A realtime observatory for laboratory simulation of planetary flows

Ravela, Sai; Marshall, John; Hill, Christopher N.; Wong, Andrew K. C.; Stransky, Scott

doi:10.1007/s00348-009-0752-0

Cited by 14 publications

(16 citation statements)

References 24 publications

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“…It is not possible to compare our results directly with the analyses produced by Ravela et al (2010), because they only compared their analyses with the observations used for assimilation (in-sample error) rather than independent observations (out-of-sample error) † . A comparison of the in-sample error is possible, however.…”

Section: Discussionmentioning

confidence: 85%

“…It might be fruitful to study this in the annulus, where specific sources of model error might be more easily identified than in atmospheric models. Ravela et al (2010) use the EnKF in their annulus assimilation work, so their method might be a good place to start.…”

Section: Discussionmentioning

confidence: 99%

“…They used an extended Kalmanfilter method on their experimental data to assimilate it into an ocean model. Secondly, Ravela et al (2010) assimilate rotating annulus data into a stripped-down General Circulation Model using the ensemble Kalman filter (Evensen , 1994). Their experimental and model setup are similar to ours but with a few important differences.…”

Section: Introductionmentioning

confidence: 99%

“…Galmiche et al (2003) used correlation image velocimetry and Ravela et al (2010) used particle image velocimetry to obtain gridded velocity observations. Presenting the observations to the assimilation in grid form is not meteorologically realistic, because in operational practice the observation network is not regularly spaced and many of the observation points move with time.…”

Section: Introductionmentioning

confidence: 99%

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Data assimilation in the laboratory using a rotating annulus experiment

Young

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2012

Quart J Royal Meteoro Soc

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The thermally driven rotating annulus is a laboratory experiment important for the study of the dynamics of planetary atmospheres under controllable and reproducible conditions. We use the analysis correction method to assimilate laboratory data into an annulus model. We analyze the 2S and 3AV regular flow regimes between rotation rates of 0.75 and 0.875 rad s −1 and the 3SV chaotic flow regime between rotation rates of 2.2 and 3.1 rad s −1 . Our assimilated observations are irregularly distributed, which is more meteorologically realistic than gridded observations as used in recent applications of data assimilation to laboratory measurements. We demonstrate that data assimilation can be used successfully and accurately in this context. We examine a number of specific assimilation scenarios: a wave-number transition between two regimes, information propagation from data-rich to datapoor regions, the response of the assimilation to a strong disturbance to the flow, and a vortex-shedding instability phenomenon at high rotation rate. At the highest rotation rates we calculated the barotropic E-vectors using unobserved variables such as temperature and the vertical structure of the velocity field that are only available via the assimilation. These showed that the mean flow is weakened by the action of eddies, going some way towards explaining why vortices are shed at the very highest rotation rates but not at lower rotation. Rossby-wave stability theory suggests that the underlying instability leading to vortex shedding may be baroclinic in character.Key Words: rotating annulus; analysis correction; data assimilation; E-vector; chaos; baroclinic instability

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Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data assimilation in the laboratory using a rotating annulus experiment

Young

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2012

Quart J Royal Meteoro Soc

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“…(16). Another approach of using image observation in laboratory data assimilation can be found in Ravela et al (2010). The authors used a computer vision system to extract measurements from the physical simulation with parallel computing and decomposition to account for observation in real time as well as using the numerical model to adapt the observing system.…”

Section: Observation Operators For Imagesmentioning

confidence: 99%

Toward the assimilation of images

Dimet

Souopgui

Titaud

et al. 2015

Nonlin. Processes Geophys.

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Abstract. The equations that govern geophysical fluids (namely atmosphere, ocean and rivers) are well known but their use for prediction requires the knowledge of the initial condition. In many practical cases, this initial condition is poorly known and the use of an imprecise initial guess is not sufficient to perform accurate forecasts because of the high sensitivity of these systems to small perturbations. As every situation is unique, the only additional information that can help to retrieve the initial condition are observations and statistics. The set of methods that combine these sources of heterogeneous information to construct such an initial condition are referred to as data assimilation. More and more images and sequences of images, of increasing resolution, are produced for scientific or technical studies. This is particularly true in the case of geophysical fluids that are permanently observed by remote sensors. However, the structured information contained in images or image sequences is not assimilated as regular observations: images are still (under-)utilized to produce qualitative analysis by experts. This paper deals with the quantitative assimilation of information provided in an image form into a numerical model of a dynamical system. We describe several possibilities for such assimilation and identify associated difficulties. Results from our ongoing research are used to illustrate the methods. The assimilation of image is a very general framework that can be transposed in several scientific domains.

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General Circulation of Planetary Atmospheres

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Pérez

Moroz

et al. 2014

Modeling Atmospheric and Oceanic Flows

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In engineering and the applied sciences, the term "model" is typically used to denote a device or concept that imitates the behavior of a physical system as closely as possible, but on a different (usually smaller) scale, possibly with some simplifications. The aim of such a model is normally to evaluate the performance of such a system for reasons connected with its exploitation for economic, social, military, or other purposes. In the context of the atmosphere or oceans, numerical weather and climate prediction models clearly fall into this category. Such models are extremely complicated entities that seek to represent the topography, composition, radiative transfer, and dynamics of the atmosphere, oceans, and surface in great detail. As a result, it is generally impossible to comprehend fully the complex interactions of physical processes and scales of motion that occur within any given simulation. The success of such models can only be judged by the accuracy of their predictions as directly verified (in the case of numerical weather prediction) against subsequent observations and measurements. Similar models used for climate prediction, however, are often comparable in complexity to those used for weather prediction but are frequently used as tools in attempts to address questions of economic, social, or political importance (e.g., concerning the impact of increasing anthropogenic greenhouse gas emissions) for which little or no verifying data may be available.In formulating such models and interpreting their results, it is necessary to make use of a different class of

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A realtime observatory for laboratory simulation of planetary flows

Abstract: Abstract

Cited by 14 publications

References 24 publications

Data assimilation in the laboratory using a rotating annulus experiment

Data assimilation in the laboratory using a rotating annulus experiment

Toward the assimilation of images

General Circulation of Planetary Atmospheres

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