Real‐time radar–rain‐gauge merging using spatio‐temporal co‐kriging with external drift in the alpine terrain of Switzerland

The problem of the optimal combination of rain‐gauge measurements and radar precipitation estimates has been investigated. A method that attempts to generalize well‐established geostatistical techniques, such as kriging with external drift, is presented. The new method, besides allowing spatial information to be incorporated into the modelling and estimation process, also allows temporal information to be incorporated. This technique employs temporal data as secondary co‐kriged variables. The approach can be considered both straightforward and practical as far as design and programming aspects are concerned. Co‐kriging with external drift leads to significant improvements in the results compared with typical radar estimates. It also seems to be more advantageous than kriging with external drift in modelling stability terms. Evidence is provided showing the advantages of co‐kriging with external drift modelling over kriging with external drift. The difference becomes particularly pronounced for non‐robust input data. The theoretical background and mathematical structure of the method is demonstrated. The method has been applied to four events, three during summer and one during winter, that took place over the complex Swiss orography. It is shown that cross‐validation skill scores improve when the aggregation period of the input data increases from ten minutes to one hour. This improvement can be attributed to the increasing robustness of the input data with the period of aggregation. Moreover, a straightforward disaggregation scheme, which starts from hourly precipitation maps, produced by means of the aforementioned geostatistical technique and generating precipitation estimates at a temporal resolution of five minutes, is proposed.