All-sky Camera Research Articles

CLOWN: The PASO Cloud Detection for Optimization of Automatic Optical Surveys

Orbiting space objects have become in the last decade a major nuisance impacting ground astronomy and orbiting space assets, from observatories to satellites and space stations. In particular with the rise of the satellite population in Low Earth Orbits, space objects are becoming an even bigger threat and a strong problem to astronomical observations. To tackle these threats, several coordinated surveillance networks composed of dedicated sensors (telescopes, radars, and laser ranging facilities) track and survey space objects, from debris to active satellites. As part of the European Space Surveillance & Tracking network, Portugal is developing the Pampilhosa da Serra Space Observatory, with both radio and optical telescopes dedicated to the Space Situational Awareness domain, deployed at a Dark Sky destination. To optimize telescope survey time, we developed CLOud Watcher at Night (CLOWN), an application interface that automatically monitors clouds in real time. This software can correctly trace cloud positions in the sky and provide accurate pointing information to the observation planning of the optical telescope to avoid cloudy areas. CLOWN only requires the use of an all-sky camera, which is already a norm in observatories with optical telescopes and can be used with any camera, including those for which no information about its model specification do exist. CLOWN does not require great computing power, and it does not require the installation of additional equipment. CLOWN results are very promising and confirm that the app can correctly identify clouds in a variety of different conditions and cloud types.

Retrieval of Solar Shortwave Irradiance from All-Sky Camera Images

The present work proposes a new model based on a convolutional neural network (CNN) to retrieve solar shortwave (SW) irradiance via the estimation of the cloud modification factor (CMF) from daytime sky images captured by all-sky cameras; this model is named CNN-CMF. To this end, a total of 237,669 sky images paired with SW irradiance measurements obtained by using pyranometers were selected at the following three sites: Valladolid and Izaña, Spain, and Lindenberg, Germany. This dataset was randomly split into training and testing sets, with the latter excluded from the training model in order to validate it using the same locations. Subsequently, the test dataset was compared with the corresponding SW irradiance measurements obtained by the pyranometers in scatter density plots. The linear fit shows a high determination coefficient (R2) of 0.99. Statistical analyses based on the mean bias error (MBE) values and the standard deviation (SD) of the SW irradiance differences yield results close to −2% and 9%, respectively. The MBE indicates a slight underestimation of the CNN-CMF model compared to the measurement values. After its validation, model performance was evaluated at the Antarctic station of Marambio (Argentina), a location not used in the training process. A similar comparison between the model-predicted SW irradiance and pyranometer measurements yielded R2=0.95, with an MBE of around 2% and an SD of approximately 26%. Although the precision provided by the SD at the Marambio station is lower, the MBE shows that the model’s accuracy is similar to previous results but with a slight overestimation of the SW irradiance. Finally, the determination coefficient improved to 0.99, and the MBE and SD are about 3% and 11%, respectively, when the CNN-CMF model is used to estimate daily SW irradiation values.

Observational Overview of the May 2024 G5-Level Geomagnetic Storm: From Solar Eruptions to Terrestrial Consequences

This study reports comprehensive observations for the G5-level geomagnetic storm that occurred from May 10 to 12, 2024, the most intense event since the 2003 Halloween storm. The storm was triggered by a series of coronal mass ejections (CMEs) originating from the merging of two active regions 13664/13668, which formed a large and complex photospheric magnetic configuration and produced X-class flares in early May 2024. Among the events, the most significant CME, driven by an X2.2 flare on May 9, caught up with and merged with a preceding slower CME associated with an X-class flare on May 8. These combined CMEs reached 1 AU simultaneously, resulting in an extreme geomagnetic storm. Geostationary satellite observations revealed changes in Earth’s magnetosphere due to solar wind impacts, increased fluxes of high-energy particles, and periodic magnetic field fluctuations accompanied by particle injections. Extreme geomagnetic storms resulting from the interaction of the solar wind with the Earth’s magnetosphere caused significant energy influx into Earth’s upper atmosphere over the polar regions, leading to thermospheric heating and changes in the global atmospheric composition and ionosphere. As part of this global disturbance, significant disruptions were also observed in the East Asian sector, including the Korean Peninsula. Ground-based observations show strong negative storm effects in the ionosphere, which are associated with thermospheric heating and resulting in decreases in the oxygen-to-nitrogen ratio (O/N2) in high-latitude regions. Global responses of storm-time prompt penetration electric fields were also observed from magnetometers over the East-Asian longitudinal sector. We also briefly report storm-time responses of aurora and cosmic rays using all-sky cameras and neutron monitors operated by the Korea Astronomy and Space Science Institute (KASI). The extensive observations of the G5-level storm offer crucial insights into Sun-Earth interactions during extreme space weather events and may help establish better preparation for future space weather challenges.

A Review and Evaluation of the State of Art in Image-Based Solar Energy Forecasting: The Methodology and Technology Used

The increasing penetration of solar energy into the grid has led to management difficulties that require high accuracy forecasting systems. New techniques and approaches are emerging worldwide every year to improve the accuracy of solar power forecasting models and reduce uncertainty in predictions. This article aims to evaluate and compare various solar power forecasting methods based on their characteristics and performance using imagery. To achieve this goal, this article presents an updated analysis of diverse research, which is classified in terms of the technologies and methodologies applied. This analysis distinguishes studies that use ground-based sensor measurements, satellite data processing, or all-sky camera images, as well as statistical regression approaches, artificial intelligence, numerical models, image processing, or a combination of these technologies and methods. Key findings include the superior accuracy of hybrid models that integrate multiple data sources and methodologies, and the promising potential of all-sky camera systems for very short-term forecasting due to their ability to capture rapid changes in cloud cover. Additionally, the evaluation of different error metrics highlights the importance of selecting appropriate benchmarks, such as the smart persistence model, to enhance forecast reliability. This review underscores the need for continued innovation and integration of advanced technologies to meet the challenges of solar energy forecasting.

Joule Heating rate at high-latitudes by Swarm and ground-based observations compared to MHD simulations

We compare Joule Heating rates as derived from ground-based magnetic field and all-sky camera data, from Low Earth Orbit satellite data (ESA Swarm) and from a MHD simulation (GUMICS-5) with each other in a case study of an auroral arc system. The observational estimates of Joule Heating rates provide information on regional scales and with high spatial resolution (10–100 km). Their comparison with global MHD results is conducted for a quiet time interval of a few minutes, just before a magnetic substorm. Analysis of the ground-based observations yields electric field with dominating North-South component pointing towards the arcs and having maxima values in the range 20–35 V/km. Combining these values with Pedersen conductance estimates from optical data (5–10 S) yields Joule Heating rates in the range 2.5–3.5 mW/m2. Swarm electric field measurements are consistent in their direction and intensity with the ground-based estimates. They also show that heating is increased particularly in the region where the conductance is low. The total amount of Joule heating in the area between the Swarm A and C satellite footprints while crossing the all-sky camera field of view is estimated to be 46 MW and the total amount energy dissipation during the 80 s overflight is around 3.6 GJ (1000 kWh). GUMICS-5 estimate of the peak Joule Heating in the magnetic local time sector of the arc system is smaller than that from the ground-based data with a factor of 2.9. Comparisons of GUMICS-5 results with Space Weather Modeling Framework (SWMF), shows that the latter gives on average larger heating rates being thus more consistent with our regional observations. However, both MHD-codes yield smaller Joule Heating rates around the time of the arcs and during the following substorm than the CTIP-e code. CTIP-e has a more detailed description of ionosphere-thermosphere interactions than the MHD-codes and its convection electric field is enhanced with a randomly varying additional component mimicking small scale structures. GUMICS-SWMF comparisons of global Joule Heating patterns in the Northern polar area reveal that the two simulations have significant differences in their spatial distribution of heating rates. Main cause for these deviations is the difference in the derivation of ionospheric Pedersen conductance. Our results emphasize the fact that future estimates of the global energetics in the magnetosphere–ionosphere–thermosphere system require better knowledge on ionospheric conductivities, both by new measurement concepts and by better understanding on the background physics controlling conductivity variations.

Development of the Cloud Monitoring Program using Machine Learning-based Python Module from the MAAO All-sky Camera Images

Development of the Cloud Monitoring Program using Machine Learning-based Python Module from the MAAO All-sky Camera Images

Noctilucent clouds altitude and particle size mapping based on spread observations by ground-based all-sky cameras

We suggest the procedure of building the maps of noctilucent clouds (NLC) zonal and meridional velocity, mean altitude and particle size based on three-color photometry by identical all-sky RGB-cameras separated by 115 km in a close-meridional direction. The procedure is applied to the bright NLC event on July 3, 2023. The altitude is measured by precise triangulation technique, and effective particle radius is estimated by comparison of each definite NLC fragment intensity and colors at different scattering angles as it is registered from different observation sites. The results are compared with existing photometric methods for average altitude and particle size measurements. A significant difference in evening and morning twilight NLC parameters is found, which is discussed in comparison with existing analysis of diurnal NLC variations.

An improved approach to determine aerosol properties from all-sky camera imagery: Sensitivity to the partially cloud scenes

We present a new approach to determine aerosol properties from radiometrically calibrated images provided by an all-sky camera. It is designed to be used regardless of the sky conditions. However, we especially focus on partially cloudy scenes, which is the main novelty of this work. Our methodology is based on using a small sector of the image that contains the principal plane of the Sun. The RGB principal plane radiances are associated to the aerosol optical depth (AOD) and Angstrom exponent (AE) AERONET observations through a Gaussian Process Regression (GPR) machine learning (ML) model. We identify the cloudy points within our working sector and the principal plane signal for the RGB radiances is averaged and smoothed. Then, we use the Pérez model to synthesize the principal plane signal in the cloudy spots. Finally, 2-year dataset has been used to test the method considering different atmospheric conditions related to the presence of clouds and aerosols, according to their amount and type. In addition, we have developed a method to evaluate the quality of predictions based on the standard deviation of the GPR. This quality assurance method may be fine-tuned according to the desired accuracy based on the application for which it is intended. Our AOD and AE predictions show an excellent overall agreement with AERONET measurements that substantially improves when our quality assurance method is applied. In that case, we obtain a high degree of correlation (R2 ¿ 0.97) and an overall MAE lower than the nominal uncertainty of AERONET measurements (0.006 and 0.05 for AOD and AE, respectively). Moreover, more than 83% and 77% of the predictions fall within the nominal uncertainty associated with AERONET measurements for AOD and AE, respectively. A comprehensive sensitivity analysis of the factors affecting the performance of the proposed methodology confirms that our method is stable and not very sensitive to external and methodological factors, especially when we apply quality assurance criteria. All this supports that our methodology is a reliable alternative to retrieve the optical properties of aerosols independently of the cloud conditions. Our results may contribute to the operational use of all-sky cameras, which may be an interesting complement regarding the study of aerosol-cloud interactions in partially cloud scenarios.

Verification of parameterizations for clear sky downwelling longwave irradiance in the Arctic

Abstract. Ground-based high resolution observations of downward longwave irradiance (DLI), surface air temperature, water vapor surface partial pressure and column amount, zenith sky infrared (IR) radiance in the atmospheric window, and all-sky camera images are regularly obtained at the Thule High Arctic Atmospheric Observatory (THAAO, 76.5° N, 68.8° W), northwestern Greenland. The datasets for the years 2017 and 2018 have been used to assess the performance of different empirical formulas used to infer clear sky DLI. An algorithm to identify clear sky observations has been developed, based on value, variability, and persistence of zenith sky IR radiance. Seventeen different formulas to estimate DLI have been tested against the THAAO dataset, using the originally determined coefficients. The formulas that combine information on total column water vapor and surface air temperature appear to perform better than others, with a mean bias with respect to the measured DLI smaller than 1 W m−2 and a root mean squared error (RMSE) around 6 W m−2. Unexpectedly, some formulas specifically developed for the Arctic are found to produce poor statistical results. This is attributed partly to limitations in the originally used dataset, which does not cover a whole year or is relative to very specific condition (i.e., the presence of an ice sheet). As expected, the bias displays a significant improvement when the coefficients of the different formulas are calculated using the THAAO dataset. The presence of 2 full years of data allows the determination and the applicability of the coefficients for singular years and the evaluation of results. The smallest values of the bias and RMSE reach 0.1 and 5 W m−2, respectively. Overall, the best results are found for formulas that use both surface parameters and total water vapor column content, and have been developed from global datasets. Conversely, formulas that express the atmospheric emissivity as a linear function of the logarithm of the column integrated water vapor appear to reproduce poorly the observations at THAAO.

Frequency and spectral analysis of pulsing aurora according to the data of the imaging photometer at the Verkhnetulomsky observatory

A Pulsating Aurora Imaging Photometers System (PAIPS) is being created using observatories of the Polar Geophysical Institute on the Kola Peninsula to study the structure of pulsating auroras (PsA) in the near ultraviolet range (300—400 nm). We presented a description of two PsA cases measured by the PAIPS system together with all-sky cameras. For the first one, a frequency analysis of the signal is given and it is shown that for an hour and a half, pulsations occur at a quasi-constant frequency of about 1 Hz. For the second one, measurements of the intensity ratio in the 337 and 391 nm wavelengths are presented. An analysis of simultaneous observations of optical cameras in the field of view of the spectrometer shows that the emission in the “on” phase is “greener”, i.e., corresponds to less energetic precipitating electrons. Stereometric observations with a camera in observatory Lovozero gives the maximum correlation coefficient for the time series of cameras at altitudes of 145—150 km and ~92 km.

Development of communication systems for a photovoltaic plant with battery energy storage system and all-sky camera

Development of communication systems for a photovoltaic plant with battery energy storage system and all-sky camera

Improvements in digital meteor spectra reduction

This study addresses the complexity and importance of developing a method of calibrating digital observations of meteor spectra with all-sky cameras. It aims to present novel approaches to spectral sensitivity, atmospheric extinction and flat-field corrections. Images of a known line emission spectrum were captured at various positions within the field of view using a camera with a fish-eye lens and plastic holographic grating. The flat-field correction was separated into a wavelength-independent and wavelength-dependent component, both dependent on the position of the spectral line in the field of view (FoV). Total profile intensities of spectra obtained from the images were compared throughout the spectral range at different positions in the FoV. The flat-field was constructed by fitting those dependencies with high-degree polynomial functions. Using a simplified atmospheric model, a novel approach was constructed to determine the atmospheric extinction curve throughout the spectral range, allowing it to be separately considered from the spectral sensitivity which was previously not the case. A comparison of the newly developed and previously used methodology was tested on several meteor spectra of the same meteor captured from different stations of the European Fireball Network. It revealed a significantly improved correspondence of the analysed spectra in the part of the spectral range unaffected by the limitations imposed by the newly developed methodology. Failing to follow the correct calibration methodology precisely may introduce varying degrees of uncertainty in computations of elemental abundances and other physical properties, depending on the equipment’s specific effect magnitude.

Development of an All-sky Camera for The Indonesian Sky Patrol Network

We developed the All-Sky Camera using a CMOS sensor, which is integrated with various other supporting devices to optimize image and video recordings. In general, CMOS cameras are not equipped with a cooling system, which can cause increased noise. Therefore, our design is equipped with an external cooling system to keep the temperature stable at night or during the day. In addition, our designs are also equipped with various other sensors, such as rain sensors, cloud sensors, light sensors, and sensors for environmental conditions such as pressure, temperature, and humidity. The development of the All-Sky Camera will also be used as a tool to monitor the sky throughout Indonesia with the Indonesian Sky Patrol Network (https://allskycam.or.id/). As a result, we managed to record several astronomical phenomena, such as meteors and space debris. For future scientific needs, the all-sky camera that we have developed is also used to monitor changes in light pollution and identify sources of light pollution, as well as the quality of the atmosphere at the ITERA Lampung Astronomical Observatory Environment (OAIL).

Cloud Identification and Reconstruction from All-sky Camera Images Based on Star Photometry Estimation

Cloud cover significantly influences ground-based optical astronomical observations, with nighttime astronomy often relying on visible light all-sky cameras for cloud detection. However, existing algorithms for processing all-sky cloud images typically require extensive manual intervention, posing challenges in identifying clouds with pronounced extinction characteristics. Furthermore, there is a lack of effective means for detailed visualization of cloud cover. To address these issues, this paper proposes a method that reconstructs the cloud distribution and thickness from all-sky images through star identification and photometry. Specifically, a high-precision star coordinate to the pixel position imaging model calibration method based on the star recognition for fisheye lenses is investigated, resulting in an all-sky rms error of less than 0.87 pixels. Based on the comprehensive reference star catalog, an optimized star extraction method based on SExtractor is developed to handle the difficulty of image source detection in all-sky cloud images. The optical thickness and distribution of cloud layers is calculated through star matching and extinction measurements. Finally, contingent upon the capability of camera and catalog star density, seven cloud layer reconstruction methods are proposed based on meshing and machine learning techniques, achieving a reconstruction accuracy of up to 1.°8. The processing results from real observed images indicate that the proposed method offers a straightforward calibration process and delivers excellent cloud cover extraction and reconstruction outcomes, thereby providing practical value in telescope dynamic scheduling, site characterization and the development of observation strategies.

The impact of drifting substorm-injected electrons on pulsating aurora initiation and intensification

Pulsating aurora, which consists of diffuse patches blinking on and off, is caused by pitch angle scattering of radiation belt electrons into the loss cone by lower-band chorus waves. Understanding the drivers of pulsating aurora is important as it is a long-lasting and widespread phenomenon, accounting for significant energy transfer from the solar wind into the ionosphere. Substorm injections, which transport electrons from the magnetotail into the inner magnetosphere, are one source of electrons in this region. Injections have been observed simultaneously with pulsating aurora during conjunctions between ground cameras and satellites. In addition, previous work has also shown that substorms can enhance chorus activity (the fundamental process that produces pulsating aurora), providing a mechanism linking substorms to pulsating aurora. To further study this connection, we used the Van Allen Probes and all-sky cameras to look at events where pulsating aurora and substorm injections were observed at different locations in Magnetic Local Time (MLT), rather than focusing only on conjunctions. To make this comparison, we calculated the drift rate of electrons originating from observed injections and projected their motion forward in time until their Magnetic Local Time was the same as the ground camera. When the electrons are located at the same MLT as the ground camera, the pulsating aurora they cause would most likely occur in the field of view of the camera. We compared the time drifting substorm-injected particles arrived at the MLT of the camera to when pulsating aurora was observed. We found several instances where the initiation or intensification of pulsating aurora was accompanied by the arrival of substorm-injected electrons. This observation gives further evidence that pulsating aurora can be enhanced by or occur after substorm injections.

Meteoroid rotation and quasi-periodic brightness variation of meteor light curves

Meteor light curves are sometimes known to display flickering: rapid, quasi-periodic variations in brightness. This effect is generally attributed to the rotational modulation of the ablation rate, which is caused by the time-varying cross section area presented by a nonspherical rotating meteoroid to the oncoming airflow. In this work we investigate the effects that the rotation of a meteoroid of given shape (spherical, cubic, or cylindrical) has on the meteor’s light curve, given state-of-the-art experimental laboratory estimates of the drag and lift coefficients of hypersonic flow (Mach number > 5) around various shaped objects. The meteoroid’s shape is important in determining these two forces, due to the different response of the drag and lift coefficients according to the angle of attack. As a case study, the model was applied to a fireball observed on 2018 April 17 by the PRISMA network, a system of all-sky cameras that achieves a systematic monitoring of meteors and fireballs in the skies over the Italian territory. The results show that this methodology is potentially able to yield a powerful diagnostic of the rotation rate of meteoroids prior to their encounter with the atmosphere, while also providing essential information on their pre-fall actual shapes.

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

In recent years, with the growing proliferation of photovoltaics (PV), accurate nowcasting of PV power has emerged as a challenge. Global horizontal irradiance (GHI), which is a key factor influencing PV power, is known to be highly variable as it is determined by short-term meteorological phenomena, particularly cloud movement. Deep learning and computer vision techniques applied to all-sky imagery are demonstrated to be highly accurate nowcasting methods, as they encode crucial information about the sky’s state. While these methods utilize deep neural network models, such as Convolutional Neural Networks (CNN), and attain high levels of accuracy, the training of image-based deep learning models demands significant computational resources. In this work, we present a computationally economical estimation technique, based on a deep learning model. We utilize both all-sky imagery and meteorological data, however, information on the sky’s state is encoded as a feature vector extracted using traditional image processing methods. We introduce six all-sky image features utilizing detailed knowledge of meteorological and physical phenomena, significantly decreasing the amount of input data and model complexity. We investigate the accuracy of the determined global and diffuse radiation for different combinations of meteorological parameters. The model is evaluated using two years of measurements from an on-site all-sky camera and an adjacent meteorological station. Our findings demonstrate that the model provides comparable accuracy to CNN-based methods, yet at a significantly lower computational cost.

Continuous sky digitalization using images from an all-sky camera

The designed networked system captures and stores high and medium resolution sky images every 2 seconds. The IP camera employed is low-cost, omnidirectional, and its images are accessible from any point with Internet connection, both in real time and to a database, thanks to the configuration of a VPN network. The images obtained by the camera can be utilised for a variety of purposes but are of particular interest for those applications where a large volume of separate images is required for the characteristics of the sky, as the system also provides an innovative method of measuring solar energy, which provides an unambiguous view of the cloud state on any given day.

Correlation between the solar wind speed and the passage of poleward-moving auroral forms into the polar cap

In 1961, Dungey suggested that magnetic reconnection occurs due to the solar-terrestrial interaction. The interplanetary magnetic field (IMF) is thought to merge with Earth’s geomagnetic field (GMF). After the reconnection process the newly formed magnetic flux tube, consisting of both the IMF and GMF, moves anti-sunward. Poleward-moving auroral forms (PMAFs) are believed to be the ionospheric signatures of this process, which transfers magnetic flux from the dayside to the nightside. This paper looks at the connection between the solar wind speed and the motion of the PMAF as it moves from the auroral oval, anti-sunward, into the polar cap. PMAFs are identified using both the meridian scanning photometer (MSP) and colored all-sky camera (ASC). Once the PMAFs are identified, the PMAF-SLOPE, vα (units of degrees per time) and the angle (αPMAF) the PMAF makes with the horizontal (Time axis), in the MSP plot are calculated. These values (vα and αPMAF) are individually plotted against the vx-component of the solar wind speed and the flow speed (total solar wind speed). The plots generate linear a relationship between PMAF-SLOPEs, vα, [or PMAF angles (αPMAF)], and the vx-component of the solar wind speed (or the flow speed). A total of 57 PMAF events from 8 different days were associated with solar wind speeds (vx-component) ranging from 344 to 679 km/s. The first linear plot, between the PMAF-SLOPE and solar wind speed (vx-component), shows a high correlation: rvα=0.944. A second linear plot, between αPMAF and the solar wind speed (vx-component) shows a very high correlation: rαPMAF=0.973. The conclusions obtained from this statistical study are: 1) both the PMAF-SLOPE vα and αPMAF are highly correlated to the vx-component of the solar wind, increasing when vx increases and vice versa, 2) PMAFs must be connected to both the IMF and GMF and are dragged anti-sunward, mostly by the vx-component of the solar wind, and 3) PMAFs are indeed the ionospheric footprints of a newly formed magnetic flux tube, due to dayside magnetic reconnection, being transferred from the dayside to nightside.

Implementation and Application of a Regional Fireball Monitoring Network Based on All-sky Camera Networking

Fireball monitoring network is the main equipment for monitoring impact from small-size near-Earth objects (NEOs), and determining meteorite fall locations. In this paper, a monitoring system using a multi-station distributed all-sky video camera network is proposed, and a regional prototype system has been constructed in Jiangsu and surrounding areas. The main processes of fireball monitoring were implemented, including network control, video capture, data processing, trajectory determination, and orbit calculation. After running for 1 yr, the results show that the limiting apparent magnitude - 1.0 for meteors in the video, and the absolute magnitude exhaustive detection regime can reach around - 2.5. The flux of fireballs is found to be 2.68×10−7 km−2· h−1. The percentages of streams and sporadic meteors are 46% and 54%, respectively, and the fraction of cometary orbits equals 72.9%, while for asteroidal-type it is 27.1%. The statistical results are close to those of the international meteor monitoring networks, which verified the monitoring capability of the network system in practical operation.