Sources of uncertainty in precipitation type determination and forecasting

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Wednesday, 5 February 2014: 4:30 PM
Room C201 (The Georgia World Congress Center )
Heather D. Reeves, CIMMS/Univ. of Oklahoma and NOAA/NSSL, Norman, OK; and K. L. Elmore, A. V. Ryzhkov, T. J. Schuur, K. L. Ortega, and J. Krause

A key component to the new dual-polarized radar winter surface hydrometeor classification algorithm is the background classification, which is derived from numerical model short-range forecasts. The accuracy of the background class is limited by the choice of algorithm, the uncertainty in the forecast system, and the horizontal variability of the precipitation type at the surface. Consideration of output from different algorithms, shows that all schemes considered are very accurate at detecting snow and rain. However, freezing rain and ice pellets are quite poor by comparison. As will be demonstrated using high-resolution observations of precipitation type, freezing rain and ice pellets often occur in mixes rather than in isolation. Using a background algorithm that allows for a freezing rain/ice pellet mix greatly improves the results. Statistical analyses as well as results from individual events will be presented that demonstrate and quantify these various sources of uncertainty.