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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