Mismatching Perturbations at the Lateral Boundaries in Limited-Area Ensemble Forecasting: A Case Study

Caron, Jean‐François

doi:10.1175/mwr-d-12-00051.1

Cited by 45 publications

(48 citation statements)

References 46 publications

(51 reference statements)

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“…To name but a few, there are the COSMO-DE-EPS (Consortium for Small-scale Modeling -EPS, Gebhardt et al, 2011;Peralta et al, 2012;Ben Bouallègue et al, 2013;Kühnlein et al, 2014) at the Deutscher Wetterdienst (DWD), the CP version of UK Met Office's MOGREPS (Met Office Global and Regional Ensemble Prediction System, Bowler et al, 2008;Caron, 2013;Hanley et al, 2013;Tennant, 2015), a storm-scale ensemble forecast (SSEF) run by the Center of Analysis and Prediction of Storms (CAPS) at the University of Oklahoma (Xue et al, 2007Clark et al, 2011;Schumacher et al, 2013;Schumacher and Clark, 2014), WRF-based CP ensemble at NCAR (e.g., Schwartz et al, 2015), and AROME-EPS (e.g., Bouttier et al, 2012) developed at Météo France. A common feature of all of these EPSs is that their horizontal mesh size is equal to or less than 4 km, but mostly between 2 and 3 km.…”

Section: Introductionmentioning

confidence: 99%

“…Some of them apply an ensemble data assimilation (EDA) approach for perturbing the initial conditions (ICs) Caron, 2013;Schumacher and Clark, 2014;Schwartz et al, 2015). The applied model perturbation methods range from a multiparameter approach (Gebhardt et al, 2011) to a stochastic physics scheme Romine et al, 2014) and to using different dynamical cores (Schumacher et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the forecast skill of a convection-permitting ensemble

Schellander-Gorgas¹,

Wang²,

Meier³

et al. 2017

Geosci. Model Dev.

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Abstract. The 2.5 km convection-permitting (CP) ensemble AROME-EPS (Applications of Research to Operations at Mesoscale -Ensemble Prediction System) is evaluated by comparison with the regional 11 km ensemble ALADIN-LAEF (Aire Limitée Adaption dynamique Développement InterNational -Limited Area Ensemble Forecasting) to show whether a benefit is provided by a CP EPS. The evaluation focuses on the abilities of the ensembles to quantitatively predict precipitation during a 3-month convective summer period over areas consisting of mountains and lowlands. The statistical verification uses surface observations and 1 km × 1 km precipitation analyses, and the verification scores involve state-of-the-art statistical measures for deterministic and probabilistic forecasts as well as novel spatial verification methods. The results show that the convectionpermitting ensemble with higher-resolution AROME-EPS outperforms its mesoscale counterpart ALADIN-LAEF for precipitation forecasts. The positive impact is larger for the mountainous areas than for the lowlands. In particular, the diurnal precipitation cycle is improved in AROME-EPS, which leads to a significant improvement of scores at the concerned times of day (up to approximately one-third of the scored verification measure). Moreover, there are advantages for higher precipitation thresholds at small spatial scales, which are due to the improved simulation of the spatial structure of precipitation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the forecast skill of a convection-permitting ensemble

Schellander-Gorgas¹,

Wang²,

Meier³

et al. 2017

Geosci. Model Dev.

View full text Add to dashboard Cite

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“…Moreover, in the context of a mesoscale ensemble system over Japan, Saito et al (2012) have shown that lateral boundary perturbations increase the ensemble spread and improve the accuracy of the ensemble mean forecast. In the context of a 1.5-km ETKF over the southern U.K., coupled to a 24-km regional ensemble, Caron (2013) has found that a mismatch between the analysis perturbations and the LBC perturbations leads to the generation of spurious gravity waves, which confirms that a careful design of LBC perturbations should be sought.…”

Section: Introductionmentioning

confidence: 79%

Sensitivity of regional ensemble data assimilation spread to perturbations of lateral boundary conditions

Ouaraini

Berre

Fischer

et al. 2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TThe implementation of a regional ensemble data assimilation and forecasting system requires the specification of appropriate perturbations of lateral boundary conditions (LBCs), in order to simulate associated errors. The sensitivity of analysis and 6-h forecast ensemble spread to these perturbations is studied here formally and experimentally by comparing three different LBC configurations for the ensemble data assimilation system of the ALADIN-France limited-area model (LAM). While perturbed initial LBCs are provided by the perturbed LAM analyses in each ensemble, the three ensemble configurations differ with respect to LBCs used at 3-and 6-h forecast ranges, which respectively correspond to: (1) perturbed LBCs provided by the operational global ensemble data assimilation system (GLBC), which is considered as a reference configuration; (2) unperturbed LBCs (ULBC) obtained from the global deterministic model; (3) perturbed LBCs obtained by adding random draws of an error covariance model (PLBC) to the global deterministic system. A formal analysis of error and perturbation equations is first carried out, in order to provide an insight of the relative effects of observation perturbations and of LBC perturbations at different ranges, in the various ensemble configurations. Horizontal variations of time-averaged ensemble spread are then examined for 6-h forecasts. Despite the use of perturbed initial LBCs, the regional ensemble ULBC is underdispersive not only near the lateral boundaries, but also in approximately one-third of the inner area, due to advection during the data assimilation cycle. This artefact is avoided in PLBC through the additional use of non-zero LBC perturbations at 3-and 6-h ranges, and the sensitivity to the amplitude scaling of the covariance model is illustrated for this configuration. Some aspects of the temporal variation of ensemble spread and associated sensitivities to LBC perturbations are also studied. These results confirm the importance of LBC perturbations for regional ensemble data assimilation. They also indicate that perturbing initial LBC is not sufficient to obtain realistic ensemble spread, whereas this can be achieved approximately by using random covariance draws for simulating LBC errors during the forecast and associated data assimilation cycling.

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“…Although the above results are mixed, these comparisons may shed some light on further improvement in present system. A practical way to take advantages of both ensemble perturbations is developing a blending technique, which has been primarily investigated by Caron [33] and Wang et al [34]. This technique can obtain blended perturbations that contain large-scale component from downscaling perturbations and small-scale component from ETKF perturbations.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of ETKF and Downscaling in a Regional Ensemble Prediction System

et al. 2015

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Based on the operational regional ensemble prediction system (REPS) in China Meteorological Administration (CMA), this paper carried out comparison of two initial condition perturbation methods: an ensemble transform Kalman filter (ETKF) and a dynamical downscaling of global ensemble perturbations. One month consecutive tests are implemented to evaluate the performance of both methods in the operational REPS environment. The perturbation characteristics are analyzed and ensemble forecast verifications are conducted; furthermore, a TC case is investigated. The main conclusions are as follows: the ETKF perturbations contain more power at small scales while the ones derived from downscaling contain more power at large scales, and the relative difference of the two types of perturbations on scales become smaller with forecast lead time. The growth of downscaling perturbations is more remarkable, and the downscaling perturbations have larger magnitude than ETKF perturbations at all forecast lead times. However, the ETKF perturbation variance can represent the forecast error variance better than downscaling. Ensemble forecast verification shows slightly higher skill of downscaling ensemble over ETKF ensemble. A TC case study indicates that the overall performance of the two systems OPEN ACCESSAtmosphere 2015, 6 342 are quite similar despite the slightly smaller error of DOWN ensemble than ETKF ensemble at long range forecast lead times.

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Mismatching Perturbations at the Lateral Boundaries in Limited-Area Ensemble Forecasting: A Case Study

Cited by 45 publications

References 46 publications

On the forecast skill of a convection-permitting ensemble

On the forecast skill of a convection-permitting ensemble

Sensitivity of regional ensemble data assimilation spread to perturbations of lateral boundary conditions

A Comparison of ETKF and Downscaling in a Regional Ensemble Prediction System

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