Editorial Type:
Article
Article Type:
Research Article

Physically Based Stochastic Perturbations (PSP) in the Boundary Layer to Represent Uncertainty in Convective Initiation

Kirstin Kober Ludwig-Maximilians-Universität München, Munich, Germany

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George C. Craig Ludwig-Maximilians-Universität München, Munich, Germany

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Print Publication:
01 Jul 2016
Collections:
Predictability and Dynamics of Weather Systems in the Atlantic-European Sector (PANDOWAE), Waves to Weather (W2W)
DOI:
https://doi.org/10.1175/JAS-D-15-0144.1
Page(s):
2893–2911
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Abstract

Stochastic perturbations allow for the representation of small-scale variability due to unresolved physical processes. However, the properties of this variability depend on model resolution and weather regime. A physically based method is presented for introducing stochastic perturbations into kilometer-scale atmospheric models that explicitly account for these dependencies. The amplitude of the perturbations is based on information obtained from the model’s subgrid turbulence parameterization, while the spatial and temporal correlations are based on physical length and time scales of the turbulent motions. The stochastic perturbations lead to triggering of additional convective cells and improved precipitation amounts in simulations of two days with weak synoptic forcing of convection but different amounts of precipitation. The perturbations had little impact in a third case study, where precipitation was mainly associated with a cold front. In contrast, an unphysical version of the scheme with constant perturbation amplitude performed poorly since there was no perturbation amplitude that would give improved amounts of precipitation during the day without generating spurious convection at other times.

Corresponding author address: Kirstin Kober, Ludwig-Maximilians-Universität München, Theresienstr. 37, 80333 Munich, Germany. E-mail: kirstin.kober@lmu.de

This article is included in the Waves to Weather (W2W) Special Collection.

Abstract

Stochastic perturbations allow for the representation of small-scale variability due to unresolved physical processes. However, the properties of this variability depend on model resolution and weather regime. A physically based method is presented for introducing stochastic perturbations into kilometer-scale atmospheric models that explicitly account for these dependencies. The amplitude of the perturbations is based on information obtained from the model’s subgrid turbulence parameterization, while the spatial and temporal correlations are based on physical length and time scales of the turbulent motions. The stochastic perturbations lead to triggering of additional convective cells and improved precipitation amounts in simulations of two days with weak synoptic forcing of convection but different amounts of precipitation. The perturbations had little impact in a third case study, where precipitation was mainly associated with a cold front. In contrast, an unphysical version of the scheme with constant perturbation amplitude performed poorly since there was no perturbation amplitude that would give improved amounts of precipitation during the day without generating spurious convection at other times.

Corresponding author address: Kirstin Kober, Ludwig-Maximilians-Universität München, Theresienstr. 37, 80333 Munich, Germany. E-mail: kirstin.kober@lmu.de

This article is included in the Waves to Weather (W2W) Special Collection.

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