Sampling Considerations Associated with the Interpretation of Disdrometric Data

Data from rain disdrometers is frequently used to parameterize raindrop size distributions, validate satellite rainfall retrieval algorithms, and develop power-law relations among bulk rain variables. However, data from multiple disdrometers placed near each other can sometimes show appreciable differences in inferred rain properties. These differences are likely due to some unknown combination of natural spatio-temporal variability, differences between instruments, and sampling uncertainties. Here, data from a dense disdrometer array in South Carolina is utilized to drive a numerical study that explores the relative contributions to uncertainty among the factors listed above. Multiple different ways of looking at this problem and strategies designed to minimize the influence of sampling uncertainty on rainfall parameter estimates are explored.