidal range together with low-lying topography leads to high risk and vulnerability from storm surge inundation. During May 2009, a severe cyclonic storm Aila struck West Bengal causing enormous destruction to life and property along coastal belts of West Bengal and Bangladesh. It was the strongest pre-monsoon cyclone in the past two decades that had landfall in West Bengal. This work reports on a numerical study for hypothetical storm surge and associated inundation from Aila using the ADCIRC model. The study covers a comprehensive qualitative analysis on water level elevation and onshore inundation for West Bengal and Bangladesh regions. The estimated peak storm surge was about 4 m in the Sunderban region that propagated into all major riverine systems, inundating the river banks as well the inland areas. Numerical simulations indicate an average inland penetration distance of 350 m with a maximum of 600 m at various coastal locations in West Bengal and Bangladesh. The study emphasizes the need and importance of inundation modeling system required for emergency preparedness and disaster managemen

A numerical study of coastal inundation and its validation for Thane cyclone in the Bay of Bengal

The numerical modeling of coastal inundation from severe cyclones is a challenging area for coastal hazard mapping, emergency planning and evacuation measures. There is a need for realistic estimate of onshore coastal inundation by the operational weather centers for precise warnings to minimize loss of life and property. At present, there is no modeling effort to evaluate the extent of coastal inundation for any coastal state in India. The operational center disseminates information only on peak surge and its location just before cyclone landfall, with no prior information about onshore inundation. To bridge this gap, the present study applies the state-of-art ADCIRC hydrodynamic model to evaluate peak surge and onshore inundation along coastal Tamil Nadu for the December 2011 Thane cyclone event. Post-storm analysis and field reconnaissance survey report from IMD and ICMAM were available for the Thane cyclone to skill assess model computation. The model that computed peak surge and onshore inundation is in good concurrence with field measurements. The study signifies that near-shore beach slope has a direct bearing on onshore inundation, and its importance in numerical modeling is highlighted. This study being first of its kind for Indian coast, emphasized that coastal inundation modeling should form an integral part in a storm surge prediction system for operational needs