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

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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 ... 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 ...

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