Perovskite Solar Cells go Outdoors Field Testing and Temperature Effects on Energy Yield

Perovskite solar cells PSC have shown that under laboratory conditions they can compete with established photovoltaic technologies. However, controlled laboratory measurements usually performed do not fully resemble operational conditions and field testing outdoors, with day amp; 8208;night cycles, changing irradiance and temperature. In this contribution, the performance of PSCs in the rooftop field test, exposed to real weather conditions is evaluated. The 1 cm2 single amp; 8208;junction devices, with an initial average power conversion efficiency of 18.5 are tracked outdoors in maximum power point over several weeks. In parallel, irradiance and air temperature are recorded, allowing us to correlate outside factors with generated power. To get more insight into outdoor device performance, a comprehensive set of laboratory measurements under different light intensities 10 to 120 of AM1.5 and temperatures is performed. From these results, a low power temperature coefficient of amp; 8722;0.17 K amp; 8722;1 is extracted in the temperature range between 25 and 85 C. By incorporating these temperature amp; 8208; and light amp; 8208;dependent PV parameters into the energy yield model, it is possible to correctly predict the generated energy of the devices, thus validating the energy yield model. In addition, degradation of the tested devices can be tracked precisely from the difference between measured and modelled powe

Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76,086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the conceptual model HBV against streamflow records for each of 9053 small to medium-sized (<50,000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence, the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite- and reanalysis-based P estimates