Predicting ocean surface currents using numerical weather prediction model and Kohonen neural network: a northern Adriatic study

Hrvoje Kalinić, Hrvoje Mihanović, Simone Cosoli, Martina Tudor, Ivica Vilibić

    Research output: Contribution to journalArticle

    6 Citations (Scopus)

    Abstract

    The paper documents a concept of ocean forecasting system for ocean surface currents based on self-organizing map (SOM) trained by high-resolution numerical weather prediction (NWP) model and high-frequency (HF) radar data. Wind and surface currents data from the northern Adriatic coastal area were used in a 6-month long training phase to obtain SOM patterns. Very high correlation between current and joined current and wind SOM patterns indicated the strong relationship between winds and currents and allowed for creation of a prediction system. Increasing SOM dimensions did not increase reliability of the forecasting system, being limited by the amount of the data used for training and achieving the lowest errors for 4 × 4 SOM matrix. As the HF radars and high-resolution NWP models are strongly expanding in coastal oceans, providing reliable and long-term datasets, the applicability of the proposed SOM-based forecasting system is expected to be high.

    Original languageEnglish
    Pages (from-to)611-620
    Number of pages10
    JournalNeural Computing and Applications
    Volume28
    DOIs
    Publication statusPublished - 1 Dec 2017

    Fingerprint Dive into the research topics of 'Predicting ocean surface currents using numerical weather prediction model and Kohonen neural network: a northern Adriatic study'. Together they form a unique fingerprint.

    Cite this