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Modal View of Atmospheric Variability


Modal View of Atmospheric Variability

Applications of Normal-Mode Function Decomposition in Weather and Climate Research
Mathematics of Planet Earth, Band 8

von: Nedjeljka Zagar, Joseph Tribbia

96,29 €

Verlag: Springer
Format: PDF
Veröffentl.: 05.12.2020
ISBN/EAN: 9783030609634
Sprache: englisch

Dieses eBook enthält ein Wasserzeichen.

Beschreibungen

<p>This book reviews the theory and applications of the normal-mode functions in numerical weather prediction and weather and climate dynamics. The normal-mode functions, the eigensolutions of the linearized primitive equations describing the evolution of atmospheric winds and mass variables, have been used for a long time. They have played an important role in the development of data assimilation schemes and the initialization of numerical weather prediction models. Chapters also present how the normal modes can be applied to many theoretical and numerical problems in the atmospheric sciences, such as equatorial wave dynamics, baroclinic instability, energy transfers, and predictability across scales. </p><br><p></p><p> </p><p>&nbsp;</p>

<p>&nbsp;</p>
<p>3D Normal Mode Functions (NMFs) Of a Global Baroclinic&nbsp;Atmospheric Model.-&nbsp;Normal Mode Functions and Initialization.-&nbsp;Normal-Mode Functions and Atmospheric Data Assimilation.-&nbsp;3D Modal Variability and Energy Transformations On The Sphere.-&nbsp;Generalization of Baroclinic Instability and Rossby Wave&nbsp;Saturation Theory.-&nbsp;Applications to Predictions And Climate Studies.</p><p><br></p>
<p>Nedjeljka Žagar is a professor of theoretical meteorology at Universität Hamburg. She received a BSc and a MSc in physics from the University of Zagreb, a PhD in dynamical meteorology from Stockholm University, and a&nbsp;postdoctoral fellowship from the Advanced Study Program of NCAR. She has&nbsp;been a professor of meteorology at the University of Ljubljana.&nbsp;Her research covers topics in atmospheric dynamics and numerical weather prediction, focusing on tropical aspects of data assimilation and predictability.&nbsp;</p><p>Joseph Tribbia is a senior scientist at the National Center for Atmospheric Research (NCAR) and Head of its Atmospheric Modeling and Predictability Section in the Climate and Global Dynamics Division. He received a Bachelor of Science in Physics from the Illinois Institute of Technology in 1971, and a masters and doctoral degree in Atmospheric Science from the University of Michigan. He has been at NCAR since 1978 and his work has focused on the numerical simulation of the atmosphere and geophysically relevant flows. His journal publications include works on the application of dynamical systems theory in atmospheric dynamics, the problems of atmospheric data analysis and numerical weather prediction, atmospheric predictability and the prediction of forecast reliability, the simulation and prediction of El Nino/Southern Oscillation and decadal climate projections.<br></p>
<p>This book reviews the theory and applications of the normal-mode functions in numerical weather prediction and weather and climate dynamics. The normal-mode functions, the eigensolutions of the linearized primitive equations describing the evolution of atmospheric winds and mass variables, have been used for a long time. They have played an important role in the development of data assimilation schemes and the initialization of numerical weather prediction models. Chapters also present how the normal modes can be applied to many theoretical and numerical problems in the atmospheric sciences, such as equatorial wave dynamics, baroclinic instability, energy transfers, and predictability across scales. </p><br>
Introduces the theory and computation of the normal-mode functions Reviews the applications of the normal modes in data assimilation and initialization of numerical weather prediction models, and for predictability research Offers an up-to-date overview of research of atmospheric variability and energy transfers in terms of the Rossby and inertia-gravity waves across scales

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