Geoinformation System for Argo Floats Drift Assessment: The Black Sea Case

E. V. Zhuk, N. V. Markova

Marine Hydrophysical Institute of RAS, Sevastopol, Russian Federation

e-mail: elena.zhuk @mhi-ras.ru

Abstract

Purpose. The work is aimed at developing and implementing a geographic information system (GIS) that provides an opportunity for online work with the Argo floats data in the Black Sea and for its application to assess the float drift velocities in different sea layers.

Methods and Results. The geoinformation system is developed based on a client-server architecture using PostgreSQL DBMS to store the Argo float data, the jQuery, Plotly and mapbox gl libraries and, therefore, to implement a user interface and a cartographic service. The floats drift velocities are calculated and analyzed using the information provided by the Argo project in the public domain. The information is received from the autonomous drifting profiling floats and includes data on their satellite positioning, drift depths and profiling. The velocities at the float drift horizon are calculated using the data on its trajectory, meanwhile GIS assumes the possibility to recalculate velocities swiftly when new observation data are received, adjust calculation methodology, expand the range of statistical characteristics as well as to add a number of additional options. The Argo data array (early 2005 – mid 2022) was included in the system current version to calculate and analyze velocities. Application of GIS made it possible to estimate floats drift velocities in the Black Sea, specify mean velocity values as compared to the previous studies and show its seasonal variability in different layers of the sea.

Conclusions. The online services of the Argo project are complemented by the developed GIS that simplifies processing and scientific analysis of the Black Sea oceanographic data significantly with no need to use additional scripts, data downloads and external visualization systems. The examples of applying the system for the assessment of floats drift velocities at different depths and in certain parts of the sea are shown. In the future, GIS can be supplemented with new modules, such as automatic downloading of Argo data, operating with similar data arrays obtained, for example, from drifters or ADCP current profilers. Besides, it can be applied to any other regions.

Keywords

geoinformation system, GIS, Argo floats, drift velocity, currents, Black Sea, database, DB

Acknowledgements

The work on development and implementation of GIS was carried out within the framework of the themes of state assignment of FSBSI FRC MHI FNNN-2024-0012 and FNNN-2024-0014. The velocity field features were studied within the framework of theme of state assignment FNNN-2024-0001. The authors are grateful to DSc. (Geogr.) V.N. Belokopytov for useful consultations while developing and testing the system, as well as to the reviewers for their attention to the work and its appreciation.

Original russian text

Original Russian Text © The Authors, 2024, published in MORSKOY GIDROFIZICHESKIY ZHURNAL, Vol. 40, Iss. 4, pp. 611–630 (2024)

For citation

Zhuk, E.V. and Markova, N.V., 2024. Geoinformation System for Argo Floats Drift Assessment: The Black Sea Case. Physical Oceanography, 31(4), pp. 562-579.

References

  1. Riser, S.C., Freeland, H.J., Roemmich, D., Wijffels, S., Troisi, A., Belbéoch, M., Gilbert, D., Xu, J., Pouliquen, S. [et al.], 2016. Fifteen Years of Ocean Observations with the Global Argo Array. Nature Climate Change, 6(2), pp. 145-153. https://doi.org/10.1038/nclimate2872X
  2. Wong, A.P.S., Wijffels, S.E., Riser, S.C., Pouliquen, S., Hosoda, S., Roemmich, D., Gilson, J., Johnson, G.C., Martini, K. [et al.], 2020. Argo Data 1999–2019: Two Million Temperature-Salinity Profiles and Subsurface Velocity Observations from a Global Array of Profiling Floats. Frontiers in Marine Science, 7, 700. https://doi.org/10.3389/fmars.2020.00700
  3. Ollitrault, M. and Rannou, J.-P., 2013. ANDRO: An Argo-Based Deep Displacement Dataset. Journal of Atmospheric and Oceanic Technology, 30(4), pp. 759-788. https://doi.org/10.1175/JTECH-D-12-00073.1
  4. Park, J.J., Kim, K., King, B.A. and Riser, S.C., 2005. An Advanced Method to Estimate Deep Currents from Profiling Floats. Journal of Atmospheric and Oceanic Technology, 22(8), pp. 1294-1304. https://doi.org/10.1175/JTECH1748.1
  5. Korotaev, G., Oguz, T. and Riser, S., 2006. Intermediate and Deep Currents of the Black Sea Obtained from Autonomous Profiling Floats. Deep Sea Research Part II: Topical Studies in Oceanography, 53(17-19), pp. 1901-1910. https://doi.org/10.1016/j.dsr2.2006.04.017X
  6. Gerasimova, S.V. and Lemeshko, E.E., 2011. Estimation of Deep-Water Current Velocities Based on ARGO Data. Environmental Control Systems, 15, pp. 187-196 (in Russian).
  7. Milanova, M. and Peneva, E., 2016. Deep Black Sea Circulation Described by Argo Profiling Floats. In: Sofia University “St. Kliment Ohridski”, 2016. Annual of Sofia University “St. Kliment Ohridski”, Faculty of Physics. Sofia: St. Kliment Ohridski University Press. Vol. 109, 12 p. Available at: https://www.phys.uni-sofia.bg/annual/archive/109/full/GSU-Fizika-109_02.pdf [Accessed: 07 July 2024].
  8. Markova, N.V. and Bagaev, A.V., 2016. The Black Sea Deep Current Velocities Estimated from the Data of ARGO Profiling Floats. Physical Oceanography, (3), pp. 23-35. https://doi.org/10.22449/1573-160X-2016-3-23-35X
  9. Ivanov, V.A. and Belokopytov, V.N., 2013. Oceanography of the Black Sea. Sevastopol: MHI, 210 p. 10 Demyshev, S.G., Dymova, O.A., Markova, N.V., Korshenko, E.A., Senderov, M.V., Turko, N.A., and Ushakov, K.V., 2021. Undercurrents in the Northeastern Black Sea Detected on the Basis of Multi-Model Experiments and Observations. Journal of Marine Science and Engineering, 9(9), 933. https://doi.org/10.3390/jmse9090933
  10. Zhuk, E., 2023. ARGO Black Sea Database: Storage and Visualization. In: K. Themistocleous, D. G. Hadjimitsis, S. Michaelides and G. Papadavid, eds., 2023. Proceedings of SPIE. SPIE. Volume 12786: Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), 127861Q. https://doi.org/10.1117/12.2681583

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