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A comparison between the spectral microphysics cloud scheme (MiStra) and the parametrized cloud scheme (PaStra) is presented. The main feature of MiStra consists of the treatment of aerosol particles, cloud droplets, and drizzle particles in a joint two-dimensional particle size distribution, whereas PaStra consists of a two-moment scheme for cloud droplets combined with a one-moment scheme for drizzle. Both cloud schemes have been implemented in a single-column model of the cloud-topped marine boundary layer. Numerical sensitivity studies are presented demonstrating that MiStra is capable of simulating in great detail the major cloud microphysical processes occurring in low-level stratiform clouds. While in MiStra no empirical parameter is available to tune the model, the empirical model parameters of PaStra have been tuned by means of the MiStra model results. By comparing the numerical results of PaStra with those of MiStra it is found that PaStra simulates the overall characteristics of the cloud-topped marine boundary layer quite well. At the same time, however, the effects of single cloud microphysical processes differ substantially from those of MiStra. Finally, it is shown that even in PaStra the inclusion of all major cloud microphysical processes is mandatory in order to obtain appropriate results. [ABSTRACT FROM AUTHOR]

A new model for nowcasting fog events in the coastal desert area of Dubai is presented, based on a machine-learning algorithm-decision-tree induction. In the investigated region high frequency observations from automatic weather stations were utilized as a database for the analysis of useful patterns. The induced decision trees yield for the first six forecasting hours increased prediction skill when compared to the coupled Weather Research and Forecasting (WRF) model and the PAFOG fog model (Bartok et al., Boundary-Layer Meteorol 145:485-506, ). The decision tree results were further improved by integrating the output of the coupled numerical fog forecasting models in the training database of the decision tree. With this treatment, the statistical quality measures, i.e. the probability of detection, the false alarm ratio, and the Gilbert's skill score, achieved values of 0.88, 0.19, and 0.69, respectively. From these results we conclude that the best fog forecast in the Dubai region is obtained by applying for the first six forecast hours the newly-developed machine-learning algorithm, while for forecast times exceeding 6 h the coupled numerical models are the best choice. [ABSTRACT FROM AUTHOR]

Modern weather prediction models use relatively high grid resolutions as well as sophisticated parametrization schemes for microphysical and other subgrid-scale atmospheric processes. Nonetheless, with these models it remains a difficult task to perform successful numerical fog forecasts since many factors controlling a particular fog event are not yet sufficiently simulated. Here we describe our efforts to create a mechanism that produces successful predictions of fog in the territory located on the north coast of the Arabian Peninsula. Our approach consists in the coupling of the one-dimensional PAFOG fog model with the three-dimensional WRF 3.0 (Weather Research and Forecast) modelling system. The proposed method allows us to construct an efficient operative road traffic warning system for the occurrence of fog in the investigated region. In total 84 historical situations were studied during the period 2008-2009. Moreover, results of operative day-by-day fog forecasting during January and February 2010 are presented. For the investigated arid and hot climate region the land-sea breeze circulation seems to be the major factor affecting the diurnal variations of the meteorological conditions, frequently resulting in the formation of fog. [ABSTRACT FROM AUTHOR]