Cirrus clouds impact the planetary energy balance and upper-tropospheric water vapor transport and are therefore relevant for climate. In this study cirrus clouds at temperatures colder than −40°C simulated by the ECHAM–Hamburg Aerosol Module (ECHAM-HAM) general circulation model are compared to Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO) satellite data. The model captures the general cloud cover pattern and reproduces the observed median ice water content within a factor of 2, while extinction is overestimated by about a factor of 3 as revealed by temperature-dependent frequency histograms. Two distinct types of cirrus clouds are found: in situ–formed cirrus dominating at temperatures colder than −55°C and liquid-origin cirrus dominating at temperatures warmer than −55°C. The latter cirrus form in anvils of deep convective clouds or by glaciation of mixed-phase clouds, leading to high ice crystal number concentrations. They are associated with extinction coefficients and ice water content of up to 1 km−1 and 0.1 g m−3, respectively, while the in situ–formed cirrus are associated with smaller extinction coefficients and ice water content. In situ–formed cirrus are nucleated either heterogeneously or homogeneously. The simulated homogeneous ice crystals are similar to liquid-origin cirrus, which are associated with high ice crystal number concentrations. On the contrary, heterogeneously nucleated ice crystals appear in smaller number concentrations. However, ice crystal aggregation and depositional growth smooth the differences between several formation mechanisms, making the attribution to a specific ice nucleation mechanism challenging.
International audienceBecause of the cloud-induced variability of the solar resource, the growing contributions of photovoltaic plants to the overall power generation challenges the stability of electricity grids. To avoid blackouts, administrations started to define maximum negative ramp rates. Storages can be used to reduce the occurring ramps. Their required capacity, durability, and costs can be optimized by nowcasting systems. Nowcasting systems use the input of upward-facing cameras to predict future irradiances. Previously, many nowcasting systems were developed and validated. However, these validations did not consider aggregation effects, which are present in industrial-sized power plants. In this paper, we present the validation of nowcasted global horizontal irradiance (GHI) and direct normal irradiance maps derived from an example system consisting of 4 all-sky cameras (“WobaS-4cam”). The WobaS-4cam system is operational at 2 solar energy research centers and at a commercial 50-MW solar power plant. Besides its validation on 30 days, the working principle is briefly explained. The forecasting deviations are investigated with a focus on temporal and spatial aggregation effects. The validation found that spatial and temporal aggregations significantly improve forecast accuracies: Spatial aggregation reduces the relative root mean square error (GHI) from 30.9% (considering field sizes of 25 m2) to 23.5% (considering a field size of 4 km2) on a day with variable conditions for 1 minute averages and a lead time of 15 minutes. Over 30 days of validation, a relative root mean square error (GHI) of 20.4% for the next 15 minutes is observed at pixel basis (25 m2). Although the deviations of nowcasting systems strongly depend on the validation period and the specific weather conditions, the WobaS-4cam system is considered to be at least state of the art
International audienceHighly spatially and temporally resolved solar irradiance maps are of special interest for predicting ramp rates and for optimizing operations in solar power plants. Irradiance maps with lead times between 0 and up to 30 min can be generated using all-sky imager based nowcasting systems or with shadow camera systems. Shadow cameras provide photos of the ground taken from an elevated position below the clouds. In this publication, we present a shadow camera system, which provides spatially resolved Direct Normal Irradiance (DNI), Global Horizontal Irradiance (GHI) and Global Tilted Irradiance (GTI) maps. To the best of our knowledge, this is the first time a shadow camera system is achieved. Its generated irradiance maps have two purposes: (1) The shadow camera system is already used to derive spatial averages to benchmark all-sky imager based nowcasting systems. (2) Shadow camera systems can potentially provide spatial irradiance maps for plant operations and may act as nowcasting systems. The presented shadow camera system consists of six cameras taking photos from the top of an 87 m tower and is located at the Plataforma Solar de Almería in southern Spain. Out of six photos, an ortho-normalized image (orthoimage) is calculated. The orthoimage under evaluation is compared with two reference orthoimages. Out of the three orthoimages and one additional pyranometer and pyrheliometer, spatially resolved irradiance maps (DNI, GHI, GTI) are derived. In contrast to satellites, the shadow camera system uses shadows to obtain irradiance maps and achieves higher spatial and temporal resolutions. he preliminary validation of the shadow camera system, conducted in detail on two example days (2015-09-18, 2015-09-19) with 911 one-minute averages, shows deviations between 4.2% and 16.7% root mean squared errors (RMSE), 1.6% and 7.5% mean absolute errors (MAE) and standard deviations between 4.2% and 15.4% for DNI maps calculated with the derived approach. The GHI maps show deviations below 10% RMSE, between 2.1% and 7.1% MAE and standard deviations between 3.2% and 7.9%. Three more days (2016-05-11, 2016-09-01, 2016-12-09) are evaluated, briefly presented and show similar deviations. These deviations are similar or below all-sky imager based nowcasts for lead time zero minutes. The deviations are small for photometrically uncalibrated, low-cost and off-the-shelf surveillance cameras, which is achieved by a segmentation approach
Wind power is seeing a strong growth around the world. At the same time, shrinking profit margins in the energy markets let wind farm managers explore options for cost reductions in the turbine operation and maintenance. Sensor-based condition monitoring facilitates remote diagnostics of turbine subsystems, enabling faster responses when unforeseen maintenance is required. Condition monitoring with data from the turbines' supervisory control and data acquisition (SCADA) systems was proposed and SCADA-based fault detection and diagnosis approaches introduced based on single-task normal operation models of turbine state variables. As the number of SCADA channels has grown strongly, thousands of independent single-target models are in place today for monitoring a single turbine. Multi-target learning was recently proposed to limit the number of models. This study applied multi-target neural networks to the task of early fault detection in drive-train components. The accuracy and delay of detecting gear bearing faults were compared to state-of-the-art single-target approaches. We found that multi-target multi-layer perceptrons (MLPs) detected faults at least as early and in many cases earlier than single-target MLPs. The multi-target MLPs could detect faults up to several days earlier than the single-target models. This can deliver a significant advantage in the planning and performance of maintenance work. At the same time, the multi-target MLPs achieved the same level of prediction stability.
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