Tuesday, 8 January 2013: 11:00 AM
Room 10A (Austin Convention Center)
A land surface snowfall rate (SR) algorithm has been developed and is running at the US National Oceanic and Atmospheric Administration (NOAA). The algorithm employs AMSU and MHS passive microwave data and performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle fall velocity and SR. Snowfall detection (Kongoli et. al, 2003) is based on a set of statistically derived criteria involving multiple channels of AMSU/MHS and ancillary data. Cloud properties are retrieved using an inversion method with a two-stream Radiative Transfer Model (RTM) (Yan et. al, 2008) that is coupled with an iteration scheme. A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle fall velocity. Finally, snowfall rate is computed by numerically solving a complex integral. This algorithm has been validated against both in-situ ground snowfall observations and radar and gauge combined snowfall product. The validation results have shown that the algorithm performs well for different types of snowfall events.
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