The interaction between model resolution, observation resolution and observation density in data assimilation: A one‐dimensional study

By Zhiquan Liu, Florence Rabier. 2002

195 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/4ec8d36a5453a16f164397a076a7e5199c1b875d

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Citations in the corpus

On the interaction of observation and prior error correlations in data assimilation (2018, unknown): “These conclusions differ from those of Liu and Rabier (2002) and Bergman and Bonner (1976), who considered only OECs with length-scales less than the BECs and only the effect on the analysis RMSE.”
Ensemble-Based Sensitivity Analysis (2008, methodology): “Unlike previously proposed observation thinning algorithms (e.g., Liu and Rabier 2002; Ochotta et al. 2005), this technique selects observations based on the forecast metric of interest.”
On the representation error in data assimilation (2018, unknown): “Daley (1993) and Liu and Rabier (2002) found that there is an optimal match between the analysis grid spacing and the instrument spatial averaging, which results in the minimum representation error.”
The Spatial Structure of Observation Errors in Atmospheric Motion Vectors from Geostationary Satellite Data (2003, background): “This has been shown in theoretical studies (e.g., Liu and Rabier 2002) and by practical experience with positive forecast impact from the assimilation of AMVs with such suboptimal settings (e.g., Bouttier and Kelly 2001; Rohn et al. 2001).”
The benefits of correlated observation errors for small scales (2015, background): “Additionally, Liu and Rabier (2002, 2003) showed that, when observation errors are correlated but the assimilation scheme treats them as uncorrelated, reducing the density of assimilated observations (‘thinning’) can improve the analysis.”
Ensemble Data Assimilation with the NCEP Global Forecast System (2008, background): “Typically, these “errors of representativeness” are accounted for by increasing the value of R. However, “representativeness errors” also have horizontal correlations, which are usually not accounted for. Liu and Rabier (2002) 466”
Representation Error of Oceanic Observations for Data Assimilation (2008, background): “Some studies have investigated how RE should be treated in data assimilation for various idealized cases (e.g., Liu and Rabier 2002; Janic and Cohn 2006).”