Refine your search
Collections
Co-Authors
- Smitha Ratheesh
- Sujit Basu
- K. V. S. R. Prasad
- Neeraj Agarwal
- V. S. R. Prasad
- Pradeep Thapliyal
- Rishi Gangwar
- Prateek Kumar
- Raj Kumar
- Aditya Chaudhary
- Surisetty V. V. Arun Kumar
- Rakesh Kumar Luhar
- Ch. Venkateswarlu
- B. Sivaiah
- Suchandra A. Bhowmick
- R. Sundar
- R. Venkatesan
- C. Anoopa Prasad
- K. N. Navaneeth
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Sharma, Rashmi
- Estimating Biological Parameters of a Coupled Physical-Biological Model of the Indian Ocean Using Polynomial Chaos
Abstract Views :195 |
PDF Views:77
Authors
Affiliations
1 Oceanic Sciences Division, Atmospheric and Oceanic Sciences Group, Space Applications Centre, Ahmedabad 380 015, IN
1 Oceanic Sciences Division, Atmospheric and Oceanic Sciences Group, Space Applications Centre, Ahmedabad 380 015, IN
Source
Current Science, Vol 110, No 8 (2016), Pagination: 1544-1549Abstract
A statistical emulator technique, namely polynomial chaos, has been used to estimate two time-dependent biological parameters of a coupled physical-biological model of the Indian Ocean. This has been achieved by minimizing a distance function representing misfit between model simulated and satellite-derived surface chlorophyll. First, the parameters have been assumed to be constant in time and optimized values have been found by minimizing a time-averaged distance function. Since no significant improvement in model simulation has been found using a fixed set of optimum parameters, minimization has been carried out daily, assuming the parameters to be time-dependent. Emulation with this set of parameters has led to a significant improvement in the simulated surface chlorophyll. Smoothing of the parameters with singular spectrum analysis has caused less noisy simulations, at the cost of increasing the model data misfit. Time-varying parameters have been found to be more suitable for the hindcast of daily averaged chlorophyll both in the Arabian Sea and the Bay of Bengal.Keywords
Coupled Physical–Biological Model, Distance Function, Polynomial Chaos, Surface Chlorophyll.- Seasonal Behaviour of Upper Ocean Freshwater Content in the Bay of Bengal:Synergistic Approach Using Model and Satellite Data
Abstract Views :270 |
PDF Views:104
Authors
Smitha Ratheesh
1,
Rashmi Sharma
1,
K. V. S. R. Prasad
2,
Neeraj Agarwal
1,
Rashmi Sharma
1,
V. S. R. Prasad
2
Affiliations
1 Space Applications Centre, Oceanic Sciences Division, Ahmedabad 380 058, IN
2 Department of Meteorology and Oceanography, Andhra University, Visakhapatnam 530 003, IN
1 Space Applications Centre, Oceanic Sciences Division, Ahmedabad 380 058, IN
2 Department of Meteorology and Oceanography, Andhra University, Visakhapatnam 530 003, IN
Source
Current Science, Vol 115, No 1 (2018), Pagination: 99-107Abstract
Any Change In Precipitation, Evaporation And River Discharge, By Virtue Of Its Impact On The Distribution Of Ocean Salinity, Leaves Its Inevitable Signature On The Freshwater Content (fwc) In The Oceans. In This Study, Synergistic Use Of Satellite Data And Numerical Ocean Circulation Model Is Explored To Examine The Seasonality Of Fwc Of The Upper 30 M Water Column Of The Bay Of Bengal (bob). For This Purpose, First The Sea Surface Salinity (sss) From Aquarius Is Assimilated Into A Model Of The Indian Ocean. Strength Of Assimilation Is Judged By Comparing Simulated Sss With Satellite And Argo Datasets. An Overall Improvement Of 39% Is Observed In Sss Over Free Run Of The Model Without Data Assimilation. Next, The Focus Is Shifted To The Spatial And Temporal Variability Of Fwc Of The Upper 30 M Of Bob In Relation To The Different Components Of Freshwater Forcing. A Delay Of Three Months In The Peak Of Fwc Is Observed With Respect To The Peak Of Net Freshwater Influx For Bob As A Whole. However, The Nature Of The Response Of Fwc To The Total Freshwater Input Forcing In The Major River-dominated Regions Of Bob Is Different From That For The Whole Bob. The Relative Role Of River Influx In Controlling Fwc In These Regions Is Well Brought Out In The Study. For The Ganga–brahmaputra Region, River Run-off Is Observed To Be A Crucial Parameter In Regulating Fwc, Whereas For Both Irrawaddy River Region And Central Bob, Precipitation Dominates The Response. The Response Of Salinity In The Uppermost Part Of The Northern Bob To The Total Freshwater Input Is Much More Rapid Than In The Other Regions.Keywords
Freshwater Content, Sea Surface Salinity, Seasonal Variability, Upper Ocean Region.References
- Sengupta, D., Bharath Raj, G. N. and Shenoi, S. S. C., Surface freshwater from Bay of Bengal runoff and Indonesian Through-flow in the tropical Indian Ocean. Geophys. Res. Lett., 2006, 33, L22609; doi:10.1029/2006GL027573.
- Perigaud, C., McCreary, J. P. and Zhang, K. Q., Impact of inter-annual rainfall anomalies on Indian Ocean salinity and temperature variability. J. Geophys. Res., 2003, 108, 3319; doi:10.1029/2002JC001699.
- Rao, R. R. and Sivakumar, R., Seasonal variability of sea surface salinity and salt budget of the mixed layer of the north Indian Ocean. J. Geophys. Res., 2003, 108, 3009; doi:10.1029/2001JC000907.
- Sharma, R., Mankad, B., Agarwal, N., Kumar, R. and Basu, S., An assessment of two different satellite-derived precipitation products in relation to simulation of sea surface salinity in the tropical Indian Ocean, J. Geophys. Res., 2012, 117, C07001; doi:10.1029/2012JC008078.
- Gadgil, S., Joseph, P. V. and Joshi, N. V., Ocean–atmosphere coupling over monsoon regions. Nature 1984, 312, 141–143.
- Neetu, S. et al., Influence of upper ocean stratification on tropical cyclone-induced surface cooling in the Bay of Bengal. J. Geophys. Res., 2012, 117, C12020, 3315–3329; doi:10.1029/2012JC008433/
- Pant, V., Girishkumar, M. S., Udaya Bhaskar, T. V. S., Ravichandran, M., Papa, F. and Thangaprakash, V. P., Observed interannual variability of near-surface salinity in the Bay of Bengal, J. Geophys. Res., 2015, 120; doi:10.1002/2014JC010340.
- Akhil, V. P. et al., A modeling study of the processes of surface salinity seasonal cycle in the Bay of Bengal. J. Geophys. Res., 2014, 119, 3926–3947; doi:10.1002/2013JC009632.
- Wiffels, S. E., Schmitt, R. W., Bryden, H. R. and Stigebrandt, A., Transport of freshwater by Oceans. J. Phys. Oceanogr., 1992, 22, 155–162.
- Behara, A. and Vinayachandran, P. N., An OGCM study of the impact of rain and river water forcing on the Bay of Bengal. J. Geophys. Res., 2016, 121, 2425–2446; doi:10.1002/2015JC011325.
- Chakraborty, A., Sharma, R., Kumar, R. and Basu, S., A SEEK filter assimilation of sea surface salinity from Aquarius in an OGCM: implication for surface dynamics and thermohaline structure. J. Geophys. Res., 2014, 119, 4777–4796; doi:10.1002/2014JC009984.
- Chakraborty, A., Sharma, R., Kumar, R. and Basu, S., Joint assimilation of Aquarius-derived sea surface salinity and AVHRR-derived sea surface temperature in an ocean general circulation model using SEEK filter: implication for mixed layer depth and barrier layer thickness. J. Geophys. Res., 2015, 120, 6927–6942; doi:10.1002/2015JC010934.
- Blumberrg, A. F. and Mellor, G. L., A description of a three-dimensional coastal ocean circulation model. In Three Dimensional Coastal Ocean Models (ed. Heaps, N. S.), Amer. Geophys. Union, Washington, DC, USA, 1987, pp. 1–16; doi:10.1029/CO004P0001.
- Mellor, G. L. and Yamada, T., Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys., 1982, 20, 851–875.
- Camerlengo, A. L. and O’Brien, J. J., Open boundary conditions in rotating fluids. J. Comput. Phys., 1980, 35, 12–35.
- Santoki, M., Ratheesh, S., Sharma, R., Joshipura, K. N. and Basu, S, Assimilation of drifter data in a circulation model of the Indian Ocean. IEEE Geosci. Remote Sensing. Lett., 2012, 9, 100–103.
- Santoki, M., George, S., Sharma, R., Joshipura, K. N. and Basu, S., Assimilation of satellite-derived ocean surface current in an Indian Ocean circulation model. Remote Sensing Lett., 2013, 4, 475–484.
- Ratheesh, S., Sharma, R. and Basu, S., Projection-based assimilation of satellite-derived surface data in an Indian Ocean circulation model. Mar. Geod., 2012, 35, 175–187.
- Ratheesh. S., Sharma, R. and Basu, S., An EnOI assimilation of satellite data in an Indian Ocean circulation model. IEEE Trans. Geosci. Remote Sensing, 2014, 52, 4106–4111.
- Oke, P. R., Brassington, G. B., Griffin, D. A. and Schiller, A., Ocean data assimilation: a case for ensemble optimal interpolation. Aust. Meteorol. Ocean J., 2010, 59, 67–76.
- Wan, L., Bertino, L. and Zhu, J., Assimilating altimetry data into a HYCOM model of the Pacific: ensemble optimal interpolation versus ensemble Kalman filter. J. Atmos. Ocean. Technol., 2010, 27, 753–776.
- Ratheesh. S., Sharma, R., Prasad, K. V. S. R. and Basu, S., Impact of SARAL/AltiKa-derived sea level anomaly in a data assimilative ocean prediction system for the Indian Ocean. Mar. Geod., 2015, 38, 354–364; doi:10.1080/01490419.2014.988833.
- Levine, M., Lagerloef, G. S. E., Colomb, R., Yueh, S. and Pellerano, F., Aquarius: an instrument to monitor sea surface salinity from space. IEEE Trans. Geosci. Remote Sensing, 2007, 45, 2040–2050.
- Ratheesh, S., Sharma, R., Sikhakolli, R., Kumar, R. and Basu, S., Assessing sea surface salinity derived by Aquarius in the Indian Ocean. IEEE Geosci. Remote Sensing Lett., 2014, 11, 719–722.
- Tang, W., Yueh, S. H., Fore, A. G. and Hayashi, A., Validation of Aquarius sea surface salinity with in situ measurements from Argo floats and moored buoys. J. Geophys. Res., 2014, 119, 6171–6189; doi:10.1002/2014JC010101.
- Kohl, A. and Serra, N., Causes of decadal changes of the freshwater content in the Arctic Ocean. J. Climate, 2014, 27(9), 3461–3475.
- Stammer, D. et al., Volume, heat, and freshwater transports of the global ocean circulation 1993–2000, estimated from a general circulation model constrained by World Ocean Circulation Experiment (WOCE) data. J. Geophys. Res., 2003, 108(C1), 3007; doi:10.1029/2001JC001115.
- Han, W., McCreary J. P. and Kohler, K. E., Influence of precipitation minus evaporation and Bay of Bengal rivers on dynamics, thermodynamics and mixed layer physics in the upper Indian Ocean. J. Geophys. Res., 2001, 106(C4), 6895–6916.
- Thadathil, P., Muraleedharan, P. M., Rao, R. R., Somayajulu, Y. K., Reddy, G. V. and Revichandran, C., Observed seasonal variability of barrier layer in the Bay of Bengal. J. Geophys. Res., 2007, 112, C02009; doi:10.1029/2006JC003651.
- Prasad, T. G., Annual and seasonal mean buoyancy fluxes for the tropical Indian Ocean. Curr. Sci., 1997, 73(8), 667–674.
- Mahadevan, A., Spiro Jaeger, G., Freilich, M., Omand, M., Shroyer, E. L. and Sengupta, D., Freshwater in the Bay of Bengal: its fate and role in air–sea heat exchange. Oceanography, 2016, 29(2), 72–81; http://dx.doi.org/10.5670/oceanog.2016.40.
- Rao, S. A. et al., Modulation of SST, SSS over northern Bay of Bengal on ISO time scale. J. Geophys. Res., 2011, 116, C09026; doi:10.1029/2010JC006804.
- Chaitanya, A. V. S. et al., Observed year-to-year sea surface Salinity variability in the Bay of Bengal during the 2009–2014 period. Ocean Dyn., 2015, 65, 173–186.
- Yu, L., A global relationship between the ocean water cycle and near-surface salinity. J. Geophys. Res., 2011, 116, C10025; doi:10.1029/2010JC006937.
- Geostationary Satellite-Based Observations for Ocean Applications
Abstract Views :181 |
PDF Views:77
Authors
Neeraj Agarwal
1,
Rashmi Sharma
1,
Pradeep Thapliyal
1,
Rishi Gangwar
1,
Prateek Kumar
1,
Raj Kumar
1
Affiliations
1 Earth, Ocean, Atmosphere and Planetary Sciences Area, Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
1 Earth, Ocean, Atmosphere and Planetary Sciences Area, Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
Source
Current Science, Vol 117, No 3 (2019), Pagination: 506-515Abstract
The study presents assessment and potential oceanographic applications of sea-surface temperature (SST), ocean net shortwave radiation (SWR) and chlorophyll concentration (CC) observations obtained from various geostationary platforms. SST and SWR from imager on-board Indian National Satellite (INSAT- 3D) and CC from Global Ocean Color Imager (GOCI) on-board communication ocean and meteorological satellite (COMS) have been used in the analysis. Relative advantages of high temporal resolution obtained from the geostationary platform compared to polar orbiting platforms are demonstrated. Comparison of INSAT-3D SST with observations gives a correlation of 0.85 and RMSE of 0.81 K. These platforms definitely provide a highly reliable source of continuous observations, which is useful in monitoring dynamic oceanic features such as thermal fronts, chlorophyll blooms, air–sea exchange fluxes, etc. on diurnal to daily timescales.Keywords
Chlorophyll Concentration, Geostationary Satellites, INSAT-3D, Sea-surface Temperature, Shortwave Radiation.References
- CGMS-46, Report of the 46th Plenary Session of the Coordination Group for Meteorological Satellites, CGMS-46, Bengaluru, 3–8 June 2018.
- Murakami, H., Ocean color estimation by Himawari-8/AHI, 2016; doi:10.1117/12.2225422.
- Kurihara, Y., Murakami, H. and Kachi, M., Sea surface temperature from the new Japanese geostationary meteorological Himawari8 satellite. Geosphys. Res. Lett., 2015; doi:10.1002/2015 GL067159.
- Nigam, R., Bhattacharya, B. K., Gunjal, K. R., Padmanabhan, N., and Patel, N. K., Formulation of time series vegetation index from Indian geostationary satellite and comparison with global product. J. Indian Soc. Remote Sensing, 2011, 40(1), 1–9.
- Temimi, M., Romanov, P., Ghedira, H., Khanbilvardi, R. and Smith, K., Sea-ice monitoring over the Caspian Sea using geostationary satellite data. Int. J. Remote Sensing, 2011, 32(6), 1575– 1593.
- Legeckis, R. and LeBorgne, P., EUMETSAT geostationary satellite monitors the sea surface temperatures of the Atlantic and Indian Oceans since 2004. Environ. Res. Eng. Manage., 2009, 3(49), 4–9.
- Clayson, C. A. and Weitlich, D., Variability of tropical diurnal sea surface temperature. J. Climate, 2007; https://doi.org/10.1175/JCLI3999.1.
- Wang, M., Son, S., Jiang, L. and Shi, W., Observations of ocean diurnal variations from the Korean geostationary ocean color imager (GOCI). Proc. SPIE 9111, Ocean Sensing and Monitoring VI, 911102, 2014; doi:10.1117/12.2053476.
- Qi, L., Hu, C., Visser, P. M. and Ma, R., Diurnal changes of cyanobacteria blooms in Taihu Lake as derived from GOCI observations. Limnol. Oceanogr., 2018; doi:10.1002/lno.10802.
- Lou, X. and Chuanmin, H., Diurnal changes of a harmful algal bloom in the East China Sea: observations from GOCI. Remote Sensing Environ., 2014, 140, 562–572; https://doi.org/10.1016/j.rse.2013.09.031.
- Park, J.-E., Park, K.-A., Ullman, D. S., Cornillon, P. C. and Park, Young-Je, Observation of diurnal variations in mesoscale eddy sea-surface currents using GOCI data. Remote Sensing Lett., 2016; https://doi.org/10.1080/2150704X.2016.1219423,1131-1140.
- Lukas, R., Observations of air–sea interaction in the western Pacific warm pool during WEPOCS. In Paper presented at the Western Pacific International Meeting and Workshop for TOGA COARE, Institut francais de Recherche scientifique pour le Developpement en Cooperation (ORSTOM), NOUMEA, New Caledonia, 1989.
- Shinoda, T., Hendon, H. H. and Glick, J., Intraseasonal variability of surface fluxes and sea surface temperature in the tropical western Pacific and Indian Oceans. J. Climate, 1998, 11, 1685–1702.
- Sengupta, D., Goswami, B. N. and Senan, R., Coherent intraseasonal oscillations of ocean and atmosphere during the Asian summer monsoon. Geophys. Res. Lett., 2001, 28, 4127–4130.
- Shahi, N. R., Thapliyal, P. K., Sharma, R., Pal, P. K. and Sarkar, A., Estimation of net surface shortwave radiation over the tropical Indian Ocean using geostationary satellite observations: algorithm and validation. J. Geophys. Res., 2011, 116, C09031; doi:10.1029/ 2011JC007105.
- Le Traon, P.-Y. et al., Use of satellite observations for operational oceanography: recent achievements and future prospects. J. Operational Oceanogr., 2015, 8(s12–s27); doi:10.1080/1755876X.2015.1022050.
- Minnett, P. J., Zhu, X., Hendee, J., Manfrino, C. and Berkelmans, R., Diurnal heating of shallow water – implications for satellite remote sensing of sea-surface temperature and monitoring coastal environments. In IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012, Munich, Germany, 22–27 July 2012.
- Stuart‐Menteth, A. C., Robinson, I. S. and Challenor, P. G., A global study of diurnal warming using satellite‐derived sea surface temperature. J. Geophys. Res. (Oceans), 2003, 108(C5), 3155; doi:10.1029/2002JC001534.
- Mathur, A., Srinivasan, I., Gohil, B. S., Sarkar, A. and Agarwal, V. K., Development of sea surface temperature retrieval algorithm for INSAT-3D. In Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions, International Society for Optics and Photonics, Goa, India, December 2006, vol. 6404, p. 64040E.
- Martin, M. et al., Group for High Resolution Sea Surface temperature (GHRSST) analysis fields inter-comparisons. Part 1: a GHRSST multi-product ensemble (GMPE). Deep Sea Res. II, 2012, 77–80, 21–30; doi.org/10.1016/j.dsr2.2012.04.013.
- Schmetz, J. and Liu, Q., Outgoing longwave radiation and its diurnal variation at regional scales derived from Meteosat. J. Geophys. Res., 1988, 93(11), 192–204.
- Venkatesan, R., Lix, J. K., Phanindra Reddy, A., Arul Muthiah, M. and Atmanand, M. A. Two decades of operating the Indian moored buoy network: significance and impact. J. Oper. Oceanogr., 2016, 9(1), 45–54.
- Shukla, M. V., Thapliyal, P. K., Bisht, J. H., Mankad, K. N., Pal, P. K. and Navalgund, R. R., Intersatellite calibration of Kalpana thermal infrared channel using AIRS hyperspectral observations. IEEE Geosci. Remote Sensing Lett., 2012, 9(4), 687–689; doi:10.1109/LGRS.2011.2178813.
- Casey, K. and Cornillon, P., A comparison of satellite and in situ– based sea surface temperature climatologies. J. Climate, 1999, 12(6), 1848–1863.
- Marra, J., Houghton, R. and Garside, C., Phytoplankton growth at the shelf-break front in the middle Atlantic bight. J. Mar. Res., 1990, 48(4), 851–868; doi:https://doi.org/10.1357/002224090784988665.
- Weller, R. A. and Anderson, S. P., Surface meteorology and air– sea fluxes in the western equatorial Pacific Warm Pool during the TOGA Coupled Ocean–Atmosphere Response Experiment. J. Climate, 1996, 9, 1959–1990; doi:10.1175/1520-0442(1996)009< 1959:SMAASF>2.0.CO;2.
- Role of Ocean Dynamics on Mesoscale and Sub-Mesoscale Variability of Ekman Pumping for the Bay of Bengal using SCATSAT-1 Forced Ocean Model Simulations
Abstract Views :245 |
PDF Views:78
Authors
Affiliations
1 Oceanic Sciences Division, Space Applications Centre, ISRO, Ahmedabad 380 015, IN
1 Oceanic Sciences Division, Space Applications Centre, ISRO, Ahmedabad 380 015, IN
Source
Current Science, Vol 117, No 6 (2019), Pagination: 993-1001Abstract
Role of ocean dynamics on vertical velocity of Ekman pumping (VVE) is analysed using simulations from very high resolution Ocean General Circulation Model (OGCM) configured for the Bay of Bengal (BoB). For this purpose, OGCM is forced with SCATSAT-1 scatterometer wind fields for 2017. Three mechanisms which modify VVE in the ocean are addressed in this study; the first results from the influence of sea surface temperature (SST) on wind field, and the other two arise from the influence of ocean surface currents (OSCs) on the wind field. Analysis for different length scales ranging from mesoscale to sub-mesoscale is also carried out. The results suggest a significant role of ocean dynamics on VVE, especially over submesoscale range (spatial scales of the order of 2– 10 km). Relative vorticity of OSC-induced Ekman pumping is found to be quite high (~3 m/day) at 2 km length scale, especially along the periphery of mesoscale eddies and along the filament structures. Impact of SST on VVE is least amongst the three factors and is observed to be significant only up to the length scales of 30 km. For length scales less than 10 km, relative vorticity-induced Ekman pumping increases drastically and the total Ekman pumping vertical velocity is predominantly controlled by the relative vorticity of OSC-induced Ekman pumping only.Keywords
Ekman Pumping, Ocean Dynamics, Scatterometers, Vertical Velocity, Wind Field.References
- Klein, P. and Lapeyre, G., The Oceanic vertical pump induced by mesoscale and submesoscale turbulence. Annu. Rev. Mar. Sci., 2009, 1, 351–375.
- Brannigan, L., Intense submesoscale upwelling in anticyclonic eddies. Geophys. Res. Lett., 2016, 43, 3360–3369; doi:10.1002/2016GL067926.
- Pickett, M. H. and Paduan, J. D., Ekman transport and pumping in the California current based on the US Navy’s high resolution atmospheric model (COAMPS). J. Geophys. Res., 2003, 108(C10), 3327; doi:10.1029/2003JC001902.
- Thushara, V. and Vinayachandran, P. N., Formation of summer phytoplankton bloom in the northwestern Bay of Bengal in a coupled physical–ecosystem model. J. Geophys. Res. Oceans, 2016, 121, 8535–8550; doi:10.1002/2016JC011987.
- Chelton, D. B., Schlax, M. G., Freilich, M. H. and Milliff, R. F., Satellite measurements reveal persistent small-scale features in ocean winds. Science, 2004, 303, 978–983.
- Sharma, R., Agarwal, N., Chakraborty, A., Mallick, S., Buckley, J., Shesu V. and Tandon, A., Large-scale air–sea coupling processes in the Bay of Bengal using space-borne observations. Oceanography, 2016, 29(2), 192–201; http://dx.doi.org/10.5670/oceanog.2016.51.
- Jensen, T. G. et al., Submesoscale features and their interaction with fronts and internal tides in a high-resolution coupled atmosphere– ocean–wave model of the Bay of Bengal. Ocean Dyn., 2018, 68, 391; https://doi.org/10.1007/s10236-018-1136-x.
- Wijesekera, H. W. et al., ASIRI: an ocean–atmosphere initiative for Bay of Bengal. Bull. Am. Meteorol. Soc., 2016, 97, 1859– 1884; https://doi.org/10.1175/BAMS-D-14- 00197.1.
- Chelton, D. B. and Xie, S., Coupled ocean–atmosphere interaction at oceanic mesoscales. Oceanogaphy, 2010, 23, 52–69; doi:10.5670/oceanog.2010.05.
- Gaube, P., Chelton, D. B., Samelson, R. M., Schlax, M. G. and O’Neill, L. W., Satellite observations of mesoscale eddy induced Ekman pumping. J. Phys. Oceanogr., 2016, 45(104), 131; doi:10.1175/JPO-D-14-0032.1.
- McGillicuddy Jr, D. J., Mechanisms of physical–biological– biogeochemical interaction at oceanic mesoscale. Annu. Rev. Mar. Sci., 2016, 8, 125–159.
- Ledwell, J. R., Watson, A. J. and Law, C. S., Evidence for slow mixing across the pycnocline from an open-ocean tracer-release experiment. Nature, 1993, 364, 701–703.
- McWilliams, J. C., Submesoscale currents in the ocean. Proc. R. Soc. A: Math. Phys. Eng. Sci., 2016, 472(2189), 20160117; http://dx.doi.org/10.1098/rspa.2016.0117.
- Mahadevan, A. and Tandon, A., An analysis of mechanisms for submesoscale vertical motion at ocean fronts. Ocean Model., 2006, 14, 241–256.
- Wei, Y., Zhang, R.-H. and Wang, H., Mesoscale wind stress–SST coupling in the Kuroshio extension and its effect on the ocean. J. Oceanogr., 2017, 73(6), 785–758; doi:10.1007/s10872-0170432-2.
- O’Neill, L. W., Wind speed and stability effects on coupling between surface wind stress and SST observed from buoys and satellite. J. Climate, 2012, 25, 1544–1569; doi:10.1175/JCLI-D-11-00121.1.
- Small, R. J. et al., Air–sea interaction over ocean fronts and eddies. Dyn. Atmosp. Oceans, 2008, 45, 274–319.
- Seo, H., Miller, A. J. and Roads, J. O., The Scripps coupled Ocean–Atmosphere Regional (SCOAR) model, with applications in the eastern Pacific sector. J. Clim., 2007, 20, 381–402.
- Okumura, Y., Xie, S.-P., Numaguti, A. and Tanimoto, Y., Tropical Atlantic air–sea interaction and its influence on the NAO. Geophys. Res. Lett., 2001, 28, 1507–1510.
- Agarwal, N., Sharma, R., Basu, S. K., Sarkar, A. and Agarwal, V. K., Evaluation of relative performance of QuikSCAT and NCEP re-analysis winds through simulations by an OGCM. Deep Sea Res. Part I: Oceanogr. Res. Pap., 2007, 54(8), 1311–1328.
- Deb, S. K., Bhowmick, S. A., Kumar, R. and Sarkar, A., Intercomparison of numerical model generated surface winds with QuikSCAT winds over the Indian Ocean. Mar. Geodesy, 2009, 32(4), 391–408.
- Jaeger, G. S. and Mahadevan, A., Submesoscale selective compensation of fronts in a salinity-stratified ocean. Sci. Adv., 2018, 4(2); doi:10.1126/sciadv.1701504.
- Adcroft, A., Hill, C., Campin, J. M., Marshall, J. and Heimbach, P., Overview of the formulation and numerics of the MIT GCM. In Proceedings of the ECMWF Seminar Series on Numerical Methods, 2004, pp. 139–149.
- Sindhu, B., Suresh, I., Unnikrishnan, A. S., Bhatkar, N. V., Neetu, S. and Michael, G. S., Improved bathymetric datasets for the shallow water regions in the Indian Ocean. J. Earth Syst. Sci., 2007, 116(3), 261–274.
- Mallick, S. K., Agarwal, N., Sharma, R. and Prasad, K. V. S. R., Sensitivity of upper ocean dynamics in high-resolution tropical Indian Ocean model to different flux parameterization: case study for the Bay of Bengal (BoB). Int. Arch. Photogramm. Remote Sensing Spatial Inf. Sci., 2018, 839–847; https://doi.org/10.5194/isprs-archives-XLII-5-839-20.
- Dee, D. et al., The era-interim reanalysis: configuration and performance of the data assimilation system. Q. J. Roy. Meteorol.Soc., 2011, 137, 553–597; doi:10.1002/qj.828.
- Papa, F., Durand, F., Rossow, W. B., Rahman, A. and Bala, S. K., Satellite altimeter‐derived monthly discharge of the Ganga– Brahmaputra River and its seasonal to interannual variations from 1993 to 2008. J. Geophys. Res.: Oceans, 2010, 115(C12); https://doi.org/10.1029/2009/C006075.
- Yaremchuk, M., Yu, Z. and McCreary, J., River discharge into the Bay of Bengal in an inverse ocean model. Geophys. Res. Lett., 2005, 32(16); https://doi.org/10.1029/2005GL023750.
- Mandal, S., Sil, S., Shee, A., Swain, D. and Pandey, P. C., Comparative analysis of SCATSAT-1 gridded winds with buoys, ASCAT and ECMWF winds in the Bay of Bengal. IEEE J. Selected Top. Appl. Earth Observ. Remote Sensing, 2018, 11(3), 845–851; doi:10.1109/JSTARS.2018.2798621.
- Stern, M., Interaction of a uniform wind stress with a geostrophic vortex. Deep–Sea Res. Oceanogr. Abstr., 1965, 12, 355–367; doi:10.1016/0011-7471(65)90007-0.
- Chelton, D., Schilax, M. G. and Samelson, R. M., Summertime coupling between sea surface temperature and wind stress in the California current system. J. Phys. Oceanogr., 2007, 495–517.
- Wortham, C. and Wunsch, C., A multidimensional spectral description of ocean variability. J. Phys. Oceanogr., 2014, 44, 944– 966; doi:10.1175/JPO-D-13-0113.1.
- Biri, S., Serra, N., Scharffenberg, M. G. and Stammer, D., Atlantic sea surface height and velocity spectra inferred from satellite altimetry and a hierarchy of numerical simulations. J. Geophys. Res., 2016, 121, 4157–4177; doi:10.1002/2015JC011503.
- Seo, H., Murtugudde, R., Jochum, M. and Miller, A. J., Modeling of mesoscale coupled ocean–atmosphere interaction and its feedback to ocean in the western Arabian Sea. Ocean Model., 2008, 25, 120–131.
- Comprehensive Remote Sensing, Volume 8:Oceans
Abstract Views :176 |
PDF Views:73
Authors
Affiliations
1 Oceanic Sciences Division, Space Applications Centre, ISRO, Ahmedabad 380 015, IN
1 Oceanic Sciences Division, Space Applications Centre, ISRO, Ahmedabad 380 015, IN
Source
Current Science, Vol 116, No 11 (2019), Pagination: 1920-1921Abstract
Comprehensive Remote Sensing is a series of nine volumes comprising allinclusive discussions across various disciplines of the earth system in the context of remote sensing. The editor-inchief, Shunlin Liang, has done a commendable job in bringing together more than 100 authors to contribute nearly 120 chapters in these series. Volume 8 of the series is specifically dedicated to the oceans. This volume comprises 11 chapters covering various aspects of ocean remote sensing.- Design And Development of a Low-Cost GNSS Drifter for Rip Currents
Abstract Views :250 |
PDF Views:84
Authors
Affiliations
1 Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Ahmedabad 380 015, IN
2 Mechanical Engineering Systems Area, Space Applications Centre (ISRO), Ahmedabad 380 015,, IN
1 Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Ahmedabad 380 015, IN
2 Mechanical Engineering Systems Area, Space Applications Centre (ISRO), Ahmedabad 380 015,, IN
Source
Current Science, Vol 118, No 2 (2020), Pagination: 273-279Abstract
Lagrangian drifters are analogues of particles that are relevant to flow-field characterization and therefore they represent realistic surface currents compared to Eulerian techniques. The use of global navigation satellite system (GNSS) in such drifters with Differential Global Positioning System mode at high frequency (5–10 Hz) sampling and post-processing kinematic results in position estimates with centimeter-level accuracy. In the complex nearshore zone, deploying expensive instruments is a risk due to greater chances of loss. To avoid this, two drifters have been designed and developed using a low-cost Emlid Reach® GNSS receivers, antennae and ‘off-the-shelf’ PVC components to measure the surface currents. The dimensions of the drifters were optimally chosen to minimize the wind and wave impacts and to increase the subsurface current drag. An analysis of relative position and velocity errors from stationary observations indicates that the drifter can resolve motion accurately with minimal errors of ±1 cm and ±2 cm/s respectively. These drifters were used to measure surf zone currents at the RK Beach, Visakhapatnam during May 2018 and to successfully identify dangerous rip current zones. This study presents the design, development aspects, error analysis and testing of GNSS drifters. Although these drifters are primarily developed to measure the rip current velocities and trajectories in the nearshore zone, they can also be operated in any marine environment like rivers, lakes, estuaries, etc. without change in the design. An extensive study using a fleet of such drifters is required to understand the complex physical processes in the marine environment.Keywords
Drifter, Error Estimation, Rip Currents, Relative Position and Velocity.References
- Poje, A. C. et al., Submesoscale dispersion in the vicinity of the Deepwater Horizon spill. Proc. Natl. Acad. Sci. USA, 2014, 111, 12693–12698.
- Stocker, R. and Imberger, J., Horizontal transport and dispersion in the surface layer of a medium-sized lake. Limnol. Oceanogr., 2003, 48, 971–982.
- Schroeder, K. et al., Targeted Lagrangian sampling of submesoscale dispersion at a coastal frontal zone. Geophys. Res. Lett., 2012, 39.
- MacMahan, J., Brown, J. and Thornton, E., Low-cost handheld global positioning system for measuring surf-zone currents. J. Coast. Res., 2009, 744–754.
- Schmidt, W. E., Woodward, B. T., Millikan, K. S., Guza, R. T., Raubenheimer, B. and Elgar, S., A GPS-tracked surf zone drifter. J. Atmos. Ocean. Techolnol., 2003, 20, 1069–1075.
- Spydell, M., Feddersen, F., Guza, R. T. and Schmidt, W. E., Observing surf-zone dispersion with drifters. J. Phys. Oceanogr., 2007, 37, 2920–2939.
- Boehm, A. B. et al., Decadal and shorter period variability of surf zone water quality at Huntington Beach, California. Environ. Sci. Technol., 2002, 36, 3885–3892.
- Nasello, C. and Armenio, V., A new small drifter for shallow water basins: application to the study of surface currents in the Muggia Bay (Italy). J. Sensors, 2016; http://dx.doi.org/10.1155/2016/65896362016.
- Earle, M. D., Riverine drifter. Technical Report, Planning Systems Inc Slidell LA, 2007.
- Suara, K., Wang, C., Feng, Y., Brown, R. J., Chanson, H. and Borgas, M., High-resolution GNSS-tracked drifter for tudying surface dispersion in shallow water. J. Atmos. Ocean. Technol., 2015, 32, 579–590.
- Murray, S. P., Trajectories and speeds of wind-driven currents wear the coast. J. Phys. Oceanogr., 1975, 5, 347–360.
- Johnson, D., Stocker, R., Head, R., Imberger, J. and Pattiaratchi, C., A compact, low-cost GPS drifter for use in the oceanic nearshore zone, lakes, and estuaries. J. Atmos. Oceanogr. Technol., 2003, 20, 1880–1884.
- Johnson, D. and Pattiaratchi, C., Transient rip currents and nearshore circulation on a swell-dominated beach. J. Geophys. Res.: Oceans, 2004, 109.
- Arun Kumar, S. V. V. and Prasad, K. V. S. R., Rip current-related fatalities in India: a new predictive risk scale for forecasting rip currents. Nat. Hazards, 2014, 70, 313–335.
- Retrieval of High-Resolution Nearshore Bathymetry from Sentinel-2 Twin Multispectral Imagers using a Multi-Scene Approach
Abstract Views :217 |
PDF Views:73
Authors
Surisetty V. V. Arun Kumar
1,
Ch. Venkateswarlu
2,
B. Sivaiah
2,
K. V. S. R. Prasad
2,
Rashmi Sharma
1,
Raj Kumar
1
Affiliations
1 Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, IN
2 Department of Meteorology and Oceanography, Andhra University, Visakhapatnam 530 004, IN
1 Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, IN
2 Department of Meteorology and Oceanography, Andhra University, Visakhapatnam 530 004, IN
Source
Current Science, Vol 119, No 11 (2020), Pagination: 1824-1830Abstract
Determining nearshore bathymetry by traditional surveying methods is a challenging task as it involves huge costs and efforts. Most of the coastal shallowwater zones worldwide either remain unmapped or not updated. Bathymetry estimations from optical satellite imageries have been increasingly implemented as an alternative tool for traditional bathymetry surveys. In this study, we examine the usefulness of freely available, five-day revisit and relatively highresolution Multi Spectral Instruments (MSI) on-board Sentinel-2A and 2B twin satellites. A process workflow has been developed which automatically incorporates a robust atmospheric correction through ACOLITE software and multi-scene compositing of several scenes to improve the reliability and no data gaps. Two study sites in India are explored owing to their variability in submarine morphology. High-resolution bathymetry maps are generated through a log-ratio transform model calibrated with minimal in situ data from the jet ski soundings. The satellite-derived bathymetry obtained has an overall bias of –0.01 and 0.02 m, and ischolar_main mean square error of 1.09 and 0.93 m respectively, at two study sites up to 15 m depth. The consistency in bathymetry retrieval indicates a potential for automated application for the benefit of operational and scientific studies. These high-resolution maps capture small-scale nearshore features like sandbars and rip channels, which are of prime importance for coastal and beach managers.Keywords
Optical Remote Sensing, Multispectral Imagers, Nearshore Bathymetry Maps, Rip Channel, Twin Satellites.- Cyclone Amphan: Oceanic Conditions Pre- and Post-Cyclone using in situ and Satellite Observations
Abstract Views :170 |
PDF Views:102
Authors
Suchandra A. Bhowmick
1,
Neeraj Agarwal
1,
Rashmi Sharma
1,
R. Sundar
2,
R. Venkatesan
2,
C. Anoopa Prasad
1,
K. N. Navaneeth
1
Affiliations
1 Space Applications Centre, Indian Space Research Organization, Ahmedabad 380 015, IN
2 National Institute of Ocean Technology, MOES, Chennai 600 100, IN
1 Space Applications Centre, Indian Space Research Organization, Ahmedabad 380 015, IN
2 National Institute of Ocean Technology, MOES, Chennai 600 100, IN