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Massive Data Sets Proceedings of a Workshop (1996) / Chapter Skim
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Massive Data Assimilation/Fusion in Atmospheric Models and Analysis: Statistical, Physical, and Computational Challenges
Pages 93-103

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 93...
... \~'e note that components of such a solution airead,v exist in statistics atmospheric, and computational sciences, but that in isolation they often fait to scale up to the massive data challenge. The prospects of synthesizing an interdisciplinary solution which will scale up to the massive data challenge are thus .
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From page 94...
... As a preamble lee describe in the next section the satellite cia,ta: volumes heterogeneity, and structure along witty some special problems sucks ciata pose. N\e then describe some existing methods and tools in section three and critically evaluate their performance with massive data sets.
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From page 95...
... (1994) reveals similar structures in the ERST/AMI monthly and annual mean for T999 as Darrell as in the Pathfinder SSM/T monthly mean wind speeds maps.
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From page 97...
... At cd ct 1 > Q Q __ o CM I Cal LO CM On en 97
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From page 98...
... The examples above underscore two serious problems with the analysis and assimilation of satellite data in atmospheric studies. The first example demonstrates the special need for the construction of efficient' inexpensive, maintainable and modular software tools for application in synoptic scale atmospheric models.
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From page 99...
... though in the phi sical world data are often imprecise. In these cases the scientist is left With the unpleasant decision of whether to ignore the imprecise information altogether and store some approximation to the precise values or to forgo the use of a standard database management system and manage the data directly.
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From page 100...
... monitoring data are tal;en over time at fixed monitoring sites, and thus provide a sequence of replicates from which to compute spatial co`-ariances. Their technique uses multidimensiona scaling to transform the geographic coordinates into a space where the correlation structure is isotropic and homogeneous so stanciard correlation estimation techniques appIv.
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From page 101...
... It is scale dependents and does not incorporate statistical error information. The SG99 estimation technique handles heterogeneous spatial correlation for small and medium data sets.
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From page 102...
... An atlas of monthly mean distribution,s of SS3~:T,surface wind speed, ARGOS buoy drifts AVHRR/2,sea surface temperature. .4 iTfl surface wind components.
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From page 103...
... PART III Additional Invited Papers 103
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Key Terms

  • massive data
  • database management
  • control set
  • data assimilation
  • atmospheric models

  • imprecise data
  • correlation structure
  • surface wind
  • error information
  • sampling pattern

  • monthly mean
  • satellite instruments
  • spatial covariance
  • error structure
  • statistical error

  • interval methods
  • existing methods
  • covariance structure
  • atmospheric studies
  • spatial correlation

  • imprecise information
  • mean wind
  • error handling
  • covariance information
  • smoothed field

  • sigmod international
  • software tools
  • handle imprecise
  • spatial information
  • massive satellite


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