Correction, gap filling and homogenization on daily level of the historical DMI station network temperature data

As climate change is amplified in the Arctic, it is crucial to have temperature records of high temporal resolution and quality in this area. This will help improve understanding of the involved physical mechanisms, assessment of the past changes and improve predictions for the future temperature development in the Arctic. In this study temperature measurements from the DMI Greenland station network spanning 1784-present day are corrected, gap-filled and homogenized on a daily level. Currently homogenized data is only available on a monthly level, and the more recent data has not been homogenized. The data is currently used for purposes like assessment and predictions of the surface mass balance of the Greenland Ice Sheet, temperature/climate reanalyses, validation of proxy data, etc.  

This study presents a method for improving the calculation of daily average temperatures, from the current practice of averaging the available measurements without considering what time of day they are from and how the measurements are distributed. The method is based on a moving average taking into consideration time of day, time of year and latitude/longitude of the station in question. An estimate of the related uncertainty is also calculated. Following the generation of daily average temperatures, different gap filling methods are tested. The different algorithms tested and compared are: simple gap filling by linear interpolation with other stations, single station temporal linear interpolation and MEM (Maximum Entropy Method). Finally, homogenization on daily level is performed. These steps will in turn also improve the monthly and annual average temperatures for the DMI Greenland station network.