Development of a Z-R Relationship with Uniform Sampling to Mitigate Sampling Variability
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O'Dell, Katelyn Ashley
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Abstract
A new method for sampling precipitation events was developed and tested on raw data and with data-based Monte-Carlo simulations. This new method separates rain events into samples of the same number of drops, unlike conventional sampling methods where each sample is taken over a fixed temporal duration. By containing a uniform number of drops per sample, the new method was expected to mitigate sampling variability and weight drops equally in determining relationships between two bulk quantities: rain rate (R) and radar reflectivity factor (Z). Using data from a two dimensional video disdrometer, this hypothesis was tested on six separate rain events. From investiga- tions with raw data, uniform sampling did not conclusively mitigate sampling variability in practical application. Subsequent investigations with data based Monte-Carlo simulations revealed very large sample sizes, on the order of 10,000 or more drops, might be necessary to mitigate sampling variability in precipitation measurements.
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Rain, Statistics, Sampling, Physics