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We propose a modification of a maximum likelihood procedure for tuning parameter values in models, based upon the comparison of their output to field data. Our methodology, which uses polynomial approximations of the sample space to…

Data Analysis, Statistics and Probability · Physics 2013-07-03 Nusret Balci , Juan M. Restrepo , Shankar C. Venkataramani

A variational Bayesian inference for measured wave intensity, such as X-ray intensity, is proposed in this paper. The data is popular to obtain information about unobservable features of an object, such as a material sample and the…

Machine Learning · Computer Science 2024-11-12 Akinori Asahara , Yoshihiro Osakabe , Yamamoto Mitsuya , Hidekazu Morita

This paper proposes a physical-statistical modeling approach for spatio-temporal data arising from a class of stochastic convection-diffusion processes. Such processes are widely found in scientific and engineering applications where…

Applications · Statistics 2020-08-07 Xiao Liu , Kyongmin Yeo , Siyuan Lu

The phosphate data of the World Ocean Database 2013 are extensively statistically analyzed by splitting the measurement results into a long scale, i.e., climatological, and a short scale part. Means, medians, absolute and relative standard…

Applications · Statistics 2019-12-17 Joscha Reimer

This paper deals with the numerical modeling of wave propagation in porous media described by Biot's theory. The viscous efforts between the fluid and the elastic skeleton are assumed to be a linear function of the relative velocity, which…

Fluid Dynamics · Physics 2015-05-20 Guillaume Chiavassa , Bruno Lombard

Modeling ocean surface waves under complex ocean current conditions is of crucial importance to many naval applications. For example, traveling ships and underwater vehicles generate spatially heterogeneous currents behind them through…

Fluid Dynamics · Physics 2022-02-15 Tianyi Li , Anqing Xuan , Lian Shen

Studies agree on a significant global mean sea level rise in the 20th century and its recent 21st century acceleration in the satellite record. At regional scale, the evolution of sea level probability distributions is often assumed to be…

Atmospheric and Oceanic Physics · Physics 2023-04-05 Fabrizio Falasca , Andrew Brettin , Laure Zanna , Stephen M. Griffies , Jianjun Yin , Ming Zhao

Atmosphere and ocean are coupled via air-sea interactions. The atmospheric conditions fuel the ocean circulation and its variability, but the extent to which ocean processes can affect the atmosphere at decadal time scales remains unclear.…

Atmospheric and Oceanic Physics · Physics 2021-07-08 Navid C. Constantinou , Andrew McC. Hogg

The ability of three wave theories to predict statistics and the crest kinematics of a seaway is quantified. The three wave theories are high-order spectral (HOS) theory, free-surface mapping (FSM), and volume-of-fluid (VOF). Issues…

Ocean mesoscale eddies enhance meridional buoyancy transport, notably in the Antarctic Circumpolar Current where they contribute to setting the deep stratification of the neighboring ocean basins. The much-needed parameterization of this…

Fluid Dynamics · Physics 2023-08-02 Julie Meunier , Benjamin Miquel , Basile Gallet

Floating offshore structures often exhibit low-frequency oscillatory motions in the horizontal plane, with amplitudes in the same order as their characteristic dimensions and larger than the corresponding wave-frequency responses, making…

Fluid Dynamics · Physics 2021-10-07 Yanlin Shao , Zhiping Zheng , Hui Liang , Jikang Chen

Accurate ocean modeling and coastal hazard prediction depend on high-resolution bathymetric data; yet, current worldwide datasets are too coarse for exact numerical simulations. While recent deep learning advances have improved earth…

Machine Learning · Computer Science 2026-03-17 Jose Marie Antonio Minoza

Interaction between atmospheric mid-latitude flow and wind-driven ocean circulation is studied coupling two idealized low-order spectral models. The barotropic Charney-DeVore model with three components simulates a bimodal mid-latitude…

Atmospheric and Oceanic Physics · Physics 2011-06-10 Oliver Bothe

Over the years, the directional distribution functions of wind-generated wave field have been assumed to be unimodal. While details of various functional forms differ, these directional models suggest that waves of all spectral components…

Atmospheric and Oceanic Physics · Physics 2019-06-20 Paul A. Hwang , David W. Wang , W. Erick Rogers , Robert N. Swift , James Yungel , William B. Krabill

Because wind-generated waves can propagate over large distances, wave spectra from a fixed point can record information about air-sea interactions in distant areas. In this study, the spectral wave climate is computed for a specific…

Atmospheric and Oceanic Physics · Physics 2019-02-14 Haoyu Jiang , Lin Mu

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,…

Climate modelers generally require meteorological information on regular grids, but monitoring stations are, in practice, sited irregularly. Thus, there is a need to produce public data records that interpolate available data to a high…

Applications · Statistics 2009-06-08 Michael L. Stein

The 'vertical modes and horizontal rays' method, commonly applied for simulating acoustic wave propagation in shallow water is advanced in this research. Our approach to this method involves the use of the so-called space-time rays, which…

Mathematical Physics · Physics 2025-01-08 Aleksandr Kaplun , Boris Katsnelson

Measuring the directional distribution of ocean waves is a difficult task in ocean wave research. For many decades, wind-generated waves are assumed to propagate primarily in the wind direction. The concept is applied either implicitly or…

Atmospheric and Oceanic Physics · Physics 2019-07-03 Paul A. Hwang , David W. Wang , James Yungel , Robert N. Swift , William B. Krabill

Unsupervised fault detection in multivariate time series plays a vital role in ensuring the stable operation of complex systems. Traditional methods often assume that normal data follow a single Gaussian distribution and identify anomalies…

Machine Learning · Computer Science 2025-07-01 Hong Liu , Xiuxiu Qiu , Yiming Shi , Miao Xu , Zelin Zang , Zhen Lei
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