An Evaluation Of The Uncertainty Of Precipitation Type...

Herein, an evaluation of the uncertainty of precipitation-type observations and its eect on the validation of forecast precipitation type is undertaken.The forms of uncertainty considered are instrument/observer bias and horizontal/temporal representivity of the observations.Instrument/observer biases are assessed by comparing observations from the Automated Surface Observing Station (ASOS) and meteorological Phenomena Identiï ¬ cation Near the Ground (mPING) networks.Relative to the augmented ASOS, mPING observations are biased toward ice pellets (PL) and away from rain (RA).Consequently, when mPING is used to validate precipitation-type forecasts, the Probabilities of Detection (PODs) for RA (PL) are decreased (increased) relative to those†¦show more content†¦Forecasts of precipitation type are created by post-processing algorithms that are applied to numerical-model output.Hence, errant diagnoses may be a product of poor algorithm assumptions.There is a wide range of a pproaches and degrees of complexity to precipitation-type algorithms.Some use bulk properties from the temperature and humidity proï ¬ les (eg.Baldwin et al.1994; Bourgouin 2000; Schuur et al.2012; Elmore et al.2015b; Chenard et al.2015), others attempt to calculate or infer the liquid-water content of falling hydrometeors (Ramer 1993; Czys et al.1996; Reeves et al.2016), while still others use mixing ratios from microphysical parameterization schemes as the primary discriminant (Th ´eriault et al.2010; Ikeda et al.2013).As a result, dierent algorithms may produce very dierent results, particularly when the environmental temperature is near 0C (Manikin et al.2004; Manikin 2005; Reeves et al.2014).In some cases, strong biases exist.For example, the Baldwin algorithm has a well-known bias toward PL.The Ramer algorithm is known to be biased toward FZRA and the algorithm described in Schuur et al.(2012) is strongly