Abstract
AbstractThe numerical weather prediction (NWP) of fog remains a challenge, with accurate forecasts relying on the representation of many interacting physical processes. The recent Local And Non‐local Fog EXperiment (LANFEX) has generated a detailed observational dataset, creating a unique opportunity to assess the NWP of fog events. We evaluate the performance of operational and research configurations of the Met Office Unified Model (MetUM) with three horizontal grid lengths, 1.5 km and 333 and 100 m, in simulating four LANFEX case studies. In general, the subkilometre (sub‐km) scale versions of MetUM are in better agreement with the observations; however, there are a number of systematic model deficiencies. MetUM produces valleys that are too warm and hills that are too cold, leading to valleys that do not have enough fog and hills that have too much. A large sensitivity to soil temperature was identified from a set of parametrisation sensitivity experiments. In all the case studies, the model erroneously transfers heat too readily through the soil to the surface, preventing fog formation. Sensitivity tests show that the specification of the soil thermal conductivity parametrisation can lead to up to a 5‐hr change in fog onset time. Overall, the sub‐km models demonstrate promise, but they have a high sensitivity to surface properties.
Highlights
Fog has large human and environmental impacts, which are often understated; the reduction in visibility caused by fog leads to huge disruptions for air, sea, and land transport
Using the LANFEX observations and the Met Office Unified model (MetUM), we evaluate the performance of three configurations of Met Office Unified Model (MetUM) with different horizontal grid lengths in simulating radiation fog events
We discuss the performance of UM1.5, UM333, and UM100 for the selected LANFEX case studies
Summary
Fog has large human and environmental impacts, which are often understated; the reduction in visibility caused by fog leads to huge disruptions for air, sea, and land transport. Fog is the second most likely cause of weather-related aviation accidents behind strong winds (Gultepe et al, 2019). Over 10,000 people died in India in 2017 from fog-related traffic accidents (Kapoor, 2019). People were reported to have died in fog-related accidents (Forthun et al, 2006). Fog can lead to persistent temperature inversions, which result in pollution stagnating in the lower atmosphere for extended periods, with consequences for human health (Tanaka et al, 1998, Nemery et al, 2001). Other methods of transport were disrupted, with speed restrictions implemented on roads, reports of traffic accidents due to the fog, and the cancellation of ferries
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