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.

The Iceland Greenland Seas Project (IGP) is a coordinated atmosphere–ocean research program investigating climate processes in the source region of the densest waters of the Atlantic meridional overturning circulation. During February and March 2018, a field campaign was executed over the Iceland and southern Greenland Seas that utilized a range of observing platforms to investigate critical processes in the region, including a research vessel, a research aircraft, moorings, sea gliders, floats, and a meteorological buoy. A remarkable feature of the field campaign was the highly coordinated deployment of the observing platforms, whereby the research vessel and aircraft tracks were planned in concert to allow simultaneous sampling of the atmosphere, the ocean, and their interactions. This joint planning was supported by tailor-made convection-permitting weather forecasts and novel diagnostics from an ensemble prediction system. The scientific aims of the IGP are to characterize the atmospheric forcing and the ocean response of coupled processes; in particular, cold-air outbreaks in the vicinity of the marginal ice zone and their triggering of oceanic heat loss, and the role of freshwater in the generation of dense water masses. The campaign observed the life cycle of a long-lasting cold-air outbreak over the Iceland Sea and the development of a cold-air outbreak over the Greenland Sea. Repeated profiling revealed the immediate impact on the ocean, while a comprehensive hydrographic survey provided a rare picture of these subpolar seas in winter. A joint atmosphere–ocean approach is also being used in the analysis phase, with coupled observational analysis and coordinated numerical modeling activities underway.

The 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.

Despite the impact it has on human activity, particularly transport, accurate forecasting of fog remains a major challenge for numerical weather prediction models. The complex interaction between various physical processes, many of which are parametrised and highly sensitive to small changes, is one of the key reasons for poor fog forecasts. One challenge for numerical models is predicting the structure of the boundary layer, which often undergoes a transition from statically stable to weakly unstable during the life cycle of a fog event. The recent local and non‐local fog experiment (LANFEX) has provided a new comprehensive and detailed observational dataset of fog events. Here, a case study has been used as the basis for an investigation of the effect of the humidity of the residual layer and wind speed on the stability of the boundary layer during a fog event. We find a very high sensitivity in the timing of the stability transition during the fog event; for example, a +3% relative humidity perturbation results in a delay of almost 3h, while a 0.45ms−1 10m wind speed perturbation results in a delay of more than 8h.

The current study highlights the importance of a detailed representation of urban processes in numerical weather prediction models and emphasizes the need for accurate urban morphology data for improving the near‐surface weather prediction over Delhi, a tropical Indian city. The Met Office Reading Urban Surface Exchange Scheme (MORUSES), a two‐tile urban energy‐budget parameterization scheme, is introduced in a high‐resolution (330‐m) model of Delhi. A new empirical relationship is established for the MORUSES scheme from the local urban morphology of Delhi. The performance is evaluated using both the newly developed empirical relationships (MORUSES‐IND) and the existing empirical relationships for the MORUSES scheme (MORUSES‐LON) against the default one‐tile configuration (BEST‐1t) for clear and foggy events and validations are performed against ground observations. MORUSES‐IND exhibits a significant improvement in the diurnal evolution of the wind speed in terms of amplitude and phase, compared with the other two configurations. Screen temperature (Tscreen$$ {T}_{\mathrm{screen}} $$) simulations using MORUSES‐IND reduce the warm bias, especially during the evening and night hours. The root‐mean‐square error of Tscreen$$ {T}_{\mathrm{screen}} $$ is reduced up to 29% using MORUSES‐IND for both synoptic conditions. The diurnal cycle of surface‐energy fluxes is reproduced well using MORUSES‐IND. The net longwave fluxes are underestimated in the model and biases are more significant during foggy events, partly due to the misrepresentation of fog. An urban cool island (UCI) effect is observed in the early morning hours during clear‐sky conditions, but it is not evident on foggy days. Compared with BEST‐1t, MORUSES‐IND represents the impact of urbanization more realistically, which is reflected in the reduction of the urban heat island and UCI in both synoptic conditions. Future works would improve the coupling between the urban surface energy budget and anthropogenic aerosols by introducing MORUSES‐IND in a chemistry aerosol framework model.