Estimating interchannel observation‐error correlations for IASI radiance data in the Met Office system †

By L. Stewart, Sarah L. Dance, Nancy Nichols, John Eyre, J. Cameron. 2014

82 citations recorded by the source corpus.

The optimal utilisation of hyper‐spectral satellite observations in numerical weather prediction is often inhibited by incorrectly assuming independent interchannel observation errors. However, in order to represent these observation‐error covariance structures, an accurate knowledge of the true variances and correlations is needed. This structure is likely to vary with observation type and assimilation system. The work in this article presents the initial results for the estimation of IASI interchannel observation‐error correlations when the data are processed in the Met Office one‐dimensional (1D‐Var) and four‐dimensional (4D‐Var) variational assimilation systems. The method used to calculate the observation errors is a post‐analysis diagnostic which utilises the background and analysis departures from the two systems.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/3e078fb62e5efdad34d6053fcee7240c6d43ee64

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Citations in the corpus

Accounting for correlated error in the assimilation of high‐resolution sounder data (2014, methodology): “However, previous work at Reading University and the Met Office (Stewart, 2009; Stewart et al., 2013) and at ECMWF (Bormann and Bauer, 2010; Bormann et al., 2010) demonstrated that correlations exist in IASI data, particularly for channels sensitive to water vapour.”
A review of operational methods of variational and ensemble‐variational data assimilation (2017, background): “There is evidence though that the correlations between errors (including representivity) of different observations should be accounted for (Stewart et al., 2014; Waller et al., 2014). c© 2016 The Authors.”
On the interaction of observation and prior error correlations in data assimilation (2018, unknown): “…, 2014a) and are found predominantly in observation types with complex observation operators (such as satellite radiances: Bormann and Bauer, 2010; Stewart et al. , 2014; Bormann et al. , 2016; Waller et al. , 2016a; Campbell et al. , 2017), observation types measuring features with natural…”
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, background): “, Σy and Σz) in the objective functions plays a significant role in parameter learning, and learning about it has been demonstrated to have considerable impact on data assimilation for weather forecasts [Dee, 1995; Stewart et al., 2014].”
On the representation error in data assimilation (2018, unknown): “As a result, it is often stated that the method will not yield an accurate result if the scales in the background and observation-error statistics are similar (Bormann and Bauer 2010; Bormann et al., 2010; Stewart et al., 2014; Weston et al., 2014).”