Abstract
Abstract The Precipitation Occurrence Sensor System (POSS) is a small Doppler radar originally designed by the Meteorological Service of Canada (MSC) to report the occurrence, type, and intensity of precipitation in automated observing stations. It is also used for real-time estimation of raindrop size distributions (DSDs). From the DSD, various rainfall parameters can be calculated and relationships established, such as between the radar reflectivity factor (Z) and the rainfall rate (R). Earlier work presented first-order estimates of the sampling errors for some POSS rainfall parameter estimates. This work combines a Monte Carlo simulation and “inverse problem” analysis to better estimate errors due to the specific sampling problems of this disdrometer type. The uncertainties are necessary to determine the statistical significance of differences between DSD estimates by the POSS and other collocated disdrometers, or between POSS measurements in different climatologies. Additionally, confidence limits can be assigned to regression coefficients for rainfall parameter relationships determined from POSS estimates. An example is given of the uncertainties in the coefficients of measured Z–R relationships.
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