The need for measuring snowfall is driven by the roles snow plays providing freshwater resources and affecting climate. Snow accumulations are an important resource for ecological and human needs and in many areas appear vulnerable to climate change. Snow cover modifies surface heat fluxes over areas extensive enough to influence climate at regional and perhaps global scales. Seasonal runoff from snowmelt, along with over-ocean snowfall, contributes to freshening in the Arctic and high-latitude North Atlantic oceans. Yet much of the Earth's area for which snowfall plays such significant roles is not well-monitored by observations.
Radar reflectivity at 94 GHz is sensitive to scattering by snow particles and CloudSat, in a near-polar orbit, provides vertically resolved measurements of 94 GHz reflectivity at latitudes from 82 N to 82 S. While not global in areal coverage, CloudSat does provide observations sampled from regions where snowfall is the dominant form of precipitation and an important component of hydrologic processes. The work presented in this study seeks to exploit these observations by developing and assessing a physically-base snowfall retrieval which uses an explicit representation of snow microphysical properties.
As the reflectivity-based snowfall retrieval problem is significantly underconstrained, a priori information about snow microphysical properties is required. The approaches typically used to develop relations between reflectivity and snowfall rate, so-called Ze-S relations, require assumptions about particle properties such as mass, area, fallspeed, and shape. Limited information about the distributions of these properties makes difficult the characterization of how uncertainties in the properties influence uncertainties in the Ze-S relations.
To address this, the study proceeded in two parts. In the first, probability distributions for snow particle microphysical properties were assessed using optimal estimation applied to multi-sensor surface-based snow observations from a field campaign. Mass properties were moderately well determined by the observations, the area properties less so. The retrieval revealed nontrivial correlations between mass and area parameters not apparent in prior studies. Synthetic testing showed that the performance of the retrieval was hampered by uncertainties in the fallspeed forward model. The mass and area properties obtained from this retrieval were used to construct particle models including 94 GHz scattering properties for dry snow. These properties were insufficient to constrain scattering properties to match observed 94 GHz reflectivities. Vertical aspect ratio supplied a sufficient additional constraint.
In the second part, the CloudSat retrieval, designed to estimate vertical profiles of snow size distribution parameters from reflectivity profiles, was applied to measurements from the field campaign and from an orbit of CloudSat observations. Uncertainties in the mass and area microphysical properties, obtained from the first part of this study, were substantial contributors to the uncertainties in the retrieved snowfall rates. Snowfall rate fractional uncertainties were typically 140% to 200%. Accumulations of snowfall calculated from the retrieval results matched observed accumulations to within 13%, however, when allowances were made for snowfall with properties likely inconsistent with the snow particle model. Information content metrics showed that the size distribution slope parameters were moderately to strongly constrained by the reflectivity observations, while the intercept parameters were determined primarily by the a priori constraints. Results from the CloudSat orbit demonstrated the ability of the CloudSat retrieval to represent a range of scene-dependent Ze-S relations.