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It is widely recognized that precipitation measurements (rain and snow) can be biased
by wind-induced errors. Measurement of snowfall is especially confounded by wind occurring
at forested measurement sites. Negative bias occurs if a precipitation gauge is shadowed
by trees, and positive bias occurs if a secondary flux occurs due to wind resuspension
of antecedent snowfall. In addition, a negative wind-induced bias occurs because
the downward vertical speed of a snow particle is decreased by the airflow distortion
near a precipitation gauge. Because the fall speed of a rain drop is much larger
than that of a snow particle the flow distortion bias is largest for the snow particle.
Fall speeds for these two particle types are about 1 m/s (snow) and 10 m/s (drop)
assuming a 4 milligram particle mass (liquid equivalent particle diameter = 2 mm).
These complications are evident in many studies of snowfall, even when using a wind shield to slow down the horizontal air velocity (and thus the vertical velocity distortion) near a gauge orifice. We propose to use a new type of precipitation sensor (the “hotplate”) in an intercomparison with conventional gauges. Both the hotplate and the conventional gauges will be deployed at a wintertime cloud seeding target site. The primary objective of the work is the development of a correction for the hotplate’s report of the precipitation rate. One M.S. student will be supported by the project and a hotplate snow sensor will be upgraded with visible and infrared radiometers reporting measurements necessary for the correction. It is anticipated that the research will advance the hotplate as a device superior to conventional gauges for measurement of snowfall.