While snowfall makes a major contribution to the hydrological cycle in the Arctic, state-of-the-art climatologies still significantly disagree. We present a satellite-based characterization of snowfall in the Arctic using CloudSat observations, and compare it with various other climatologies. First, we examine the frequency and phase of precipitation as well as the snowfall rates from CloudSat over 2007–10. Frequency of solid precipitation is higher than 70% over the Arctic Ocean and 95% over Greenland, while mixed precipitation occurs mainly over North Atlantic (50%) and liquid precipitation over land south of 70°N (40%). Intense mean snowfall rates are located over Greenland, the Barents Sea, and the Alaska range (>500 mm yr−1), and maxima are located over the southeast coast of Greenland (up to 2000 mm yr−1). Then we compare snowfall rates with the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim, herein ERA-I) and Arctic System Reanalysis (ASR). Similar general geographical patterns are observed in all datasets, such as the high snowfall rates along the North Atlantic storm track. Yet, there are significant mean snowfall rate differences over the Arctic between 58° and 82°N between ERA-I (153 mm yr−1), ASR version 1 (206 mm yr−1), ASR version 2 (174 mm yr−1), and CloudSat (183 mm yr−1). Snowfall rates and differences are larger over Greenland. Phase attribution is likely to be a significant source of snowfall rate differences, especially regarding ERA-I underestimation. In spite of its nadir-viewing limitations, CloudSat is an essential source of information to characterize snowfall in the Arctic.

In this study, we present a new module for the Snow retrievaL ALgorithm fOr gMi (SLALOM) that retrieves surface snowfall rate using Global Precipitation Measurement (GPM) Microwave Imager measurements together with humidity and temperature vertical profiles. This module, named Surface Snowfall Rate Module, is tuned using colocated surface snowfall observations of the Cloud Profiling Radar onboard CloudSat. Using this new module, the SLALOM algorithm is able to predict surface snowfall rate with a relative bias of −13%, a root‐mean‐square error of 0.08 mm/hr, and a correlation coefficient of 0.7. Surface Snowfall Rate Module is then used to retrieve snowfall rate for three case studies and to provide a unique, 70°S to 70°N high‐resolution distribution of average surface snowfall rate from 2014 to 2017. This new product will be useful for surface precipitation analyses, global water budget estimation, and climatological analyses.

This study evaluates the potential use of the Microwave Humidity Sounder (MHS) for snowfall detection in the Arctic. Using two years of colocated MHS and CloudSat observations, we develop an algorithm that is able to detect up to 90% of the most intense snowfall events (snow water path ≥400 g m−2 and 50% of the weak snowfall rate events (snow water path ≤50 g m−2. The brightness temperatures at 190.3 GHz and 183.3 ± 3 GHz, the integrated water vapor, and the temperature at 2 m are identified as the most important variables for snowfall detection. The algorithm tends to underestimate the snowfall occurrence over Greenland and mountainous areas (by as much as −30%), likely due to the dryness of these areas, and to overestimate the snowfall occurrence over the northern part of the Atlantic (by up to 30%), likely due to the occurrence of mixed phase precipitation. An interpretation of the selection of the variables and their importance provides a better understanding of the snowfall detection algorithm. This work lays the foundation for the development of a snowfall rate quantification algorithm.