Fog is a high-impact weather phenomenon affecting human activity, including aviation, transport, and health. Its prediction is a longstanding issue for weather forecast models. The success of a forecast depends on complex interactions among various meteorological and topographical parameters; even very small changes in some of these can determine the difference between thick fog and good visibility. This makes prediction of fog one of the most challenging goals for numerical weather prediction. The Local and Nonlocal Fog Experiment (LANFEX) is an attempt to improve our understanding of radiation fog formation through a combined field and numerical study. The 18-month field trial was deployed in the United Kingdom with an extensive range of equipment, including some novel measurements (e.g., dew measurement and thermal imaging). In a hilly area we instrumented flux towers in four adjacent valleys to observe the evolution of similar, but crucially different, meteorological conditions at the different sites. We correlated these with the formation and evolution of fog. The results indicate new quantitative insight into the subtle turbulent conditions required for the formation of radiation fog within a stable boundary layer. Modeling studies have also been conducted, concentrating on high-resolution forecast models and research models from 1.5-km to 100-m resolution. Early results show that models with a resolution of around 100 m are capable of reproducing the local-scale variability that can lead to the onset and development of radiation fog, and also have identified deficiencies in aerosol activation, turbulence, and cloud micro- and macrophysics, in model parameterizations.

A 100 m resolution simulation of radiation fog observed during the Local And Non‐local Fog EXperiment (LANFEX) was performed over the Shropshire hills (UK) in order to understand the impact of local circulation on valley fog formation. The model correctly resolves all valleys and their different geometries, their associated dynamical features, and the different fog conditions between the measurement sites. Passage of stratocumulus during the night led to fog dissipation and gave the opportunity to study two fog formation stages. In the narrow valleys, fog formed at the valley floor and non‐local drainage‐flow processes acted to dissipate it. This equilibrium determined the fog optical thickness, which varied within and between valleys. Wider basins were more subject to dense fog conditions (due to local formation) than narrower valleys, where advecting fog events are locally observed through basins overflowing. The largest and most open valley of Jay Barns was impacted by numerous circulations from narrower tributary valleys, and their complex interactions affected fog formation differently between events. The impact of cloud microphysics on the simulated fog is studied by comparing simulations with one‐moment and two‐moment schemes. The use of a two‐moment scheme brings improvements when the prognostic number concentration is used to compute cloud optical properties, meaning that the radiative impact of droplet concentration is greater than its gravitational settling effect.

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.