Articles | Volume 8, issue 10
https://doi.org/10.5194/gmd-8-3105-2015
https://doi.org/10.5194/gmd-8-3105-2015
Development and technical paper
 | 
06 Oct 2015
Development and technical paper |  | 06 Oct 2015

Assessment of valley cold pools and clouds in a very high-resolution numerical weather prediction model

J. K. Hughes, A. N. Ross, S. B. Vosper, A. P. Lock, and B. C. Jemmett-Smith

Abstract. The formation of cold air pools in valleys under stable conditions represents an important challenge for numerical weather prediction (NWP). The challenge is increased when the valleys that dominate cold pool formation are on scales unresolved by NWP models, which can lead to substantial local errors in temperature forecasts. In this study a 2-month simulation is presented using a nested model configuration with a finest horizontal grid spacing of 100 m. The simulation is compared with observations from the recent COLd air Pooling Experiment (COLPEX) project and the model's ability to represent cold pool formation, and the surface energy balance is assessed. The results reveal a bias in the model long-wave radiation that results from the assumptions made about the sub-grid variability in humidity in the cloud parametrization scheme. The cloud scheme assumes relative humidity thresholds below 100 % to diagnose partial cloudiness, an approach common to schemes used in many other models. The biases in radiation, and resulting biases in screen temperature and cold pool properties are shown to be sensitive to the choice of critical relative humidity, suggesting that this is a key area that should be improved for very high-resolution modeling.

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Short summary
The formation of cold air pools in valleys under stable conditions represents an important challenge for numerical weather prediction (NWP). In this study a two-month cold pool simulation is presented using a high-resolution NWP model. Results are compared to observations and assumptions made in the cloud parametrization scheme about the sub-grid variability of humidity are shown to dominate model bias. Our results show that this is a key area for very high resolution modelling development.