A snowfall detection algorithm over land utilizing high-frequency passive microwave measurements-Application to ATMS

This paper presents a snowfall detection algorithm over land from high-frequency passive microwave measurements. The algorithm computes the probability of snowfall using logistic regression and the principal components of the seven high-frequency brightness temperature measurements at Atmospheric Technology Microwave Sounder (ATMS) channel frequencies 89 GHz and above. The oxygen absorption channel 6 (53.6 GHz) is utilized as temperature proxy to define the snowfall retrieval domain. Ground truth surface meteorological data including snowfall occurrence were collected over Conterminous U.S. and Alaska during two winter seasons in 2012-2013 and 2013-2014. Statistical analysis of the in situ data matched with ATMS measurements showed that in relatively warmer weather, snowfall tends to be associated with lower high-frequency brightness temperatures than no snowfall, and the brightness temperatures are negatively correlated with measured snowfall rate. In colder weather conditions, however, snowfall tends to occur at higher microwave brightness temperatures than no-snowfall, and the brightness temperatures are positively correlated with snowfall rate. The brightness temperature decrease and the negative correlations with snowfall rate in warmer weather are attributed to the scattering effect. It is hypothesized that the scattering effect is insignificant in colder weather due to the predominance of lighter snowfall and emission. Based on these results, a two-step algorithm is developed that optimizes snowfall detection over these two distinct temperature regimes. Evaluation of the algorithm shows skill in capturing snowfall in variable weather conditions as well as the remaining challenges in the retrieval of lighter and colder snowfall.