Challenges and Prospects for Numerical Techniques in Atmospheric Modeling

Author:

Li J.11,Li Y.22,Steppeler J.33,Laurian A.44,Fang F.55,Knapp D.66

Affiliation:

1. International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China;

2. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China;

3. Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany;

4. Meteorologisches Institut, Ludwig-Maximilians-Universität, Munich, Germany;

5. Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, London, United Kingdom;

6. Deutsches Zentrum für Luft- und Raumfahrt (DLR), Cologne, Germany

Publisher

American Meteorological Society

Subject

Atmospheric Science

Reference5 articles.

1. Opportunities and challenges for machine learning in weather and climate modelling: Hard, medium and soft AI;Chantry, M.,2021

2. Behavior of flow over step orography;Gallus, W. A.,2000

3. A unified approach to energy conservation and potential vorticity dynamics for arbitrarily-structured C-grids;Ringler, T.,2010

4. Conservative transport schemes for spherical geodesic grids: High-order flux operators for ODE-based time integration;Skamarock, W. C.,2011

5. Numerical representation of geostrophic modes on arbitrarily structured C-grids;Thuburn, J.,2009

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