A LAM model for regions with complex orography
The increase in planetary temperature has led to an increase in the thermal energy resources available for convective phenomena, to an increase in the evaporative potential of our seas and, consequently, to an increase in the frequency of extreme weather events. Extreme weather events are characteristics of the Mediterranean coastal areas and often cause flash floods. During these episodes, the precipitations that occur in a few hours often exceed the rain accumulations that normally occur in several months. Local factors such as the presence of orographic reliefs along to the coastline often determine their intensity. The geographical position and the complex orography of European regions positioned around the Mediterranean sea often cause extreme weather events. In early autumn, the Mediterranean cyclones that originate from the contrast between air masses with very different temperatures and humidity interacting with seawater high temperature affect the surrounding regions. These conditions can cause extreme weather events characterized by sudden and heavy rainfalls and dangerous flash floods. From the geographic point of view, these regions are characterized by a peculiar orography where the warm and moist air coming from Africa is lifted over the orographic barriers, losing its humidity because of the cooling, and these modifications can cause heavy rains. This occurs especially in the autumn seasons when the sea is still warm and able to transfer large amounts of humidity into the atmosphere.
While the climate modeling community is performing runs typically at grid spacing of 100 km, 50 km or at most, 10 km, higher resolutions are needed for places with complex topography and dynamical downscaling seems to be the way to go. The Meteorological Models local circulations accurate simulation ability will rely strongly on resolving the important terrain features over focused area. Since the terrain height depends on the grid resolution model, it is essential that the simulation uses an adequate grid size in order to resolve the terrain forcing over the analysed area.
Technology demonstrated in relevant environment.
Representative model or prototype system, which is well beyond that of TRL 5, is tested in a relevant environment. Represents a major step up in a technology’s demonstrated readiness. Examples include testing a prototype in a high-fidelity laboratory environment or in a simulated operational environment.
Representative model or prototype system, which is well beyond that of TRL 5, is tested in a relevant environment. Represents a major step up in a technology’s demonstrated readiness. Examples include testing a prototype in a high-fidelity laboratory environment or in a simulated operational environment.
The innovation has a TRL 6 because the product has been tested in a relevant environment. This environment is the region of Sicily (I), a region with a complex orography.
The innovation has been tested through numerous case studies in which extreme meteorological events of the past have been analyzed. In this framework, we analyzed and discussed, as a case study, the heavy rains that occurred in Sicily during the night of 10 October 2015 [1]. In just 9 hours, a Mediterranean depression, centered on the Tunisian coast, produced a violent storm of mesoscale located on the Peloritani Mountains with a maximum rainfall of about 200 mm. The analysis was based on the comparison of the model's performance with the data collected by the networks of weather stations available in Sicily.
The obtained results consented to clearly show that the improvement of the model grid spacing, together with the use of more accurate geographic data and the land use data, more suitable for the description of the territory, are the key elements for the prediction accuracy. This is especially true for geographic areas like Sicily that are characterized by the presence of complex orographic structures.
How does it work?
The proposed innovation is based on the development of a WRF (Weather Research and Forecasting model), with ARW (Advanced Research WRF) core, specifically optimized for territories with complex orography, through developments that significantly affect the use of initial high-resolution static orographic data, soil and vegetative coverage data and sea temperature data. A further optimization process of the model is based on the different physical parametrizations The numerical forecasts provided by the models used and the data from the surveys carried out are processed using numerical multiscaling approaches, with particular reference to the wavelets, to identify correlations, trends and anomalies.
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