| Section S | S index | 951-959 of 1376 terms |
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stationary process—A stochastic process, X(t), with properties that do not change over time or space. This means that the marginal distributions of the variate (its mean, variances, and similar characteristics) are time independent. Furthermore, the joint distribution of the values of the process at two (or more) times, X(t) and X(s), can only depend on the time difference(s), t − s.
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stationary source—A type of air pollution source that releases emissions from a specific location and is permanent or semipermanent structures at that location. Examples are smokestacks, vents, power plants, mines, farms, buildings, and trees. Compare mobile source.
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stationary-state hypothesis—(Also called steady-state hypothesis.) A chemical species is said to be in steady state when the rate of its formation and the rate of its destruction are approximately equal, such that the concentration of the species remains nearly constant. The lifetime is then the atmospheric burden divided by the source or sink rate.
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stationary time series—A time series having stable statistical properties in the following sense. Let x(t) denote the value of the variable at time t. Hold t fixed and imagine an indefinite series of repetitions of essentially the same generating process, giving rise to a population (ensemble) of values of x(t). For a stationary time series, the ensemble probability distribution of x(t) is independent of t. When the probability distribution changes very gradually with t, the time series is called quasi-stationary.
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stationary wave—A wave with nodes that are stationary relative to the given coordinate system. Any permanent wave may be rendered stationary by appropriately chosen coordinates. In meteorology the coordinate system is usually fixed with respect to the earth, so that a stationary wave usually refers to one that is stationary relative to the earth's surface. See standing wave.
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statistical–dynamical model—A climate model in which the statistical behavior of synoptic-scale dynamical processes is represented parametrically. In contrast to general circulation models, statistical–dynamical models offer computational advantages, but the parameterizations adopted in these models can only roughly approximate the effects of synoptic-scale eddies.
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