Interpolation of Hourly Surface Air Temperature—Application of a Spatio‐Temporal Statistical Model in Complex Terrain

ABSTRACT At present, grid datasets of in situ climate observations are mostly constructed in daily time resolution. An extension to sub‐daily resolution is desirable, particularly for surface air temperature, in order to represent diurnal variations. Here, we devise and investigate a method to produce hourly temperature grids in a region of complex topography (Switzerland). The method, a dynamic linear model (DLM), extends a model widely used in spatial climatology (kriging with external drift, KED), into a spatio‐temporal statistical model, while preserving its flexibility to adapt to the specifics of a region. We configure the method with covariates for known mesoscale effects (inversions, cold‐air pooling, valley‐ and lake effects), assuming quasi‐harmonic evolutions in the related coefficients. The method is applied to a wintertime inversion episode and a summertime high‐pressure episode, with data from 259 stations. We compare DLM to a sequentially applied KED and evaluate the predictions in cross‐validation. The results demonstrate that DLM can accurately reproduce complex spatio‐temporal variations, including the gradual build‐up of an inversion and the distinct temperature patterns between day‐ and night‐time. The mean absolute error ranges from 0.65°C in flat and hilly terrain (both episodes) to 1.6°C in mountain valleys in winter. DLM yields temporally more continuous effect coefficients than KED, which corroborates the expected advantage from spatio‐temporal modelling. However, the prediction errors of DLM are not significantly smaller, except in experiments where the station data was artificially reduced. It seems that the spatio‐temporal approach had little advantage over a spatial model in the data‐rich conditions of our application. However, its advantage may be more prominent in conditions with fewer observations or with more covariates. DLM is a versatile method for developing sub‐daily climate datasets and this study provides valuable insight on its adoption to the climatic conditions of a specific region.