Falling Snow Retrieval Algorithm Development Work for Gpm

Retrievals of falling snow from space represent one of the next important challenges for the atmospheric, hydrological, and energy budget scientific communities. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. The Global Precipitation Measurement (GPM) mission has a requirement to detect falling snow. Historically, retrievals of falling snow have been difficult due to the relative insensitivity of satellite rain-based channels as used in the past. We emphasize the use of high frequency passive microwave channels (85-200 GHz) since these are more sensitive to the ice in clouds (e. ensures a physical consistency between the underlying cloud and atmospheric state and the brightness temperature observations. Three of the major challenges associated with falling snow retrievals include: (1) assessing the effects of land surface signatures and environmental state variables that obscure falling snow signatures in brightness temperature observations; (2) adequately modeling the physical characteristics of precipitating snow in radiative transfer calculations for database construction; and (3) ensuring that the retrieval database is representative of falling snow events. Here we focus on testing the sensitivity of the detection and estimation results to surface emission, particle shape, and environmental state to help assess uncertainty in the retrievals based on these input variables. This work also allows for an improved understanding of the relationships between radiative properties associated with radar reflectivities, brightness temperatures (TB), and the physical properties of frozen precipitation within a cloud.