Impacts on sea ice analyses from the assumption of uncorrelated ice thickness observation errors: Experiments using a 1D toy model
| Autor: | Mark Buehner, K. Andrea Scott, Graham Stonebridge |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences lcsh:QC851-999 010502 geochemistry & geophysics Oceanography Atmospheric sciences 01 natural sciences Physics::Geophysics lcsh:Oceanography Data assimilation Sea ice lcsh:GC1-1581 data assimilation Physics::Atmospheric and Oceanic Physics 0105 earth and related environmental sciences correlated errors geography Toy model geography.geographical_feature_category sea ice Uncorrelated Ice thickness lcsh:Meteorology. Climatology Astrophysics::Earth and Planetary Astrophysics Geology ice thickness |
| Zdroj: | Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 70, Iss 1, Pp 1-13 (2018) |
| ISSN: | 1600-0870 |
| DOI: | 10.1080/16000870.2018.1445379 |
| Popis: | Sea ice prediction centres are moving toward the assimilation of ice thickness observations under the simplifying assumption that the observation errors are uncorrelated. The assumption of uncorrelated observation errors is attractive because the errors can be represented by a diagonal observation error covariance matrix, which is inexpensive to invert. In this paper a set of idealized experiments are carried out to investigate the impact of this assumption on sea ice analyses. A background error covariance matrix is generated using a 1D toy model for sea, i.e. forced with idealized models of the ocean and atmosphere. Analysis error covariance matrices are then calculated using this $ \boldsymbol{ \mathrm B } $ matrix for both correlated and uncorrelated observation error covariance matrices, $ \boldsymbol{ \mathrm R } $. The results indicate when the true $ \boldsymbol{ \mathrm R } $ is correlated, using a diagonal approximation results in an analysis that is overconfident at the large scales, in that the analysis error standard deviation at the large scales is underestimated. It is also shown that for the largest observation error correlation length scale tested, 150 km, the analysis error standard deviation for ice thickness is reduced by 10.8% relative to the background error standard deviation when $ \boldsymbol{ \mathrm R } $ has the correct correlation length scale of 150 km, whereas when a diagonal approximation to $ \boldsymbol{ \mathrm R } $ is used in combination with an inflation factor, the reduction is to 6.3%. |
| Databáze: | OpenAIRE |
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