# Difference between revisions of "Optimum interpolation"

From Glossary of Meteorology

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− | <div class="definition"><div class="short_definition">Commonly known as OI, this procedure provides an estimate of the state of the [[atmosphere]] by a weighted least squares fit to observations and a [[background field]], usually provided by a [[NWP]] model forecast.</div><br/> <div class="paragraph">The weights are the inverse of the [[error]] covariance matrices for the observations and the background field. The word | + | <div class="definition"><div class="short_definition">Commonly known as OI, this procedure provides an estimate of the state of the [[atmosphere]] by a weighted least squares fit to observations and a [[background field]], usually provided by a [[NWP]] model forecast.</div><br/> <div class="paragraph">The weights are the inverse of the [[error]] covariance matrices for the observations and the background field. The word "optimum" is misleading, because in practice it is difficult to define the error covariances accurately. A more appropriate term is "statistical interpolation."</div><br/> </div><div class="reference"> Daley, R. 1991. Atmospheric Data Analysis. 98–184. </div><br/> |

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## Latest revision as of 15:47, 20 February 2012

## optimum interpolation

Commonly known as OI, this procedure provides an estimate of the state of the atmosphere by a weighted least squares fit to observations and a background field, usually provided by a NWP model forecast.

The weights are the inverse of the error covariance matrices for the observations and the background field. The word "optimum" is misleading, because in practice it is difficult to define the error covariances accurately. A more appropriate term is "statistical interpolation."

Daley, R. 1991. Atmospheric Data Analysis. 98–184.

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