Abstract
Abstract. The ability of single-frequency, millimeter-wavelength radar reflectivity observations to provide useful constraints for retrieval of snow particle size distribution (PSD) parameters, snowfall rates, and snowfall accumulations is examined. An optimal estimation snowfall retrieval that allows analyses of retrieval uncertainties and information content is applied to observations of near-surface W-band reflectivities from multiple snowfall events during the 2006–2007 winter season in southern Ontario. Retrieved instantaneous snowfall rates generally have uncertainties greater than 100 %, but single-event and seasonal snow accumulations from the retrieval results match well with collocated measurements of accumulations. Absolute fractional differences are mainly below 30 % for individual events that have more substantial accumulations and, for the season, 12.6 %. Uncertainties in retrieved snowfall rates are driven mainly by uncertainties in the retrieved PSD parameters, followed by uncertainties in particle model parameters and, to a lesser extent, the uncertainties in the fall-speed model. Uncertainties attributable to assuming an exponential distribution are negligible. The results indicate that improvements to PSD and particle model a priori constraints provide the most impactful path forward for reducing uncertainties in retrieved snowfall rates. Information content analyses reveal that PSD slope is well-constrained by the retrieval. Given the sensitivity of PSD slope to microphysical transformations, the results show that such retrievals, when applied to radar reflectivity profiles, could provide information about microphysical transformations in the snowing column. The PSD intercept is less well-constrained by the retrieval. While applied to near-surface radar observations in this study, the retrieval is applicable as well to radar observations aloft, such as those provided by profiling ground-based, airborne, and satellite-borne radars under lighter snowfall conditions when attenuation and multiple scattering can be neglected.
Highlights
20 Radar observations focused on snowfall from platforms outside the established weather surveillance radar networks have become ubiquitous over the last two decades, largely due to increased interest in the role of snowfall in mid- and high-latitude microphysics, hydrology and climate
Given the sensitivity of particle size distribution (PSD) slope to microphysical transformations, the results show that such retrievals, when applied to radar reflectivity profiles, could provide information about microphysical transformations in the snowing column
To quantify global hydrometeor and precipitation properties. These satellite-borne radars ( the CloudSat mission’s Cloud Profiling Radar (CPR) (Tanelli et al, 2008) and the Global Precipitation Measurement (GPM) mission’s Dual-frequency 25 Precipitation Radar (DPR) (Toyoshima et al, 2015), with two others anticipated to launch in the coming decade) are capable solely of measuring vertical profiles of radar reflectivity factor along with path integrated attenuation under certain conditions
Summary
20 Radar observations focused on snowfall from platforms outside the established weather surveillance radar networks have become ubiquitous over the last two decades, largely due to increased interest in the role of snowfall in mid- and high-latitude microphysics, hydrology and climate. 3. Ground-based radar and precipitation observations allow the retrieval to be tested, showing that size distribution width is best constrained by the retrieval and that uncertainties in retrieved size distribution parameters 85 (but not uncertainties due to the assumed exponential form of the PSD itself) are the strongest contributors to uncertainties in estimated snowfall rates With even simple parameterized expressions for particle mass, shape, and size distribution (PSD), single-frequency observations of radar reflectivity alone are insufficient to reasonably constrain the resulting set of parameters. To address this insufficiency, retrievals must incorporate a priori information about particle microphysical and scattering properties.
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