Access to high quality weather and climate data is crucial for a wide range of societal and economic issues. It allows optimising industrial processes with respect to efficiency. The goal of the research project FAIR is to simplify the information exchange between the DWD and economical players. This paper summarizes the results of the requirement analysis of three use cases and presents the deduced technical architecture and FAIR services aiming at a user-friendly exchange of weather data.

Reanalyses are long-term meteorological datasets that are based on numerical weather prediction models and the assimilation of historic observations. The regional model COSMO of Germany’s national meteorological service (Deutscher Wetterdienst) has been used to develop regional reanalyses with spatial resolution of up to 2 km. In this paper, we provide an overview of evaluation results and application examples at the European and national German level with a focus on renewable energy.

In order to analyze the impact on long time series by the transition from manual to automatic sunshine duration devices, parallel measurements of German climate reference stations are used. The different measurement principles (glass sphere and photodiode) cause systematic differences between the observations. With a linear regression model (used to adjust the automatic measurements) we want to prevent breaks in long time series of daily sunshine duration.

In this study, we assess balancing effects between photovoltaics and wind energy. On average, the seasonal cycles complement each other in Germany as well as in Europe. The frequency of events with a risk of low electricity generation is analyzed. The results illustrate that the number of such events is reduced when offshore regions are included, or when a combined system of PV and wind energy is considered. A European-wide analysis also leads to a distinct reduction of such events.

During recent years, Germany’s national meteorological service has significantly expanded the open access to its climate observations. To improve the interactive and user-friendly access to the data, a new data portal has been developed. The portal serves a variety of user requirements that result from the broad range of applications of DWD’s climate data. The focus of the recent developments was on improved access to German station data.

As one element of the SASSCAL initiative (a cooperation of Angola, Botswana, Namibia, Zambia, South Africa and Germany) networks of automatic weather stations have been installed or improved in Southern Africa. Here we compare interpolation methods for monthly minimum and maximum temperatures which were calculated from hourly measurements. The best interpolation results have been achieved combining multiple linear regression with three dimensional inverse distance weighted interpolation.

Germany's national meteorological service (DWD) operates a network of so-called climate reference stations. At these stations parallel measurements are performed in order to allow the comparison of manual and automatic observations. We present an analysis of parallel measurements of temperature at 2 m height. It is shown that the automation of stations did not cause an artificial increase in the series of daily mean temperature. A bias occurs for maximum temperature in specific configurations.

Wind speeds derived from the regional reanalysis COSMO-REA6 and from two global reanalyses of ECMWF (ERA-Interim and ERA-20C) have been compared to tower measurements covering heights from 10 m up to 140 m, located in the North Sea, in The Netherlands, and in Northeast Germany. In general, the monthly and hourly mean values of COSMO-REA6 are at least as close to the measurements as the global reanalyses, and in the correlation of daily means an added value of COSMO-REA6 is found.

To respond to the challenges of climate change, Angola, Botswana, Germany, Namibia, South Africa and Zambia have initiated the regional competence centre SASSCAL. As part of the initiative, Deutscher Wetterdienst (DWD) cooperates with the meteorological services of Angola, Botswana and Zambia to improve the management of climate data. First results of the cooperation between DWD and the Angolan Meteorological Services (INAMET) are presented in order to provide hints for comparable activities.

Within the European Union’s seventh Framework Programme project Uncertainties in Ensembles of Regional Re-Analyses (UERRA), ensembles of RRAs covering the European area are produced and their uncertainties are quantified. In this study, we discuss different methods for quantifying the uncertainty of RRAs in order to answer the question to which extent the smaller scale information (or resulting statistics) provided by the RRAs can be relied on.

Wind speed measured at the German stations correlate well with reanalysis fields. Monthly means from two global reanalyses (ERA-20C, ERA-Interim) and one regional reanalysis (COSMO-REA6) were analysed and correlate well for the majority of the German stations. Thus we conclude that the monthly and seasonal anomalies recorded at these stations can be understood as representative for a spatial area comparable to the resolution of the reanalyses, at least for the recent years.

One task of the “Southern African Science Service Centre for Climate Change and Adaptive Land Management” (www.sasscal.org) is the provision of climate data for Southern Africa. Extension and improvements of observational networks in Angola, Botswana, Namibia, Zambia and South Africa are supported. This effort is complemented by an improvement of climate data management at national weather authorities, capacity building activities and an extension of the data bases with historical climate data.

Many users of climate information are unaware of the availability of reanalysis feedback data and input observations, and uptake of feedback data is rather low. The most important factors limiting the use of this data is that the users feel that there is no easy interface to get the data or they do not find it at all. The relevant communities should invest resources to develop tools and provide training to bridge the gap between current capabilities and comprehensive exploitation of the data.

Plant phenology is the study of periodically recurring patterns of growth and development of plants during the year. First plant phenological observations have been performed in Germany already in the 18th century. Today, Germany’s national meteorological service (Deutscher Wetterdienst, DWD) maintains a dense phenological observation network and a database with phenological observations.