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

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doi:10.5194/amt-2017-26
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
15 Feb 2017
Review status
This discussion paper is under review for the journal Atmospheric Measurement Techniques (AMT).
A variational technique to estimate snowfall rate from coincident radar, snowflake, and fallspeed observations
Steven J. Cooper1, Norman B. Wood2, and Tristan S. L'Ecuyer3 1Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT, USA
2Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, Madison, WI, USA
3Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI, USA
Abstract. Estimates of snowfall rate as derived from radar reflectivities alone are non-unique. Different combinations of snowflake microphysical properties and particle fallspeeds can conspire to produce nearly identical snowfall rates for given radar reflectivity signatures. Such ambiguities can result in retrieval uncertainties on the order of 100–200 % for individual events. Here, we use observations of particle size distribution (PSD), fallspeed, and snowflake habit from the Multi-Angle Snow Camera (MASC) to constrain estimates of snowfall derived from Ka-band Zenith Radar (KAZR) measurements at the ARM NSA Barrow Climate Facility site. MASC measurements of microphysical properties with uncertainties are introduced into a modified form of the optimal-estimation CloudSat snowfall algorithm (2C-SNOW-PROFILE) via the a priori guess and variance terms. Use of MASC fallspeed, MASC PSD, and CloudSat snow particle model as base assumptions resulted in retrieved total accumulations with a −18 % difference relative to nearby National Weather Service observations over five snow events. Use of different but reasonable combinations of retrieval assumptions resulted in estimated snowfall accumulations with differences ranging from −64 % to +94 % for the same storm events. Retrieved snowfall rates were particularly sensitive to assumed fallspeed and habit, suggesting that in-situ measurements can help to constrain key snowfall retrieval uncertainties. More accurate knowledge of these properties dependent upon location and meteorological conditions should help refine and improve ground and space-based radar estimates of snowfall.

Citation: Cooper, S. J., Wood, N. B., and L'Ecuyer, T. S.: A variational technique to estimate snowfall rate from coincident radar, snowflake, and fallspeed observations, Atmos. Meas. Tech. Discuss., doi:10.5194/amt-2017-26, in review, 2017.
Steven J. Cooper et al.
Steven J. Cooper et al.
Steven J. Cooper et al.

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Short summary
Estimates of snowfall rate as derived from radar observations can suffer large uncertainties due to great natural variability in snowflake microphysical properties. Here, we used in-situ observations of particle size, shape, and fallspeed to refine radar-based estimates of snowfall for five snow events at the ARM Barrow Climate Facility. Estimated snowfall amounts agreed well with nearby snow gauge observations and demonstrated significant sensitivity to both particle shape and fallspeed.
Estimates of snowfall rate as derived from radar observations can suffer large uncertainties due...
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