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Cloud-Resolving Modeling of Convective Processes

Cloud-Resolving Modeling of Convective Processes
Author: Xiaofan Li
Publisher: Springer
Total Pages: 364
Release: 2016-05-17
Genre: Science
ISBN: 3319263609

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This is an updated and revised second edition of the book presenting new developments in the field of cloud-resolving modeling. The first edition of the book introduces the framework of cloud-resolving model, methodologies for analysis of modeling outputs, and validation of simulations with observations. It details important scientific findings in the aspects of surface rainfall processes, precipitation efficiency, dynamic and thermodynamic processes associated with tropical convection, diurnal variations, radiative and cloud microphysical processes associated with development of cloud clusters, air-sea coupling on convective scales, climate equilibrium states, and remote sensing applications. In additional to the content from the first edition of the book, the second edition of the book contains the new scientific results in the development of convective-stratiform rainfall separation scheme, the analysis of structures of precipitation systems, the thermal effects of doubled carbon dioxide on rainfall, precipitation predictability, and modeling depositional growth of ice crystal. The book will be beneficial both to graduate students and to researchers who do cloud, mesoscale and global modeling.


Physical Processes in Clouds and Cloud Modeling

Physical Processes in Clouds and Cloud Modeling
Author: Alexander P. Khain
Publisher: Cambridge University Press
Total Pages: 643
Release: 2018-07-05
Genre: Nature
ISBN: 0521767431

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Provides a comprehensive analysis of modern theories of cloud microphysical processes and their representation in numerical cloud models.


Modeling of Atmospheric Chemistry

Modeling of Atmospheric Chemistry
Author: Guy P. Brasseur
Publisher: Cambridge University Press
Total Pages: 631
Release: 2017-06-19
Genre: Science
ISBN: 1108210953

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Mathematical modeling of atmospheric composition is a formidable scientific and computational challenge. This comprehensive presentation of the modeling methods used in atmospheric chemistry focuses on both theory and practice, from the fundamental principles behind models, through to their applications in interpreting observations. An encyclopaedic coverage of methods used in atmospheric modeling, including their advantages and disadvantages, makes this a one-stop resource with a large scope. Particular emphasis is given to the mathematical formulation of chemical, radiative, and aerosol processes; advection and turbulent transport; emission and deposition processes; as well as major chapters on model evaluation and inverse modeling. The modeling of atmospheric chemistry is an intrinsically interdisciplinary endeavour, bringing together meteorology, radiative transfer, physical chemistry and biogeochemistry, making the book of value to a broad readership. Introductory chapters and a review of the relevant mathematics make this book instantly accessible to graduate students and researchers in the atmospheric sciences.


Mean-state Acceleration of Cloud-resolving Models and Large Eddy Simulations

Mean-state Acceleration of Cloud-resolving Models and Large Eddy Simulations
Author:
Publisher:
Total Pages: 18
Release: 2015
Genre:
ISBN:

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In this study, large eddy simulations and cloud-resolving models (CRMs) are routinely used to simulate boundary layer and deep convective cloud processes, aid in the development of moist physical parameterization for global models, study cloud-climate feedbacks and cloud-aerosol interaction, and as the heart of superparameterized climate models. These models are computationally demanding, placing practical constraints on their use in these applications, especially for long, climate-relevant simulations. In many situations, the horizontal-mean atmospheric structure evolves slowly compared to the turnover time of the most energetic turbulent eddies. We develop a simple scheme to reduce this time scale separation to accelerate the evolution of the mean state. Using this approach we are able to accelerate the model evolution by a factor of 2-16 or more in idealized stratocumulus, shallow and deep cumulus convection without substantial loss of accuracy in simulating mean cloud statistics and their sensitivity to climate change perturbations. As a culminating test, we apply this technique to accelerate the embedded CRMs in the Superparameterized Community Atmosphere Model by a factor of 2, thereby showing that the method is robust and stable to realistic perturbations across spatial and temporal scales typical in a GCM.


Current Trends in the Representation of Physical Processes in Weather and Climate Models

Current Trends in the Representation of Physical Processes in Weather and Climate Models
Author: David A. Randall
Publisher: Springer
Total Pages: 372
Release: 2019-01-31
Genre: Science
ISBN: 9811333963

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This book focuses on the development of physical parameterization over the last 2 to 3 decades and provides a roadmap for its future development. It covers important physical processes: convection, clouds, radiation, land-surface, and the orographic effect. The improvement of numerical models for predicting weather and climate at a variety of places and times has progressed globally. However, there are still several challenging areas, which need to be addressed with a better understanding of physical processes based on observations, and to subsequently be taken into account by means of improved parameterization. And this is all the more important since models are increasingly being used at higher horizontal and vertical resolutions. Encouraging debate on the cloud-resolving approach or the hybrid approach with parameterized convection and grid-scale cloud microphysics and its impact on models’ intrinsic predictability, the book offers a motivating reference guide for all researchers whose work involves physical parameterization problems and numerical models.


Precipitation Modeling and Quantitative Analysis

Precipitation Modeling and Quantitative Analysis
Author: Xiaofan Li
Publisher: Springer Science & Business Media
Total Pages: 251
Release: 2011-11-03
Genre: Science
ISBN: 9400723814

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The book examines surface rainfall processes through cloud-resolving modeling and quantitative analysis of surface rainfall budget and summarizes modeling and analysis results in recent seven years. The book shows validation of precipitation modeling against observations and derives a set of diagnostic precipitation equations. The book provides detailed discussions of the applications of precipitation equations to the examination of effects of sea surface temperature, vertical wind shear, radiation, and ice clouds on torrential rainfall processes in the tropics and mid-latitudes, and to the studies of sensitivity of precipitation modeling to uncertainty of the initial conditions and to the estimate of precipitation efficiency. The book can be used as a text book for graduate students and will be beneficial to researchers and forecasters for precipitation process studies and operational forecasts.


Evaluating the Representation and Impact of Convective Processes in the NCAR's Community Climate System Model

Evaluating the Representation and Impact of Convective Processes in the NCAR's Community Climate System Model
Author:
Publisher:
Total Pages:
Release: 2008
Genre:
ISBN:

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Convection and clouds affect atmospheric temperature, moisture and wind fields through the heat of condensation and evaporation and through redistributions of heat, moisture and momentum. Individual clouds have a spatial scale of less than 10 km, much smaller than the grid size of several hundred kilometers used in climate models. Therefore the effects of clouds must be approximated in terms of variables that the model can resolve. Deriving such formulations for convection and clouds has been a major challenge for the climate modeling community due to the lack of observations of cloud and microphysical properties. The objective of our DOE CCPP project is to evaluate and improve the representation of convection schemes developed by PIs in the NCAR (National Center for Atmospheric Research) Community Climate System Model (CCSM) and study its impact on global climate simulations.


Mesoscale-Convective Processes in the Atmosphere

Mesoscale-Convective Processes in the Atmosphere
Author: Robert J. Trapp
Publisher: Cambridge University Press
Total Pages: 359
Release: 2013-03-25
Genre: Science
ISBN: 1107328217

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This new textbook seeks to promote a deep yet accessible understanding of mesoscale-convective processes in the atmosphere. Mesoscale-convective processes are commonly manifested in the form of thunderstorms, which are fast evolving, inherently hazardous, and can assume a broad range of sizes and severity. Modern explanations of the convective-storm dynamics, and of the related development of tornadoes, damaging 'straight-line' winds and heavy rainfall, are provided. Students and weather professionals will benefit especially from unique chapters devoted to observations and measurements of mesoscale phenomena, mesoscale prediction and predictability, and dynamical feedbacks between mesoscale-convective processes and larger-scale motions.


The Representation of Cumulus Convection in Numerical Models

The Representation of Cumulus Convection in Numerical Models
Author: Kerry Emanuel
Publisher: Springer
Total Pages: 242
Release: 2015-03-30
Genre: Science
ISBN: 1935704133

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This book presents descriptions of numerical models for testing cumulus in cloud fields. It is divided into six parts. Part I provides an overview of the problem, including descriptions of cumulus clouds and the effects of ensembles of cumulus clouds on mass, momentum, and vorticity distributions. A review of closure assumptions is also provided. A review of "classical" convection schemes in widespread use is provided in Part II. The special problems associated with the representation of convection in mesoscale models are discussed in Part III, along with descriptions of some of the commonly used mesoscale schemes. Part IV covers some of the problems associated with the representation of convection in climate models, while the parameterization of slantwise convection is the subject of Part V.


Deep Learning for the Earth Sciences

Deep Learning for the Earth Sciences
Author: Gustau Camps-Valls
Publisher: John Wiley & Sons
Total Pages: 436
Release: 2021-08-18
Genre: Technology & Engineering
ISBN: 1119646162

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DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.