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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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Atmos. Meas. Tech., 8, 5009-5021, 2015
https://doi.org/10.5194/amt-8-5009-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
01 Dec 2015
Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden
L. Norin1, A. Devasthale1, T. S. L'Ecuyer2, N. B. Wood3, and M. Smalley2 1Atmospheric Remote Sensing Unit, Research Department, Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
2Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
3Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, Madison, Wisconsin, USA
Abstract. Accurate snowfall estimates are important for both weather and climate applications. Ground-based weather radars and space-based satellite sensors are often used as viable alternatives to rain gauges to estimate precipitation in this context. In particular, the Cloud Profiling Radar (CPR) on board CloudSat is proving to be a useful tool to map snowfall globally, in part due to its high sensitivity to light precipitation and its ability to provide near-global vertical structure. CloudSat snowfall estimates play a particularly important role in the high-latitude regions as other ground-based observations become sparse and passive satellite sensors suffer from inherent limitations. In this paper, snowfall estimates from two observing systems – Swerad, the Swedish national weather radar network, and CloudSat – are compared. Swerad offers a well-calibrated data set of precipitation rates with high spatial and temporal resolution, at very high latitudes. The measurements are anchored to rain gauges and provide valuable insights into the usefulness of CloudSat CPR's snowfall estimates in the polar regions. In total, 7.2 × 105 matchups of CloudSat and Swerad observations from 2008 through 2010 were intercompared, covering all but the summer months (June to September). The intercomparison shows encouraging agreement between the two observing systems despite their different sensitivities and user applications. The best agreement is observed when CloudSat passes close to a Swerad station (46–82 km), where the observational conditions for both systems are comparable. Larger disagreements outside this range suggest that both platforms have difficulty with shallow snow but for different reasons. The correlation between Swerad and CloudSat degrades with increasing distance from the nearest Swerad station, as Swerad's sensitivity decreases as a function of distance. Swerad also tends to overshoot low-level precipitating systems further away from the station, leading to an underestimation of snowfall rate and occasionally to missing precipitation altogether. Several statistical metrics – including the probability of detection, false alarm rate, hit rate, and Pierce's skill score – are calculated. The sensitivity of these metrics to the snowfall rate, as well as to the distance from the nearest radar station, are summarised. This highlights the strengths and the limitations of both observing systems at the lower and upper ends of the snowfall distributions as well as the range of uncertainties that can be expected from these systems in high-latitude regions.

Citation: Norin, L., Devasthale, A., L'Ecuyer, T. S., Wood, N. B., and Smalley, M.: Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden, Atmos. Meas. Tech., 8, 5009-5021, https://doi.org/10.5194/amt-8-5009-2015, 2015.
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Short summary
The ability to estimate snowfall accurately is important for both weather and climate applications. In this work we have intercompared snowfall estimates from two observing systems: the space-based Cloud Profiling Radar on board NASA's CloudSat satellite and Swerad, the ground-based Swedish national weather radar network. The intercomparison shows encouraging agreement between these two observing systems despite their different sensitivities and user applications.
The ability to estimate snowfall accurately is important for both weather and climate...
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