We invite you to submit to our AGU session on “ Applications of Machine Learning Algorithms in Modeling Atmospheric Aerosols, Clouds and Radiation ” . Our motivation for this session is to foster talks and discussions that can enhance predictive understanding of Earth System Models by applications of novel machine learning algorithms and tools with a focus on improving the accuracy and speed of model predictions that are constrained by atmospheric physics and chemistry. The deadline for abstract submission is Wednesday July 29th, 2020.
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The Session Co-Conveners
Manish Shrivastava, Anthony Wexler, Ziheng Sun, Daniel Tong
Applications of Machine Learning Algorithms in Modeling Atmospheric Aerosols, Clouds and Radiation
Recent breakthroughs in machine learning algorithms and artificial intelligence provide unprecedented tools for enhancing predictive understanding of the existing Earth System Models. These tools could be used for a wide range of problems, such as replacing computationally expensive modules, representing stochastic processes in radiative transfer models, using physics informed neural networks to understand unknown processes, and representing and propagating uncertainties in atmospheric models across different spatial and temporal scales. However, progress in scientific applications has been comparatively slow. It is challenging to generalize the ML models for a larger scale, interpret the trained models, and improve the reproducibility. We invite talks on innovative machine learning algorithms focused on modeling atmospheric aerosols, clouds and radiation. These could range from replacing existing computationally expensive modules to reducing model error, interpreting physical processes and uncertainty quantification, and integrating AI models into the established numeric models to achieve better quality and lower cost.
Session ID: 105898
Session Title: . Applications of Machine Learning Algorithms in Modeling Atmospheric Aerosols, Clouds and Radiation
Section: Atmospheric Sciences
View Session Details: https://agu.confex.com/agu/fm20/prelim.cgi/Session/105898