A Nonparametric Procedure to Assess the Accuracy of the Normality Assumption for Annual Rainfall Totals, Based on the Marginal Statistics of Daily Rainfall: An Application to the NOAA/NCDC Rainfall Database

Abstract We develop a nonparametric procedure to assess the accuracy of normality assumption for annual rainfall totals (ART), based on marginal statistics daily rainfall. The is addressed practitioners and hydrologists that operate in data-poor regions. To do so we use 1) goodness-of-fit metrics conclude approximate convergence empirical distribution normal shape classify 3007 time series from NOAA/NCDC Global Historical Climatology Network database, with at least 30 years recordings, into Gaussian (G) non-Gaussian (NG) groups; 2) logistic regression analysis identify are most descriptive G/NG classification; 3) random-search algorithm set constraints allows classification ART samples basis rain rates. shows Anderson–Darling (AD) test statistic conservative one determining Gaussianity (followed by Cramer–Von Mises Lilliefors’s version Kolmogorov–Smirnov) fraction wet days f wd < 0.1 skewness coefficient positive rates sk > 5.92 deviate significantly shape. In addition, find continental climate (type D) exhibits highest distributed (i.e., 74.45%; AD α = 5% significance level), followed warm temperate C; 72.80%), equatorial A; 68.83%), polar E; 62.96%), arid B; 60.29%) climates.