Remote sensing of liquid precipitation (rain) has had decades to mature into a robust retrieval product. With the launch of the Global Precipitation Measurement (GPM) Core Observatory satellite, along with other satellite sensors, remotely sensed retrievals of falling snow now have the opportunity to develop into useful products for scientific research and societal benefit. The research project will involve retrieval algorithm enhancement and validation for estimates of atmospheric snow above the melting layer and, especially, snow that falls at the Earth's surface. The research will also include activities such as identifying various ice growth processes (e.g., columnar or planar growth, aggregation, riming) from remote sensing observations. The approaches will be multi-faceted (radar-only, radiometer-only, combined) and will address some of the challenging aspects surrounding retrievals (e.g., melting layer processes, microphysical and radiative properties of frozen particles, vertical velocities, oriented particles and their relationship to polarized radiometer observations). Retrievals will be for satellite radiometers (frequencies from 10 to 183 GHz), satellite radars (frequencies from Ku, Ka, and W band), and aircraft remote sensors (both active and passive). Validations and improvements to the algorithms will rely on observations and measurements from existing and future satellite, aircraft and ground instrumentation. Radiative transfer and cloud model simulations will also be used in the analysis. Comparisons with other satellite frozen precipitation estimates will be performed. The application and analysis of this research will help define capabilities and sensor requirements for future instruments.
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Remote Sensing of Atmospheric Snow
ORAU, Greenbelt, MD
Earth Science: Atmospheric Dynamics and Cloud Remote Sensing
ORAU, Greenbelt, MD