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A Single-Column Comparison of Model-Error Representations for Ensemble Prediction

Abstract : Various perturbation approaches have been proposed for representing model error in convection-permitting ensemble prediction. Their evaluation usually relies on time-averaged ensemble prediction statistics and on complex case studies. In this work, their detailed physical behaviour is studied in order to understand their differences, and to help their optimization. A process-level intercomparison framework is used to investigate the widely used SPPT (stochastic perturbations of physics tendencies), independent SPPT, and random-parameters model-perturbation approaches. Ensemble predictions with the single-column version of the Arome numerical-weather-prediction model are evaluated on three different boundary-layer regimes: cumulus convection, stratocumulus-topped boundary layer, and radiation fog. The independent SPPT approach is found to produce more dispersion than the SPPT approach, particularly when several physics parametrizations are in near equilibrium. It also appears to be more numerically stable near the surface. The random parameters approach perturbations are structurally very different from the other approaches, particularly regarding cloud structure. The independent SPPT and random parameters approaches have very different sensitivities to the atmospheric conditions, which suggests that intercomparisons of ensemble-model-error approaches should carefully account for situation dependency. Substantial forecast biases are produced by random parameters with respect to the unperturbed model. These results suggest that the independent SPPT approach can bring major improvements over the SPPT approach with minimal effort, that there is some complementarity between the independent SPPT and random parameters approaches, but that implementing random-parameters-type approaches in operational applications may require careful tuning to avoid creating forecast biases.
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https://hal-insu.archives-ouvertes.fr/insu-03668387
Contributor : Nathalie Pothier Connect in order to contact the contributor
Submitted on : Saturday, May 14, 2022 - 5:18:23 PM
Last modification on : Monday, May 16, 2022 - 8:20:31 AM

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INSU | OMP | METEO | CNRS

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François Bouttier, Axelle Fleury, Thierry Bergot, Sébastien Riette. A Single-Column Comparison of Model-Error Representations for Ensemble Prediction. BOUNDARY-LAYER METEOROLOGY, 2022, 183, pp.167-197. ⟨10.1007/s10546-021-00682-6⟩. ⟨insu-03668387⟩

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