Accounting for observation uncertainty and bias due to unresolved scales with the Schmidt-Kalman filter

Data assimilation combines observations with numerical model data, to provide a best estimate of a real system. Errors due to unresolved scales arise when there is a spatio-temporal scale mismatch between the processes resolved by the observations and model. We present theory on error, uncertainty a...

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Veröffentlicht in:	Tellus. Series A, Dynamic meteorology and oceanography Dynamic meteorology and oceanography, 2020-01, Vol.72 (1), p.1-21
Hauptverfasser:	Bell, Zackary, Dance, Sarah L., Waller, Joanne A.
Format:	Artikel
Sprache:	eng
Schlagworte:	Bias data assimilation Data collection error due to unresolved scales Errors Kalman filters Mathematical models observation bias observation uncertainty Schmidt-Kalman filter Uncertainty
Online-Zugang:	Volltext
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