Preprints
https://doi.org/10.5194/acp-2021-832
https://doi.org/10.5194/acp-2021-832

  15 Oct 2021

15 Oct 2021

Review status: a revised version of this preprint is currently under review for the journal ACP.

Demistify: an LES and SCM intercomparison of radiation fog

Ian Boutle1, Wayne Angevine2, Jian-Wen Bao3, Thierry Bergot4, Ritthik Bhattacharya5, Andreas Bott6, Leo Ducongé4, Richard Forbes7, Tobias Goecke8, Evelyn Grell9, Adrian Hill1, Adele Igel10, Innocent Kudzotsa11, Christine Lac4, Bjorn Maronga12, Sami Romakkaniemi11, Juerg Schmidli5, Johannes Schwenkel12, Gert-Jan Steeneveld13, and Benoît Vié4 Ian Boutle et al.
  • 1Met Office, Exeter, UK
  • 2CIRES, University of Colorado, and NOAA Chemical Sciences Laboratory, Boulder, USA
  • 3NOAA Physical Sciences Laboratory, Boulder, USA
  • 4CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • 5University of Frankfurt, Germany
  • 6University of Bonn, Germany
  • 7ECMWF, Reading, UK
  • 8DWD, Offenbach, Germany
  • 9CIRES, University of Colorado, and NOAA Physical Sciences Laboratory, Boulder, USA
  • 10UC Davis, USA
  • 11FMI, Kuopio, Finland
  • 12University of Hannover, Germany
  • 13Wageningen University, Netherlands

Abstract. An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the LANFEX field campaign. 7 of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst 3 are research-grade SCMs designed for fog simulation, and the LES are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality, and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number (CDNC) conditions. The main SCM bias appears to be toward over-development of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP-SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parametrization, as it is to the underlying aerosol or CDNC.

Ian Boutle et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-832', Robert Tardif, 10 Nov 2021
  • RC2: 'Comment on acp-2021-832', Anonymous Referee #2, 12 Nov 2021

Ian Boutle et al.

Ian Boutle et al.

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Short summary
Fog forecasting is one of the biggest problems for numerical weather prediction. By comparing many models used for fog forecasting with others used for fog research, we hoped to help guide forecast improvements. We show some key processes that, if improved, will help improve fog forecasting, such as how water is deposited on the ground. We also showed that research models were not themselves a suitable baseline for comparison, and discuss what future observations are required to improve them.
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