Correlated observation errors in data assimilation

Stewart, Laura; Dance, Sarah L.; Nichols, Nancy K.

doi:10.1002/fld.1636

Cited by 93 publications

(141 citation statements)

References 5 publications

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“…The results also showed that the analysis became less accurate in certain combinations of the error correlation and the angle in the observation operator. The results agree very well in the conceptual model based on the Shannon information content (Stewart et al 2008;MKL13).…”

Section: Summary and Discussionsupporting

confidence: 77%

“…MKL13 suggested that for analysis accuracy in data assimilation the available information in the observations be essential rather than the amount of information based on the Shannon information content (Stewart et al 2008). Recent studies suggested that some observations have correlated errors, but the observation error covariance matrix R is assumed to be diagonal in the operational numerical weather prediction systems.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…However, the observation error correlations, or off-diagonal components of the observation error covariance matrix, are not considered in the current operational numerical weather prediction (NWP) systems to the best of the authors' knowledge. Stewart et al (2008) showed that assuming a diagonal observation error correlation matrix for the error-correlated observations results in a significant loss of information using an idealized dataset. Moreover, Stewart et al (2012) suggested that it be better to include an approximated observation error correlation structure than to make an incorrect assumption of no error correlation.…”

Section: Introductionmentioning

confidence: 99%

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Data Assimilation with Error-Correlated and Non-Orthogonal Observations: Experiments with the Lorenz-96 Model

Terasaki

Miyoshi

2014

SOLA

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This study aims to investigate the impact of observation error correlations and non-orthogonal observation operators on analysis accuracy using a chaotic dynamical model known as the Lorenz-96 40-variable model, extending the previous study by Miyoshi et al. using a simple two-dimensional conceptual model. The results corroborate Miyoshi et al.'s conceptual study and show that the analysis is more accurate when the row vectors of a linear observation operator are correlated positively (negatively) with negatively (positively) correlated observation error. The online estimation of the observation error covariance matrix based on the Desroziers diagnostics is successful when we have reasonable a priori knowledge about the observation error correlations.(Citation: Terasaki, K., and T. Miyoshi, 2014: Data assimilation with error-correlated and non-orthogonal observations: Experiments with the Lorenz-96 model. SOLA, 10, 210−213,

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Section: Summary and Discussionsupporting

confidence: 77%

Section: Summary and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Assimilation with Error-Correlated and Non-Orthogonal Observations: Experiments with the Lorenz-96 Model

Terasaki

Miyoshi

2014

SOLA

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“…Nevertheless, attempts have been 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;Stewart, 2010;Stewart et al, 2012). Using diagnosed correlations such as these in an operational assimilation system is far from straightforward: early attempts by the UK Met Office using IASI and AIRS data have resulted in conditioning problems with the 4D-Var minimisation (Weston, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Under these assumptions, 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). Stewart et al (2008) and Stewart (2010) showed that the observation information content in the analysis is severely degraded under the incorrect assumption of independent observation errors. Such studies, combined with examples demonstrating that ignoring correlation structure hinders the use of satellite data [e.g.…”

Section: Introductionmentioning

confidence: 99%

Data assimilation with correlated observation errors: experiments with a 1-D shallow water model

Stewart¹,

Dance²,

Nichols

2013

Tellus A: Dynamic Meteorology and Oceanography

108

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A B S T R A C T Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information. As operational centres move towards higher-resolution forecasting, there is a requirement to retain data providing detail on appropriate scales. Thus an alternative approach to dealing with observation error correlations is needed. In this article, we consider several approaches to approximating observation error correlation matrices: diagonal approximations, eigendecomposition approximations and Markov matrices. These approximations are applied in incremental variational assimilation experiments with a 1-D shallow water model using synthetic observations. Our experiments quantify analysis accuracy in comparison with a reference or 'truth' trajectory, as well as with analyses using the 'true' observation error covariance matrix. We show that it is often better to include an approximate correlation structure in the observation error covariance matrix than to incorrectly assume error independence. Furthermore, by choosing a suitable matrix approximation, it is feasible and computationally cheap to include error correlation structure in a variational data assimilation algorithm.

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Changes in nitrogen oxides emissions in California during 2005–2010 indicated from top‐down and bottom‐up emission estimates

et al. 2014

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In California, emission control strategies have been implemented to reduce air pollutants. Here we estimate the changes in nitrogen oxides (NO x = NO + NO 2 ) emissions in 2005-2010 using a state-of-the-art four-dimensional variational approach. We separately and jointly assimilate surface NO 2 concentrations and tropospheric NO 2 columns observed by Ozone Monitoring Instrument (OMI) into the regional-scale Sulfur Transport and dEposition Model (STEM) chemical transport model on a 12 × 12 km 2 horizontal resolution grid in May 2010. The assimilation generates grid-scale top-down emission estimates, and the updated chemistry fields are evaluated with independent aircraft measurements during the NOAA California Nexus (CalNex) field experiment. The emission estimates constrained only by NO 2 columns, only by surface NO 2 , and by both indicate statewide reductions of 26%, 29%, and 30% from~0.3 Tg N/yr in the base year of 2005, respectively. The spatial distributions of the emission changes differ in these cases, which can be attributed to many factors including the differences in the observation sampling strategies and their uncertainties, as well as those in the sensitivities of column and surface NO 2 with respect to NO x emissions. The updates in California's NO x emissions reduced the mean error in modeled surface ozone in the Western U.S., even though the uncertainties in some urban areas increased due to their NO x -saturated chemical regime. The statewide reductions in NO x emissions indicated from our observationally constrained emission estimates are also reflected in several independently developed inventories:~30% in the California Air Resources Board bottom-up inventory,~4% in the 2008 National Emission Inventory, and~20% in the annual mean top-down estimates by Lamsal et al. using the global Goddard Earth Observing System (GEOS)-Chem model and OMI NO 2 columns. Despite the grid-scale differences among all top-down and bottom-up inventories, they all indicate stronger emission reductions in the urban regions. This study shows the potential of using space-/ground-based monitoring data and advanced data assimilation approach to timely and independently update NO x emission estimates on a monthly scale and at a fine grid resolution. The well-evaluated results here suggest that these approaches can be applied more broadly.

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Correlated observation errors in data assimilation

Cited by 93 publications

References 5 publications

Data Assimilation with Error-Correlated and Non-Orthogonal Observations: Experiments with the Lorenz-96 Model

Data Assimilation with Error-Correlated and Non-Orthogonal Observations: Experiments with the Lorenz-96 Model

Data assimilation with correlated observation errors: experiments with a 1-D shallow water model

Changes in nitrogen oxides emissions in California during 2005–2010 indicated from top‐down and bottom‐up emission estimates

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