Abstract
Determining an oceanographic parameter on regular grid positions, using a set of data at random locations both in space and time, is the most sought after typical problem since long in the field of oceanography. This is usually called the gridding problem, and the outcome is useful for many applications such as data analysis, graphical display, forcing or initialization of models, etc. In the present study temperature and salinity profiles data obtained from Argo profiling floats were used, and data on regular grids were generated. Data-interpolating variational analysis (DIVA) method was chosen for generating the gridded product. Extensive analysis was done to obtain correct choices of correlation length (L) and signal-to-noise ratio (λ), which results in an optimal gridded product. The gridded data obtained for different choices of L and λ were later validated with datasets deliberately set aside before performing the analyses. For each combination of L and λ, the resultant gridded data was also validated with subsurface data from OMNI buoys. Based on the statistics of comparison with OMNI, the best-fit choice for L and λ was concluded. Later, a comparative analysis was performed with the obtained gridded products from DIVA against the gridded product obtained from objective analysis (OA) to demonstrate the method's reliability. The resultant optimal combination of L and λ will be used for generating Argo gridded data, which will be subsequently used for generating value-added products like mixed layer depth, ocean heat content, D20, etc., and will be made available on INCOIS Live Access Server.
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References
Barth A, Alvera Azcárate A, Troupin C, Ouberdous M and Beckers J M 2010 A web interface for gridding arbitrarily distributed in-situ data based on data-interpolating variational analysis (DIVA); Adv. Geosci. 28 29–37.
Beckers J M, Barth A, Troupin C and Alvera Azcarate A 2014 Approximate and efficient methods to assess error fields in spatial gridding with data interpolating variational analysis (DIVA); J. Atmos. Ocean. Technol. 31(2) 515–530.
Bhaskar T V S U, Ravichandran M and Devender R 2007 An operational objective analysis system at INCOIS for generation of Argo value-added products; Tech. Report, INCOIS-MOG-ARGO-TR-04-2007.
Bhaskar T V S U, Swain D and Ravichandran M 2006 Inferring mixed-layer depth variability from Argo observations in the western Indian Ocean; J. Mar. Res. 64(3) 393–406.
Brankart J M and Brasseur P 1996 Optimal analysis of in-situ data in the western Mediterranean using statistics and cross-validation; J. Atmos. Ocean. Tech. 13 477–491.
Brasseur P, Beckers J M, Brankart J M and Schoenauen R 1996 Seasonal temperature and salinity fields in the Mediterranean Sea: Climatological analyses of a historical data set; Deep-Sea Res. I 43 159–192.
Daley R 1991 Atmospheric data analysis, Cambridge Atmospheric and Space Science Series; Cambridge Univ. Press, New York.
Freeland H, Roemmich D, Garzoli S, Traon P Y, Ravichandran M, Riser S, Thierry V, Wijffels S, Belbéoch M, Gould W, Grant F, Mark I, King B, Klein B, Mork K, Owens B, Pouliquen S, Sterl A, Suga T and Xu J 2010 Argo – A decade of progress; In: Proceedings of the OceanObs 09: Sustained Ocean Observations and Information for Society Conference (eds) Hall J, Harrison D E and Stammer D, Vol. 2, Venice, Italy, 21–25 September 2009, ESA Publication, WPP 306.
Gandin L S 1965 Objective analysis of meteorological fields; Israel Program for Scientific Translations, Jerusaleum, 242p.
Jha R K, Ugail H, Haron H and Iglesias A 2018 Multiresolution Discrete finite difference masks for rapid solution approximation of the Poisson’s Equation; 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), Phnom Penh, Cambodia, pp. 1–7.
Kambhammettu B V N P, Allena P and King J P 2011 Application and evaluation of universal kriging for optimal contouring of groundwater levels; J. Earth Syst. Sci. 120 413.
Kessler and McCreary 1993 The annual wind-driven Rossby wave in the subthermocline equatorial Pacific; J. Phys. Oceanogr. 23 1192–1207.
Krige D G 1951 A statistical approach to some basic mine valuation problems on the Witwatersrand; J. Geophys. Res. 95 13529–13541.
Menemenlis D, Fieguth P, Wunsch C and Willsky A 1997 Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data; J. Geophys. Res. 102(C5) 10,573–10,584.
Pattabhi Rama Rao E, Udaya Bhaskar T V S, Venkat Seshu R, Srinivasa Rao N, Suprit K and Geetha G 2018 Marine Data Services at National Oceanographic Data Centre-India; Data Sci. J. 17 11.
Rixen M, Beckers J M, Brankart J M and Brasseur P 2000 A numerically efficient data analysis method with error map generation; Ocean Model 2 45–60.
Shesu R V, Bhaskar T U, Rao E P R, Devender R and Rao T H 2013 Open source architecture for web-based oceanographic data services; Data Sci. J. 12 47–55.
Simi Mathew, Shamji V R, Vengatesan G, Arul Muthiah M and Venkatesan R 2016 Systematic validation of conductivity and temperature from ocean moored buoy data in the northern Indian Ocean with in-situ ship-based measurements; Indian J. Geo-Mar. Sci. 45(2) 224–229.
Smith D M, Cusack S, Colman A W, Folland C K, Harris G R and Murphy J M 2007 Improved surface temperature prediction for the coming decade from a global climate model; Science 317 796–799.
Troupin C, Barth A, Sirjacobs D, Ouberdous M, Brankart J M, Brasseur P, Rixen M, Alvera Azcárate A, Belounis M, Capet A, Lenartz F, Toussaint M E and Beckers J M 2012 Generation of analysis and consistent error fields using the data interpolating variational analysis (DIVA); Ocean Modelling 52–53 90–101.
Troupin C, Ouberdous M, Sirjacobs D, Alvera Azcárate A, Barth A, Toussaint M E and Beckers J M 2013 Diva User Guide.
Acknowledgements
The authors are grateful to the Director, Indian National Centre for Ocean Information Services (INCOIS), Hyderabad for his constant encouragement and providing the facilities to carry out the work. We also encourage the efforts of the scientific team of Argo data collection and distribution. The authors express gratitude for the DIVA software developer at GHER, ULiège, Belgium (modb.oce.ulg.ac.be/mediawiki/index.php/DIVA). We wish to acknowledge the use of the Ferret program, a product of NOAA’s Pacific Marine Environmental Laboratory, for analysis and graphics in this paper. We thank the anonymous reviewers for their constructive remarks in improving the manuscript. This is INCOIS contribution number 424.
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Ravi Kumar Jha has performed all the analysis, picture generations, and prepared the original manuscript. T V S Udaya Bhaskar established the methods configuration, reviewed and edited the original manuscript.
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Communicated by C Gnanaseelan
Supplementary material pertaining to this article is available on the Journal of Earth System Science website (http://www.ias.ac.in/Journals/Journal_of_Earth_System_Science).
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Jha, R.K., Udaya Bhaskar, T.V.S. Optimal parameters for generation of gridded product of Argo temperature and salinity using DIVA. J Earth Syst Sci 130, 170 (2021). https://doi.org/10.1007/s12040-021-01675-2
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DOI: https://doi.org/10.1007/s12040-021-01675-2