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      Evaluation of the CloudSat surface snowfall product over Antarctica using ground-based precipitation radars

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          Abstract

          <p><strong>Abstract.</strong> In-situ observations of snowfall over the Antarctic Ice Sheet are scarce. Currently, continent-wide assessments of snowfall are limited to information from the Cloud Profiling Radar on board of CloudSat, which has not been evaluated up to now. In this study, snowfall derived from CloudSat is evaluated using three ground-based vertically profiling 24-GHz precipitation radars (Micro Rain Radars; MRRs). Firstly, using the MRRs long-term measurement records, an assessment of the uncertainty caused by the low temporal sampling rate of CloudSat (one revisit per 2.1 to 4.5 days) is performed. The 10&amp;ndash;90th percentile temporal sampling uncertainty on the snowfall climatology varies between 30&amp;ndash;40<span class="thinspace"></span>% depending on the latitudinal location and revisit time of CloudSat. Secondly, an evaluation of the snowfall climatology indicates that the CloudSat product, derived at a resolution of 1° latitude by 2° longitude, is able to accurately represent the snowfall climatology at the three MRR sites (biases<span class="thinspace"></span><<span class="thinspace"></span>15<span class="thinspace"></span>%), outperforming ERA-Interim. For coarser and finer resolutions, the performance drops due to higher omission errors by CloudSat. Moreover, the CloudSat product does not perform well in simulating individual snowfall events. Since the difference between the MRRs and the CloudSat climatology are limited and the temporal uncertainty is lower than current CMIP5 snowfall variability, our results imply that the CloudSat product is valuable for climate model evaluation purposes.</p>

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          Author and article information

          Journal
          The Cryosphere Discussions
          The Cryosphere Discuss.
          Copernicus GmbH
          1994-0440
          June 13 2018
          : 1-21
          Article
          10.5194/tc-2018-111
          5d85e214-4210-4198-8cdf-8a31f7b8f9f9
          © 2018

          https://creativecommons.org/licenses/by/4.0/

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