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Cited by 1,891Oceanographic and Atmospheric ProcessesMeteorological Phenomena and SimulationsClimate variability and models

Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation

Vallis, Geoffrey K.·2017·Cambridge University Press eBooks
DOI 10.1017/9781107588417
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Abstract

The atmosphere and ocean are two of the most important components of the climate system, and fluid dynamics is central to our understanding of both. This book provides a unified and comprehensive treatment of the field that blends classical results with modern interpretations. It takes the reader seamlessly from the basics to the frontiers of knowledge, from the equations of motion to modern theories of the general circulation of the atmosphere and ocean. These concepts are illustrated throughout the book with observations and numerical examples. As well as updating existing chapters, this full-color second edition includes new chapters on tropical dynamics, El Niño, the stratosphere and gravity waves. Supplementary resources are provided online, including figures from the book and problem sets, making this new edition an ideal resource for students in the atmospheric, oceanic and climate sciences, as well as in applied mathematics and engineering.

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Crossref · 10.1017/9781107588417

Cites

280 of 280 references verified0 citation errors · 0 not checkable

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  • Typical correlation times for climate data are of the order of 3-5 days.

    “Notably, it is shown that the occurrence of heatwaves can be predicted up to τ = 15 days in advance, thus significantly beyond typical correlations times for climate data of the order of 3-5 days (Vallis, 2017).”

    From Deep Learning-Based Extreme Heatwave Forecast (2022) · cites this paper as Vallis, 2017

  • The prediction of long-lasting heatwaves up to 15 days ahead is significantly beyond typical correlation times for climate data of the order of 3-5 days.

    “Notably, it is shown that the occurrence of heatwaves can be predicted up to τ = 15 days in advance, thus significantly beyond typical correlations times for climate data of the order of 3-5 days (Vallis, 2017).”

    From Deep Learning-Based Extreme Heatwave Forecast (2022) · cites this paper as Vallis, 2017

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Related

Authors
Vallis, G. K.
Affiliations
University of Exeter (GB)
Corresponding author
Vallis, Geoffrey K.
ORCID
Vallis, Geoffrey K.
Venue
Cambridge University Press eBooks
Publisher
Cambridge University Press
Published
2017-06-09
DOI
10.1017/9781107588417
OpenAlex
W3015681226
Cited by
1,891
References
318
Citations by year
2026: 141 · 2025: 163 · 2024: 155 · 2023: 166 · 2022: 155 · 2021: 165 · 2020: 143 · 2019: 152 · 2018: 109 · 2017: 76 · 2016: 61 · 2015: 67 · 2014: 57 · 2013: 72 · 2012: 60
FWCI
75.63
Type
book
Language
en
Open access
closed
Licence
www.cambridge.org/core/terms (unspecified)
Locations
Cambridge University Press eBooks (publishedVersion)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
Keywords
equations of motion, atmospheric general circulation, El Niño, stratosphere, gravity waves, climate system

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