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Fast Processes in Large-Scale Atmospheric Models

Progress, Challenges, and Opportunities

Yangang Liu (Brookhaven National Laboratory, USA) Pavlos Kollias (Brookhaven National Laboratory; Stony Brook University, USA) Leo J. Donner

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English
American Geophysical Union
12 January 2024
Improving weather and climate prediction with better representation of fast processes in atmospheric models

Many atmospheric processes that influence Earth’s weather and climate occur at spatiotemporal scales that are too small to be resolved in large scale models. They must be parameterized, which means approximately representing them by variables that can be resolved by model grids.

Fast Processes in Large-Scale Atmospheric Models: Progress, Challenges and Opportunities explores ways to better investigate and represent multiple parameterized processes in models and thus improve their ability to make accurate climate and weather predictions.

Volume highlights include:

Historical development of the parameterization of fast processes in numerical models Different types of major sub-grid processes and their parameterizations Efforts to unify the treatment of individual processes and their interactions Top-down versus bottom-up approaches across multiple scales Measurement techniques, observational studies, and frameworks for model evaluation Emerging challenges, new opportunities, and future research directions

The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
Edited by:   , ,
Consultant editor:  
Imprint:   American Geophysical Union
Country of Publication:   United States
Weight:   1.733kg
ISBN:   9781119528999
ISBN 10:   1119528992
Series:   Geophysical Monograph Series
Pages:   480
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active

Yangang Liu, Brookhaven National Laboratory, USA. Pavlos Kollias, Brookhaven National Laboratory and Stony Brook University, USA.

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