Sub‐km scale numerical weather prediction model simulations of radiation fog

Abstract

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.

A review on factors influencing fog formation, classification, forecasting, detection and impacts

“…The IGP region is shrouded in a layer of thick fog, and apart from the regional meteorology, an adequate amount of moisture provided to the atmosphere comes from the wet soil, which is a result of irrigation (Izhar et al 2020 ; Ghude et al 2017 ). In his research, Smith et al ( 2021 ) found that the fog was highly sensitive to the thermal conductivity of soil, causing the onset time of fog to shift by 5 h. Adhikari and Wang ( 2020 ) made the first attempt to study the influence of soil moisture on fog. The study was conducted at two sites of Namib Desert, one is a gravel plain, and the other is a sand dune.…”

“…The influence of soil properties on fog formation has not been analytically studied, neither in field studies nor as an important parameterization factor in prediction models. Smith et al ( 2021 ) have studied the impact of both grass and bare soil on fog; nevertheless, they have also suggested that future research is needed on to what extent grass can act as an insulator between soil and atmosphere.…”

Coupling High-Resolution Numerical Weather Prediction and Computational Fluid Dynamics: Auckland Harbour Case Study

“…The model set up is 300 × 300 horizontal grid-points, centred on the Auckland Harbour Bridge with a terrain-following 140-level vertical coordinate system over the lowest 40 km of the atmosphere. This increased resolution has shown significant improvements of near surface temperatures [30,31]. The Auckland Model is one-way nested inside NIWA's 4-km resolution NZLAM (New Zealand Limited Area Model), which is data-assimilating and uses the Global Atmosphere 6 science configuration [32] with an intermediate nest at 1.5 km resolution to ease the transition from 4-km to 333-m resolution.…”

Surface deposition of marine fog and its treatment in the Weather Research and Forecasting (WRF) model

“…is greatest closest to the surface, where they increase the amount of (Q c ) removed from the lowest model levels". Boutle et al (2016Boutle et al ( , 2018 and Smith et al (2021) have adjusted the MetUM taper parameters and obtained improved matches with visibility observations of fog, including the LANFLEX (Price et al, 2018) study. It seems to work as a "tuning parameter", but the taper curve approach could be considered somewhat unphysical.…”

“…13c from Bergot et al (2005), generally show Q c maxima at the surface. COBEL has also been coupled with WRF (Stolaki et al, 2012) and used to simulate advectionradiation fog conditions at Thessaloniki Airport. Ducongé et al (2020) report on recent radiation fog modelling studies with Meso-NH downscaled from the Métèo-France operational model of AROME.…”