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| Titel |
Evaluation criteria on the design for assimilating remote sensing data using variational approaches |
| VerfasserIn |
Sha Lu, Arnold Heemink, Hai Xiang Lin, Arjo Segers, Guangliang Fu |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250137423
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| Publikation (Nr.) |
 EGU/EGU2017-120.pdf |
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| Zusammenfassung |
| Remote sensing, as a powerful tool for monitoring atmospheric phenomena, has been playing
an increasingly important role in inverse modeling. Remote sensing instruments measure
quantities that combine several state variables. This creates correlations between the state
variables which share the same observation variable. Sometimes strong Sensor-Induced
Artificial (SIA) correlations are introduced between physically unrelated states or parameters.
This may cause numerical problems resulting in a low convergence rate or inaccurate
estimates in gradient-based variational assimilation.
In this work, two criteria or scoring rules are proposed to quantify the effectiveness of
assimilating a specific set of remote sensing observations and to quantify the reliability of the
estimates of the parameters. The criteria are derived by analyzing how the SIA correlations
are created via shared observation data and how they influence the process of variational data
assimilation. Experimental tests are conducted and show a good agreement with theory. The
results illustrate the capability of the criteria to indicate the reliability of the assimilation
process. Both criteria can be used as prejudge before the assimilation methodology is
implemented. |
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