A challenge of an energy system that nowadays more strongly depends on wind power generation is the spatial and temporal variability in winds. Nocturnal low-level jets (NLLJs) are typical wind phenomena defined as a maximum in the vertical profile of the horizontal wind speed. A NLLJ has typical core heights of 50–500 m a.g.l. (above ground level), which is in the height range of most modern wind turbines. This study presents NLLJ analyses based on new observations from Doppler wind lidars. The aim is to characterize the temporal and spatial variability in NLLJs on the mesoscale and to quantify their impacts on wind power generation. The data were collected during the Field Experiment on Submesoscale Spatio-Temporal Variability (FESSTVaL) campaign from June to August 2020 in Lindenberg and Falkenberg (Germany), located at about 6 km from each other. Both sites have seen NLLJs in about 70 % of the nights with half of them lasting for more than 3 h. Events longer than 6 h occurred more often simultaneously at both sites than shorter events, indicating the mesoscale character of very long NLLJs. Very short NLLJs of less than 1 h occurred more often in Lindenberg than Falkenberg, indicating more local influences on the wind profile. We discussed different meteorological mechanisms for NLLJ formation and linked NLLJ occurrences to synoptic weather patterns. There were positive and negative impacts of NLLJs on wind power that we quantified based on the observational data. NLLJs increased the mean power production by up to 80 % and were responsible for about 25 % of the power potential during the campaign. However, the stronger shear in the rotor layer during NLLJs can also have negative impacts. The impacts of NLLJs on wind power production depended on the relative height between the wind turbine and the core of the NLLJ. For instance, the mean increase in the estimated power production during NLLJ events was about 30 % higher for a turbine at 135 m a.g.l. compared to one at 94 m a.g.l. Our results imply that long NLLJs have an overall stronger impact on the total power production, while short events are primarily relevant as drivers for power ramps.

Low‐level jets (LLJs), vertical profiles with a wind speed maxima in the lowest hundred meters of the troposphere, have multiple impacts in the Earth system, but a global present‐day climatology based on contemporary data does not exist. We use the spatially and temporally complete data set from ERA5 reanalysis to compile a global climatology of LLJs for studying the formation mechanisms, characteristics, and trends during the period of 1992–2021. In the global mean, LLJs are detected 21% of the time with more cases over land (32%) than over the ocean (15%). We classified the LLJs into three categories: non‐polar land (LLLJ), polar land (PLLJ), and coastal (CLLJ) LLJs. For LLLJ, the averaged frequency of occurrence is 20% and 75% of them are associated with a near‐surface temperature inversion as a prerequisite for an inertial oscillation. PLLJs are also associated with a temperature inversion and occur even more frequently with 59% of the time. These are also the lowest and the strongest LLJs among the three categories. CLLJs are particularly frequent in some marine hotspots, situated along the west coast of continents, with neutral to unstable stratification close to the surfaces and a stably stratified layer aloft. We found distinct regional trends in both the frequency and intensity of LLJs over the past decades, which can have implications for the emission and transport of aerosols, and the transport of atmospheric moisture. Future studies could address changes in LLJs and the associated implications in more detail, based on the here released ERA5‐based LLJ data.

PV modules tilted and oriented toward east and west directions gain gradually more importance as an alternative to the presently-preferred south (north in the Southern Hemisphere) orientation and it is shown to become economically superior even under the reimbursement of feed-in tariff (FIT). This is a consequence of the increasing spread between the decreasing costs of self-consumed solar power and the costs for power from the grid. One-minute values of irradiance were measured by silicon sensors at different orientations and tilt angles in Hannover (Germany) over three years. We show that south-oriented collectors give the highest electrical power during the day, whereas combinations of east and west orientations (E-W) result in the highest self-consumption rate (SC), and combinations of southeast and southwest (SE-SW) orientations result in the highest degree of autarky (AD), although they reduce the yearly PV Power by 5–6%. Moreover, the economic analysis of PV systems without FIT shows that the SE-SW and E-W combinations have the lowest electricity cost and they are more beneficial in terms of internal rate of return (IRR), compared to the S orientation at the same tilt. For PV systems with FIT, the S orientation presently provides the highest transfer of money from the supplier. However, as a consequence of the continuing decline of FIT, the economic advantage of S orientation is decreasing. E-W and SE-SW orientations are more beneficial for the owner as soon as FIT decreases to 7 Ct/kWh. East and west orientations of PV modules do not only have benefits for the individual owner but avoid high costs for storing energy—regardless who would own the storage facilities—and by avoiding high noon peaks of solar energy production during sunny periods, which would become an increasing problem for the grid if more solar power is installed. Furthermore, two types of commonly used PV software (PVSOL and PVsyst) were used to simulate the system performance. The comparison with measurements showed that both PV software underestimate SC and AD for all studied orientations, leading to the conclusion that improvements are necessary in modelling.

We present the first observational evidence for convectively generated cold pools (CP) as driving mechanism for low‐level jets (LLJ). Our findings are based on a unique campaign data set that allowed us to perform a systematic assessment of the process. During the three‐month campaign in Germany, 6.8% of all identified LLJ profiles were connected to a CP (CPLLJ). Most measured CPLLJs appeared with the CP front and lasted for up to two hours. Moreover, we have observed a CP favoring the formation of a several‐hours long LLJ. In that case, a strong LLJ and cooling of the atmosphere between the surface and at least 400 m a.g.l. were seen when the density current reached the measurement site. The development led to the formation of a near‐surface temperature inversion during daytime as a prerequisite for the LLJ, not unlike the mechanism of nocturnal LLJs.

A novel high-resolution method for forecasting cloud motion from all-sky images using deep learning is presented. A convolutional neural network (CNN) was created and trained with more than two years of all-sky images, recorded by a hemispheric sky imager (HSI) at the Institute of Meteorology and Climatology (IMUK) of the Leibniz Universität Hannover, Hannover, Germany. Using the haze indexpostprocessing algorithm, cloud characteristics were found, and the deformation vector of each cloud was performed and used as ground truth. The CNN training process was built to predict cloud motion up to 10 min ahead, in a sequence of HSI images, tracking clouds frame by frame. The first two simulated minutes show a strong similarity between simulated and measured cloud motion, which allows photovoltaic (PV) companies to make accurate horizon time predictions and better marketing decisions for primary and secondary control reserves. This cloud motion algorithm principally targets global irradiance predictions as an application for electrical engineering and in PV output predictions. Comparisons between the results of the predicted region of interest of a cloud by the proposed method and real cloud position show a mean Sørensen–Dice similarity coefficient (SD) of 94 ± 2.6% (mean ± standard deviation) for the first minute, outperforming the persistence model (89 ± 3.8%). As the forecast time window increased the index decreased to 44.4 ± 12.3% for the CNN and 37.8 ± 16.4% for the persistence model for 10 min ahead forecast. In addition, up to 10 min global horizontal irradiance was also derived using a feed-forward artificial neural network technique for each CNN forecasted image. Therefore, the new algorithm presented here increases the SD approximately 15% compared to the reference persistence model.

A challenge of an energy system, that nowadays more strongly depends on wind power generation, is the spatial and temporal variability of winds. Nocturnal low-level jets (NLLJ) are typical wind phenomena defined as a maximum in the vertical profile of the horizontal wind speed. A NLLJ has typical core heights of 50–500 m above ground level (a.g.l.), which is in the height range of most modern wind turbines. This study presents NLLJ analyses based on new observations from Doppler wind LiDARs. The aim is to characterize the temporal and spatial variability of NLLJs on the mesoscale and to quantify their impacts on wind power generation. The data was collected during the Field Experiment on Submesoscale Spatio-Temporal Variability (FESSTVaL) campaign from June to August 2020 in Lindenberg and Falkenberg (Germany), located at about 6 km from each other. Both sites have seen NLLJs in about 70 % of the nights with half of them lasting for more than 3 hours. Events longer than 6 hours occurred more often simultaneously at both sites than shorter events, indicating the mesoscale character of very long NLLJs. Very short NLLJs of less than one hour occurred more often in Lindenberg than Falkenberg, indicating more local influences on the wind profile. We discussed different meteorological mechanisms for NLLJ formation and linked NLLJ occurrences to synoptic weather patterns. There were positive and negative impacts of NLLJs on wind power that we quantified based on the observational data. While NLLJs increased the mean power production by up to 80 %, the stronger shear in the rotor layer during NLLJs had also negative impacts. The impacts of NLLJs on wind power production depended on the relative height between the wind turbine and the core of the NLLJ. For instance, the mean increase of the estimated power production during NLLJ events was about 30 % higher for a turbine at 135 m a.g.l. compared to one at 94 m a.g.l.. Our results imply that long NLLJs have an overall stronger impact on the total power production, while short events are primarily relevant as driver for power ramps.