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Quality Control: Methodology and Applications

  • Pierre Gauthier
  • Clément Chouinard
  • Bruce Brasnett
Part of the NATO Science Series book series (NAIV, volume 26)

Abstract

All assimilation methods rely on data collected from many sources. There are the conventional data sources like surface observations, radiosonde data, aircraft and ship data to which is now added an increasing amount of satellite data (see also the chapters Observing the atmosphere by R. Swinbank and Assimilation of remote sensing observations in Numerical Weather Prediction, by J.N. Thépaut). Each instrument is prone to error that could be systematic due to an incorrect calibration or random, reflecting the accuracy and representativeness of the measurement. The assimilation methods presented during this course assume that the data used in the assimilation have unbiased errors and are devoid of any serious error due to a malfunction of the instrument. Such errors are referred to as gross errors.

Keywords

Data Assimilation Numerical Weather Prediction Observation Error Background Error Assimilation Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Pierre Gauthier
    • 1
  • Clément Chouinard
    • 1
  • Bruce Brasnett
    • 1
  1. 1.Meteorological Service of CanadaDorvalCanada

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