Anna Sommer

Research Scientist at National Centre for Atmospheric Science
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Contact Information
us****@****om
(386) 825-5501
Location
Reading, England, United Kingdom, UK
Languages
  • Russian Native or bilingual proficiency
  • French Native or bilingual proficiency
  • English Full professional proficiency

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Credentials

  • Deep Learning Specialization
    Coursera
    Nov, 2021
    - Sep, 2024

Experience

  • National Centre for Atmospheric Science
    • United Kingdom
    • Research Services
    • 1 - 100 Employee
    • Research Scientist
      • Feb 2023 - Present

      High-resolution ocean and climate model development. High-resolution ocean and climate model development.

  • University of East Anglia
    • United Kingdom
    • Higher Education
    • 700 & Above Employee
    • Senior Research Associate
      • Jan 2020 - Jan 2023

      Work on the improvement of global biogeochemical ocean model PlankTOM using Machine Learning. Identification of the linkages between surface environmental and ecosystem structure and particulate organic carbon distribution within the ocean interior using real observations, PlankTOM outputs and Machine Learning. Analysis of the optimal number of classes for particulate organic matter that should be presented in the global biogeochemical models to adequately represent the carbon flux. Work on the improvement of global biogeochemical ocean model PlankTOM using Machine Learning. Identification of the linkages between surface environmental and ecosystem structure and particulate organic carbon distribution within the ocean interior using real observations, PlankTOM outputs and Machine Learning. Analysis of the optimal number of classes for particulate organic matter that should be presented in the global biogeochemical models to adequately represent the carbon flux.

  • CEA
    • Chile
    • Industrial Machinery Manufacturing
    • Postdoctoral Researcher
      • May 2019 - Dec 2019

      Improvement of meso-scale parametrisation in ocean models using Machine Learning: reconstruction of sub-grid-scale buoyancy fluxes from large-scale ocean variables. Improvement of meso-scale parametrisation in ocean models using Machine Learning: reconstruction of sub-grid-scale buoyancy fluxes from large-scale ocean variables.

  • Laboratoire des sciences du climat et de l'environnement
    • Postdoctoral Researcher
      • Jan 2017 - Aug 2018

      The application of statistical approaches for reconstruction of pCO2 and the analysis of uncertainties of models and data: neural network, VGAM (R). Work on reports and presentation of results in the AtlantOS project, H2020. Outputs of developed Machine Learning model are distributed by Copernicus Marine Environment Monitoring Service. Data are used for analysis in annual Carbon Budget rapport since 2019. The application of statistical approaches for reconstruction of pCO2 and the analysis of uncertainties of models and data: neural network, VGAM (R). Work on reports and presentation of results in the AtlantOS project, H2020. Outputs of developed Machine Learning model are distributed by Copernicus Marine Environment Monitoring Service. Data are used for analysis in annual Carbon Budget rapport since 2019.

Education

  • Pierre and Marie Curie University
    Doctor of Philosophy - PhD, Physical Oceanography
    2013 - 2016
  • Université Grenoble Alpes
    Master's degree, Master's Program in Environmental Fluid Mechanics
    2012 - 2013
  • Novosibirsk State University (NSU)
    Master's degree, Mechanics, specialty: mathematical modelling
    2009 - 2011
  • Novosibirsk State University (NSU)
    Bachelor's degree, Mathematics, specialty: Applied Mathematics and Computer Science
    2005 - 2009

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