Estimates of spatial and interchannel observation‐error characteristics for current sounder radiances for numerical weather prediction. I: Methods and application to ATOVS data

By Niels Bormann, Peter Bauer. 2010

166 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ad4ee48a2aaa3876b8c8a9e9a0e4f7aa6fda0bd7

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Citations in the corpus

The benefits of correlated observation errors for small scales (2015, unknown): “We remark that Schlatter and Branstator (1979) and Bormann et al. (2010) discuss estimation of observation-error covariance for satellite observations. Hollingsworth and Lönnberg (1986) describe a technique for estimating observation-error variances that assumes spatially uncorrelated observation er”
Data assimilation with correlated observation errors: experiments with a 1-D shallow water model (2013, background): “…made to quantify error correlation structure for a few different observation types such as Atmospheric Motion Vectors (Bormann et al., 2003) and satellite radiances (Sherlock et al., 2003; Stewart et al., 2009; Bormann and Bauer, 2010; Bormann et al., 2010; Stewart, 2010; Stewart et al., 2012).”
On the representation error in data assimilation (2018, unknown): “Bormann and Bauer (2010) and Bormann et al. (2010) applied the method to ATOVS, AIRS and IASI data used in the ECMWF analysis, again demonstrating considerable correlation structures in certain wavelength bands.”
Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere (2017, methodology): “2 K) as diagnosed by Bormann and Bauer (2010).”
Observation errors in all‐sky data assimilation (2011, methodology): “It is quite common to use the FG departures as a guide to the size of observation errors and a number of methods can be used to estimate the partitioning between background and observation error (e.g. Hollingsworth and Lönnberg, 1986; Desroziers et al., 2005; Bormann and Bauer, 2010).”
Error covariance estimation methods based on analysis residuals: theoretical foundation and convergence properties derived from simplified observation networks (2016, background): “In these studies, generally conducted with a single iteration, we also find spatially correlated structures in the estimated observation-error covariance (e.g. Bormann and Bauer, 2010; Bormann et al., 2010, 2011).”
Estimating interchannel observation‐error correlations for IASI radiance data in the Met Office system † (2014, unknown): “Bormann and Bauer (2010) and Bormann et al. (2010) applied the estimation technique on a larger scale, estimating the error covariance structure for clear-sky sounder radiances used in the ECMWF assimilation system.”
On the interaction of observation and prior error correlations in data assimilation (2018, unknown): “…(Waller et al. , 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…”
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.”