A One-dimensional Ensemble Forecast and Assimilation System for Fog Prediction

Abstract

Abstract-A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG. The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3-D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts.

Role of Fog in Urban Heat Island Modification in Kraków, Poland

“…The situation is even more difficult in regions of complex and heterogeneous terrain where mainly during radiative weather conditions cold-air drainage flows from the mountains or hills into the valleys which is one of fogfavoring factors (Gultepe et al, 2006;Müller et al, 2007). The study realized in Zurich airport area showed that the spatial distribution of air temperature, humidity and fog characteristics is very diversified, especially in vertical profile, with the most dense fog occurrence in the valley bottom (Gultepe et al, 2006).…”

Three‐dimensional fog forecasting in complex terrain

“…A comparison of several models was recently published by Bergot et al (2007), however the intercomparison was limited to very few cases and no model with spectral or second-moment cloud water was considered. Müller et al (2007) have compared the PAFOG parametrization to a one-moment microphysics model (COBEL-NOAH) in a seasonal ensemble forecast, but could not determine a clear winner. We decided to use the PAFOG parametrization as it can circumvent the problem of specifying a threshold value for autoconverion, as well as the specification of a fixed settling velocity for cloud water.…”

“…This type of elevated fog is very difficult to forecast as it can break up in the early morning or be persistent over days. As advection is very important, the application of 1D models is difficult in this area, as was demonstrated by Müller et al (2007), using ensemble forecasting for entire fog seasons and a statistical base of over 50 000 simulations.…”

“…Furthermore, Müller et al (2007) used PAFOG-1D with horizontal advection derived from mesoscale models. The advection was introduced into the 1D model as a source/sink tendency.…”

Characterization and Modeling of Fog in the Mexico Basin

“…For these reasons, a good understanding of these mechanisms is necessary to develop adequate tools for fog modeling and forecasting. Both one-and threedimensional models with several combinations of physical and statistical parameterizations are frequently used in fog numerical simulations (Bergot and Guédalia, 1994;Bott and Trautmann, 2002;Steppeler et al, 2003;Müller et al, 2007). Although mesoscale models -such as the Weather Research and Forecasting (WRF) model -have been mainly concerned with the simulation of advection fogs (Nakanishi and Niino, 2006;Román-Cascón et al, 2016), they have been also utilized for modeling radiation-advection ( Van der Velde et al, 2010;Román-Cascón et al, 2011), mountain (Shimadera et al, 2012), and ice fogs (Gultepe et al, 2015).…”