Cloud Cover Parameterization in Numerical Models PDF Download

Author: European Centre for Medium Range Weather Forecasts
Publisher:
ISBN:
Category :
Languages : en
Pages : 310

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Author: David J. Stensrud
Publisher: Cambridge University Press
ISBN: 0521865409
Category : Science
Languages : en
Pages : 408

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Contents: 1.

Author:
Publisher:
ISBN:
Category : Atmospheric circulation
Languages : en
Pages : 310

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Book Description

Author: David J. Stensrud
Publisher: Cambridge University Press
ISBN: 1107469651
Category : Science
Languages : en
Pages : 594

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Book Description
Numerical weather prediction models play an increasingly important role in meteorology, both in short- and medium-range forecasting and global climate change studies. The most important components of any numerical weather prediction model are the subgrid-scale parameterization schemes, and the analysis and understanding of these schemes is a key aspect of numerical weather prediction. This book provides in-depth explorations of the most commonly used types of parameterization schemes that influence both short-range weather forecasts and global climate models. Several parameterizations are summarised and compared, followed by a discussion of their limitations. Review questions at the end of each chapter enable readers to monitor their understanding of the topics covered, and solutions are available to instructors at www.cambridge.org/9780521865401. This will be an essential reference for academic researchers, meteorologists, weather forecasters, and graduate students interested in numerical weather prediction and its use in weather forecasting.

Author: David A. Randall
Publisher: Springer
ISBN: 9811333963
Category : Science
Languages : en
Pages : 377

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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.

Author: WORKSHOP ON CLOUD COVER PARAMETERIZATION IN NUMERICAL MODELS 1984, SHINFIELD PARK, READING, UK
Publisher:
ISBN:
Category :
Languages : en
Pages : 310

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Book Description

Author: Robert S Plant
Publisher: World Scientific
ISBN: 1783266929
Category : Technology & Engineering
Languages : en
Pages : 1169

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Book Description
Precipitating atmospheric convection is fundamental to the Earth's weather and climate. It plays a leading role in the heat, moisture and momentum budgets. Appropriate modelling of convection is thus a prerequisite for reliable numerical weather prediction and climate modelling. The current standard approach is to represent it by subgrid-scale convection parameterization.Parameterization of Atmospheric Convection provides, for the first time, a comprehensive presentation of this important topic. The two-volume set equips readers with a firm grasp of the wide range of important issues, and thorough coverage is given of both the theoretical and practical aspects. This makes the parameterization problem accessible to a wider range of scientists than before. At the same time, by providing a solid bottom-up presentation of convection parameterization, this set is the definitive reference point for atmospheric scientists and modellers working on such problems.Volume 1 of this two-volume set focuses on the basic principles: introductions to atmospheric convection and tropical dynamics, explanations and discussions of key parameterization concepts, and a thorough and critical exploration of the mass-flux parameterization framework, which underlies the methods currently used in almost all operational models and at major climate modelling centres. Volume 2 focuses on the practice, which also leads to some more advanced fundamental issues. It includes: perspectives on operational implementations and model performance, tailored verification approaches, the role and representation of cloud microphysics, alternative parameterization approaches, stochasticity, criticality, and symmetry constraints.

Author: Kerry Emanuel
Publisher: Springer
ISBN: 1935704133
Category : Science
Languages : en
Pages : 242

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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.

Author: Guy P. Brasseur
Publisher: Cambridge University Press
ISBN: 1108210953
Category : Science
Languages : en
Pages : 631

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Book Description
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.

Author: Arthur Grundner
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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This thesis delves into the improvement of cloud parameterizations in climate models through machine learning trained on coarse-grained output from high-resolution simulations. Utilizing the ICOsahedral Non-hydrostatic (ICON) modeling framework, it specifically targets the enhancement of cloud cover parameterization within the ICON Earth System Model. Three types of neural networks (NNs) differing in vertical locality are developed to estimate cloud cover, with globally trained NNs even applicable to distinct regional simulations. Interpretability analysis exposes model-specific biases and local relationships with the thermodynamic environment. Despite achieving high predictive performance, NNs necessitate post-hoc interpretation tools. To tackle this issue, a combined hierarchical modeling framework incorporating symbolic regression, feature selection, and physical constraints is proposed. The resulting equations, characterized by simplicity and physical consistency, attain performance comparable to NNs while demonstrating superior transferability to other realistic datasets. Our best equation adeptly captures cloud cover distributions across various regimes, notably excelling in representing marine stratocumulus clouds by learning to utilize the vertical relative humidity gradient. This research underscores the potential of deep learning in achieving accurate cloud parameterizations and emphasizes the effective role of symbolic regression in deriving interpretable, consistent equations for cloud cover.