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The research area Climate Monitoring and Diagnostics of the Hans-Ertel-Centre for Weather Research advances the fundamental science basis for energy meteorology and translates the physical knowledge into stakeholder-relevant information for a climate neutral energy system in Europe. We show recent successes building on a kilometre-scale reanalysis dataset for Europe that was developed by the HErZ research area at the Universities of Cologne and Bonn, and produced by the German Weather Service. Paired with contemporary models for renewable power production, we carry out transdisciplinary research across meteorology and economics to answer urgent questions for the energy transition from currently primarily fossil fuels to a future energy system that relies more on renewables energy sources. Our research follows a seamless approach ranging from the assessment of weather and climate processes for wind and photovoltaic power production via economic system modelling and price estimates to communicating our research results to stakeholders in industry and politics, engaging in public outreach on national level, and teaching the next generation about our HErZ research. Scientific results from our works highlight (1) balancing potentials of natural variability and extremes in the production of wind and photovoltaic power across Europe, (2) distinct synoptic-scale weather patterns for dark doldrums with a possibility of compensating power shortfalls with sufficient storage capacities for a secure power supply, (3) nocturnal low-level jets having both beneficial and adverse impacts on wind power production, and (4) different biases in gridded irradiance datasets for estimating photovoltaic power production in comparison to on-site measurements. The research area closely collaborates with the Institute of Energy Economics at the University of Cologne (EWI). This cooperation informs the research agenda from an energy system perspective and facilitates the rapid transfer of research results into policy-relevant work, such as e.g. a contribution to EWI's report for the "dena Study Integrated Energy Transition" (dena, 2021). The dena Study, commissioned by the German Energy Agency (dena), has been widely received in the German policy debate, and it raised substantial public awareness e.g. by way of press releases and interviews for major newspapers. Future directions of the fruitful collaboration will involve blending climate change scenarios into the work of the research team for knowing possible extreme weather impacts on a redesigned energy system before they occur.

Accurate irradiance data is necessary for model estimates of expected photovoltaic (PV) power production. Such data is freely available from reanalysis and satellite products with a high temporal and spatial resolution, including locations without ground-based measurements. Gridded irradiance data is therefore used for the characterization of solar resources at specific locations and larger areas, e.g. by power system modellers. Past assessments of irradiance data for PV modelling often relied on the evaluation of global horizontal irradiance (QGHI). However, the direct and diffuse irradiance components as well as differences in seasonal characteristics can strongly affect the PV capacity factors (C) potentially leading to larger biases in C than for QGHI. We therefore systematically assess differences in QGHI, direct and diffuse horizontal irradiance (Qdir and Qdif) and quantify the subsequent bias propagation from individual radiation components to C in a contemporary PV power model. Our PV model simulations use seven different gridded irradiance data sets, namely ERA5, COSMO-REA6, COSMO-REA6pp, COSMO-REA2, CAMS radiation service, SARAH-2 and CERES Syn1Deg. All data sets provide Qdir and Qdif as separate time series spanning seven to 43 years and with a temporal resolution of 15 minutes to one hour. The results are compared against seven years of simulations based on reference measurements from 30 weather stations of the German Weather Service. We compute metrics characterizing biases in seasonal and annual spatial means, day-to-day variability and extremes in C, considering single stations and a simulated PV fleet. Our results highlight biases of -1.4 % (COSMO-REA6) to +8.2 % (ERA5) in annual and spatial means of C at single stations across Germany, while the bias in QGHI is -3 % for COSMO-REA6 to +3.6 % for ERA5. We also show the bias on days of very low PV production, relevant for extreme event analysis: The days within the lowest ten percent of daily PV production in a PV fleet show a bias of +70.2 % in ERA5, while it is only +4 % in the post-processed COSMO-REA6 data (COSMO-REA6pp). SARAH-2 and COSMO-REA6pp outperform the other products for many metrics, but also cause some biases in C. For instance, SARAH-2 yields good results in summer, but overestimates C in winter by 16 % averaged across all stations. COSMO-REA6pp represents day-to-day variability in C of a simulated PV fleet very well and has a relatively small bias of +0.5 % in the annual spatial means, but this is partly due to compensating biases from individual stations. Our results suggest that gridded irradiance data should be used with caution for site assessments and should ideally be complemented by local measurements. For power system modellers, our results may provide guidance for the quantification of uncertainties caused by gridded irradiance data. Reference:Kenny, D., and Fiedler, S., in press, Which gridded irradiance data is best for modelling photovoltaic power production in Germany?, Solar Energy.

Highlights • Broad analysis of bias propagation with seven irradiance data sets in PV model. • We identify seasonal and regional biases in irradiance and PV power. • There is no single data set performing best in all metrics for means and variability. Abstract Model estimates of expected photovoltaic (PV) power production rely on accurate irradiance data. Reanalysis and satellite products freely provide irradiance data with a high temporal and spatial resolution including locations for which no ground-based measurements are available. We assess differences in such gridded irradiance data and quantify the subsequent bias propagation from individual radiation components to capacity factors in a contemporary PV model. PV power production is simulated based on four reanalysis (ERA5, COSMO-REA6, COSMO-REA6pp, COSMO-REA2) and three satellite products (CAMS, SARAH-2, CERES Syn1Deg). The results are compared against simulations using measurements from 30 weather stations of the German Weather Service. We compute metrics characterizing biases in seasonal and annual means, day-to-day variability and extremes in PV power. Our results highlight a bias of −1.4% (COSMO-REA6) to ＋8.2% (ERA5) in annual and spatial means of PV power production for Germany. No single data set is best in all metrics, although SARAH-2 and the postprocessed COSMO-REA6 data (COSMO-REA6pp) outperform the other products for many metrics. SARAH-2 yields good results in summer, but overestimates PV output in winter by 16% averaged across all stations. COSMO-REA6pp represents day-to-day variability in the PV power production of a simulated PV fleet best and has a particularly small bias of 0.5% in annual means. This is at least in parts due to compensating biases in local and seasonal means. Our results imply that gridded irradiance data should be used with caution for site assessments and ideally be complemented by local measurements.