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On the Utility of Individual Tendency Output: Revealing Interactions between Parameterized Processes during a Marine Cold Air Outbreak

On the Utility of Individual Tendency Output: Revealing Interactions between Parameterized... AbstractForecasts of marine cold air outbreaks critically rely on the interplay of multiple parameterization schemes to represent subgrid-scale processes, including shallow convection, turbulence, and microphysics. Even though such an interplay has been recognized to contribute to forecast uncertainty, a quantification of this interplay is still missing. Here, we investigate the tendencies of temperature and specific humidity contributed by individual parameterization schemes in the operational weather prediction model AROME-Arctic. From a case study of an extensive marine cold air outbreak over the Nordic seas, we find that the type of planetary boundary layer assigned by the model algorithm modulates the contribution of individual schemes and affects the interactions between different schemes. In addition, we demonstrate the sensitivity of these interactions to an increase or decrease in the strength of the parameterized shallow convection. The individual tendencies from several parameterizations can thereby compensate each other, sometimes resulting in a small residual. In some instances this residual remains nearly unchanged between the sensitivity experiments, even though some individual tendencies differ by up to an order of magnitude. Using the individual tendency output, we can characterize the subgrid-scale as well as grid-scale responses of the model and trace them back to their underlying causes. We thereby highlight the utility of individual tendency output for understanding process-related differences between model runs with varying physical configurations and for the continued development of numerical weather prediction models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Weather and Forecasting American Meteorological Society

On the Utility of Individual Tendency Output: Revealing Interactions between Parameterized Processes during a Marine Cold Air Outbreak

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References (51)

Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0434
eISSN
1520-0434
DOI
10.1175/WAF-D-21-0014.1
Publisher site
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Abstract

AbstractForecasts of marine cold air outbreaks critically rely on the interplay of multiple parameterization schemes to represent subgrid-scale processes, including shallow convection, turbulence, and microphysics. Even though such an interplay has been recognized to contribute to forecast uncertainty, a quantification of this interplay is still missing. Here, we investigate the tendencies of temperature and specific humidity contributed by individual parameterization schemes in the operational weather prediction model AROME-Arctic. From a case study of an extensive marine cold air outbreak over the Nordic seas, we find that the type of planetary boundary layer assigned by the model algorithm modulates the contribution of individual schemes and affects the interactions between different schemes. In addition, we demonstrate the sensitivity of these interactions to an increase or decrease in the strength of the parameterized shallow convection. The individual tendencies from several parameterizations can thereby compensate each other, sometimes resulting in a small residual. In some instances this residual remains nearly unchanged between the sensitivity experiments, even though some individual tendencies differ by up to an order of magnitude. Using the individual tendency output, we can characterize the subgrid-scale as well as grid-scale responses of the model and trace them back to their underlying causes. We thereby highlight the utility of individual tendency output for understanding process-related differences between model runs with varying physical configurations and for the continued development of numerical weather prediction models.

Journal

Weather and Forecasting – American Meteorological Society

Published: Dec 26, 2021

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