Indian National Centre For Ocean Information Services Research Articles

An integrated buoy-satellite based coastal water quality nowcasting system: India's pioneering efforts towards addressing UN ocean decade challenges

The Indian coastal waters are stressed due to a multitude of factors, such as the discharge of industrial effluents, urbanization (municipal sewage), agricultural runoff, and river discharge. The coastal waters along the eastern and western seaboard of India exhibit contrasting characteristics in terms of seasonality, the magnitude of river influx, circulation pattern, and degree of anthropogenic activity. Therefore, understanding these processes and forecasting their occurrence is highly necessary to secure the health of coastal waters, habitats, marine resources, and the safety of tourists. This article introduces an integrated buoy-satellite based Water Quality Nowcasting System (WQNS) to address the unique challenges of water quality monitoring in Indian coastal waters and to boost the regional blue economy. The Indian National Centre for Ocean Information Services (INCOIS) has launched a first-of-its-kind WQNS, and positioned the buoys at two important locations along the east (Visakhapatnam) and west (Kochi) coast of India, covering a range of environmental conditions and tourist-intensive zones. These buoys are equipped with different physical-biogeochemical sensors, data telemetry systems, and integration with satellite-based observations for real-time data transmission to land. The sensors onboard these buoys continuously measure 22 water quality parameters, including surface current (speed and direction), salinity, temperature, pH, dissolved oxygen, phycocyanin, phycoerythrin, Coloured Dissolved Organic Matter, chlorophyll-a, turbidity, dissolved methane, hydrocarbon (crude and refined), scattering, pCO2 (water and air), and inorganic macronutrients (nitrite, nitrate, ammonium, phosphate, silicate). This real-time data is transmitted to a central processing facility at INCOIS, and after necessary quality control, the data is disseminated through the INCOIS website. Preliminary results from the WQNS show promising outcomes, including the short-term changes in the water column oxic and hypoxic regimes within a day in coastal waters off Kochi during the monsoon period, whereas effluxing of high levels of CO2 into the atmosphere associated with the mixing of water, driven by local depression in the coastal waters off Visakhapatnam. The system has demonstrated its ability to detect changes in the water column properties due to episodic events and mesoscale processes. Additionally, it offers valuable data for research, management, and policy development related to coastal water quality.

Investigating Physics Behind the Rapid Intensification and Catastrophic Landfall of Cyclone ‘Titli’ (2018) in the Bay of Bengal

The present study delineates the role of ocean conditions in the genesis and rapid intensification (RI) of a very severe cyclonic storm (VSCS) ‘Titli’ (2018). The tropical cyclone (TC) formed over the warm waters of the east-central Bay of Bengal during 08-13 October 2018. According to the India Meteorological Department (IMD), the cyclone was the most damaging storm to hit any coast of India in the year 2018, making it a special case of analysis. In the present study, 10 m winds , Sea Surface Temperature (SST), Latent heat flux, and relative vorticity (RV) during the lifespan of the cyclone are studied using ECMWF reanalysis V5 (ERA5) prepared by European Centre for Medium Range Weather Forecasts (ECMWF). Further, the Tropical Cyclone Heat Potential (TCHP) data generated by the Indian National Centre for Ocean Information Services (INCOIS) in Hyderabad is used to study the important information about the oceanic conditions of the TC. The investigation of the TC’s sea surface temperature data from satellites reveals that a relatively warmer SST prevailed during the cyclone’s occurrence, which may have been the primary factor in the TC’s rapid intensification. Further, the latent Heat flux (LHF) and TCHP values were also found high in conjunction with SST values. Our in-depth analysis reveals that the 10 m winds embedded into the TC were extremely strong, exceeding 12 m/s prior to the landfall. A positive and large value of RV was found when the TC was about to hit the coast. This may be one of the reasons behind the ‘catastrophic landfall’ of the cyclone.

Improved prediction of oil drift pattern using ensemble of ocean currents

ABSTRACT Indian Coast Guard and oil spill responders utilise the operational oil spill advisory services of the Indian National Centre for Ocean Information Services (INCOIS) during the event of oil spills for setting up spill response operations. Oil drifts in marine zones are dominated by ocean currents. Ocean currents generated by numerical models have errors and uncertainties due to model approximations, inaccurate initial and boundary conditions in the model setup. An attempt was made to generate an oil spill advisory using a weighted ensemble of ocean currents. In this study, the oil spill trajectory model, General National Oceanic and Atmospheric Administration (NOAA) Operational Modeling Environment (GNOME) was forced using a weighted ensemble of ocean currents for the Heavy Furnace Oil (HFO) spill reported off Ennore port during 0400 hours (IST) of 28 January 2017. The inverse-variance weighting method was used to estimate weights by comparing zonal and meridional components of individual model ocean currents, with that of High Frequency (HF) Radar currents. The zone of HFO spread obtained while using an ensemble of ocean currents was compared with oil slick signatures obtained from Synthetic Aperture Radar (SAR) data on 0600 hours (IST) of 29 January 2017. It was noted that the trajectory patterns obtained from the weighted ensemble of ocean currents were well within the observed zone of oil slicks, compared to that of individual model ocean currents.

Investigation of Waves Generated by Tropical Cyclone Kyarr in the Arabian Sea: An Application of ERA5 Reanalysis Wind Data

In this study, the wave conditions in the Arabian Sea induced by tropical cyclone Kyarr (2019) have been simulated by employing the 3rd generation wave model MIKE 21 SW. The model was run from 24 October to 1 November 2019, a total of 8 days. The MIKE 21 SW model was forced by reanalyzed ERA5 wind data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results are compared with buoy data from the Indian National Centre for Ocean Information Services (INCOIS), which is located at 67.44° E, 18.50° N. In addition, the satellite altimeter data (CryoSat-2, SARAL and Jason-3 satellite altimeter data) was utilized for validation. Three wave parameters are considered for the validation: the significant wave height; the peak wave period; and the mean wave direction. The validation results showed that the significant wave height, the peak wave period, and the mean wave direction could be reasonably predicted by the model with reanalysis wind data as input. The maximum significant wave height reached to 10.7 m (with an associated peak wave period of 12.5 s) on 28 October 2019 at 23:00:00 in the middle of the Arabian Sea. For coastal areas, the significant wave height along the Iran and Pakistan (north Arabian Sea) coasts increased to a range of 1.4–2.8 m when tropical cyclone Kyarr moved northward. This wave height along with elevated sea level may cause severe coastal erosion and nearshore inland flooding. Impacts of cyclones on coastal zones critical facilities and infrastructure can be reduced by timely and suitable action before the event, so coastal managers should understand the effect of cyclones and their destructive consequences. The validated model developed in this study may be utilized as input data of evaluating the risk to life and infrastructure in this area.

HE-DFNETS: A Novel Hybrid Deep Learning Architecture for the Prediction of Potential Fishing Zone Areas in Indian Ocean Using Remote Sensing Images.

The Indian subcontinent is known for its larger coastline spanning, over 8100 km and is considered the habitat for many millions of people. The livelihood of their habitat is purely dependent upon the fishing activities. Often, the search for fish requires more time for catching and more resources, thus increasing the operational cost leading to low profitability. With the advent of artificial intelligence algorithms, designing intelligent algorithms for an effective prediction of fishing areas has reached new heights in terms of high accuracy (Acy) and less time. But still, predicting the location of potential fishing zones (PFZs) is always a daunting task. To reduce these issues, this work presented the novel hybrid prediction architecture of PFZs using remote sensing images. The proposed architecture integrates the deep convolutional layers and flitter bat optimized long short-term memory (FB-LSTM)-based recurrent neural networks (RNN). These convolutional layers are utilized to remove the various color features such as chlorophyll, sea surface temperature (SST), and GPS location from the satellite images, and FB-LTSM is utilized to predict the potential locations for fishing. The extensive experimentations are carried out utilizing the satellite data from Indian National Centre for Ocean Information Services (INCOIS) and implemented using TensorFlow 1.18 with Keras API. The performance metrics such as prediction Acy, precision (Pscn), recall (Rcl) or sensitivity (Sty), specificity (Sfy), and F1-score and compared with other existing intelligent learning models. From our observations, the proposed architecture (99% prediction Acy) has outperformed the other existing algorithms and finds its best place in designing an intelligent system for better predicting of PFZs.

Understanding CYGNSS wind performance against in-situ and satellite winds during low-to-moderate and cyclonic conditions

An assessment of the performance of CYGNSS operational wind products is done against a multitude of reference datasets which include in-situ wind speed from Indian National Centre for Ocean Information Services (INCOIS) network of buoys (hereafter, INCOIS-buoys) in the Indian Ocean, SCATSAT-1 operational wind products and cyclone best track data from India Meteorological Department (IMD). The validation study is done for CYGNSS data version 2.1 for the period 17th March 2017 till 31st December 2017. The performance of the operational wind products is examined through a two-tier approach- first one covering low-to-moderate wind regimes (less than or equal to 15 m/s) where in-situ data from INCOIS-buoys and rain-free SCATSAT-1 products are used. While the former gives an idea of CYGNSS winds in the Indian Ocean basin, the latter can be treated as global reference dataset. In the second approach, high wind (greater than15 m/s) performance of CYGNSS winds are evaluated on a case study basis during some of the tropical cyclones (TC) of 2017. The first approach gives good accuracy of minimum variance estimator (MVE) wind speed products in terms of standard deviations of 1.85 m/s (INCOIS-buoys) and 2.07 m/s (SCATSAT-1). MVE is the best product amongst all the five different operational wind products viz. MVE, FDS-NBRCS, FDS-LES, YSLF-NBRCS, YSLF-LES of CYGNSS. Time series analyses of high wind performance during the TC- Mora and Ockhi show better sensitivity of young sea wind products especially YSLF-NBRCS compared to YSLF-LES. However, the fully developed wind product, FDS-NBRCS shows equally good sensitivity of wind speed.

Seasonality in recurrent Potential Fishing Zones along India’s Southwest coast and its relationship to the prevailing hydrographic settings

This study presents the linkage between recurrent (repeat) Potential Fishing Zones (PFZ) and seasonal hydrographic conditions along India’s Southwest (Kerala) coast in the Southeastern Arabian Sea (SEAS). The PFZ advisories released by the Indian National Centre for Ocean Information Services (INCOIS), Hyderabad, India are based on the fact that ocean processes that enhance biological production leave imprints on surface ocean parameters that can be tracked using satellite remote sensing techniques and used to forecast fish availability. Sea surface temperature and chlorophyll-a features of satellite remote sensing data sets usually coincide in regions and situations where physical processes and biological responses are closely coupled, which is used by INCOIS to create PFZ advisories. Nonetheless, it is unknown how PFZ frequency varies seasonally or how the recurrent PFZs are related to the region’s predominant seasonal hydrographic settings, which have been addressed here based on a retrospective analysis of PFZs disseminated by INCOIS in advisories distributed over five years (2008–2012). For the current investigation, 432 PFZ advisories over five years were analysed, and the regions of repeat PFZs were demarcated every month along the Kerala coast in the SEAS. This study found that almost all of the very prominent repeat PFZs are formed within the 50 m depth contour and the most frequent and pronounced repeat PFZs were observed across the entire SEAS during the Northeast Monsoon in December, January, and February. On the other hand, the lowest repeat PFZs were discovered in April and May, during the Pre-monsoon season. We show that that the observed increase in repeat PFZs in the study area during the Northeast Monsoon is primarily due to the hydrographical setting created by the intrusion of low-salinity Bay of Bengal waters, which has the potential to increase the occurrence of mesoscale and sub-mesoscale ocean surface features such as fronts and eddies that might get imprinted as more frequent and prominant repeat PFZs in the study area. Instead, very weak winds and thermally stratified surface waters during the Pre-monsoon result in fewer cases of physical processes to enhance plankton production, resulting in a low frequency of imprints in satellite data sets and low repeat PFZs.

Implementation of altimeter data assimilation on a regional wave forecasting system and its impact on wave and swell surge forecast in the Indian Ocean

In the present work, we have operationally implemented a data assimilation (DA) scheme in the wave forecasting system at the Indian operational agency, Indian National Centre for Ocean Information Services (INCOIS). Significant Wave Height (SWH) measurements from the SARAL/AltiKa, Jason-2 and Jason-3 altimeters were assimilated using the Optimal Interpolation technique. The impact of altimeter DA towards improving the reliability of wave predictions in the Indian Ocean is evaluated by validating the forecasted wave parameters with buoy observations. The assimilation of altimeter data showed considerable improvement in the wave predictions. SWH forecast in the northern Indian Ocean region improved up to ~15 % in the first 24 h period. The improvement in forecasted wave parameters were due to the correction in swell forecast, which persists throughout the forecast period. For wind-sea forecast, impact of DA was less visible (~4–6% improvement up to forecast lead time of 24 h), as it is primarily driven by local wind fields. The positive impact of DA on the swell forecast is further established considering a swell surge event, named Kallakkadal.

Development of small vessel advisory and forecast services system for safe navigation and operations at sea

ABSTRACT Fishing is the most dangerous profession in the world. To reduce the number of accidents caused by capsizing of vessels, Indian National Centre for Ocean Information Services (INCOIS) has designed and developed an advisory and forecast services system for the Indian ocean regime, which warns users against potential zones and times at which vessel overturning can take place, three days in advance. This paper discusses the development and various aspects of such a system, its decision-making mechanism, and the Standard Operating Procedure (SOP). Warning advisories issued by the system are based upon a newly coined index-Boat Safety Index (BSI). The advisory system is verified with a few real-life incidents of the past. The refinement of the advisory system is planned by updating suitable thresholds for the indices based upon regular feedback from the users. The boats operating along the Indian coast are classified into different categories based upon their beam size, and category-specific advisories are issued.

A Report on the Indian National Centre for Ocean Information Services (INCOIS), Hyderabad

A Report on the Indian National Centre for Ocean Information Services (INCOIS), Hyderabad

Ensemble based regional ocean data assimilation system for the Indian Ocean: Implementation and evaluation

A high-resolution ocean circulation model for the Indian Ocean (IO) using Regional Ocean Modeling System (ROMS) is operational at Indian National Centre for Ocean Information Services (INCOIS) which provides ocean state forecasts for the Bay of Bengal (BoB) and the Arabian Sea (AS) to the Indian Ocean rim countries. To provide an improved estimate of ocean state, a variant of Ensemble Kalman Filter (EnKF), viz., the Local Ensemble Transform Kalman Filter (LETKF) has been developed and interfaced with the present basin-wide operational ROMS. This system assimilates in-situ temperature and salinity profiles and satellite track data of sea-surface temperature (SST). The ensemble members of the assimilation system are initialized with different parameters like diffusion and viscosity coefficients and are subjected to an ensemble of atmospheric fluxes. In addition, one half of the ensemble members respond to K profile parameterization mixing scheme while the other half is subjected to Mellor–Yamada mixing scheme. This strategy aids in arresting the filter divergence which has always been a challenging task. The assimilated system simulates the ocean state better than the present operational ROMS. Improvements permeate to deeper ocean depths with better correlation and reduced root-mean-squared deviation (RMSD) with respect to observations particularly in the northern Indian Ocean which is data rich in density. Analysis shows domain averaged RMSD reduction of about 0.2–0.4 °C in sea surface temperature and 2–4 cm in sea level anomaly. The assimilated system also manages to significantly improve the thickness of the temperature inversion layers and the duration of its occurrence in northern Bay of Bengal. The most profound improvements are seen in currents, with an error reduction of 15 cm/s in zonal currents of central Bay of Bengal.

The Use of Generalized Additive Model (GAM) To Assess Fish Abundance and Spatial Occupancy in North-West Bay of Bengal

The remote sensing data, such as sea surface temperature & chlorophyll concentration obtained from various satellites are utilized by Indian National Centre for Ocean Information Services (INCOIS) to provide Potential Fishing Zone (PFZ) advisories to the Indian fishing community which plays a vital role in national GDP. The data on Sea Surface Temperature (SST) is retrieved regularly from thermal-infrared channels of NOAA-AVHRR and chlorophyll concentration (CC) from optical bands of Oceansat-II and MODIS Aqua satellites for the identification of Potential Fishing Zones (PFZ) in Indian water. PFZ information has certain limitations, such as it can't predict the type of fish available in the notified fishing zone. In this dissertation, I have worked towards the development of short-term Hilsa shad predictive capabilities in a sustainable way. An effort has been taken to categorize all essential biological, environmental and climatic signals that have a direct or indirect impact on the Hilsa shad distribution. Remote sensing, ocean biogeochemical modelling, and statistical modelling approach have gained an increasing importance to study the marine ecosystems as-well-as for understanding the dynamics of the oceanic environment. Shad habitat has been studied from the geo-tagged fish catch data and oceanic/ecological indicators as predictor variables. For short-term prediction, the variables have been derived from a biophysical model, configured at INCOIS, using Regional Ocean Model System (ROMS) and remote sensing data. Using generalized additive model (GAM) Catch per Unit Effort (kg h?1) has been calculated as a response variable. Probability maps of predicted habitat with no fishing zone information have been generated using geographic information system.

Improved ocean analysis for the Indian Ocean

ABSTRACTThe National Centers for Environmental Prediction (NCEP) and the Indian National Centre for Ocean Information Services (INCOIS) produce global ocean analyses based on the Global Ocean Data Assimilation System (GODAS). This system uses a state of the art ocean general circulation model named moduler ocean model (MOM) and the 3D-Variational (3DVar) data assimilation technique. In this study we have evaluated the INCOIS-GODAS operational analysis products with an upgrade of the physical model from MOM4p0d to MOM4p1. Two experiments were performed with same atmospheric forcing fields:(i) using MOM4p0d (GODAS_p0), and (ii) using MOM4p1 (GODAS_p1). Observed temperature and salinity profiles were assimilated in both experiments. Validation with independent observations show improvement of sea surface temperature(SST), sea surface salinity (SSS) and surface currents in the new analysis GODAS_p1 as compared to the old analysis GODAS_p0.

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.

Marine Data Services at National Oceanographic Data Centre-India

In this paper we introduce about the marine data archived at Indian National Centre for Ocean Information Services (INCOIS), Ministry of Earth Sciences, India. Heterogeneous data from in situ, remote sensing and ocean models are archived. In-situ ocean observations includes data from Lagrangian as well Eulerian platforms like Argo floats, moored buoys etc, while remote sensing include data from NOAA satellite series, OceanScat etc. The data generated is translated into ocean information services through analysis and modelling. Data is disseminated to users using variety of means like web with GIS features, ERDDAP, Live Access server with facilities to search, visualize and download.

Development of the wave spectrum of the northern region of the Bay of Bengal based on Indian National Centre for Ocean Information Services (INCOIS) moored buoy data

The wave spectrum of the northern region of the Bay of Bengal is developed and analyzed. A meticulous analysis shows variations of the wave spectrum of this region in the four meteorological seasons namely spring (March, April, May), summer (June, July, August), autumn (September, October, November) and winter (December, January, February). Real time wave data have been collected from a moored buoy of Indian National Centre for Ocean Information Service (INCOIS) located at 89.675° E longitude and 18.191° N latitude. Varyingtwo-parameter (mean wave period and significant wave height) wave spectra for the four seasons are obtained and formulae for all the four spectra are derived. The adequacy of the different spectra is indicated by comparing with different wave spectra of known formulations.

Finding the high probabilistic potential fishing zone by accelerated SVM classification

Potential fishing zones (PFZs) advisories play an important role in forecasting the spots for precised and optimised fishing. The PFZ advisories obtained from Indian National Centre for Ocean Information Services (INCOIS), during November 2003 to December 2011 of Kilakarai coast was analysed to understand the effective and high probabilistic fishing spot location. The original data for Kilakarai had four directions; they are south, southeast, southwest and east. INCOIS data is converted into FFT features to classify the areas using support vector machine (SVM) based on directions and plot the support vectors to distinguish locations. The identified support vectors for given direction combinations namely, south-southeast, south-southwest, south-east, southeast-southwest, southeast-east and southwest-east helps to a greater extent for classification of direction zone. SVM, a clustering technique, used to plot the support vectors to differentiate direction zones. We have accelerated the process by fine tuning the box constraint value which is used to plot the support vectors. In this work the high probabilistic potential fishing zones are effectively cropped to get the assured fishing zones.

Coral Bleaching Along Andaman Coast Due to Thermal Stress During Summer Months of 2016: A Geospatial Assessment

Coral bleaching reported in the North Bay, Andaman Islands during the April last week of 2016 due to thermal stress. The Coral Bleaching Alert System (CBAS) a service providing by Indian National Centre for Ocean Information Services (INCOIS) has reported the warning and the bleaching has been confirmed from the field survey conducted on April 26, 2016. As coral reef is important fragile eco-system of the shallow marine environment suffers due to human and natural impacts. Bleaching has been initiated as weather predictions of the various agencies have suggested the elevated temperatures in the Indian subcontinent this year. We have put forward our remote sensing analysis of coral bleaching warning as Alert Level-1 followed by the initiation of the coral bleaching in the in-situ observation at Andaman Islands enhances the credibility of satellite observation and the service of INCOIS. These results were useful for the coastal management authority for taking suitable measure and policy on important coral eco-system.

Real-time storm surge and inundation forecast for very severe cyclonic storm ‘Hudhud’

The high population density along the coastal stretch of India necessitates a real-time storm surge warning system. Keeping this in view, the Earth System Science Organization (ESSO) - Indian National Centre for Ocean Information Services (INCOIS) initiated the Storm Surge Early Warning System (SSEWS) for Indian coasts using the ADCIRC (Advanced Circulation) model. ADCIRC is a finite-element based, depth-integrated shallow water model that can be used to model storm surges and for other coastal applications. This warning system utilizes the automated Decision Support System (DSS) based on Geographic Information System (GIS) and database technology. Wind and pressure fields are generated using the Jelesnianski and Taylor dynamic wind model which make use of track forecasts from IMD. While DSS was initially tested for the very severe cyclonic storm ‘Phailin’ (October, 2013) in experimental mode, it was used for the first time to provide real-time storm surge and inundation forecasts during cyclone ‘Hudhud’ (October, 2014). In this paper, we highlight the performance of SSEWS during this event while comparing with observations. Forecasts from SSEWS were found to be quite promising and proving its capability.

Impact of an upgraded model in the NCEP Global Ocean Data Assimilation System: The tropical Indian Ocean

The National Centers for Environmental Prediction (NCEP) and the Indian National Centre for Ocean Information Services (INCOIS) produces global ocean analysis based on the Global Ocean Data Assimilation System (GODAS). This study shows how upgrades to the forward model simulations from MOM4p0d to MOM4p1 impact ocean analyses over the tropical Indian Ocean in GODAS. Three experiments were performed with same atmospheric forcing fields: (i) using MOM4p0d (GODAS_p0), (ii) using MOM4p1 (GODAS_p1), both using observed temperature and synthetic salinity, and (iii) using MOM4p1 (GODAS_p1S) assimilating both observed temperature and observed salinity. Validation with independent observations shows significant improvement of subsurface temperature and salinity in the new analysis using MOM4p1 versus MOM4p0d. There is also improvement in the upper ocean current of the equatorial Indian Ocean. The impact of observed salinity on the upper ocean surface current is marginal, but there is significant improvement in the subsurface current. The seasonal and inter-annual variability of the Wyrtki jet and the equatorial undercurrent is improved in GODAS_p1 versus GODAS_p0. All analyses reproduced the Indian Ocean dipole, with the GODAS_p1S simulated sea surface temperature (SST) the most accurate. The temperature inversion over the north Bay of Bengal (BoB) is reproduced only in GODAS_p1S. The mean sea level over BoB and equatorial Indian Ocean improved in GODAS_p1S as compared with AVISO observation. The combined model upgrade and assimilation of observed salinity led to reduced root-mean-square deviation and higher correlation coefficient values in the sea level anomaly (SLA) when compared with satellite observations.