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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.
Sommario
List of contributors vii
Preface xi
1 Progress in Understanding and Parameterizing Fast Physics in Large-Scale Atmospheric Models 1
Yangang Liu and Pavlos Kollias
Part I Processes and Parameterizations
2 Radiative Transfer and Atmospheric Interactions 13
Yu Gu and Kuo-Nan Liou
3 AerosolsandClimateEffects 53
Xiaohong Liu
4 Entrainment, Mixing, and Their Microphysical Influences 87
Chunsong Lu, Yangang Liu, Xiaoqi Xu, Sinan Gao, and Cheng Sun
5 Deep Convection and Convective Clouds 121
Leo J. Donner
6 Stratus, Stratocumulus, and Remote Sensing 141
Xiquan Dong and Patrick Minnis
7 Planetary Boundary Layer and Processes 201
Virendra P. Ghate and David B. Mechem
8 Human Impacts on Land Surface-Atmosphere Interactions 213
Michael Barlage and Fei Chen
9 Gravity Wave Drag Parameterizations for Earth's Atmosphere 229
Christopher G. Kruse, Jadwiga H. Richter, M. Joan Alexander, Julio T. Bacmeister, Christopher Heale, and Junhong Wei
Part II Unifying Efforts
10 Higher-Order Equations Closed by the Assumed PDF Method: Suitability for Parameterizing Cumulus Convection 259
Vincent E. Larson
11 An Introduction to the Eddy-Diffusivity/Mass-Flux (EDMF) Approach: A Unified Turbulence and ConvectionParameterization 271
João Teixeira, Kay Suselj, and Marcin J. Kurowski
12 Application of Machine Learning to Parameterization Emulation and Development 283
Vladimir Krasnopolsky and Alexei Belochitski
13 Top-DownApproachestotheStudyofCloudSystems 313
Graham Feingold and Ilan Koren
Part III Measurements, Model Evaluation, and Model-measurement Integration
14 Ground-Based Remote-Sensing of Key Properties 329
Katia Lamer, Pavlos Kollias, Vassilis Amiridis, Eleni Marinou, Ulrich Loehnert, Sabrina Schnitt, and Allison McComiskey
15 Satellite and Airborne Remote Sensing of Clouds and Aerosols 361
Alexander Marshak and Anthony B. Davis
16 In Situ and Laboratory Measurements of Cloud Microphysical Properties 399
Kamal Kant Chandrakar and Raymond A. Shaw
17 Frameworks for Testing and Evaluating Fast Physics: Parameterizations in Climate and Weather Forecasting Models 425
Wuyin Lin and Shaocheng Xie
18 Future Research Outlook: Challenges and Opportunities 445
Yangang Liu and Pavlos Kollias
Index 451
Info autore
Yangang Liu, Brookhaven National Laboratory, USA.
Pavlos Kollias, Brookhaven National Laboratory and Stony Brook University, USA.
Riassunto
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.
