A Method to Infer Observation Time Based on Day-to-Day Temperature Variations

Abstract

A method to infer the observation time of a station at annual resolution is developed and tested at stations in the United States. The procedure is based on a tendency for the percentiles of the monthly distribution of positive day-to-day maximum temperature changes (i.e., day n + 1 > day n) to exceed the corresponding absolute percentiles of the distribution of negative day-to-day changes at afternoon stations. Similarly absolute percentiles of negative day-to-day minimum temperature change tend to exceed the corresponding positive interdiurnal changes at morning observation sites. Equal percentiles are generally found at stations that use a midnight observation hour. Based on annual and seasonal summations of these monthly percentile differences, discriminant functions are developed that are capable of differentiating between afternoon, morning, and midnight observation schedules. Across the majority of the United States observation time is correctly classified in over 90% of the station-years tested. Classification success is generally highest for morning and afternoon observations and somewhat lower for midnight observations. Although geographic biases in classification success are not apparent, the procedure’s ability to estimate observation time decreases considerably at stations where the average annual interdiurnal temperature range is less than 1.7°C. In the United States such stations are limited to coastal California, parts of Arizona, and extreme southern portions of Texas and Florida. Application of the procedure to a subset of U.S. climatic normals stations indicates the presence of errors in the corresponding observation time metadata file.