Collaboration in radar meteorology with the EPFL

Collaboration in radar meteorology between EPFL (Environmental Remote Sensing Lab) and MeteoSwiss (Radar, Satellite and Nowcasting). The renewed weather radar network is a fundamental pillar of the national meteorological observing system. It is an indispensable basis for the generation of alerts and the management of natural hazards related to heavy rainfall, severe thunderstorms and hail, which are top priority tasks of MeteoSwiss. The collaboration in radar meteorology with the École Polytechnique Fédérale de Lausanne (EPFL) aims at a thorough exploitation of the large potential of the novel radar technology of the renewed weather radar network. Key topics are hydrometeors, microphysics, hail, winter precipitation, quantitative precipitation estimation, dual-polarisation Doppler signatures of severe convection. This is combined with data from other observing systems, advanced integration in the forecasting system “COSMO” and novel data processing technology such as machine learning. At the national level, this collaboration is an example of a fruitful mutual interaction between academy (under EAER - The Federal Department of Economic Affairs, Education and Research) and government (under FDHA - The Federal Department of Home Affairs) and represents a modern set-up for capacity building and professional training with young researchers and senior scientists who closely interact on a daily basis remotely and face-to-face. It unifies the academic context and strive for scientific excellence of EPFL with the user-oriented context of MeteoSwiss and its long tradition in applied research and the supervision of PhD students. EPFL has many students eager to learn and work hard, while MeteoSwiss can offer a unique set of observational data, large libraries of algorithms and many research topics with high practical significance. A key element is also the scientific and professional training of PhD students and young Post-Doc scientists in a field that is relevant for MeteoSwiss.