Multivariate testing of spatio-temporal consistence of daily precipitation records

Abstract

Abstract. The project KLIDADIGI of the German Meteorological Service (DWD) systematically rescues historical daily climate data of Germany by keying and imaging. Up to now, daily nearly gap-free precipitation time series at 118 locations for the period 1901-2000 are collected and extended by digitalization of handwritten protocols. To screen the spatio-temporal consistence of these raw data, we apply principal component analysis (PCA) in S (spatial) mode for daily precipitation records as well as for indices such as the number of rainy days above a certain threshold, intensity and absolute daily maximum in monthly, seasonal or annual resolution. Results of this screening test indicate that the PCA is a useful tool for detection of questionable stations and data preprocessing for further quality control and homogenization.

Monitoring of climate change in Germany – data, products and services of Germany's National Climate Data Centre

“…by taking photographs of the paper documents. A focus is the expansion and completion of data for stations with long time series (Mächel and Kapala, 2013).…”

“…The interpolation method has been verified and compared with other (GIS-based) interpolation methods (Maier and Müller-Westermeier, 2010) and it was shown that other standard interpolation procedures do not lead to better results.…”

Data rescue of national and international meteorological observations at Deutscher Wetterdienst

“…After digitization, the time series are quality controlled (e.g. Mächel and Kapala, 2013) Original historic documents with phenological observations (Kaspar et al, 2014) have been handed over to the Austrian and Czech meteorological services. Digitized data of 13 land stations have been provided to the China Meteorological Administration (CMA) and data of signal stations at the Polish coast were given to the Institute of Meteorology and Water Management (IMWG).…”