Atmosph Model Data Assimil Predict
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Atmosph Model Data Assimil Predict
This comprehensive text and reference work on numerical weather prediction covers for the first time, not only methods for numerical modeling, but also the important related areas of data assimilation and predictability. It incorporates all aspects of environmental computer modeling including an historical overview of the subject, equations of motion and their approximations, a modern and clear description of numerical methods, and the determination of initial conditions using weather observations (an important new science known as data assimilation). Finally, this book provides a clear discussion of the problems of predictability and chaos in dynamical systems and how they can be applied to atmospheric and oceanic systems. Professors and students in meteorology, atmospheric science, oceanography, hydrology and environmental science will find much to interest them in this book, which can also form the basis of one or more graduate-level courses.
Atmospheric Modeling, Data Assimilation and Predictability
Author: Eugenia Kalnay
language: en
Publisher: Cambridge University Press
Release Date: 2003
This book, first published in 2002, is a graduate-level text on numerical weather prediction, including atmospheric modeling, data assimilation and predictability.
Quantification of the Downstream Impact of Extratropical Transition for Typhoon Jangmi and Other Case Studies
Author: Grams, Christian Michael Warnfrid
language: en
Publisher: KIT Scientific Publishing
Release Date: 2015-03-30
The impact of extratropical transition on the midlatitude flow is quantified based on potential vorticity inversion. The detailed study of Typhoon Jangmi (2008) reveals the diabatically enhanced net transport of low-PV air to the tropopause as the key physical process determining the direct impact of ET. Relocation experiments and further case studies show the crucial role of the relative position of the TC and the midlatitude flow for the downstream impact of ET and the reduced predictability.