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The definition of dynamic areas of searching for shipwrecks, and/or the movement of pollution across waters of Szczecin Lagoon first requires the knowledge and specification of hydro-meteorological conditions across the area. This article compares wind parameters from various sources of meteorological stations located close to Szczecin Lagoon. The wind speed and direction were obtained from Ueckermuende, I Brama Torowa (Urząd Morski Szczecin), and Kopice (wind meter of Szczecin Maritime Academy). Wind direction data analysis was based on directional statistics methods and tools.
Rocznik
Tom
Strony
83--93
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
- Maritime University of Szczecin 1-2 Wały Chrobrego St., 70-500 Szczecin, Poland
autor
- Maritime University of Szczecin 1-2 Wały Chrobrego St., 70-500 Szczecin, Poland
autor
- Maritime University of Szczecin 1-2 Wały Chrobrego St., 70-500 Szczecin, Poland
Bibliografia
- 1. Bartoka, J., Habalab, O., Bednar, P., Gazaka, M. & Hluch, L. (2012) Data Mining and Integration for Predicting Significant Meteorological Phenomena. Procedia Computer Science 1(1), pp. 37–46.
- 2. Berens, P. (2009) CircStat: A MATLAB Toolbox for Circular Statistics. Journal of Statistical Software 31 (10), doi: 10.18637/jss.v031.i10.
- 3. Biuro Hydrograficzne Marynarki Wojennej (2009) Locja Bałtyku. Wybrzeże Polskie. Wydanie IX. Gdynia.
- 4. Caccamo, M.T., Calabrö, E., Cannuli, A. & Magazü, S. (2016) Wavelet Study of Meteorological Data Collected by ArduinoWeather Station: Impact on Solar Energy Collection Technology. MATEC Web of Conference 55, 02004, doi: 10.1051/matecconf/20165502004.
- 5. Chakraborty, A. & Gangopadhyay, A. (2016) Development of a High-Resolution Multiscale Modeling and Prediction System for Bay of Bengal, Part II: An Application to October 2008. Open Journal of Marine Science 6(1), pp. 125–144, doi: 10.4236/ojms.2016.61012.
- 6. Colston, J.M., Ahmed, T., Mahopo, C., Kang, G., Kosek, M., de Sousa Junior, F., Shrestha, P.S., Svensen, E., Turab, A. & Zaitchik, B. (2018) Evaluating meteorological data from weather stations, and from satellites and global models for a multi-site epidemiological study. Environmental Research 165, pp. 91–109.
- 7. Estévez, J., Gavilán, P. & Giráldez, J.V. (2011) Guidelines on validation procedures for meteorological data from automatic weather stations. Journal of Hydrology 402(1–2), pp.144–154, doi: 10.1016/j.jhydrol.2011.02.031.
- 8. Fang, W., Sheng, V., Wen, X. & Pan, W. (2014) Meteorological Data Analysis Using MapReduce. The Scientific World Journal 3, 646497, doi: 10.1155/2014/646497.
- 9. Feng, S., Hu, Q. & Qian, W. (2004) Quality control of daily meteorological data in China, 1951–2000: a new dataset. International Journal of Climatology 24(7), pp. 853–870, doi: 10.1002/joc.1047.
- 10. Gospodarka Morska (2017) Trwają poszukiwania załogi jachtu na Zalewie Szczecińskim. Available from: www.gospodarkamorska.pl/MW,Sluzby-Morskie/trwaja-poszukiwania-zalogi-jachtu-na-zalewie-szczecinskim.html [Accessed: October 27, 2017].
- 11. Guo, Q., Huang, R., Zhuang, L., Zhang, K. & Huang, J. (2019) Assessment of China’s Offshore Wind Resources Based on the Integration of Multiple Satellite Data and Meteorological Data. Remote Sensing 11 (22), 2680, doi: 10.3390/rs11222680.
- 12. Haidvogel, D.B. & Beckmann, A. (1999) Numerical ocean circulation modeling. London: Imperial College Press.
- 13. Heaney, A., Little, E., Ng, S. & Shaman, J. (2016) Meteorological variability and infectious disease in Central Africa: a review of meteorological data quality. Annals of the New York Academy of Sciences 1382 (1), doi: 10.1111/ nyas.13090.
- 14. IMO (2013) International Aeronautical and Maritime Search and Rescue Manual. Volume II Mission Coordination. London: IMO/ICAO.
- 15. Jammalamadaka, R.S. & SenGupta, A. (2001) Topics in Circular Statistics. Singapore: World Scientific.
- 16. Kampel, M. & Camayo, R. (2021) Comparison of Multiple Surface Ocean Wind Products with Buoy Data over Blue Amazon (Brazilian Continental Margin). Advances in Meteorology 2021, 6680626, doi: 10.1155/2021/6680626.
- 17. Kantha, L. & Clayson, C. (2000) Small Scale Processes in Geophysical Fluid Flows. Amsterdam: Academic Press.
- 18. Korotenko, K.A., Mamedov, R.M., Kontar, A.E. & Korotenko, L.A. (2004) Particle tracking method in the approach for prediction of oil slick transport in the sea: modelling oil pollution resulting from river input. Journal of Marine Systems 48 (1–4), pp. 159–170, doi: 10.1016/j. jmarsys.2003.11.023.
- 19. Lagouvardos, K., Kotroni, V., Bezes, A., Koletsis, I., Kopania, T., Lykoudis, S., Mazarakis, N., Papagiannaki, K. & Vougioukas, S. (2017) The automatic weather stations NOANN network of the National Observatory of Athens: operation and database. Geoscience Data Journal 4(1), doi: 10.1002/gdj3.44.
- 20. Lazarevic, B. & Popovic, T. (2020) Meteorological Data Aggregation System for Application in Agriculture 2.0. Proceedings: 7th International Conference on Electrical, Electronic and Computing Engineering IcETRAN 2020, Belgrade, pp. 615–772.
- 21. Lo, Y.-H., Blancoa, J.A., Seelya, B., Welhama, C. & (Hamish) Kimminsa, J.P. (2011) Generating reliable meteorological data in mountainous areas with scarce presence of weather records: the performance of MTCLIM in interior British Columbia, Canada. Environmental Modelling & Software 26(5), pp. 644–657, doi: 10.1016/j.envsoft.2010.11.005.
- 22. Mardia, K. & Jupp, P. (1999) Directional Statistics. Wiley Series in Probability and Statistics, John Wiley & Sons Ltd.
- 23. Mayer, M.J. (2021) Effects of the meteorological data resolution and aggregation on the optimal design of photovoltaic power plants. Energy Conversion and Management 241(8), 114313, doi: 10.1016/j.enconman.2021.114313.
- 24. Montgomery, D.C. & Runger, G.C. (1994) Applied Statistics and Probability for Engineers. New York: John Wiley & Sons.
- 25. Tammelin, B., Vihma, T. & Atlaskin, E. et al. (2013) Production of the Finnish Wind Atlas. Wind Energy 16, pp. 19– 35.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d11a3aea-a824-4c68-a6bb-f2331ba0b6c0
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