Observations and WRF simulations of fog events at the Spanish Northern Plateau

Abstract

Abstract. The prediction of fogs is one of the processes not well reproduced by the Numerical Weather Prediction (NWP) models. In particular, the role of turbulence in the formation or dissipation of fogs is one of the physical processed not well understood, and therefore, not well parameterized by the NWP models. Observational analysis of three different periods with fogs at the Spanish Northern Plateau has been carried out. These periods have also been simulated with the Weather Research and Forecasting (WRF) numerical model and their results have been compared to observations. The study includes a comparison of the skill of different planetary boundary layer (PBL) parameterizations, surface layer schemes and a test of the gravitational settling of clouds/fogs droplets option. A statistical analysis of this comparison has been evaluated in order to study differences between the periods and between the various parameterizations used. The model results for each PBL parameterization were different, depending on the studied period, due to differences in the features of each fog. This fact made it difficult to obtain generalized conclusions, but allowed us to determine which parameterization performed better for each case. In general, judging from the models results of liquid water content (LWC), none of the PBL schemes were able to correctly simulate the fogs, being Mellor-Yamada Nakanishi and Niino (MYNN) 2.5 level PBL scheme the best one in most of the cases. This conclusion is also supported by the root mean square error (RMSE) calculated for different meteorological variables.

Forecasting radiation fog at climatologically contrasting sites: evaluation of statistical methods and WRF

“…For the microphysics scheme, the WRF double-moment sixclass (WDM6) was used. The Mellor-Yamada-Nakanishi-Niino (MYNN) 2.5-level parametrization (Nakanishi and Niino, 2004) was used for the PBL and surface layer schemes, which has also been proved to perform adequately for fog conditions (Román-Cascón et al, 2012). RRTM (Mlawer et al, 1997) and Dudhia (1989) parametrizations were used for short-wave and long-wave radiation respectively.…”

“…Many studies analyse the sensitivity of NWP models to different technical configurations or physical parametrizations (e.g. Bergot and Guedalia, ; Pagowski et al , ; Van der Velde et al , ; Román‐Cascón et al , ). Thus, there is a continuous effort by the modelling community to improve fog forecasting through the incorporation of new physical parametrizations (e.g.…”

“…Despite the numerous influences of fog on daily life, it is still poorly forecasted by numerical weather prediction (NWP) models (Bergot et al, 2007;Müller et al, 2010;Román-Cascón et al, 2012;Price et al, 2015;Steeneveld et al, 2015). One of the causes of the unsuccessful prediction of fog is the interaction of several processes (Gultepe et al, 2009): radiative cooling during fog formation and heating during fog dissipation, pre-fog turbulence intensity, turbulence generated by fog itself (Cuxart and Jiménez, 2012), synoptic advection, concentration of cloud condensation nuclei (CCN), surface water availability, synopticscale conditions and local features of the area, influencing local winds, nearby advections, etc.…”

Radiation and cloud-base lowering fog events: Observational analysis and evaluation of WRF and HARMONIE

“…Thus, mesoscale NWP models need to simulate correctly all these physical processes to perform successful simulations of fog. However, the fog forecasting can be also affected by issues related to the model itself: possible errors in initial and boundary conditions (e.g., Bergot and Guedalia, 1994;Hu et al, 2014), appropriate spin-up times (e.g., Román-Cascón et al, 2016a), limitations in the vertical/horizontal resolution (e.g., Philip et al, 2016;Boutle et al, 2016) or inappropriate parameterizations of sub-grid scale processes (e.g., Román-Cascón et al, 2012;Chaouch et al, 2017). These are some of the reasons why in other cases, the use of 1D models and statistical downscaling techniques are also used, especially in predictions needed at specific points, e.g., in airports (Cornejo-Bueno et al, 2017).…”

An analogue dynamical model for forecasting fog‐induced visibility: validation over Delhi

“…E-mail: pgoswami@nistads.res.in model (COBEL-ISBA) and an ensemble system showed the forecasts to be very sensitive to local initial conditions and mesoscale forcings (Roquelaure and Bergot, 2008). Despite the major economic impact of radiation fog on human society, numerical weather prediction models have relatively low skill in forecasting both the onset and the development of radiation fog (Teixeira, 1999;Gultepe et al, 2007;Tudor, 2010;Zhou et al, 2011;Román-Cascón et al, 2012). Forecasting radiation fog is still somewhat problematic because local topography, moisture availability, vegetation and soil conditions introduce spatial variability into the model results and forecast products (Golding, 1993;Meyer and Rao, 1999).…”