Analysis of diurnal and seasonal cycles and trends in climatic records with arbitrary observation times

Many long‐term records of climate variables have missing data or have had changes in their times of observation. Here we present a technique to analyze such inhomogeneous records. We assume that the underlying climatic processes are nonstationary, where the observations contain a long‐term trend superimposed on periodic shorter time seasonal and diurnal cycles. The seasonal and diurnal variations are approximated using a limited number of Fourier harmonics, while the trend is represented by a monotonic function of time whose amplitude can also vary seasonally and diurnally. A least squares method is used to estimate the unknown Fourier coefficients. As an example of the technique, we present an analysis of multi‐decadal hourly observations of surface air temperature obtained from several meteorological stations within the United States.