Integrating Early Career Stem Research Through Machine Learning by the NASA Climate Change Research Initiative (CCRI)

Satellites are particularly well-suited to provide spatially distributed observations of global snow. For hydrological research and applications, Snow Water Equivalent (SWE) is the most important observation, but it is also our biggest gap in snow remote sensing. Currently, no satellite sensor has the ability to measure SWE globally at the accuracy, resolution and frequency needed, because of a number of factors that impact the signals such as forests, mountains, clouds and the snow characteristics themselves. The NASA Climate Change Research Initiative (CCRI) SnowEx team uses machine learning approaches to attempt to address the unanswered questions of snow science. The team has collaborated with other similar NASA wide programs and leveraged skills and resources which led to the formation of a community machine learning (ML) working group.