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Fast Processes in Large-Scale Atmospheric Models
Progress, Challenges, and Opportunities
Yangang Liu (Edited by), Y Liu (Author), Pavlos Kollias (Edited by), Leo J. Donner (Consultant editor)
9781119528999, Wiley
Hardback, published 12 January 2024
480 pages
28.3 x 22.4 x 3.4 cm, 1.733 kg
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: 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.
List of contributors vii Preface xi 1 Progress in Understanding and Parameterizing Fast Physics in Large-Scale Atmospheric Models 1 Part I Processes and Parameterizations 2 Radiative Transfer and Atmospheric Interactions 13 3 AerosolsandClimateEffects 53 4 Entrainment, Mixing, and Their Microphysical Influences 87 5 Deep Convection and Convective Clouds 121 6 Stratus, Stratocumulus, and Remote Sensing 141 7 Planetary Boundary Layer and Processes 201 8 Human Impacts on Land Surface-Atmosphere Interactions 213 9 Gravity Wave Drag Parameterizations for Earth’s Atmosphere 229 Part II Unifying Efforts 10 Higher-Order Equations Closed by the Assumed PDF Method: Suitability for Parameterizing Cumulus Convection 259 11 An Introduction to the Eddy–Diffusivity/Mass–Flux (EDMF) Approach: A Unified Turbulence and ConvectionParameterization 271 12 Application of Machine Learning to Parameterization Emulation and Development 283 13 Top-DownApproachestotheStudyofCloudSystems 313 Part III Measurements, Model Evaluation, and Model-measurement Integration 14 Ground-Based Remote-Sensing of Key Properties 329 15 Satellite and Airborne Remote Sensing of Clouds and Aerosols 361 16 In Situ and Laboratory Measurements of Cloud Microphysical Properties 399 17 Frameworks for Testing and Evaluating Fast Physics: Parameterizations in Climate and Weather Forecasting Models 425 18 Future Research Outlook: Challenges and Opportunities 445 Index 451
Yangang Liu and Pavlos Kollias
Yu Gu and Kuo-Nan Liou
Xiaohong Liu
Chunsong Lu, Yangang Liu, Xiaoqi Xu, Sinan Gao, and Cheng Sun
Leo J. Donner
Xiquan Dong and Patrick Minnis
Virendra P. Ghate and David B. Mechem
Michael Barlage and Fei Chen
Christopher G. Kruse, Jadwiga H. Richter, M. Joan Alexander, Julio T. Bacmeister, Christopher Heale, and Junhong Wei
Vincent E. Larson
João Teixeira, Kay Suselj, and Marcin J. Kurowski
Vladimir Krasnopolsky and Alexei Belochitski
Graham Feingold and Ilan Koren
Katia Lamer, Pavlos Kollias, Vassilis Amiridis, Eleni Marinou, Ulrich Loehnert, Sabrina Schnitt, and Allison McComiskey
Alexander Marshak and Anthony B. Davis
Kamal Kant Chandrakar and Raymond A. Shaw
Wuyin Lin and Shaocheng Xie
Yangang Liu and Pavlos Kollias
Subject Areas: Science: general issues [PD]
