Abstmct-In the choice of an eduutor for the spectnrm of a a t i o n~rythlleseriestrom~fiaitesunpleoftheprocecs,theprobkmsofb~ control and codstency, or "moothiug," are dominant.L n t h i s p . p e r w e p n s e n t a n e w w t h o d b~o n a " l o a l " e i g e nerrp~ntoestinutethespectnrmmtermsofthesolutionofaain-tegd equation. Comprrtationdly this method is equivalent to k g the weighted .ver a@ of a aeries of d i r e c t -estimates based on orthogonal data widows (discrete prolate Spher0id.l sequences) to treat both the bias and moothing problems.Some of the a t h e t h t e a m of this estimate are: thexe are no mbimay windowr;it is asmaUgmpietheory;itisconsistent;itprovidesauaualysin-of-nriinQtestfor~componeng;andithnshigh resolution.We also show relations of this estimate to maximum4ikelihood estimates, show that the esffmotion cqwcity of the estimate is high, and show appticationa to cohe~nce md polysphum estimates.

Many different random-walk models of dispersion in inhomogeneous or unsteady turbulence have been proposed and several criteria have emerged to distinguish good models from bad. In this paper the relationships between the various criteria are examined for a very general class of models and it is shown that most of the criteria are equivalent. It is also shown how a model can be designed to satisfy these criteria exactly and to be consistent with inertial-subrange theory. Some examples of models that obey the criteria are described. As an illustration some calculations of dispersion in free-convective conditions are presented.

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The ability of a large-eddy simulation to represent the large-scale motions in the interior of a turbulent flow is well established. However, concerns remain for the behaviour close to rigid surfaces where, with the exception of low-Reynolds-number flows, the large-eddy description must be matched to some description of the flow in which all except the larger-scale ‘inactive’ motions are averaged. The performance of large-eddy simulations in this near-surface region is investigated and it is pointed out that in previous simulations the mean velocity profile in the matching region has not had a logarithmic form. A number of new simulations are conducted with the Smagorinsky (1963) subgrid model. These also show departures from the logarithmic profile and suggest that it may not be possible to eliminate the error by adjustments of the subgrid lengthscale. An obvious defect of the Smagorinsky model is its failure to represent stochastic subgrid stress variations. It is shown that inclusion of these variations leads to a marked improvement in the near-wall flow simulation. The constant of proportionality between the magnitude of the fluctuations in stress and the Smagorinsky stresses has been empirically determined to give an accurate logarithmic flow profile. This value provides an energy backscatter rate slightly larger than the dissipation rate and equal to idealized theoretical predictions (Chasnov 1991).

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A new stochastic model for the motion of particle pairs in isotropic high-Reynolds-number turbulence is proposed. The model is three-dimensional and its formulation takes account of recent improvements in the understanding of one-particle models. In particular the model is designed so that if the particle pairs are initially well mixed in the fluid, they will remain so. In contrast to previous models, the new model leads to a prediction for the particle separation probability density function which is in qualitative agreement with inertial subrange theory. The values of concentration variance from the model show encouraging agreement with experimental data. The model results suggest that, at large times, the intensity of concentration fluctuations (i.e. standard deviation of concentration divided by mean concentration) tends to zero in stationary conditions and to a constant in decaying turbulence.

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S U M M A R YRobust magnetotelluric response function estimators are now in standard use in electromagnetic induction research. Properly devised and applied, these have the ability to reduce the influence of unusual data (outliers) in the response (electric field) variables, but are often not sensitive to exceptional predictor (magnetic field) data, which are termed leverage points. A bounded influence estimator is described which simultaneously limits the influence of both outliers and leverage points, and has proven to consistently yield more reliable magnetotelluric response function estimates than conventional robust approaches. The bounded influence estimator combines a standard robust M-estimator with leverage weighting based on the statistics of the hat matrix diagonal, which is a standard statistical measure of unusual predictors. Further extensions to magnetotelluric data analysis are proposed, including a generalization of the remote reference method which utilizes multiple sites instead of a single one and a two-stage bounded influence estimator which effectively removes correlated noise in the local electric and magnetic field variables using one or more uncontaminated remote references. These developments are illustrated using a variety of magnetotelluric data.