Scientific Machine Learning at DMI

Hi!

Welcome to the website for people interested in scientific machine learning (SciML) at the Danish Meteorological Institute (DMI) - SciML is the discipline of combining machine learning with scientific computing see e.g. sciml.ai for an overview. This website serves to organise our work and allow others to join us.

If you’re based at DMI and would like to join you can simply make a pull-request to add yourself to this website: people.md

We meet the first Monday of every month (think “ML Mondays”) at 11am local time at DMI - everyone is welcome!

People

  • Leif Denby

    Self-supervised/unsupervised learning, convolutional networks

    meso-scale cloud organisation, denoising of LIDAR water vapour observations

  • Kasper Hintz

    XGBoost, Random Forest

    Quality control of smartphone pressure observations

  • Marion Devilliers

    beginner, interested in CNN for downscaling

    climate models

  • Peter Thejll

    supervised learning of CNN, CNN for image regression (and classification)

    Radiometry: determine terrestrial albedo from Moon images

  • Maxime Beauchamp

    Supervised/unsupervised learning, Neural data assimilation schemes

    Geosciences, CAO (Climate, Atmosphere, Ocean), Data assimilation, Spatial Statistics

  • Søren Borg Thorsen

    Beginner

    Post-processing of NWP data and downstream applications

  • Peter Ukkonen

    RNNs, MLPs, CNNs

    ESM parameterizations, e.g. radiative transfer

  • Phillip Aarestrup

    LSTMs, CNNs

    Forecasting and simulation of river networks

  • Tommaso Benacchio

    Supervised learning, Physics-informed neural networks

    dynamical cores

  • Ruth Mottram

    Still learning, convolutional neural networks

    Regional climate model emulation, Surface mass balance modelling in Greenland and Antarctica

  • Fabrizio Baordo

    interested in supervised/unsupervised learning

    ai data driven NWP forecasts, ML for remote sensing products and satellite data assimilation within NWP systems

  • Villy Mik-Meyer

    interested in supervised learning

    Data driven models for extreme WL predictions

  • Kristian H. Møller

    Beginner

    Atmospheric Dispersion

  • Tore Wulf

    supervised/self-supervised learning, vision models, uncertainty quantification

    earth observation (e.g. sea ice mapping in SAR imagery), data-driven forecast models

  • Carlos Peralta

    Beginner/intermediate. Hands-on experience with most python ML libraries., Interested in supervised/unsupervised machine learning

    Model verification and post-processing, Interested in data-driven forecast models, downscaling, time-series forecasting

  • Kristian Pagh Nielsen

    Beginner (in AI)

    weather and climate radiative transfer (forward) modelling, remote sensing (inverse) modelling

  • Thomas Bøvith

    Machine learning

    Nowcasting of precipitation, Data quality

  • Rashpal Gill

    Machine learning

    Hydrometeor classification, Data quality

  • Flemming Vejen

    Machine learning, Data quality

    Precipitation estimation

  • André Düsterhus

    Interested in supervised/unsupervised machine learning

    Effect of North Atlantic system on European Climate, Chances and limitation of ML methods

  • Nicolaj Hansen

    Machine learning, Data quality, Emulators

    Build emulators that can downscale precipitation over ice sheets

  • Michael Andersen

    GNNs, XGBoost, Hyperparameter Optimization, ResNet/CNN, Word2Vec text embedding, OCR, Neo4j, GraphDB

    Fraud detection, Image classification, Similarity/context searches

  • Henriette Rilling

    Supervised/unsupervised learning, Physics-informed neural networks

    Precipitation time series forecasting, spatial + temporal gap-filling, ML for radar products

Posts