Buch, Englisch, Band 11, 338 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 694 g
Reihe: Mathematics of Planet Earth
STUOD 2022 Workshop, London, UK, September 26-29
Buch, Englisch, Band 11, 338 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 694 g
Reihe: Mathematics of Planet Earth
ISBN: 978-3-031-40093-3
Verlag: Springer Nature Switzerland
All topics of these proceedings are essential to the scientific foundations of oceanography which has a vital role in climate science. Studies convened in this volume focus on a range of fundamental areas, including:
- Observations at a high resolution of upper ocean properties such as temperature, salinity, topography, wind, waves and velocity;
- Large scale numerical simulations;
- Data-based stochastic equations for upper ocean dynamics that quantify simulation error;
- Stochastic data assimilation to reduce uncertainty.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Mathematik | Informatik EDV | Informatik Informatik Berechenbarkeitstheorie, Komplexitätstheorie
- Mathematik | Informatik Mathematik Mathematische Analysis
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
Weitere Infos & Material
Internal tides energy transfers and interactions with the mesoscale circulation in two contrasted areas of the North Atlantic.- Sparse-stochastic model reduction for 2D Euler equations.- Effect of Transport Noise on Kelvin–Helmholtz instability.- On the 3D Navier-Stokes Equations with Stochastic Lie Transport.- On the interactions between mean flows and inertial gravity waves in the WKB approximation.- Toward a stochastic parameterization for oceanic deep convection.- Comparison of Stochastic Parametrization Schemes using Data Assimilation on Triad Models.- An explicit method to determine Casimirs in 2D geophysical flows.- Correlated structures in a balanced motion interacting with an internal wave.- Linear wave solutions of a stochastic shallow water model.- Analysis of Sea Surface Temperature variability using machine learning.- Data assimilation: A dynamic homotopy-based coupling approach.- Constrained random diffeomorphisms for data assimilation.- Stochastic compressible Navier–Stokes equations under location uncertainty.- Data driven stochastic primitive equations with dynamic modes decomposition.