92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Tuesday, 24 January 2012
Snowfall Rate Retrieval Using Passive Microwave Measurements
Hall E (New Orleans Convention Center )
Huan Meng, NOAA/NESDIS/STAR, College Park, MD; and B. Yan and R. R. Ferraro

A land surface snowfall rate (water equivalent) algorithm has been developed using measurements from National Oceanic and Atmospheric Administration's (NOAA) Advanced Microwave Sounding Unit (AMSU-A and AMSU-B) and European Organization for the Exploitation of METeorological SATellites' (EUMATSAT) Microwave Humidity Sounder (MHS). The algorithm includes four components: snowfall detection, Ice Water Path (IWP) retrieval, cloud thickness determination, and snowflake fall velocity computation. Snowfall detection is based on an AMSU Snowfall Detection (SD) algorithm developed by Kongoli et. al (2003). This algorithm applies a set of statistically derived criteria involving multiple channels of AMSU/MHS to detect snowfall and filter out false alarms triggered by snow on the surface. Improvement was recently made to the original SD algorithm by utilizing the temperature and water vapor profiles from a NWP model. Criteria for temperature and relative humidity at various heights are used to reclassify precipitation types, including reclassify detected snowfall to rain or vice versa. IWP is derived using an inversion method with a two-stream Radiative Transfer Model (RTM) coupled with an iteration scheme. The RTM is parameterized for cold season and therefore is particularly suitable for retrieving environmental properties of winter precipitation. Input to the retrieval system is a combination of measurements from AMSU/MHS ‘window' and water vapor channels as well as the ‘first guesses' of some variables to be retrieved such as IWP. The system outputs retrieved quantities when the differences between the RTM simulated and the measured brightness temperatures fall under predefined thresholds. In this study, the ‘cloud top' used to compute cloud thickness is confined to 6 km or lower due to the fact that the microwave sensors used in this study are less sensitive to higher level cloud particles. The cloud thickness is derived using an empirical method based on GDAS water vapor and temperature profiles. Some simplifications are made regarding the distributions and fall velocities of snow particles which allow the computation of snowfall rate from the derived IWP and cloud thickness. Validation of the snowfall rate algorithm is conducted using ground hourly observations from the Continental United States (CONUS) and shows reasonable agreement between the retrievals and the observations. The presentation will include validation results and case studies of some major snow storms in the CONUS.

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