Evaluation of the CloudSat surface snowfall product over Antarctica using ground-based precipitation radars
Niels Souverijns (Katholieke Universiteit Leuven)
Alexandra Gossart (Katholieke Universiteit Leuven)
Stef Lhermitte (TU Delft - Mathematical Geodesy and Positioning)
Irina V. Gorodetskaya (University of Aveiro)
Jacopo Grazioli (Federal Office of Meteorology and Climatology MeteoSwiss, École Polytechnique Fédérale de Lausanne)
Alexis Berne (École Polytechnique Fédérale de Lausanne)
Claudio Duran-Alarcon (Université Grenoble Alpes)
Brice Boudevillain (Université Grenoble Alpes)
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Abstract
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 the CloudSat satellite, 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 MRR 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 10-90th-percentile temporal sampling uncertainty in the snowfall climatology varies between 30% and 40% 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<15 %), outperforming ERA-Interim. For coarser and finer resolutions, the performance drops as a result of 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 Climate Model Intercomparison Project Phase 5 (CMIP5) snowfall variability, our results imply that the CloudSat product is valuable for climate model evaluation purposes.
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