Abstract
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 Local and Non-local Fog Experiment (LANFEX) field campaign. Seven of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst three are research-grade SCMs designed for fog simulation, and the LESs 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 concentration (CDNC) conditions. The main SCM bias appears to be toward the overdevelopment 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 parameterisation, as it is to the underlying aerosol or CDNC.
Highlights
Most operational numerical weather prediction (NWP) centres will list errors in fog forecasting amongst their top model problems, with the requirement for improvement considered high priority (Hewson, 2019)
This is consistent with the observations until 08:00 UTC, when the upper level cloud arrived at the site and is responsible for the sharp increase in liquid water path (LWP) after this time
The level of comparability between our most detailed process models – large-eddy simulation (LES) – is much lower than has been seen in previous intercomparison studies of other boundary-layer or cloud regimes (Beare et al, 2006; van der Dussen et al, 2013). This is largely due to the huge role microphysics plays in fog development and uncertainties inherent in the representation of a process which is still entirely parameterised in LES
Summary
Most operational numerical weather prediction (NWP) centres will list errors in fog forecasting amongst their top model problems, with the requirement for improvement considered high priority (Hewson, 2019). In the worst case, fog can be a significant danger and is the second most likely cause of weather-related accidents (Gultepe et al, 2019; Leung et al, 2020). Despite this importance, there is no international community working together on improving fog modelling. The initial phase of work, documented in this paper, will constrain the surface properties and focus primarily on the atmospheric development of fog This will document the current state of LES and NWP fog modelling within the community and provide guidance on opportunities for improvements applicable to many models. Further stages of the project will consider feedbacks through the land surface, more complicated cases with non-local forcing, and the representation of fog in climate models, something which has rarely been looked at in the literature
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