Linearized Physics for Data Assimilation at ECMWF

Janisková, Marta; Lopez, Philippe

doi:10.1007/978-3-642-35088-7_11

Cited by 39 publications

(41 citation statements)

References 46 publications

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“…The second singular vector configuration uses a tangent‐linear model that is expected to approximate better the evolution of finite‐amplitude initial perturbations in the nonlinear forecast model. It uses a higher horizontal resolution of T L 159 and moist linearized physics (Janisková and Lopez, 2013). Otherwise, the configuration is the same as for the lower resolution singular vectors.…”

Section: Methodsmentioning

confidence: 99%

On the reliability of ensemble variance in subspaces defined by singular vectors

Leutbecher

Lang

2013

Quart J Royal Meteoro Soc

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In a statistically consistent ensemble forecast, ensemble variance agrees with the mean squared error of the ensemble mean when looking at a large sample of independent realizations. Here, the first goal is to introduce a new diagnostic that quantifies the reliability of ensemble variance in subspaces spanned by singular vectors of the model propagator. The second goal is to apply this technique to ensemble forecast experiments with the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) in order to gain new insight into essential approximations made when the sources of forecast uncertainties are represented with initial perturbations based on the leading initial singular vectors. Diagnostics are based on extratropical singular vectors optimized for growth of total energy over 48 hours. For the initial perturbations as well as the singular vector diagnostics, two versions of the tangent‐linear model are compared, which differ in terms of the accuracy of the tangent‐linear approximation. When model uncertainties are not represented, initial perturbations based solely on the leading singular vectors can achieve reliable ensemble variances in the subspace spanned by the singular vectors evolved to their optimization time. However, it is impossible to achieve at the same time reliable total variances with up to about 100 singular vectors in the extratropics. Far more singular vectors would be required to do so. Ensembles using initial perturbations based on up to 100 leading singular vectors with amplitudes tuned such that total variance matches total error suffer from overdispersion in the subspace spanned by the leading singular vectors. In the operational configuration of the ECMWF ensemble, the singular vector initial perturbations introduce overdispersion in the subspace of the leading 50 extratropical singular vectors.

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Section: Methodsmentioning

confidence: 99%

On the reliability of ensemble variance in subspaces defined by singular vectors

Leutbecher

Lang

2013

Quart J Royal Meteoro Soc

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“…While the tangent-linear model is extensively tested (Janisková and Lopez 2012), do the assumptions of linearity break down in fast-maturing instability events such as MCSs? To obtain the tangent-linear model, simplifications are made to the nonlinear physics, discontinuities are smoothed, and processes are linearized.…”

Section: Improving the Modelmentioning

confidence: 99%

Characteristics of Occasional Poor Medium-Range Weather Forecasts for Europe

Rodwell

Magnusson

Bauer

et al. 2013

Bull. Amer. Meteor. Soc.

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174

221

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“…The resolution of the forecast model was T511 (;0.358 grid spacing on reduced linear Gaussian grid), with 91 vertical levels. The data assimilation, ECMWF's 4D-Var system, uses a full nonlinear trajectory at T511 L91 (outer loop) and a linearized model (Janiskova and Lopez 2012) at the resolutions T159, T159, and T255 for the three minimization inner loops, respectively. The ECMWF 4D-Var system also used background error variances ''of the day'' as estimated from the low resolution (T399 L91 outer loop, linearized T159 inner loops) ensemble data assimilation (Bonavita et al 2011).…”

Section: Targeted Data Model and Data Assimilation Systemmentioning

confidence: 99%

Impact of Targeted Winter Storm Reconnaissance Dropwindsonde Data on Midlatitude Numerical Weather Predictions

Hamill

Yang

Cardinali

et al. 2013

Monthly Weather Review

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The impact of assimilating data from the 2011 Winter Storm Reconnaissance (WSR) program on numerical weather forecasts was assessed. Parallel sets of analyses and deterministic 120-h numerical forecasts were generated using the ECMWF four-dimensional variational data assimilation (4D-Var) and Integrated Forecast System. One set of analyses was generated with all of the normally assimilated data plus WSR targeted dropwindsonde data, the other with only the normally assimilated data. Forecasts were then generated from the two analyses. The comparison covered the period from 10 January to 28 March 2011, during which 98 flights and 776 total dropwindsondes were deployed from four different air bases in the Pacific basin and the United States. The dropwindsondes were deployed in situations where guidance indicated the potential for high-impact weather and/or the potential for large subsequent forecast errors. Downstream target verification regions where the high-impact weather was expected were identified for each case. Forecast errors around the target verification regions were evaluated using an approximation to the total-energy norm. Precipitation forecasts were also evaluated over the contiguous United States using the equitable threat score and bias.Forecast impacts were generally neutral and thus smaller than reported in previous studies, most from over a decade ago, perhaps because of the improved forecast and assimilation system and the somewhat denser observation network. Target areas may also have been undersampled in this study. The neutral results from 2011 suggest that it may be more beneficial to explore other targeted observation concepts for the midlatitudes, such as assimilation of a denser set of cloud-drift winds and radiance data in dynamically sensitive regions.

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Linearized Physics for Data Assimilation at ECMWF

Cited by 39 publications

References 46 publications

On the reliability of ensemble variance in subspaces defined by singular vectors

On the reliability of ensemble variance in subspaces defined by singular vectors

Characteristics of Occasional Poor Medium-Range Weather Forecasts for Europe

Impact of Targeted Winter Storm Reconnaissance Dropwindsonde Data on Midlatitude Numerical Weather Predictions

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