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Demistify: an LES and SCM intercomparison of radiation fog

Authors :
Evelyn Grell
Innocent Kudzotsa
Tobias Goecke
Adele L. Igel
Ritthik Bhattacharya
Andreas Bott
Leo Ducongé
Björn Maronga
Gert-Jan Steeneveld
Adrian Hill
Thierry Bergot
Jian-Wen Bao
Richard G. Forbes
Juerg Schmidli
Benoît Vié
Ian A. Boutle
Christine Lac
Johannes Schwenkel
Sami Romakkaniemi
Wayne M. Angevine
Centre national de recherches météorologiques (CNRM)
Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS)
Publication Year :
2021
Publisher :
HAL CCSD, 2021.

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.

Details

Language :
English
ISSN :
16807324
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....e20a4aa48dfb6d9b3dca542c39af7cf3