Modeling of atmospheric temperature fluctuations by translations of oscillatory random processes with application to spacecraft atmospheric re-entry

Field, Richard V.; Edwards, Timothy S.; Rouse, Jerry W.

doi:10.1016/j.probengmech.2010.07.005

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Atmospheric processes produce and respond to temporal and spatial fluctuations at all scales (e.g., Charney, 1971; Chinita et al., 2018; Hasselmann, 1976; Monin & Yaglom, 1971; Taylor, 1938, and references therein). Temperature fluctuations at high altitudes affect the vibrations of a spacecraft entering and descending through the Martian atmosphere, as they do on Earth (e.g., Field et al., 2011). The statistical properties of environmental fluctuations are used in numerical models of Martian weather to parameterize processes that cannot be resolved.…”

Section: Introductionmentioning

confidence: 99%

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Diurnal Cycle of Rapid Air Temperature Fluctuations at Jezero Crater: Probability Distributions, Exponential Tails, Scaling, and Intermittency

et al. 2023

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Section: Introductionmentioning

confidence: 99%

“…

Temperature fluctuations at high altitudes affect the vibrations of a spacecraft entering and descending through the Martian atmosphere, as they do on Earth (e.g., Field et al, 2011). The statistical properties of environmental

…”

mentioning

confidence: 99%

Diurnal Cycle of Rapid Air Temperature Fluctuations at Jezero Crater: Probability Distributions, Exponential Tails, Scaling, and Intermittency

et al. 2023

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“…The translation random field model defined by Eq. (1) is a very flexible model and has proved effective for a variety of applications in stochastic mechanics (see, for example, [4,5]) and the modeling of various environmental phenomena [8,9].…”

Section: Model Definitionmentioning

confidence: 99%

Statistical Surrogate Models for Prediction of High-Consequence Climate Change

Field

Constantine

Boslough

2013

Int.J.UncertaintyQuantification

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In safety engineering, performance metrics are defined using probabilistic risk assessments focused on the low-probability, high-consequence tail of the distribution of 3 possible events, as opposed to best estimates based on central tendencies. We frame the climate change problem and its associated risks in a similar manner. To properly explore the tails of the distribution requires extensive sampling, which is not possible with existing coupled atmospheric models due to the high computational cost of each simulation. We therefore propose the use of specialized statistical surrogate models (SSMs) for the purpose of exploring the probability law of various climate variables of interest. A SSM is different than a deterministic surrogate model in that it represents each climate variable of interest as a space/time random field. The SSM can be calibrated to available spatial and temporal data from existing climate databases, e.g., the Program for Climate Model Diagnosis and Intercomparison (PCMDI), or to a collection of outputs from a General Circulation Model (GCM), e.g., the Community Earth System Model (CESM) and its predecessors. Because of its reduced size and complexity, the realization of a large number of independent model outputs from a SSM becomes computationally straightforward, so that quantifying the risk associated with low-probability, high-consequence climate events becomes feasible. A Bayesian framework is developed to provide quantitative measures of confidence, via Bayesian credible intervals, in the use of the proposed approach to assess these risks.4

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