Satellite-based snowfall detection and estimation: challenges and future perspectives within HSAF
Giulia Panegrossi presents the challenges and recent advancements in satellite-based snowfall quantification and global monitoring.
Snow plays an important role in the Earth energy exchange processes, and is a fundamental element of the water cycle. The use of satellites for snowfall monitoring and quantification and for retrieving snow cover properties and variability is necessary to globally quantify water resources. Recent studies have evidenced how space borne multi-channel microwave (MW) radiometer measurements respond to both snowfall and snow cover properties. Improvement in both monitoring of high latitude precipitation and in our understanding on microphysical and dynamical processes that influence high latitude precipitation patterns, intensity and type must be driven by concerted observations of active radars and passive microwave radiometers. This has been recently demonstrated through the development of machine learning-based algorithms for snowfall detection and retrieval, exploiting global observational datasets built from passive and active microwave space borne sensors. In this presentation the challenges and recent advancements in satellite-based snowfall quantification and global monitoring will be discussed. Moreover, retrieval strategies based on machine learning approaches that are being adopted within the EUMETSAT H SAF in view of the future EPS-SG mission, will be presented.