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Demistify: an LES and SCM intercomparison of radiation fog
- 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.
- Subjects :
- [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere
010504 meteorology & atmospheric sciences
Meteorology
business.industry
Cloud computing
Numerical weather prediction
01 natural sciences
Radiation fog
010305 fluids & plasmas
Aerosol
13. Climate action
0103 physical sciences
Cloud droplet
Environmental science
Parametrization (atmospheric modeling)
business
Droplet size
Field campaign
0105 earth and related environmental sciences
Subjects
Details
- Language :
- English
- ISSN :
- 16807324
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....e20a4aa48dfb6d9b3dca542c39af7cf3