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Full Description
This open access proceedings volume brings selected, peer-reviewed contributions presented at the Stochastic Transport in Upper Ocean Dynamics (STUOD) 2021 Workshop, held virtually and in person at the Imperial College London, UK, September 20-23, 2021. The STUOD project is supported by an ERC Synergy Grant, and led by Imperial College London, the National Institute for Research in Computer Science and Automatic Control (INRIA) and the French Research Institute for Exploitation of the Sea (IFREMER). The project aims to deliver new capabilities for assessing variability and uncertainty in upper ocean dynamics. It will provide decision makers a means of quantifying the effects of local patterns of sea level rise, heat uptake, carbon storage and change of oxygen content and pH in the ocean. Its multimodal monitoring will enhance the scientific understanding of marine debris transport, tracking of oil spills and accumulation of plastic in the sea.
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.
These fundamental subjects in modern science and technology are urgently required in order to meet the challenges of climate change faced today by human society. This proceedings volume represents a lasting legacy of crucial scientific expertise to help meet this ongoing challenge, for the benefit of academics and professionals in pure and applied mathematics, computational science, data analysis, data assimilation andoceanography.
Contents
Blow-up of strong solutions of the Thermal Quasi-Geostrophic equation (R. Mensah).- Modeling under location uncertainty: a convergent large-scale representation of the Navier-Stokes equations.- (E. Mémin).- A stochastic Benjamin-Bona-Mahony type equation (E. Dinvay).- Observation-based noise calibration: an efficient dynamics for the Ensemble Kalman filter (B. Dufée).- A two-step numerical scheme in time for surface quasi geostrophic equations under location uncertainty (C. Fiorini).- The Dissipation Properties of Transport Noise (F. Flandoli).- Existence and Uniqueness of Maximal Solutions to a 3D Navier-Stokes Equation with Stochastic Lie Transport (D. Goodair).- Ponderomotive coupling of waves to sea surface currents via horizontal density gradients (R. Hu).- Variational Stochastic Parameterisations and their Applications to Primitive Equation Models (S. Patching).- A pathwise parameterisation for stochastic transport (O. Lang).- Stochastic parameterization withdynamic mode decomposition (L. Li).- Deep Learning for the Benes Filter (A. Lobbe). End-to-End Kalman Filter in a High Dimensional Linear Embedding of the Observations (S. Ouala).- Dynamical Properties of Weather Regime Transitions (P. Platzer).- Frequentist perspective on robust parameter estimation using the ensemble Kalman filter (S. Reich).- Random ocean swell-rays: a stochastic framework (V. Resseguier).- Modified (hyper-)viscosity for coarse-resolution ocean models (L. Thiry).- Boussinesq equations under location uncertainty: theoretical description and models development (L. Li).- Bridging Koopman Operator and time-series auto-correlation based Hilbert-Schmidt operator (Y. Zhen).
