Heavy snow events over the Mediterranean basin: applications of a new GPM Microwave Imager snowfall retrieval algorithm

In many regions of the Mediterranean basin snowfall can be considered as an extreme event and can cause troubles to transportations and serious damages. Snowfall monitoring by ground-based in situ measurements is limited due to scarce accessibility and arduous maintenance of the instrumentation. Moreover, ground-based radar snowfall rate estimates are often affected by large uncertainties. Satellites can offer valuable tools for snowfall detection and monitoring. In the last decades, the availability of high-frequency channel assortment by most satellite microwave radiometers has determined many advances in snow detection from space. Moreover CloudSat Cloud Profiling Radar (CPR) sensitivity has proved extremely valuable for global snowfall research. However, snowfall detection and rate estimation from satellite microwave observations represent an extremely challenging task due to the complex microphysics of snow clouds, and to the many sources of signal contamination (e.g., frozen background, presence of supercooled droplets). Compiling and exploiting coincident spaceborne active and passive microwave sensor datasets can effectively enhance our understanding of high-frequency microwave channels sensitivity to snowfall. In a recent study (Panegrossi et al., 2017), we have analyzed the impact of falling snow on the high frequencies channels of Global Precipitation Measurement (GPM) Microwave Imager (GMI) on the basis of matched GPM GMI and CloudSat CPR snowfall observations (mainly occurring at latitudes between 55° and 65°N). In particular we have analyzed the sensitivity of GMI high-frequency channels to falling snow, as identified by CPR, under different environmental conditions (atmospheric water vapor content, 2-meter temperature, background surface), and the impact on the GMI snowfall signal of supercooled droplets as identified by CALIPSO/CPR observations. Starting from these results we developed a brand new algorithm to retrieve snowfall, based on the exploitation of the same global GMI/CloudSat snowfall coincidence dataset. This algorithm makes use of all GMI channels brightness temperature measurements and of ancillary environmental variables. In this study, the new GMI snowfall retrieval algorithm is briefly described, and applications to heavy snowfall events over the Mediterranean basin are shown. In particular we focus on extensive snowfall cases occurred over Italy and Morocco in 2017 and 2018, for which several GMI overpasses are available, showing distinctive signature of falling snow on the high-frequency brightness temperatures. We compare our findings with ground-based radar data (where available), NWP model snowfall-related fields, and with the NASA GPM precipitation products. The potentials of the applicability of the algorithm to mid-low latitude heavy snowfall events, beyond those represented in the GMI/CloudSat snowfall coincidence dataset, will be discussed.