Estimates of turbulence parameters from Lagrangian data using a stochastic particle model

Griffa, Annalisa; Owens, K.; Piterbarg, Leonid I.; Rozovskiĭ, B. L.

doi:10.1357/0022240953213151

Cited by 66 publications

(44 citation statements)

References 15 publications

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“…Actually, the model we apply is a straightforward generalization of models by Csanady [1974] and Griffa et al [1995] for the case of shear mean flow. The velocity of a particle is presented as a sum of a mean flow U and a "turbulent" velocity fluctuation u.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Estimating horizontal diffusivity in the East Sea (Sea of Japan) and the northwest Pacific from satellite‐tracked drifter data

Oh¹,

Zhurbas²,

Park³

2000

J. Geophys. Res.

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Section: Simulation Experimentsmentioning

confidence: 99%

Estimating horizontal diffusivity in the East Sea (Sea of Japan) and the northwest Pacific from satellite‐tracked drifter data

Oh¹,

Zhurbas²,

Park³

2000

J. Geophys. Res.

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“…The most classical method was proposed by Taylor (1922), which, however, is quite sensitive to the asymptotic behavior of the autocorrelation at large time lags (Lumpkin et al 2002). Griffa et al (1995) tried to fit the autocorrelation with a known-shape curve prescribed by some parameters, so that the problems with the asymptotic behavior of autocorrelation could be avoided by only estimating the parameters. Davis (1987Davis ( , 1991 took into account the inhomogeneous velocity field, which is not applicable for Taylor's theorem, and made the estimation of diffusivity hold true for the shear mean flows (Zhurbas and Oh 2003).…”

Section: Introductionmentioning

confidence: 99%

Eulerian and Lagrangian Statistics in the South China Sea as Deduced from Surface Drifters

Qian

Peng

2013

Journal of Physical Oceanography

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Eulerian and Lagrangian statistics of the surface flows over the South China Sea (SCS) are investigated based on observations of satellite-tracked Lagrangian drifters of the Surface Velocity Program. Although the dataset contains the most recent observations up to 2011, the spatial and temporal distributions of the data are quite inhomogeneous. The Luzon Strait as well as the northern and western parts of the SCS are heavily sampled by drifters whereas the internal SCS is less sampled. The overall Eulerian mean circulation derived from all drifter observations resembles the wintertime circulations because of the seasonal sampling bias toward wintertime. It is found that the calculation of the diffusivity may be misled by the low-frequency temporal variabilities such as annual cycle, which could be removed using the Gauss-Markov decomposition. In the regions around Luzon Strait, typical values of diffusivity, time scale, and length scale are 3.7-11.9 3 10 7 cm 2 s 21 , 0.8-1.9 days, and 16.4-44.4 km, respectively. In the regions around Hainan Island and the western boundary of the SCS, the typical estimated diffusivity and length scale are slightly smaller, that is, 2.3-6.9 3 10 7 cm 2 s 21 and 12.8-33.7 km while the time scale (0.8-2.0 days) is approximately the same as that in the regions around Luzon Strait. Although these regions are close to the coast, the ''flux versus gradient'' relationship is still valid in these coastal regions.

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“…In this paper we will use the terminology that is common in econometrics and apply our estimator to a stochastic volatility model. We emphasize also that similar problems arise in many other areas where the process of interest is modeled using an SDE, such as, for example, oceanography [18,1]. In such cases one is interested in estimating the eddy diffusivity from noisy Lagrangian observations.…”

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confidence: 88%

“…Introduction. Diffusion (Itô) processes are being used as models in many applications such as physics, biology, finance, and atmosphere/ocean science [4,15,18,24,31,33]. Some examples include the stochastic modeling of epidemics, the theory of derivative pricing, and stochastic modeling in oceanography.…”

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confidence: 99%

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Frequency Domain Estimation of Integrated Volatility for Itô Processes in the Presence of Market-Microstructure Noise

Olhede

Sykulski

Pavliotis

2010

Multiscale Model. Simul.

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Abstract. This paper proposes a novel multiscale estimator for the integrated volatility of an Itô process in the presence of market microstructure noise (observation error). The multiscale structure of the observed process is represented frequency by frequency, and the concept of the multiscale ratio is introduced to quantify the bias in the realized integrated volatility due to the observation error. The multiscale ratio is estimated from a single sample path, and a frequency-by-frequency bias correction procedure is proposed, which simultaneously reduces variance. We extend the method to include correlated observation errors and provide the implied time-domain form of the estimation procedure. The new method is implemented to estimate the integrated volatility for the Heston and other models, and the improved performance of our method over existing methods is illustrated by simulation studies.

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Estimates of turbulence parameters from Lagrangian data using a stochastic particle model

Cited by 66 publications

References 15 publications

Estimating horizontal diffusivity in the East Sea (Sea of Japan) and the northwest Pacific from satellite‐tracked drifter data

Estimating horizontal diffusivity in the East Sea (Sea of Japan) and the northwest Pacific from satellite‐tracked drifter data

Eulerian and Lagrangian Statistics in the South China Sea as Deduced from Surface Drifters

Frequency Domain Estimation of Integrated Volatility for Itô Processes in the Presence of Market-Microstructure Noise

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