Thermal structure and dynamical modeling of a Mediterranean tropical-like cyclone

In the present work, a detailed analysis of a Mediterranean TLC occurred in January 2014 has been conducted. The author is not aware of other studies regarding this particular event at the publication of this thesis. In order to outline the cyclone evolution, observational data, including weather-stations data, satellite data, radar data and photographic evidence, were collected at first. After having identified the cyclone path and its general features, the GLOBO, BOLAM and MOLOCH NWP models, developed at ISAC-CNR (Bologna), were used to simulate the phenomenon. Particular attention was paid on the Mediterranean phase as well as on the Atlantic phase, since the cyclone showed a well defined precursor up to 3 days before the minimum formation in the Alboran Sea. The Mediterranean phase has been studied using different combinations of GLOBO, BOLAM and MOLOCH models, so as to evaluate the best model chain to simulate this kind of phenomena. The BOLAM and MOLOCH models showed the best performance, by adjusting the path erroneously deviated in the National Centre for Environmental Prediction (NCEP) and ECMWF operational models. The analysis of the cyclone thermal phase shown the presence of a deep-warm core structure in many cases, thus confirming the tropical-like nature of the system. Furthermore, the results showed high sensitivity to initial conditions in the whole lifetime of the cyclone, while the Sea Surface Temperature (SST) modification leads only to small changes in the Adriatic phase. The Atlantic phase has been studied using GLOBO and BOLAM model and with the aid of the same methodology already developed. After tracing the precursor, in the form of a low-pressure system, from the American East Coast to Spain, the thermal phase analysis was conducted. The parameters obtained showed evidence of a deep-cold core asymmetric structure during the whole Atlantic phase, while the first contact with the Mediterranean Sea caused a sudden transition to a shallow-warm core structure. The examination of Potential Vorticity (PV) 3-dimensional structure revealed the presence of a PV streamer that individually formed over Greenland and eventually interacted with the low-pressure system over the Spanish coast, favouring the first phase of the cyclone baroclinic intensification. Finally, the development of an automated system that tracks and studies the thermal phase of Mediterranean cyclones has been encouraged. This could lead to the forecast of potential tropical transition, against with a minimum computational investment.

Private sensors and crowdsourced rainfall data: accuracy and effectiveness for monitoring severe events and mapping pluvial flooding in urban areas

The increasing number of extreme rainfall events, combined with the high population density and the imperviousness of the land surface, makes urban areas particularly vulnerable to pluvial flooding. In order to design and manage cities to be able to deal with this issue, the reconstruction of weather phenomena is essential. Among the most interesting data sources which show great potential are the observational networks of private sensors managed by citizens (crowdsourcing). The number of these personal weather stations is consistently increasing, and the spatial distribution roughly follows population density. Precisely for this reason, they perfectly suit this detailed study on the modelling of pluvial flood in urban environments. The uncertainty associated with these measurements of precipitation is still a matter of research. In order to characterise the accuracy and precision of the crowdsourced data, we carried out exploratory data analyses. A comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national weather services is presented. The crowdsourced stations have very good skills in rain detection but tend to underestimate the reference value. In detail, the accuracy and precision of crowd- sourced data change as precipitation increases, improving the spread going to the extreme values. Then, the ability of this kind of observation to improve the prediction of pluvial flooding is tested. To this aim, the simplified raster-based inundation model incorporated in the Saferplaces web platform is used for simulating pluvial flooding. Different precipitation fields have been produced and tested as input in the model. Two different case studies are analysed over the most densely populated Norwegian city: Oslo. The crowdsourced weather station observations, bias-corrected (i.e. increased by 25%), showed very good skills in detecting flooded areas.