Sources of Uncertainty in Precipitation-Type Forecasting

Abstract

ive implicit precipitation-type algorithms are assessed using observed and model-forecast sounding data in order to measure their accuracy and to gauge the effects of model uncertainty on algorithm performance. When applied to observed soundings, all algorithms provide very reliable guidance on snow and rain (SN and RA). However, their skills for ice pellets and freezing rain (IP and FZRA) are comparatively low. Most misclassifications of IP are for FZRA and vice versa. Deeper investigation reveals that no method used in any of the algorithms to differentiate between IP and FZRA allows for clear discrimination between the two forms. The effects of model uncertainty are also considered. For SN and RA, these effects are minimal and each algorithm performs reliably. Conversely, IP and FZRA are strongly impacted. When the range of uncertainty is fully accounted for, their resulting wet-bulb temperature profiles are nearly indistinguishable, leading to very poor skill for all algorithms. Although currently available data do not allow for a thorough investigation, comparison of the statistics from only those soundings that are associated with long-duration, horizontally uniform regions of FZRA shows there are significant differences between these profiles and those that are from more transient, highly variable environments. Hence, a five-category (SN, RA, IP, FZRA, and IP?FZRA mix) approach is advocated to differentiate between sustained regions of horizontally uniform FZRA (or IP) from more mixed environments.