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Volume 10, issue 7 | Copyright
Atmos. Meas. Tech., 10, 2557-2571, 2017
https://doi.org/10.5194/amt-10-2557-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 20 Jul 2017

Research article | 20 Jul 2017

A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations

Steven J. Cooper1, Norman B. Wood2, and Tristan S. L'Ecuyer3 Steven J. Cooper et al.
  • 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 fall speeds 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), fall speed, and snowflake habit from the Multi-Angle Snowflake Camera (MASC) to constrain estimates of snowfall derived from Ka-band ARM zenith radar (KAZR) measurements at the Atmospheric Radiation Measurement (ARM) North Slope Alaska (NSA) Climate Research Facility site at Barrow. 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 the MASC fall speed, 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 (NWS) observations over five snow events. The average error was 36% for the individual events. Use of different but reasonable combinations of retrieval assumptions resulted in estimated snowfall accumulations with differences ranging from −64 to +122% for the same storm events. Retrieved snowfall rates were particularly sensitive to assumed fall speed 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.

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