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A new approximation to the Stokes drift velocity profile based on the exact solution for the Phillips spectrum is explored. The profile is compared with the monochromatic profile and the recently proposed exponential integral profile.…

Atmospheric and Oceanic Physics · Physics 2016-02-02 Øyvind Breivik , Jean-Raymond Bidlot , Peter A. E. M. Janssen

Periodic water waves generate Stokes drift as manifest from the orbits of Lagrangian particles not fully closing. Stokes drift can contribute to the transport of floating marine litter, including plastic. Previously, marine litter objects…

The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained may lead to performance degradation during the system's…

Machine Learning · Computer Science 2022-03-22 Firas Bayram , Bestoun S. Ahmed , Andreas Kassler

Reinforcement learning (RL) agents typically assume stationary environment dynamics. Yet in real-world applications such as healthcare, robotics, and finance, transition probabilities or reward functions may evolve, leading to model drift.…

Machine Learning · Computer Science 2025-09-16 Chang-Hwan Lee , Alexander Shim

A current-aided inertial navigation framework is proposed for small autonomous underwater vehicles in long-duration operations (> 1 hour), where neither frequent surfacing nor consistent bottom-tracking are available. We instantiate this…

Robotics · Computer Science 2017-10-17 Zhuoyuan Song , Kamran Mohseni

Forecasting ocean drift trajectories are important for many applications, including search and rescue operations, oil spill cleanup and iceberg risk mitigation. In an operational setting, forecasts of drift trajectories are produced based…

Computation · Statistics 2020-03-26 Håvard Heitlo Holm , Martin Lilleeng Sætra , Peter Jan van Leeuwen

Flight delays impose challenges that impact any flight transportation system. Predicting when they are going to occur is an important way to mitigate this issue. However, the behavior of the flight delay system varies through time. This…

Machine Learning · Computer Science 2022-02-01 Lucas Giusti , Leonardo Carvalho , Antonio Tadeu Gomes , Rafaelli Coutinho , Jorge Soares , Eduardo Ogasawara

In this paper, we model the trajectory of sea vessels and provide a service that predicts in near-real time the position of any given vessel in 4', 10', 20' and 40' time intervals. We explore the necessary tradeoffs between accuracy,…

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate-models. In this context, the…

Geophysics · Physics 2021-03-11 B. T. Nadiga

Variability in sea ice conditions, combined with strong couplings to the atmosphere and the ocean, lead to a broad range of complex sea ice dynamics. More in-situ measurements are needed to better identify the phenomena and mechanisms that…

By providing mathematical estimates, this paper answers a fundamental question -- "what leads to Stokes drift"? Although overwhelmingly understood for water waves, Stokes drift is a generic mechanism that stems from kinematics and occurs in…

Fluid Dynamics · Physics 2024-02-27 Anirban Guha , Akanksha Gupta

This study evaluates data-driven models from a dynamical system perspective, such as unstable fixed points, periodic orbits, chaotic saddle, Lyapunov exponents, manifold structures, and statistical values. We find that these dynamical…

Dynamical Systems · Mathematics 2021-11-10 Miki U Kobayashi , Kengo Nakai , Yoshitaka Saiki , Natsuki Tsutsumi

A dataset of sea surface temperature (SST) estimates is generated from the temperature observations of surface drifting buoys of NOAA's Global Drifter Program. Estimates of SST at regular hourly time steps along drifter trajectories are…

Atmospheric and Oceanic Physics · Physics 2022-09-01 Shane Elipot , Adam Sykulski , Rick Lumpkin , Luca Centurioni , Mayra Pazos

Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. A typical use includes training a predictive model with data from sensors operating under normal conditions and using the model…

Machine Learning · Computer Science 2021-07-29 Bang Xiang Yong , Yasmin Fathy , Alexandra Brintrup

Data-driven weather prediction models implicitly assume that the statistical relationship between predictors and targets is stationary. Under anthropogenic climate change, this assumption is violated, yet the structure of the resulting…

Atmospheric and Oceanic Physics · Physics 2025-11-26 Haokun Zhou

The purpose of this review-and-research paper is twofold: (i) to review the role played in climate dynamics by fluid-dynamical models; and (ii) to contribute to the understanding and reduction of the uncertainties in future climate-change…

Dynamical Systems · Mathematics 2010-06-16 Michael Ghil , Mickaël D. Chekroun , Eric Simonnet

In this communication we address some hydrodynamic aspects of recently revisited drift mechanism of biogenic mixing in the ocean (Katija and Dabiri, Nature vol. 460, pp. 624-626, 2009). The relevance of the locomotion gait at various…

Fluid Dynamics · Physics 2015-05-19 A. M. Leshansky , L. M. Pismen

Drifters designed to mimic floating marine debris and small patches of pelagic \emph{Sargassum} were satellite tracked in four regions across the North Atlantic. Though subjected to the same initial conditions at each site, the tracks of…

Atmospheric and Oceanic Physics · Physics 2020-10-28 P. Miron , M. J. Olascoaga , F. J. Beron-Vera , N. F. Putman , J. Trinanes , R. Lumpkin , G. J. Goni

We study the estimation of time-homogeneous drift functions in multivariate stochastic differential equations with known diffusion coefficient, from multiple trajectories observed at high frequency over a fixed time horizon. We formulate…

Machine Learning · Statistics 2026-02-23 Marcos Tapia Costa , Nikolas Kantas , George Deligiannidis

The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time. We are interested in an identification of those features, that are most relevant for the observed drift.…

Machine Learning · Computer Science 2020-12-02 Fabian Hinder , Jonathan Jakob , Barbara Hammer