Land-ATmosphere Interactions in Cold Environments – LATICE

LATICE aims to advance the knowledge base concerning land atmosphere interactions through improved model representation of snow, permafrost, hydrology and large-scale vegetation processes representative of high latitudes, including; 1) New ground observations (gap filling using ML); 2) Land surface model parametrization using data science methods; 3) Seasonal snow cover dynamics using data assimilation and Earth observations (e.g. satellite data, drones).

Read more about the project (mn.uio.no)

Tags: Bayesian inference, Bayesian machine learning, Data fusion/integration, Deep learning/neural networks, Dynamical systems and differential equations, Gaussian processes, Model selection, Monte Carlo methods, Non-linear dynamics, Spatial statistics, Stochastic mathematical modeling, Time series, Uncertainty quantification, Statistical methods
Published July 6, 2023 1:24 PM - Last modified Oct. 18, 2023 1:49 PM
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