Figure 4 - available via license: Creative Commons Attribution 4.0 International
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Wind rose of cloud motion directions derived from ASI UOL, indicating a dominance of clouds coming from western directions (a), and the distribution of the cloud base height (CBH) in the analyzed period (b).
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Cloud base height (CBH) is an important parameter for many applications such as aviation, climatology or solar irradiance nowcasting (forecasting for the next seconds to hours ahead). The latter application is of increasing importance for the operation of distribution grids and photovoltaic power plants, energy storage systems and flexible consumer...
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... on this, we consider the estimation of cloud motion directions from ASI UOL as being sufficiently accurate for this statistical evaluation. Figure 4 (left) shows the distribution of cloud motion directions estimated with the ASI in the sense of a wind rose representing the directions from which clouds approach the urban area. Seen are two main lobes at azimuthal angles of 240 • N (west to southwest) and 290 • N (west to northwest), while other directions of cloud motion are observed rather seldom. ...
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... distribution of the CBH at the site of Oldenburg for the full measuring period is given in Fig. 4 (right). As in general in this study, the analysis is based only on the lowest cloud layer detected by the ceilometer. The majority of all ceilometer readings (54 %) indicates a CBH smaller than 2 km. Similarly, within the interval CBH ∈]0, 2[ km, all values are observed with a similar frequency. This includes the lowest bin of CBH ∈]0, 0.5[ ...
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... the range of reference CBH > 10 km, the ASI network constantly returns a CBH of around 10 km. In the studied climate, the reference CBH in this range is comparably rare (see Fig. 4). Therefore, corresponding grid cells of the conditional probability distributions, used by the estimation procedure, were approximated coarsely based on a small number of observations. The ASI network's combination method, using cumulative likelihood, is intended to avoid deviations resulting from these inaccuracies and, thus, to ...
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... and ASI network are analyzed separately for five ranges of the reference CBH defined by the bounds {0, 1, 2, 4, 8 and 12} km. The number of CBH measurements included in this evaluation is given in Table 1 for each of these ranges. The interval bounds are spaced irregularly to correspond better to the distribution of the CBH at the site (see also Fig. 4). Table 1 also shows the number of observations excluded from the validation, as a significant temporal variability in the CBH was detected for these observations. While a significant fraction of the readings is sorted out, the representation of the CBH ranges remains widely comparable to the original data set (see Fig. 2; left). Only ...
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... In recent years, various methods have been proposed for investigating cumulus (Cu) cloud geometry and dynamics using both stereo camera systems (e.g., Beekmans et al., 2016;Crispel & Roberts, 2018;Nguyen & Kleissl, 2014) and multi-camera networks (Blum et al., 2021;Nouri et al., 2019;Romps & Öktem, 2018). To our knowledge there has not been an attempt yet to emulate such hemispheric camera images from LES output using path-tracing rendering, and to subsequently use these renderings to three-dimensionally reconstruct the simulated cloud field. ...
... ;Blum et al. (2021), and networks Same asFigure 7a, but extended to include the cloud field. The cameras' positions are denoted by a black cross near the surface. ...
The complex spatial and temporal structure of cumulus clouds complicates their representation in weather and climate models. Classic meteorological instrumentation struggles to fully capture these features. Networks of multiple high‐resolution hemispheric cameras are increasingly used to fill this data gap, and provide information on this missing multi‐dimensional spatial information. In this study, a path‐tracing algorithm is used to generate virtual camera images of resolved clouds in large‐eddy simulations (LES). These images are then used as a camera network simulator, allowing reconstructions of three‐dimensional cloud edges from the model output. Because the actual LES cloud field is fully known, the combined path‐tracing and reconstruction method can be statistically analyzed. The method is applied to LES realizations of summertime shallow cumulus at the Jülich Observatory for Cloud Evolution (JOYCE), Germany, which also routinely operates a camera network. We find that the path‐tracing method allows accurate reconstruction of up to 70% of the visible cloud edges. Additional sensitivity tests show that the method is robust for changes in its hyperparameters. The sensitivity to cloud optical thickness is also investigated, finding a cloud boundary placement error of approximately 182 m. This error can be considered typical for cloud boundary reconstruction using real stereo camera imagery. The results provide proof of principle for future use of the method for evaluating LES clouds against camera network imagery, and for further optimizing the configuration of such camera networks.
... The methods of cloud height measurement mainly include using sounding data for inversion, using microwave radiometers to obtain atmospheric boundary layer height, and combining photoelectric detection technology to measure cloud height. Holzworth measured cloud heights using dry adiabatic curves based on sounding data [1], Blum et al. proposed and validated the use of an all-sky imager to measure cloud boundaries [2], and Kuhn et al. obtained cloud height based on data from the European Weather Forecast Center and three low-cost ground-based systems and then compared the cloud height measurements with 59 days of different cloud conditions in southern Spain [3]. All three ground systems used in Kuhn et al.'s work derive the cloud velocity in meters/second absolute units and determine the cloud height using the cloud velocity derived from the all-day imager [4]. ...
At present, the methods for measuring cloud height and thickness mainly include using micro-pulse lidar and microwave radiometer data. To further study cloud height and thickness, a superconducting nanowire single-photon detector (SNSPD) is applied to a lidar system for the first time, to the best of our knowledge, to analyze the cloud height and thickness. In the experiment, a 1.2-m-diameter horizon telescope is used for laser emitting and echo receiving, a 1064 nm near-IR pulse laser with a single pulse energy of 4 mJ is used as the system emission laser, and a 4-pixel SNSPD array detector is used as the end receiver to complete the echo photon reception. By analyzing the experimental data, the distributions of cloud height and cloud thickness can be obtained using the laser ranging system. The cloud cover condition on a certain day was measured, and the obtained cloud bottom height was about 1222 m, cloud top height was about 1394 m, and cloud cover thickness was about 172 m. The difference between the cloud cover thickness and the forecast value was 28 m. The cloud cover height and thickness measured by this method are true and credible.
... Early works tend to first extract features from ground-based sky images, such as red-to-blue ratio (RBR), cloud coverage and cloud motion vectors, and then use these features for building physical deterministic models [11,12,13] or training machine learning models such as artificial neural networks [14,15,16,17,18]. In addition, several all-sky cameras can be used in stereo-vision mode to model the cloud cover in three dimensions to provide local irradiance maps [19,20,21,22,23]. In recent 5 years, with the development of computer vision techniques, efforts have been shifted to build end-to-end deep learning models to predict irradiance or PV power output based on a historical sky image sequence, which generally achieve state-of-the-art performance despite some limitations in their anticipation ability [24]. ...
... Industrial sky imagers such as EKO's SRF-02 and ASI-16 are commonly used in sky image collection, e.g., [87,89]. Surveillance network cameras, such as Hikvision DS-2CD6365GOE-IVS and MOBOTIX Q25, are also used by researchers for solar forecasting [23,137]. Though much cheaper than industrial sky imagers, they work well for providing images for solar forecasting [4,25,38,152,153]. ...
This study presents a comprehensive survey of open-source ground-based sky image datasets for very short-term solar forecasting. Related research areas which could potentially help improve solar forecasting methods, including cloud segmentation, cloud classification, and cloud motion prediction are also considered. We first identify 72 open-source sky image datasets that satisfy the needs of machine/deep learning. Then a database of information about various aspects of the datasets is constructed. To evaluate each surveyed datasets, we further develop a multi-criteria ranking system based on 8 dimensions of the datasets which could potentially have important impacts on usage of the data. Finally, we provide insights on the usage of these datasets in the open literature. We hope this paper provide an overview for researchers who are looking for datasets for training deep learning models for very short-term solar forecasting, cloud analysis, and atmospheric modeling.
... The ASIs record an image every half and full minute. Further details on the exposure and other camera settings are described by Blum et al. [37,53]. Each ASI was calibrated geometrically by an intrinsic calibration which describes a fisheye camera's image distortion and by the ASI's external orientation. ...
... Firstly, one of the ASIs should be located right next to an RSI as measurements of DNI, DHI, relative humidity and dry bulb temperature are required as inputs to this system. A small camera distance is preferred as cloud base height (CBH) is rather low for most timestamps at this site [53]. Additionally, both ASIs should have a mostly free field of view up to a zenith angle of 78 • . ...
... The height over ground of this map is fixed to the local CBH which may vary spatially. CBH is estimated by the technique presented by Blum et al. [53]. These maps and all other maps of parameters derived from the ASI network use a single coordinate system and a spatial resolution of 50 m × 50 m. ...
All-sky imagers (ASIs) can be used to model clouds and detect spatial variations of cloud attenuation. Such cloud modeling can support ASI-based nowcasting, upscaling of photovoltaic production and numeric weather predictions. A novel procedure is developed which uses a network of ASIs to model clouds and determine cloud attenuation more accurately over every location in the observed area, at a resolution of 50 m × 50 m. The approach combines images from neighboring ASIs which monitor the cloud scene from different perspectives. Areas covered by optically thick/intermediate/thin clouds are detected in the images of twelve ASIs and are transformed into maps of attenuation index. In areas monitored by multiple ASIs, an accuracy-weighted average combines the maps of attenuation index. An ASI observation’s local weight is calculated from its expected accuracy. Based on radiometer measurements, a probabilistic procedure derives a map of cloud attenuation from the combined map of attenuation index. Using two additional radiometers located 3.8 km west and south of the first radiometer, the ASI network’s estimations of direct normal (DNI) and global horizontal irradiance (GHI) are validated and benchmarked against estimations from an ASI pair and homogeneous persistence which uses a radiometer alone. The validation works without forecasted data, this way excluding sources of error which would be present in forecasting. The ASI network reduces errors notably (RMSD for DNI 136 W/m2, GHI 98 W/m2) compared to the ASI pair (RMSD for DNI 173 W/m2, GHI 119 W/m2 and radiometer alone (RMSD for DNI 213 W/m2), GHI 140 W/m2). A notable reduction is found in all studied conditions, classified by irradiance variability. Thus, the ASI network detects spatial variations of cloud attenuation considerably more accurately than the state-of-the-art approaches in all atmospheric conditions.
... In comparison to the other options discussed here, a ceilometer is considered to measure CBH accurately but only at the location of the instrument. This was discussed e.g., by Blum et al. [49] and was indicated by a number of previous studies [50][51][52]. ...
Fluctuations of the incoming solar irradiance impact the power generation from photovoltaic and concentrating solar thermal power plants. Accurate solar nowcasting becomes necessary to detect these sudden changes of generated power and to provide the desired information for optimal exploitation of solar systems. In the framework of the International Energy Agency's Photovoltaic Power Systems Program Task 16, a benchmarking exercise has been conducted relying on a bouquet of solar nowcasting methodologies by all-sky imagers (ASI). In this paper, four ASI systems nowcast the Global Horizontal Irradiance (GHI) with a time forecast ranging from 1 to 20 min during 28 days with variable cloud conditions spanning from September to November 2019 in southern Spain. All ASIs demonstrated their ability to accurately nowcast GHI, with RMSE ranging from 6.9% to 18.1%. Under cloudy conditions, all ASIs' nowcasts outperform the persistence models. Under clear skies, three ASIs are better than persistence. Discrepancies in the observed nowcasting performance become larger at increasing forecast horizons. The findings of this study highlight the feasibility of ASIs to reliably nowcast GHI at different sky conditions, time intervals and horizons. Such nowcasts can be used either to estimate solar power at distant times or detect sudden GHI fluctuations.
... Here, the usage of a network of cameras is reducing nowcasting errors by of up to 50% in highly variable conditions compared to the status quo of 2-ASI systems [22,23]. Additionally, Blum et al. [24] demonstrate more accurate cloud height estimations with the network of ASI in Oldenburg compared to pairs of ASI. ...
The Eye2Sky network is a measurement network in north-western Germany consisting of multiple all-sky imagers (ASI), meteorological and solar irradiance measurements. The network provides high temporal and spatial resolution data for meteorological and especially solar energy related applications. With increasing photovoltaic (PV) capacity in electrical grids fluctuations in solar irradiance due to changing cloud cover may have adverse effects on the grid stability. Within Eye2Sky, new technologies and methodologies facing the demand for more accurate solar irradiance forecasts are being developed. The ASIs used in Eye2Sky record 180° field of view hemispherical sky images from fish-eye lensed cameras. Accompanied with local measurements of solar irradiance components (global, direct and diffuse) a very short-term forecast of the solar resource is possible. These nowcasts provide minutely updated information up to 20 minutes ahead with 1-minute temporal and 50 m x 50 m spatial resolution. This approach shows more precise forecasting results for the next minutes ahead compared to traditional and less detailed methods based on satellite or numerical weather prediction models. In the network, multiple ASIs are used to enlarge the spatial coverage and the forecast horizon requested by many applications. Moreover, the forecast error can be reduced with a network of cameras. In this article, the Eye2Sky network, its research results and applications are introduced.
... Cloud-Base Height (CBH) estimation is an increasingly crucial task: this parameter is required in many applications [1]. It is exploited to validate climate models [2] and to improve Numeric Weather Prediction (NWP) models [3]; for instance, it improves the definition of the cloud-drift vector field, allowing a more effective modeling of the atmosphere dynamics [4]. ...
... In the scientific literature, several studies are aimed at estimating CBH through different procedures. In [1], seven All Sky Imagers (ASIs) belonging to the Eye2Sky ASI network, located in the city of Oldenburg, are exploited to estimate CBH. In detail, an independent CBH estimation is derived considering each possible pair of ASIs, and then all the different estimations are properly merged into a final and more reliable one. ...
... In order to make the results of ground-based cloud observation more objective, automatic algorithms are in great need. Many automatic algorithms are proposed for ground-based cloud observation including cloud base height measurement [8]- [10], cloud type classification [11], [12], and cloud cover estimation [13]- [15]. In this article, we focus on cloud cover estimation for ground-based cloud observation. ...
Cloud image segmentation plays an important role in ground-based cloud observation. Recently, most existing methods for ground-based cloud image segmentation learn feature representations using the convolutional neural network (CNN), which results in the loss of global information because of the limited receptive field size of the filters in the CNN. In this article, we propose a novel deep model named TransCloudSeg, which makes full use of the advantages of the CNN and transformer to extract detailed information and global contextual information for ground-based cloud image segmentation. Specifically, TransCloudSeg hybridizes the CNN and transformer as the encoders to obtain different features. To recover and fuse the feature maps from the encoders, we design the CNN decoder and the transformer decoder for TransCloudSeg. After obtaining two sets of feature maps from two different decoders, we propose the heterogeneous fusion module to effectively fuse the heterogeneous feature maps by applying the self-attention mechanism. We conduct a series of experiments on Tianjin Normal University large-scale cloud detection database and Tianjin Normal University cloud detection database, and the results show that our method achieves a better performance than other state-of-the-art methods, thus proving the effectiveness of the proposed TransCloudSeg.
... Dense networks of ASIs with overlapping viewing angles can lead to further noticeable performance enhancements of ASI based nowcasts. [33] Such networks can also increase significantly the nowcasts horizon while maintaining the same resolution. Future research will evaluate in detail the nowcasts potential to improve the performance of electrical grids and power plants under variable conditions, despite the nowcasts uncertainties. ...
The share of distributed solar power generation is continuously growing. This increase, combined with the intermittent nature of the solar resource, creates new challenges for all relevant stakeholders from generation to transmission and demand. Insufficient consideration of intra-minute and intra-hour variabilities might lead to grid instabilities. Therefore, the relevance of nowcasts (shortest-term forecasts) is steadily increasing. Nowcasts are suitable for fine-grained control applications in order to operate solar power plants in a grid friendly way and to secure stable operations of electrical grids. In space and time highly resolved nowcasts can be obtained by all sky imager (ASI) systems. ASI systems create hemispherical sky images. The associated software analyzes the sky conditions and derives solar irradiance nowcasts. Accuracy is the decisive factor for the effective use of nowcasts. Therefore, the goal of this work is to increase the nowcast accuracy by combining ASI nowcasts and persistence nowcasts, which persist the prevailing irradiance conditions, while maintaining the spatial coverage and resolution obtained by the ASI system. This hybrid approach combines the strengths while reducing the respective weaknesses of both approaches. Results of a validation show reductions of the root mean square deviation of up to 12% due to the hybrid approach.
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... A network of ASIs and ceilometers called Eye2Sky is being deployed in the Oldenburg region in Germany, which will reach 38 stations at full extent. The high spatial density of ASI pairs enables to reduce errors associated with the inference of cloud base height compared to a single pair of ASI [8]. A probabilistic procedure integrates cloud base heights and cloud segmentation relying on machine learning to produce nowcasted irradiance maps, such as the Global Horizontal Irradiance map surrounding a PV site shown in Fig. 4. The predicted irradiance can then be imported in a PV forecasting model. ...
