Enhancing COSMO-DE ensemble forecasts by inexpensive techniques

Bouallègue, Zied Ben; Theis, Susanne; Gebhardt, Christoph

doi:10.1127/0941-2948/2013/0374

Cited by 48 publications

(51 citation statements)

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“…They also argued that more members are required for skillful forecasts of rare events 25 or in low-predictability regimes. Bouallegue et al (2013) found an improvement in the reliability and resolution of precipitation ensemble forecasts by increasing the number of members from 20 to 60 in a 2.8 km grid-length version of the COSMO-DE model. Ménétrier et al (2014) discussed the characteristics of the forecast error variances and correlation length scales for small (6 member) and large (84 member) ensembles of forecasts using the convection-permitting AROME model (2.5 km grid-length) over France, and focused on a case study characterized by strong convection.…”

Section: Sensitivity To Ensemble Size In Convective-scale Systemsmentioning

confidence: 94%

Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble

Bannister

Migliorini

Rudd

et al. 2017

Preprint

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Abstract. Ensemble-based predictions are increasingly used as an aid to weather forecasting and to data assimilation, where the aim is to capture the range of possible outcomes consistent with the underlying uncertainties. Constraints on computing resources mean that ensembles have a relatively small size, which can lead to an incomplete range of possible outcomes, and to inherent sampling errors. This paper discusses how an existing ensemble can be relatively easily increased in size, it develops a range of standard and extended diagnostics to help determine whether a given ensemble is 'large enough' to 5 be useful for forecasting and data assimilation purposes, and it applies the diagnostics to a convective-scale case study for illustration. Diagnostics include the effect of ensemble size on various aspects of rainfall forecasts, kinetic energy spectra, and (co)-variance statistics in the spatial and spectral domains.The work here extends the Met Office's 24 ensemble members to 93. It is found that the extra members do develop a significant degree of linear independence, they increase the ensemble spread (although with caveats to do with non-Gaussianity), 10 they reduce sampling error in many statistical quantities (namely variances, correlations, and length-scales), and improve the effective spatial resolution of the ensemble. The extra members though do not improve the probabilistic rain rate forecasts.It is assumed that the 93-member ensemble approximates the error-free statistics, which is a practical assumption, but the data suggests that this number of members is ultimately not enough to justify this assumption, and therefore more ensembles are likely required for such convective-scale systems to further reduce sampling errors, especially for ensemble data assimilation 15 purposes.Copyright statement.

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Section: Sensitivity To Ensemble Size In Convective-scale Systemsmentioning

confidence: 94%

Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble

Bannister

Migliorini

Rudd

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…In this study, the ensembles are generated following the "time-lagged ensemble" method prescribed by the German Weather Service [11]. According to the aforementioned method, series of ensemble model runs are performed for both DBC and CBC by varying the initialization time of the three model components (COSMO-DE, CLM3.5, and ParFlow) of TerrSysMP.…”

Section: Setup Of Numerical Experimentsmentioning

confidence: 99%

The subsurface–land surface–atmosphere connection under convective conditions

Rahman

Sulis

Kollet³

2015

Advances in Water Resources

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“…It is now a common approach to blend different NWP models for improving forecast performance. For instance, Ben Bouallègue et al (), Raynaud et al (), and Raynaud and Bouttier () showed that combining time‐lagged ensemble forecasts allows for an increase of the sample size and of the probabilistic performance at no additional cost. However, since different forecasts have different characteristics and skills, the question of how to optimally weight the combined forecasts is important in order to extract the most relevant information.…”

Section: Object‐based Weighting Of Rainfall Forecastsmentioning

confidence: 99%

Object‐based verification metrics applied to the evaluation and weighting of convective‐scale precipitation forecasts

Raynaud

Pechin

Arbogast

et al. 2019

Quart J Royal Meteoro Soc

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Traditional pointwise verification scores are not always appropriate for the evaluation of high‐resolution precipitation forecasts because of double‐penalty problems. An alternative approach, based on the identification of homogeneous rainfall areas called “precipitating objects”, allows forecast evaluation at a larger and thus more predictable scale, and specific information about the nature of errors (e.g. location, size, intensity) can be obtained. A novel object detection method is first introduced and the object‐based verification of precipitation forecasts from the convective‐scale deterministic and ensemble models Arome and Arome‐EPS is then discussed, using several scores and diagnostics. Three types of precipitating objects characterizing total, moderate and heavy rainfall are considered. In the second part, object‐based metrics are used to compute objective weights for time‐lagged ensemble forecasts, based on their performance at early forecast ranges. The weights obtained clearly depend on the meteorological situation and on the precipitation type, reflecting for instance the lower predictability of moderate precipitation compared to total precipitation. There is also a dependence on the production time with, on average, slightly larger and more homogeneous weights associated with the most recent run. However, in some situations of moderate and heavy rainfall, a relevant signal can be extracted from older runs. It is finally shown that object‐based weights are better suited than classical quadratic weights to improve nowcasting performance.

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Enhancing COSMO-DE ensemble forecasts by inexpensive techniques

Cited by 48 publications

References 0 publications

Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble

Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble

The subsurface–land surface–atmosphere connection under convective conditions

Object‐based verification metrics applied to the evaluation and weighting of convective‐scale precipitation forecasts

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