A method for filtering out raingauge representativeness errors from the verification distributions of radar and raingauge rainfall

The study presents a conditional distribution transformation (CDT) method for improving radar rainfall (RR) verifications that use sparse raingauge networks as the ground reference (GR). Large differences between the sampling areas of radar and raingauge measurements render direct comparisons problematic. The purpose of the CDT method is to filter out the raingauge representativeness errors from radar–raingauge verification samples. Our objective is to test the validity and evaluate the accuracy of this method. These analyses are based on two large data samples from high-density research networks covering the Goodwin Creek watershed in Mississippi and the Little Washita watershed in Oklahoma. An example implementation in a quasi operational situation is also presented, and sample size requirements are investigated using Monte Carlo simulations. Our tests indicate that the CDT method performs with satisfactory accuracy and can considerably improve on the currently applied RR verification practices.