Mending and extending observational temperature series by linear and nonlinear regression

While time series of meteorological measurements from land-based weather stations still represent one of the basic types of data employed in climate research, it not uncommon for these records to be incomplete, interrupted by periods of missing or otherwise compromised values. Such gaps typically need to be filled before a subsequent analysis can be performed, and records from other nearby measuring sites are frequently used for this purpose. In this presentation, results of central European daily temperatures estimation from other concurrent measurements by various statistical methods are showcased, with a particular emphasis on assessing potential benefits of application of nonlinear regression techniques. Using multi-decadal daily temperature series originating from a dense network of weather stations covering the territory of the Czech Republic, we show that while nonlinear regression does not always outperform its linear counterpart, it can substantially improve accuracy of temperature estimates for some target locations. The gain is shown to be especially prominent for sites exhibiting atypical behavior compared to their local geographic neighborhood, such as isolated mountain-based stations. In addition to regression-based restoration of compromised segments in the temperature records, use of this methodology for extending the temperature records beyond their original period of measurements is also discussed, as well as its potential for homogeneity testing.