Patterns and trend analysis of rain-on-snow events using passive microwave satellite data over the Canadian Arctic Archipelago since 1987

Abstract

Climate change has a profound effect on Arctic meteorology extreme events, such as rain-on-snow (ROS), which affects surface state variable spatial and temporal variability. Passive microwave satellite images can help detect such events in polar regions where local meteorological and snow information are scarce. In this study, we use a detection algorithm using a high-resolution passive microwave data to monitor spatial and temporal variability of ROS over the Canadian Arctic Archipelago from 1987 to 2019. The method is validated using data from several meteorological stations and atmospheric corrections have been applied to the passive microwave dataset. Our approach to detect ROS is based on two methods: 1) over a fixed time-period (i.e. November 1st to May 31st) throughout the study period and 2) using an a-prior detection for snow presence before applying our ROS algorithm (i.e. length of studied winter varies yearly). Event occurrence is analyzed for each winter and separated by island groups of the Canadian Arctic Archipelago. Results show an increase in absolute ROS occurrence, mainly along the coasts, although no statistically significant trends are observed.