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Saturday, August 1, 2020 | History

4 edition of Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data found in the catalog.

Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data

Dimitris Menemenlis

Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data

by Dimitris Menemenlis

  • 281 Want to read
  • 31 Currently reading

Published by National Aeronautics and Space Administration in [Washington, D.C .
Written in English

    Subjects:
  • Hydrographic surveying,
  • Oceanography -- Observations

  • Edition Notes

    Other titlesJournal of geophysical research 0148-0227.
    StatementDimitris Menemenlis ... [et al.].
    GenreObservations.
    SeriesNASA-CR -- 204802., NASA contractor report -- NASA CR-204802.
    The Physical Object
    FormatMicrofilm
    Pagination1 microfiche (14 fr.)
    Number of Pages14
    ID Numbers
    Open LibraryOL17604914M
    OCLC/WorldCa47441449

    Interpolation of measuring data on the standard oceanographic levels. Estimating of interpolating data in square nodes of geographic grid, and. Presentation of output results in GIS form. Other oceanographic data had been passed a similar control procedure that depending of nature and distribution of data. The intended use of this program is to assimilate SNOTEL precipitation data with WRF model data. - nicksilver/optimal_interpolation.

    Spatial interpolation is a method to estimate the data in contiguous area and forecast the unknown points (the information is missing or cannot be obtained) with available observation data (Chai et al. ; Losser et al. ), including geo-statistical interpolation and deterministic interpolation. Geo-statistical interpolation consists of. Optimal interpolation To derive the optimal interpolation method (Gandin,), it is convenient to introduce the concept of \state vector". The state vector x is a column vector containing all unknowns that we want to estimate from the observations. For our gridding .

    The AWAP Data. This dataset has been developed by the Australian Water Availability Project (AWAP) and details on the creation of AWAP can be found in Jones et al. [] and Raupach et al. [].The AWAP data were produced using the WaterDyn model [] for continental Australia and the datasets include rainfall, maximum and minimum temperature, and vapour pressure surfaces obtained by. Algorithm design! Fundamental issue in biomedic al ima ging ¥ Projective mapping ¥Aspect ratio, l ¥Magnetic field distortions Endoscopy C-Arm fluoroscopy Classical image interpolation 14 Discrete image data f [k ], k = (k 1,ááá,k p)! Z p Contin uous image mode l.


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Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data by Dimitris Menemenlis Download PDF EPUB FB2

Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data Dimitris Menemenlis • Paul Fieguth 2 Carl Wunsch • and Alan Willsky a Abstract.

A fast, recently developed, multiscale optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographic data. The multiscale algorithm itself, published elsewhere, is not the focus of this paper.

However, the algorithm requires statistical models having a very particular multiscale structure; it is the development of a class of multiscale statistical models, appropriate for oceanographic mapping problems, with which we concern ourselves in this by: Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data: Publication Type: Journal Article: Year of Publication: ISBN Number: Abstract: A fast, recently developed, multiscale optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographic data.

This. A fast, recently developed, multiscale optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographic data. This algorithm. Abstract. A fast, recently developed, multiscale optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographic by: BibTeX @MISC{Menemenlis97adaptationof, author = {Dimitris Menemenlis and Paul Fieguth and Carl Wunsch and Alan Willsky}, title = {Adaptation of a Fast Optimal Interpolation Algorithm to the Mapping of Oceanographic Data}, year = {}}.

Adaptation of a Fast Optimal Interpolation Algorithm to the Mapping of Oceanographic Data By Dimitris Menemenlis, Paul Fieguth, Carl Wunsch and Alan Willsky Abstract.

Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data Dimitris Menemenlis,1 Paul Fieguth,2 Carl Wunsch,1 and Alan Willsky3 1 Abstract.

A fast, recently developed, multiscale optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographic data. A multiresolution optimal interpolation scheme is described and used to map the sea level anomaly of the Mediterranean Sea based on TOPEX/Poseidon and ERS-1 data.

The principal advantages of the multiresolution scheme are its high computational efficiency, the requirement for explicit statistical models for the oceanographic signal and the.

Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data. Journal of Geophysical Research: Oceans, Vol.Issue. Journal of. Menemenlis, D., P.

Fieguth, C. Wunsch, and A. Willsky, "Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data", Journal of Geophysical Research, vol.issue C5, pp.

-BibTeX. Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data Dimitris Menemenlis, 1 Paul Fieguth, 2 Carl Wunsch, a and Alan Willsky a Abstract. A fast, recently developed, multiscale optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographi c data.

Menemenlis D, Fieguth P, Wunsch C, Willsky A () Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data. J Geophys Res Oceans (–) – Article; Google Scholar.

Adaptation of a Fast Optimal Interpolation Algorithm to the Mapping of Oceangraphic Data optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographic.

Interpolation of measuring data on the standard oceanographic levels -Estimating of interpolating data in square nodes of geographic grid, and -Presentation of output results in GIS form.

Other oceanographic data had been passed a similar control procedure that depending of nature and distribution of data. Optimal Interpolation Optimal Interpolation or OI is a commonly used and fairly simple but powerful method of data assimilation. Most weather centers around the world used OI for operational numerical weather forecasts throughout the s and 80s.

In fact, Canada was the flrst to implement an OI for. ging techniques. Section 3 describes the adaptation of self-organizing maps to the spatial interpolation problem.

The results of actual data interpolation in an oceanographic problem are presented and discussed. The last section draws conclusions and perspectives.

van Leeuwen et al. (Eds.): IFIP TCS, LNCSpp. {, Several attempts have been made to improve the retrieval quality of LAI data. One way is to continue developing sophisticated algorithms, such as introducing spatial [2] or temporal [3] prior knowledge.

The other way is post-process current LAI products using various methods such as optimal interpolation [4], temporal spatial filter [5]. Thus, we presented a new local interpolation algorithm, which works well also when the amount of data is very large, is fast, and achieves a good accuracy.

In particular, this optimized implementation of the modified spherical version of Shepard’s method is obtained by applying a spherical zone nearest neighbour searching procedure.

Get this from a library. Adaptation of a fast optimal interpolation algorithm to the mapping of oceanographic data. [Dimitris Menemenlis; United States.

As discussed previously in MAP estimation, Model interpolation using training data, when adapting LMs using context dependent interpolation, two sets of weights are available. These are obtained from the training data nPP estimation and test adaptation.() STABLE – a stability algorithm for parametric model reduction by matrix interpolation.

Mathematical and Computer Modelling of Dynamical Systems() A hybrid adaptive sampling algorithm for obtaining reduced order models for .A fast algorithm for optimal linear interpolation Abstract: A fast algorithm for computing the optimal linear interpolation filter is developed.

The algorithm is based on the Sherman-Morrison inversion formula for symmetric matrices. The relationship between the derived algorithm and the Levinson algorithm is illustrated.