Fast Processes in Large Scale Atmospheric Models: Progress, Challenges, and Opportunities (Geophysical Monograph)

ISBN 10: 1119528992 / ISBN 13: 9781119528999

Published by American Geophysical Union, 2023

About this Item

Seller Inventory # 389447686

Bibliographic Details

• Title: Fast Processes in Large Scale Atmospheric Models: Progress, Challenges, and Opportunities (Geophysical Monograph)
• Publisher: American Geophysical Union
• Publication Date: 2023
• Language: English
• ISBN 10: 1119528992
• ISBN 13: 9781119528999
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

About the Author

Yangang Liu, Brookhaven National Laboratory, USA.

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