Quality Control of Weather Data during Extreme Events

Abstract

Quality assurance (QA) procedures have been automated to reduce the time and labor necessary to discover outliers in weather data. Measurements from neighboring stations are used in this study in a spatial regression test to provide preliminary estimates of the measured data points. The new method does not assign the largest weight to the nearest estimate but, instead, assigns the weights according to the standard error of estimate. In this paper, the spatial test was employed to study patterns in flagged data in the following extreme events: the 1993 Midwest floods, the 2002 drought, Hurricane Andrew (1992), and a series of cold fronts during October 1990. The location of flagged records and the influence zones for such events relative to QA were compared. The behavior of the spatial test in these events provides important information on the probability of making a type I error in the assignment of the quality control flag. Simple pattern recognition tools that identify zones wherein frequent flagging occurs are illustrated. These tools serve as a means of resetting QA flags to minimize the number of type I errors as demonstrated for the extreme events included here.