The potential of high‐density observations for numerical weather prediction: A study with simulated observations

By Zhiquan Liu, Florence Rabier. 2003

134 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/042bd635b5f289516ea96cc102b3a71296896853

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Citations in the corpus

Estimating interchannel observation‐error correlations for IASI radiance data in the Met Office system † (2014, background): “Mistreating observation errors as uncorrelated has been shown to be detrimental to the analysis accuracy and information content when using hyperspectral satellite observations (Liu and Rabier, 2003; Rabier et al., 2003; Collard, 2004; Stewart et al., 2008).”
On the representation error in data assimilation (2018, unknown): “When observation errors are incorrectly assumed to be uncorrelated, increasing the observation density beyond some threshold value has been shown to yield little or no improvement in analysis accuracy (Liu and Rabier, 2003; Berger and Forsythe, 2004; Dando et al., 2007; Jacques and Zawadzki, 2014).”
Impact of correlated observation errors on inverted CO2 surface fluxes from OCO measurements (2007, methodology): “A prominent illustration is given by thenumericalweatherpredictionsystems,sincemostofthem assume uncorrelated observation errors (but correlated prior errors).Toattenuatetheeffectsofsucharoughsimplification, these systems include two empirical adjustments [e.g., Liu and Rabier, 2003, and reference”
Estimates of spatial and interchannel observation‐error characteristics for current sounder radiances for numerical weather prediction. I: Methods and application to ATOVS data (2010, background): “Guidance for selecting optimal thinning scales can be taken from Liu and Rabier (2003), who found that thinning scales of a thresholderror correlation value of around 0.2 produced the smallest analysis error when error correlations are neglected and the diagonal observation errors are not inflated.”
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.”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW) (2009, methodology): “In defining C in (12), we use the compactly supported second-order autoregressive function as horizontal correlation model (Liu and Rabier 2003):”
On the interaction of observation and prior error correlations in data assimilation (2018, unknown): “Evaluating the problems caused by erroneously not including OECs has been the subject of many recent articles, e.g. Liu and Rabier (2002, 2003), Stewart et al. (2008, 2013), Miyoshi et al. (2013), Jacques and Zawadzki (2014) and Rainwater et al. (2015).”