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  • 1.
    Hieronymus, Jenny
    et al.
    SMHI, Research Department, Oceanography.
    Eilola, Kari
    SMHI, Research Department, Oceanography.
    Hieronymus, Magnus
    SMHI, Research Department, Oceanography.
    Meier, Markus
    SMHI, Research Department, Oceanography.
    Saraiva, Sofia
    Karlson, Bengt
    SMHI, Research Department, Oceanography.
    Causes of simulated long-term changes in phytoplankton biomass in the Baltic proper: a wavelet analysis2018In: Biogeosciences, ISSN 1726-4170, E-ISSN 1726-4189, Vol. 15, no 16, p. 5113-5129Article in journal (Refereed)
    Download full text (pdf)
    fulltext
  • 2.
    Hieronymus, Magnus
    et al.
    SMHI, Research Department, Oceanography.
    Hieronymus, Jenny
    SMHI, Research Department, Oceanography.
    Arneborg, Lars
    SMHI, Research Department, Oceanography.
    Sea level modelling in the Baltic and the North Sea: The respective role of different parts of the forcing2017In: Ocean Modelling, ISSN 1463-5003, E-ISSN 1463-5011, Vol. 118, p. 59-72Article in journal (Refereed)
  • 3.
    Hieronymus, Magnus
    et al.
    SMHI, Research Department, Oceanography.
    Hieronymus, Jenny
    SMHI, Research Department, Oceanography.
    Hieronymus, Fredrik
    On the Application of Machine Learning Techniques to Regression Problems in Sea Level Studies2019In: Journal of Atmospheric and Oceanic Technology, ISSN 0739-0572, E-ISSN 1520-0426, Vol. 36, no 9, p. 1889-1902Article in journal (Refereed)
    Abstract [en]

    Long sea level records with high temporal resolution are of paramount importance for future coastal protection and adaptation plans. Here we discuss the application of machine learning techniques to some regression problems commonly encountered when analyzing such time series. The performance of artificial neural networks is compared with that of multiple linear regression models on sea level data from the Swedish coast. The neural networks are found to be superior when local sea level forcing is used together with remote sea level forcing and meteorological forcing, whereas the linear models and the neural networks show similar performance when local sea level forcing is excluded. The overall performance of the machine learning algorithms is good, often surpassing that of the much more computationally costly numerical ocean models used at our institute.

    Download full text (pdf)
    fulltext
  • 4. Skyllas, Nomikos
    et al.
    Bintanja, Richard
    Buma, Anita G. J.
    Brussaard, Corina P. D.
    Groger, Matthias
    SMHI, Research Department, Oceanography.
    Hieronymus, Jenny
    SMHI, Research Department, Oceanography.
    van de Poll, Willem H.
    Validation of Stratification-Driven Phytoplankton Biomass and Nutrient Concentrations in the Northeast Atlantic Ocean as Simulated by EC-Earth2019In: GEOSCIENCES, ISSN 2076-3263, Vol. 9, no 10, article id 450Article in journal (Refereed)
    Download full text (pdf)
    fulltext
1 - 4 of 4
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