Mediterranean cyclones are among the most important natural hazards in the region, affecting more than 135 million people. The physical mechanisms that make Mediterranean cyclones evolve into severe storms are not yet fully understood due to many uncertainties in the underlying atmospheric processes. These processes are mainly associated with deep convection and air-sea interactions at meso-scales, as well as with large-scale atmospheric systems that affect the region. The scientific objective of the project is to show how these processes lead to extreme rainfall in the Mediterranean and contribute to the better forecasting of cyclones induced extreme rainfall. Analysis within ExMeCy is based on a multi-methodological approach that includes fundamental atmospheric dynamical analysis, analysis of lightning and satellite observational datasets, and modelling adapted to the project needs. First the Researcher will detect the cyclones causing extreme rainfall. Then, he will classify these cyclones according to the contribution of the main processes associated with extreme rainfall, namely the deep convection and the airstreams of warm conveyor belts. Different cyclone groups will be thus formed, which will be analyzed separately using new modelling and diagnostic techniques, applied for the first time to Mediterranean cyclones. The decomposition of the complex interactions of atmospheric processes that lead to cyclones intensification and extreme rainfall will constitute ExMeCy’s original results. These results will be eventually used to assess European services models to predict cyclones-related high impact weather in the Mediterranean. The project will be hosted at the Institute for Environmental Research & Sustainable Development (IERSD) of the National Observatory of Athens (NOA).

The recent IPCC report identifies mineral dust and the associated uncertainties in climate projections as key topics for future research. Dust size distribution in climate models controls the dust-radiation-cloud interactions and is a major contributor to these uncertainties. Observations show that the coarse mode of dust can be sustained during long-range transport, while current understanding fails in explaining why the lifetime of large airborne dust particles is longer than expected from gravitational settling theories. This discrepancy between observations and theory suggests that other processes counterbalance the effect of gravity along transport. D-TECT envisages filling this knowledge gap by studying the contribution of the triboelectrification (contact electrification) on particle removal processes. Our hypothesis is that triboelectric charging generates adequate electric fields to hold large dust particles up in the atmosphere. D-TECT aims to (i) parameterize the physical mechanisms responsible for dust triboelectrification; (ii) assess the impact of electrification on dust settling; (iii) quantify the climatic impacts of the process, particularly the effect on the dust size evolution during transport, on dry deposition and on CCN/IN reservoirs, and the effect of the electric field on particle orientation and on radiative transfer. The approach involves the development of a novel specialized high-power lidar system to detect and characterize aerosol particle orientation and a large-scale field experiment in the Mediterranean Basin using unprecedented ground-based remote sensing and airborne in-situ observation synergies. Considering aerosol-electricity interactions, the observations will be used to improve theoretical understanding and simulations of dust lifecycle. The project will provide new fundamental understanding, able to open new horizons for weather and climate science, including biogeochemistry, volcanic ash and extraterrestrial dust research.

Over the past few years hyperspectral (HS) imaging has been broadly applied in a wealth of different applications with remote sensing of the environment being the most prominent one. HS imaging provides a rich amount of information by generating images and videos of high spectral resolution captured at a wide range of the electro-magnetic spectrum. Recently, HS data have been shown to offer remarkable advances to a new field of significant interest i.e., medical HS (mHS) imaging. The high spectral resolution of HS data makes them amenable to identifying even subtle spectral differences related to various pathological conditions. In view of that, mHS images and videos have received considerable attention lately. mHS data have already been used for non-invasive diagnosis of several types of cancer e.g. brain, tongue cancer, as well as for diabetic foot diagnosis and surgical guidance. mHS imaging is anticipated to remarkably flourish in the years to come taking into account the recent advances that have occurred in the development of micro-size and low-cost HS cameras. However, despite this large progress in HS imaging hardware, sophisticated algorithms capable to interpret these data are still missing. HyPPOCRATES aims at deriving new powerful mHS image and video interpretation schemes tailored to mHS data processing, by applying novel machine learning ideas. To this end, the problems of subspace clustering and unmixing will be investigated for performing refined mHS image and video understanding. Along those lines, constrained matrix and tensor factorization approaches will be explored for devising computationally efficient and scalable machine learning algorithms. Overall, the main objective of the project is to bridge the gap between the recent advances in mHS imaging and those in machine learning research. This way, the researcher aspires to go the diagnostic process of several serious diseases, such as various types of cancer, one step further.