Using CloudSat‐Derived Snow Accumulation Estimates to Constrain Gridded Snow Water Equivalent Products

Abstract

Abstract Changes in the quantity of terrestrial Arctic snow have far‐reaching implications for the global water‐energy budget, ecosystem development, and cold region flooding. Snow water equivalent (SWE) is a useful metric for monitoring these changes, however only a sparse observing network is available in the Arctic. Space‐based remote sensing offers the potential to fill these measurement gaps, and the CloudSat cloud profiling radar (CPR) has been shown to provide high‐quality estimates of surface snowfall rates across the Arctic. We propose a novel method to leverage monthly information from CloudSat to identify data quality issues in the Blended‐4 (B4) gridded SWE product. A regression of estimated monthly mean snow accumulation between CloudSat and B4 provides a confidence interval to objectively flag individual months in B4 with data quality issues. Applying this method to a case study in the Canadian high Arctic identifies an unphysical January melt event in B4 which is traced back to a measurement error in the assimilated station observations. We generalize this technique to a one‐degree grid and find a total of 4,885 cases in B4 with low data quality. We find that the low‐quality SWE product values are not random errors, and that they introduce a systematic bias of −3.1 mm on B4's estimates of mean Arctic SWE. CloudSat is uniquely positioned as one of the only observational data sets for observing snowfall at high latitudes, and when combined with the methodology developed here, its estimates can be used to further enhance the accuracy of current gridded SWE products.

Keywords

• CloudSat
• snow
• remote sensing
• Arctic
• reanalysis
• SWE