Most of the effects caused by fog are negative for humans. Yet, numerical weather prediction (NWP) models still have problems to simulate fog properly, especially in operational forecasts. In the case of radiation fog, this is partially caused by the large sensitivity to many aspects, such as the synoptic and local conditions, the near-surface turbulence, the aerosol and droplet microphysics, or the surface characteristics, among others. This work focuses on an interesting 8-day period with several alternating radiation and cloud-base lowering (CBL) fog events observed at the Research Centre for the Lower Atmosphere (CIBA) in the Spanish Northern Plateau. On the one hand, radiation fog events are associated with strong surface cooling leading to high stability close to the surface and low values of turbulence, giving rise to shallow fog. The evolution of this type of fog is markedly sensitive to the dynamical conditions close to the surface (i.e., wind speed and turbulence). On the other hand, CBL fog presents deeper thickness associated with higher values of turbulence and less stability. Subsequently, we evaluated the fog-forecasting skill of two mesoscale models (WRF and HARMONIE) configured as similar as possible. Both models present more difficulties simulating radiation fog events than CBL ones. However, the duration and vertical extension of the CBL fog events is normally overestimated. This extended-fog avoids the surface radiative cooling needed to simulate radiation fog events formed the following nights. Therefore, these periods with alternating CBL and radiation fog are especially challenging for NWP models.
- Model skill