Statistics and Physical Oceanography (1993) / Chapter Skim
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6: Interpolation, Nonlinear Smoothing, Filtering, and Prediction
6: Interpolation, Nonlinear Smoothing, Filtering, and Prediction
Pages 37-44
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From page 37... ...
__~ ~ ~ = ¢ ~ 7 ~ ~ ~ ~~v4~ an 1~ ~11~ Calf The most extensive of these newly available and soon to be available data sets are remotely sensed from space. Active and passive instruments operating in the microwave, infrared, and visible portions of the electromagnetic spectrum provide spatial and temporal coverage of the ocean unavailable from any other source, but present new challenges in interpretation.
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From page 38... ...
are time dependent, the composite images represent only some average picture of sea surface temperature distribution for the penod covered. Therefore, it is important to know how this picture and its statistical properties are connected with the statistical properties ot cloud Gelds, and now representative the composite image is with respect to the ensemble average of the temperature field (see, e.g., Chelton and Schiax, 1991~.
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From page 39... ...
Upper panel shows the ground tracks traced out during days 1 to 3 (solid lines) , days 4 to 6 (dashed lines)
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From page 40... ...
The disadvantage of schemes with-fixed weights is clear: they are unable to adapt to data of varying accuracy, even though they do a decent job at adapting to inhomogeneous data distnbutions. The practical disadvantage for both objective mapping and successive corrections is that spatially inhomogeneous scales and anisotropy are not easily treated, and require breaking up the problem into several regional ones.
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From page 41... ...
The ocean modeling literature naturally overlaps with the numerical weather prediction literature on this subject, and the two fields share a common interest in qualitative results, but systematic studies are few, and those that exist are elementary. Direct approaches to applying statistically based data assimilation methods to nonlinear problems have so far been based on generalizations of linear methods.
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From page 42... ...
INVERSE MEIEIODS Some oceanographers consider that, in some larger sense, all of physical oceanography can be described in terms of an inverse problem: given data, describe the ocean from which the data were sampled. Obviously direct inversion of the sampling process is impossible, but the smoothing process is occasionally viewed as some generalized inverse of the sampling process, with the laws of ocean physics used as constraints (see, e.g., Wunsch, 1978, 1988; Bennett, 1992~.
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From page 43... ...
Filtering and smoothing for the systems in which the dynamics are given by discontinuous functions of the state variables; 2. Parameter estimation for randomly perturbed equations of physical oceanography; 3.
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From page 44... ...
410) has noted: One of the major challenges from both the atmospheric and ocean sciences is to merge and integrate in situ and remotely sensed interdisciplinary data sets which have differing spatial and temporal resolution and encompass differing scale ranges ....
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