Birs- 23w5093: Mathematical Approaches of Atmospheric Constituents Data Assimilation and Inverse Modeling

  1. Title:
    NOAA efforts in chemical data assimilation and inverse modeling, an overview

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  2. Title:
    CAMS air quality and GHG and methane monitoring and inversion

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  3. Title:
    Mathematics of chemical data assimilation and inverse modeling

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  4. Title:
    Continuous formulation of advective dynamics and variance loss

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  5. Title:
    On the observability quest for chemistry data assimilation and emission inversion

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    The representativeness of observations and emissions for air quality analyses on regional scales

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  7. Title:
    Constituent data assimilation plans of the GMAO at NASA

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  8. Title:
    Challenges and opportunities in inverse modeling: A case with Carbon Monoxide sources

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  9. Title:
    Characterizing model errors in inverse modelling of CH4 and CO2 fluxes using weak constraint 4D-Var

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  10. Title:
    Assessing wildfire emissions of CO using 4D-Var inverse modeling

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  11. Title:
    On variational Fokker-Planck filters and smoothers

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  12. Title:
    Coupling of meteorology and tracers in data assimilation systems

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  13. Title:
    Development of the ECCC’s national carbon flux inversion system

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  14. Title:
    Applying analytical inversion and 4D-Var to estimate sources of methane and air pollutants

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  15. Title:
    Using process control theory to estimate sea spray emissions in GEOS-Chem

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  16. Title:
    Inverse modeling with HYSPLIT Lagrangian dispersion model

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  17. Title:
    Utilizing machine learning to optimize the choice of error distribution in data assimilation

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  18. Title:
    Online model error correction with neural networks – from theory to the ECMWF forecasting system

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  19. Title:
    Towards CO2 plume detection and source inversion from satellites using deep neural networks

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  20. Title:
    Overview of the role of Machine learning in atmospheric research

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