Cloud-Resolving Modeling of Convective Processes
Cloud-Resolving Modeling of Convective Processes

Author: Xiaofan Li

Publisher: Springer

Published: 2016-05-17

Total Pages: 364

ISBN-13: 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

Published: 2018-07-05

Total Pages: 643

ISBN-13: 0521767431

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

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

Author:

Publisher:

Published: 2015

Total Pages: 18

ISBN-13:

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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.

Modeling of Atmospheric Chemistry
Modeling of Atmospheric Chemistry

Author: Guy P. Brasseur

Publisher: Cambridge University Press

Published: 2017-06-19

Total Pages: 631

ISBN-13: 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.

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

Published: 2019-01-31

Total Pages: 377

ISBN-13: 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.

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:

Published: 2008

Total Pages:

ISBN-13:

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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.

Deep Learning for the Earth Sciences
Deep Learning for the Earth Sciences

Author: Gustau Camps-Valls

Publisher: John Wiley & Sons

Published: 2021-08-18

Total Pages: 436

ISBN-13: 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.

ARM - Midlatitude Continental Convective Clouds
ARM - Midlatitude Continental Convective Clouds

Author:

Publisher:

Published: 2012

Total Pages:

ISBN-13:

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Convective processes play a critical role in the Earth's energy balance through the redistribution of heat and moisture in the atmosphere and their link to the hydrological cycle. Accurate representation of convective processes in numerical models is vital towards improving current and future simulations of Earths climate system. Despite improvements in computing power, current operational weather and global climate models are unable to resolve the natural temporal and spatial scales important to convective processes and therefore must turn to parameterization schemes to represent these processes. In turn, parameterization schemes in cloud-resolving models need to be evaluated for their generality and application to a variety of atmospheric conditions. Data from field campaigns with appropriate forcing descriptors have been traditionally used by modelers for evaluating and improving parameterization schemes.

Studies Of Cloud, Convection And Precipitation Processes Using Satellite Observations
Studies Of Cloud, Convection And Precipitation Processes Using Satellite Observations

Author: William B Rossow

Publisher: World Scientific

Published: 2022-10-26

Total Pages: 333

ISBN-13: 9811256926

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Clouds, convection and precipitation processes are central components of Earth's weather and climate. They are produced by atmospheric motions across a very wide range of space-time scales from local weather to long-term global climate variation. They feedback on these motions by perturbing the heating/cooling that drive the atmospheric circulation. These processes also perturb the oceanic circulation and land surface properties that affect the atmospheric circulation.Because of the coupling of the atmosphere-ocean-land system across all scales by cloud, convection and precipitation processes, studying their behaviors requires measurements in space-time variations across all these scales simultaneously. Satellite constellations with global coverage and high time resolution offer the ideal platforms for such observations. This book summarizes some of the latest research using combinations of various satellite observations to study these processes and to evaluate their representations in global weather and climate models.Included with this publication are downloadable electronic slides and accompanying notes of each lecture for students, teachers, and public speakers around the world to be better able to understand cloud, convection and precipitation processes.

The Representation of Cumulus Convection in Numerical Models
The Representation of Cumulus Convection in Numerical Models

Author: Kerry Emanuel

Publisher: Springer

Published: 2015-03-30

Total Pages: 242

ISBN-13: 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.