Characterization of Snowfall Properties at High Latitude Sites through Coincident Radar, Snowflake, and Fallspeed Observations

Estimates of snowfall rate 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 a given radar reflectivity signature. Such ambiguities can result in retrieval uncertainties on the order of 100-200% for individual events. Here, we use observations of snowflake particle size distribution, fallspeed, and habit from the Precipitation Imaging Package (PIP) and Multi-Angle Snow Camera (MASC) to constrain estimates of snowfall rate as derived from radar measurements for sites in both Alaska and Norway. Measurements of microphysical properties and 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. Initial results focus on the MASC and Ka-band ARM Zenith Radar (KAZR) measurements at the ARM NSA Barrow Climate Facility site. Use of MASC fallspeed, MASC PSD, and a CloudSat particle model as base assumptions resulted in retrieved total accumulations with a -17% difference relative to nearby National Weather Service observations averaged over five snow events. Use of different but reasonable combinations of retrieval assumptions resulted in estimated snowfall accumulations with differences ranging from -63% to + 86% for the same storm events. Retrieved snowfall rates were particularly sensitive to assumed fallspeed and habit. Preliminary results also will be presented for the deployment of a Micro Rain Radar (MRR), MASC, and PIP to a mountainous site near Haukeliseter, Norway, during winter season 2016-17. Retrieval performance will be evaluated against double-fenced, single-fenced, and unshielded snow gauge observations as a function of meteorological conditions such as wind speed. It is hoped more accurate knowledge of snowfall properties dependent upon location and environment will be useful for both weather and climate applications.