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Jurnal

Työväentutkimus Vuosikirja

ISSN 0784-1272 · 4 artikel di indeks

  1. Artikel Jurnal Teks lengkap

    Implementation of the lake parameterisation scheme FLake into the numerical weather prediction model COSMO

    Dmitrii Mironov; Erdmann Heise; Ekaterina Kourzeneva dkk.

    Työväentutkimus Vuosikirja · 2024 · Inggris · OpenAlex

    The application of the lake model FLake to represent the effect of lakes in numerical weather prediction (NWP) and climate models is discussed.As a lake parameterisation scheme FLake is implemented into the limited-area NWP model COSMO.Results from a numerical experiment with the coupled…

  2. Artikel Jurnal

    Formation and growth of fresh atmospheric aerosols: eight years of aerosol size distribution data from SMEAR II, Hyytiälä, Finland

    Miikka Dal Maso; Markku Kulmala; Ilona Riipinen dkk.

    Työväentutkimus Vuosikirja · 2024 · Inggris · OpenAlex

    We analyzed size distributions measured continuously at a boreal forest measurement site at Hyytiälä, Finland between 1996 and 2003. From the eight-year data we identified days when new aerosol particle formation was taking place as well as days when no formation was detected, removing days with…

  3. Artikel Jurnal

    Climate change projections for Finland during the 21st century

    Kirsti Jylhä; Heikki Tuomenvirta; Kimmo Ruosteenoja

    Työväentutkimus Vuosikirja · 2024 · Inggris · OpenAlex

    On the basis of fifteen global model simulations of future climate, using the SRES emissions scenarios for greenhouse gases and aerosols, we have constructed national-scale seasonal and annual climate change scenarios for Finland during the 21st century. In approximate terms, the annual mean…

  4. Artikel Jurnal

    Uncovering the structure of clinical EEG signals with self-supervised learning

    Hubert J. Banville; Omar Chehab; Aapo Hyvärinen dkk.

    Työväentutkimus Vuosikirja · 2021 · Inggris · OpenAlex

    Objective. Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), where labeling can be costly in terms of specialized expertise and human…