A Method for Operational Detection of Daily Observation-Time Changes

Abstract

A method to detect observation-time changes within weeks to months of their occurrence was developed using hourly observations from a set of 32 first-order stations across the United States. The procedure operationally requires only daily maximum and minimum temperatures. Development of the detection procedure depends on the utilization of nonclimatic biases that are artificially introduced into simulated daily temperature datasets constructed from hourly data at each of the 32 development stations. These biases are quantified by using interdiurnal temperature differences, which are used as the basis of observation-time-change detection. The procedure is tested on simulated daily temperature datasets constructed from hourly data at nine independent first-order stations and is applied to actual daily temperature observations from a sample of stations that make up the Cooperative Observer Network. The probability of detection is largest over the Midwest and High Plains regions of the United States and is smallest for maritime stations in the southwestern and southeastern regions of the United States. False-alarm occurrence is highest over the southern United States, particularly in the southeastern region and southern California. Overall detection performance is improved in all regions by incorporating more data into the detection tests, but the median amount of time between the occurrence of an observation-time change and its detection also increases as a result.