A Data Assimilation Scheme to Improve the Wave Predictions in the Black Sea

被引:3
|
作者
Butunoiu, Dorin [1 ]
Rusu, Eugen [1 ]
机构
[1] Univ Dunarea de Jos, Dept Mech Engn, Galati, Romania
来源
关键词
Black Sea; waves; SWAN; data assimilation; satellite data; COASTAL ENVIRONMENT; ROMANIAN NEARSHORE; ALTIMETER DATA; WIND; IMPACT; MODELS;
D O I
10.1109/OCEANS-Genova.2015.7271242
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
The objective of the work is to evaluate a data assimilation scheme for improving the reliability of the wave predictions in the Black Sea. A wave modeling system, SWAN based, has been implemented in the Black Sea and it was extensively tested against both in situ and remotely sensed measurements. Subsequently, this system was focused on various coastal environments in a multi-level nested scheme. In order to assimilate the satellite data, for the wave generation level that covers the entire basin of the Black Sea, an Optimal Interpolation (OI) technique has been implemented. SWAN model simulations have been performed covering a 10-year time interval (1999-2008). For this period, Hs measurements from the following satellites were available: ERS-2, Poseidon, JASON-1, JASON-2, GEOSAT Follow-On (GFO), ENVISAT, TOPEX. The assimilation technique was performed for a time window of 24h considering the first 5 satellites for assimilation and the last two (ENVISAT and TOPEX) for validations. The results show that the assimilation scheme implemented induces a visible improvement of all the statistical parameters evaluated, increasing in this way the reliability of the wave predictions. The work is still ongoing being focused on the prediction of the major storms and as a further step all the satellites will be considered for assimilation, while validations are going to be performed against various in situ measurements.
引用
收藏
页数:6
相关论文
共 50 条
  • [41] A model for monitoring the evolution of the Black Sea ecosystem on the basis of remote sensing data assimilation
    Dorofeyev, V.
    Sukhikh, L.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2018, 39 (24) : 9339 - 9355
  • [42] AN ENSEMBLE KALMAN FILTER DATA ASSIMILATION SCHEME FOR MODELING THE WAVE CLIMATE IN PERSIAN GULF
    Serpoushan, Nima
    Zeinoddini, Mostafa
    Golestani, Maziar
    PROCEEDINGS OF THE ASME 32ND INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING - 2013 - VOL 5, 2013,
  • [43] Neural-Network-Based Data Assimilation to Improve Numerical Ocean Wave Forecast
    Deshmukh, Aditya N.
    Deo, M. C.
    Bhaskaran, Prasad K.
    Nair, T. M. Balakrishnan
    Sandhya, K. G.
    IEEE JOURNAL OF OCEANIC ENGINEERING, 2016, 41 (04) : 944 - 953
  • [44] WAVE DATA ASSIMILATION IN THE WAM WAVE MODEL
    DELASHERAS, MM
    BURGERS, G
    JANSSEN, PAEM
    JOURNAL OF MARINE SYSTEMS, 1995, 6 (1-2) : 77 - 85
  • [45] North Sea Wave Analysis Using Data Assimilation and Mesoscale Model Forcing Winds
    Caires, S.
    Marseille, G. J.
    Verlaan, M.
    Stoffelen, A.
    JOURNAL OF WATERWAY PORT COASTAL AND OCEAN ENGINEERING, 2018, 144 (04)
  • [46] A DRP-4DVar Data Assimilation Scheme for Typhoon Initialization Using Sea Level Pressure Data
    Zhao, Ying
    Wang, Bin
    Liu, Juanjuan
    MONTHLY WEATHER REVIEW, 2012, 140 (04) : 1191 - 1203
  • [47] Black Sea wave energy atlas from 13 years hindcasted wave data
    Aydogan, Burak
    Ayat, Berna
    Yuksel, Yalcin
    RENEWABLE ENERGY, 2013, 57 : 436 - 447
  • [48] Local assimilation of wave model predictions for weather routing systems
    Goncalves, Marta
    Soares, C. Guedes
    OCEAN ENGINEERING, 2022, 266
  • [49] Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions
    Musuuza, Jude Lubega
    Gustafsson, David
    Pimentel, Rafael
    Crochemore, Louise
    Pechlivanidis, Ilias
    REMOTE SENSING, 2020, 12 (05)
  • [50] Assessment of SWE data assimilation for ensemble streamflow predictions
    Franz, Kristie J.
    Hogue, Terri S.
    Bank, Muhammad
    He, Minxue
    JOURNAL OF HYDROLOGY, 2014, 519 : 2737 - 2746