Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting
Authors
Keywords
-
Journal
Journal of Advances in Modeling Earth Systems
Volume 13, Issue 7, Pages -
Publisher
American Geophysical Union (AGU)
Online
2021-06-17
DOI
10.1029/2021ms002477
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI
- (2021) Matthew Chantry et al. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Reduced‐precision parametrization: lessons from an intermediate‐complexity atmospheric model
- (2020) Leo Saffin et al. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
- Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
- (2020) Janni Yuval et al. Nature Communications
- A Fortran-Keras Deep Learning Bridge for Scientific Computing
- (2020) Jordan Ott et al. Scientific Programming
- Accelerating Radiation Computations for Dynamical Models With Targeted Machine Learning and Code Optimization
- (2020) Peter Ukkonen et al. Journal of Advances in Modeling Earth Systems
- Can machines learn to predict weather? Using deep learning to predict gridded 500‐hPa geopotential height from historical weather data
- (2019) Jonathan A. Weyn et al. Journal of Advances in Modeling Earth Systems
- Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse‐graining
- (2019) Noah D. Brenowitz et al. Journal of Advances in Modeling Earth Systems
- The Application of Machine Learning Techniques to Improve El Niño Prediction Skill
- (2019) Henk A. Dijkstra et al. Frontiers in Physics
- Could Machine Learning Break the Convection Parameterization Deadlock?
- (2018) P. Gentine et al. GEOPHYSICAL RESEARCH LETTERS
- Prognostic Validation of a Neural Network Unified Physics Parameterization
- (2018) N. D. Brenowitz et al. GEOPHYSICAL RESEARCH LETTERS
- Sensitivity of the Brewer–Dobson Circulation and Polar Vortex Variability to Parameterized Nonorographic Gravity Wave Drag in a High-Resolution Atmospheric Model
- (2018) I. Polichtchouk et al. JOURNAL OF THE ATMOSPHERIC SCIENCES
- Impact of Parametrized Nonorographic Gravity Wave Drag on Stratosphere-Troposphere Coupling in the Northern and Southern Hemispheres
- (2018) Inna Polichtchouk et al. GEOPHYSICAL RESEARCH LETTERS
- Deep learning to represent subgrid processes in climate models
- (2018) Stephan Rasp et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events
- (2018) Paul A. O'Gorman et al. Journal of Advances in Modeling Earth Systems
- Single Precision in Weather Forecasting Models: An Evaluation with the IFS
- (2017) Filip Váňa et al. MONTHLY WEATHER REVIEW
- Significance of changes in medium-range forecast scores
- (2016) Alan J. Geer TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY
- Improved Middle Atmosphere Climate and Forecasts in the ECMWF Model through a Nonorographic Gravity Wave Drag Parameterization
- (2010) Andrew Orr et al. JOURNAL OF CLIMATE
- A Reduced Radiation Grid for the ECMWF Integrated Forecasting System
- (2008) Jean-Jacques Morcrette et al. MONTHLY WEATHER REVIEW
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started