Estimation of snow microphysical properties with application to millimeter-wavelength radar retrievals for snowfall rate

• Main Author: Wood, Norman Bryce
• Other Authors: Stephens, Graeme L., Cotton, William R., Fassnacht, Steven R., Kummerow, Christian D., Matrosov, Sergey Y.
• Format: Text
• Language: English
• Published: Colorado State University. Libraries 2007
• Subjects: Arctic; Climate change; North Atlantic
• Online Access: http://hdl.handle.net/10217/48170

2011 Summer. Includes bibliographical references. 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 ...