## 2 Materials and Methods
### The Weather Model  
The NWP (Numerical Weather Prediction) model used is the WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) version 3.9, developed by the NCAR (National Centre for Atmospheric Research). The model is run operationally by the US National Weather Service and, being open-source and easily portable, it is widely widespread around the world for research and  
Figure 1: The four experimental sites where the Meteo Expert Centre (MEC) algorithm is implemented: The Autostrada Pedemontana Lombarda motorway (**a**), the Milano Linate airport airstrip (**b**), the maize field in the Chiese Consortium (**c**), and the tomato field in the Capitanata Consortium (**d**). weather forecasts [22]. The WRF model is designed to be a flexible, state-of-the-art model, and it is developed as a collaborative effort of several institutes. It is supported as a community model and it offers a number of different parameterization for the different physical processes. It is designed to serve both operational forecasting and atmospheric research needs: it features a 3-dimensional and 4-dimensional variational (3 DVAR and 4 DVAR) data assimilation system, and a software architecture allowing computational parallelism and system extensibility. The WRF is originally built as a Mesoscale or Local Area Model (LAM), i.e., it simulates the weather in geographical areas smaller than the entire globe. The governing equations, describing the dynamic and thermodynamic evolution of the atmosphere [23], are solved numerically on grid points over the area of interest that constitutes the domain of the model which is three-dimensional. On the horizontal plane models may allow different map projections and utilize a specific grid structure. In the present case, the WRF-ARW employs the Arakawa C-grid [24]. This grid is staggered, which means that not all governing equations are solved on the same grid points: dynamic physical quantities are solved on so-called \"flux points\", while mass related physical quantities are solved on \"mass points\". Vertically, a terrain-following coordinate is used [22; 23], and vertical levels are denser near the Earth surface and grow more distant from one another when moving towards the top of the atmosphere. The diagonal distance between two mass grid points on the same vertical level defines the model horizontal resolution, which can be chosen by the user too. Local area models require initialization and boundary conditions from a larger LAM or a global model. Initialization data provided the values of all the necessary atmospheric and surface fields at the beginning of the simulation and contour data provided the evolution of these fields outside of the LAM domain. The set-up of this implemented model can be used in an ensemble configuration with eight WRF instances, each of them with a different physical configuration and with a different set of initial and boundary conditions, which compose the ensemble [25; 26]. The ensemble approach has been proved to be a valid instrument in reducing the biases of the physical parameterizations of the model, and in reducing the errors present in the initial and boundary data, especially for meteorological forecasts, which depend on highly non-linear equations [27]. The first-level approach in extracting the information from an ensemble model is the use of average values of the different meteorological parameters: in such a way the meteorological output can be treated as the output of a deterministic forecast. Alternatively, the use of the probabilistic distribution function, which comes as the output of an ensemble forecast system, allows the user to have a full view of all possible scenarios, and to evaluate the probability of occurrence of every single deterministic configuration. The initial and contour data come from the Global Ensemble Forecast System (GEFS) model with a temporal step of 3-h [28].