Different Representation of Mesoscale Convective Systems in Convection-Permitting and Convection-Parameterizing NWP Models and Its Implications for Large-Scale Forecast Evolution

Karsten Peters,Cathy Hohenegger,Daniel Klocke

doi:10.3390/atmos10090503

Karsten Peters, Cathy Hohenegger + Show 1 more

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https://doi.org/10.3390/atmos10090503

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Abstract

Representing mesoscale convective systems (MCSs) and their multi-scale interaction with the large-scale atmospheric dynamics is still a major challenge in state-of-the-art global numerical weather prediction (NWP) models. This results in potentially defective forecasts of synoptic-scale dynamics in regions of high MCS activity. Here, we quantify this error by comparing simulations performed with a very large-domain, convection-permitting NWP model to two operational global NWP models relying on parameterized convection. We use one month’s worth of daily forecasts over Western Africa and focus on land regions only. The convection-permitting model matches remarkably well the statistics of westward-propagating MCSs compared to observations, while the convection-parameterizing NWP models misrepresent them. The difference in the representation of MCSs in the different models leads to measurably different synoptic-scale forecast evolution as visible in the wind fields at both 850 and 650 hPa, resulting in forecast differences compared to the operational global NWP models. This is quantified by computing the correlation between the differences and the number of MCSs: the larger the number of MCSs, the larger the difference. This fits the expectation from theory on MCS–mean flow interaction. Here, we show that this effect is strong enough to affect daily limited-area forecasts on very large domains.

Highlights

What is it in numerical atmospheric models that puts the largest constraints on our ability to provide more trustworthy simulations of weather and climate? When asked this question, many model deficiencies would come to mind, most of them related to the conceptual models used to approximate the effect of physical processes unresolved by the atmospheric models’ grid spacing on the evolution of the resolved dynamical and thermodynamical model fields
Our results were consistent with previous studies, i.e., using a model relying on the explicit representation of convection significantly improved the representation of mesoscale convective systems (MCSs)
We first assessed the ability of the models to represent the statistics of westward-propagating organized precipitation over Western Africa by applying the identification algorithm detailed in Section 2.2 to Hovmöller diagrams of three-hourly mean precipitation over West Africa

Summary

Introduction

What is it in numerical atmospheric models that puts the largest constraints on our ability to provide more trustworthy simulations of weather and climate? When asked this question, many model deficiencies would come to mind, most of them related to the conceptual models used to approximate the effect of physical processes unresolved by the atmospheric models’ grid spacing on the evolution of the resolved dynamical and thermodynamical model fields. The ever-increasing availability of computational resources has sparked serious interest in attenuating the “cumulus parameterization problem” (coined so by [8]) by focusing efforts on exploring effective ways of running atmospheric models at convection-permitting grid spacings on large domains, even global, and long time periods (e.g., [9,10,11,12,13,14,15,16]). Such models use grid spacings of a few kilometers, allowing them to resolve convective storms explicitly

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