Misspecification of extreme rainfall population statistics and frequency inference due to measurement bias and variability

It is shown that: (a) unaccounted measurement error may potentially cause a considerable degree of misspecification about the conventional moment and L-moment coefficients of variation, and the conventional moments of skewness and kurtosis; (b) the presence of measurement variability alone can cause significant and nonlinear quantile bias which further strongly increases with the additional presence of measurement bias; and (c) maximum likelihood estimation provides a general and efficient tool for assessing measurement error in extreme rainfall frequency analysis.