Cloud Motion Research Articles

Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection

Solar energy supply is usually highly volatile, limiting its integration into the power grid. Accurate probabilistic intraday forecasts of solar resources are essential to increase the share of photovoltaic (PV) energy in the grid and enable cost-efficient balancing of power demand and supply. Solar PV production mainly depends on downwelling surface solar radiation (SSR). By estimating SSR from geostationary satellites, we can cover large areas with high spatial and temporal resolutions, allowing us to track cloud motion. State-of-the-art probabilistic forecasts of solar resources from satellite imagery account only for the advective motion of clouds. They do not consider the evolution of clouds over time, their growth, and dissipation, even though these are major sources of forecast uncertainty. To address the uncertainty of cloudiness evolution, we present SolarSTEPS, the first optical-flow probabilistic model able to simulate the temporal variability of cloudiness. We demonstrate that forecasting the autocorrelated scale-dependent evolution of cloudiness outperforms state-of-the-art probabilistic advection-based forecasts by 9% in continuous ranked probability score (CRPS). This corresponds to an extension of the forecast lead time by about 45 min at constant CRPS. Our work is motivated by the scale-dependent predictability of cloud growth and decay. We demonstrate that cloudiness is more variable in time at smaller spatial scales than at larger ones. Specifically, we show that the temporal autocorrelation of cloudiness is related to its spatial scale by an inverse power law. We also demonstrate that decomposing cloudiness into multiple spatial scales in the forecasts further improves the forecast skill, reducing the CRPS by 10% and the RMSE by 9%.

A TCN-Based Hybrid Forecasting Framework for Hours-Ahead Utility-Scale PV Forecasting

This paper presents a Temporal Convolutional Network (TCN) based hybrid PV forecasting framework for enhancing hours-ahead utility-scale PV forecasting. The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven fluctuation forecasting (FF) model. Three TCNs are integrated in the framework for: i) blending the inputs from different Numerical Weather Prediction sources for the TF model to achieve superior performance on forecasting hourly PV profiles, ii) capturing spatial-temporal correlations between detector sites and the target site in the FF model to achieve more accurate forecast of intrahour PV power drops, and iii) reconciling TF and FF results to obtain coherent hours-ahead PV forecast with both hourly trends and intra-hour fluctuations well preserved. To automatically identify the most contributive neighboring sites for forming a detector network, a scenario-based correlation analysis method is developed, which significantly improves the capability of the FF model on capturing large power fluctuations caused by cloud movements. The framework is developed, tested, and validated using actual PV data collected from 95 PV farms in North Carolina. Simulation results show that the performance of 6 hours ahead PV power forecasting is improved by 20% -30% compared with state-of-the-art methods.

Влияние лермонтовской традиции на современного иранского поэта Шафии Кадкани

Man and nature are two inseparable elements of the world, and the unbreakable connection and coexistence between them is the origin of all kinds of literary works. From ancient times to the present day, we often see poems about nature both in Russian and Persian literature. Using the Natural objects and phenomena in the form of myths and symbols in literary works and giving them personality have various reasons, including political ones. Sometimes poems may be composed under the influence of a poet from another country. This influence can be seen in contemporary Iranian literature. Among the contemporary Iranian poets who are very familiar with the ideas and works of Russian poets and writers is the Iranian naturalist (пейзажист) poet Shafiei Kadkani. He composed his poem “Happy Journeys!”) based on Lermontov’s “The Rock” and “The black Clouds” poems. Both poets have used natural images such as plant (растение), breeze (ветерок), blossoms (цветения), rain (дождь), desert (пустыня), dust (пыль), horror desert (пустыня ужаса), rock / cliff (утес) and black clouds (тучи), which have a symbolic role. By comparing the poems, we can see that they are similar in terms of content and have the same message. And some of the symbols of Shafiei Kadkani are the same as the symbols of Lermontov, such as the desert, or they are chosen in terms of meaning very close to the symbols of Lermontov, such as “rock / cliff and milkvetch” or “small clouds and breeze”, as well as the dynamic movement of small clouds and breeze, which are the symbol of transition and change. Like Lermontov, Shafiei Kadkani also complains about the situation of his society, but symbols like “blossoms and rain” at the end of Shafiei Kadkani’s poem, unlike Lermontov’s poems, promise the morning of freedom, prosperity and transition, but with doubts.

Frequency mixing property of electron cloud in a Penning trap at room temperature

The excited motion of the electron cloud stored in a Penning trap was observed at the resonance frequency of the detection circuit by exploiting the frequency mixing property of the cloud. Direct application of the excitation signal at the resonance frequency of the detection circuit results in coupling with the detection system, making it difficult to isolate the response of the cloud. To address this issue, two different frequencies were applied to the two different trap electrodes, with a combined frequency equal to the resonance frequency of the detection circuit. The cloud mixed the applied excitations and enabled the detection of cloud's response at the resonance frequency and thus, helps to circumvent the direct feedthrough. This technique was found to provide an observable signal in the situations where the conventional resonance absorption technique provides signals that are difficult to disentangle from noisy backgrounds at room temperature.

High-resolution APEX/LAsMA 12CO and 13CO (3–2) observation of the G333 giant molecular cloud complex

Context. Hub-filament systems are suggested to be the birth cradles of high-mass stars and clusters. Aims. We investigate the gas kinematics of hub-filament structures in the G333 giant molecular cloud complex using 13CO (3–2) observed with the APEX/LAsMA heterodyne camera. Methods. We applied the FILFINDER algorithm to the integrated intensity maps of the 13CO J = 3–2 line to identify filaments in the G333 complex, and we extracted the velocity and intensity along the filament skeleton from moment maps. Clear velocity and density fluctuations are seen along the filaments, allowing us to fit velocity gradients around the intensity peaks. Results. The velocity gradients we fit to the LAsMA and ALMA data agree with each other over the scales covered by ALMA observations in the ATOMS survey (<5 pc). Changes in velocity gradient with scale indicate a funnel structure of the velocity field in position-position-velocity (PPV) space. This is indicative of a smooth, continuously increasing velocity gradient from large to small scales, and thus is consistent with gravitational acceleration. The typical velocity gradient corresponding to a 1 pc scale is ~1.6 km s−1 pc−1. Assuming freefall, we estimate a kinematic mass within 1 pc of ~1190 M⊙, which is consistent with typical masses of clumps in the ATLASGAL survey of massive clumps in the inner Galaxy. We find direct evidence for gravitational acceleration from a comparison of the observed accelerations to those predicted by freefall onto dense hubs with masses from millimeter continuum observations. On large scales, we find that the inflow may be driven by the larger-scale structure, consistent with the hierarchical structure in the molecular cloud and gas inflow from large to small scales. The hub-filament structures at different scales may be organized into a hierarchical system extending up to the largest scales probed through the coupling of gravitational centers at different scales. Conclusions. We argue that the funnel structure in PPV space can be an effective probe for the gravitational collapse motions in molecular clouds. The large-scale gas inflow is driven by gravity, implying that the molecular clouds in the G333 complex may be in a state of global gravitational collapse.

Simulations of the interaction between fiber bundles/yarns and the energy absorption of the fabric under hypervelocity impact

In this work, the interaction between fiber bundles/yarns and the energy absorption of the multiple fabrics under hypervelocity impact (HVI) are studied by numerical simulation and experiments. In numerical model, an anisotropic elastic-plastic constitutive equation coupled with the Mie-Grüneisen equation of state (EOS) is adopted to describe deviatoric deformation and strongly nonlinear volumetric compression induced by HVI. Considering the internal interactions within fabrics that possess a hierarchical structure and the evolution of impact-induced debris clouds, a meso‑scale model for woven fabrics is developed using the finite element-smoothed particle hydrodynamics (FEM-SPH) adaptive method. The meso‑scale fabric model is validated through HVI experiments, and the simulation results demonstrate excellent agreement with the experimental data. Notably the model reveals an approximately linear increase relationship between the absorbed energy by the fabric and the impact energy of the projectile. Moreover, the meso‑scale fabric model exhibits enhanced predictive capability in modeling the movement and dispersion of debris clouds. Furthermore, the results of the sensitivity analysis of yarn partition indicate that the model is capable of capturing the interactions between fiber bundles/yarns within the fabric during HVI. With increasing impact velocity, the impact time decreases, which reduces the interaction between fabric components and subsequently leading to a decrease in the generated frictional energy.

Design of MPPT for Buck-boost Converter based on GA to Optimize Solar Power Generation

Maximizing power in the Solar Power Plant system was something that needs attention. Indonesia was a tropical country that has two seasons, where the changing weather or cloud movements are always erratic, especially in the southern region of Java Island. To overcome this problem, an inverter equipped with maximum power point tracking (MPPT) is used. Currently the switching system in MPPT is still not optimal with a system efficiency of around 90%. In this study, the installation of MPPT was carried out which aims to maximize power in the solar photovoltaic (PV) system due to fluctuations or instability of sunlight at PT. Jatinom Indah Agri, Blitar City. The maximum power generated by solar photovoltaic could be achieved by using the combination of DC - DC converter and artificial intelligence. In this research, modeling of the solar PV system was made using MATLAB software, where the design of the solar PV system consists of a PV module with capacity 240W, DC to DC converter, battery and MPPT. The MPPT based on Genetic Algorithm (GA) has been tested and compared with MPPT based on Particle Swarm Optimization (PSO) and conventional MPPT, where MPPT based on GA works well in finding the maximum power point in the solar photovoltaic system. it was found that MPPT GA produces a maximum power point close to PV power with an efficiency of 92%, while MPPT PSO has an efficiency of 85% and for conventional MPPT it was 79%. In selecting the method for designing MPPT, a method is needed that has a wide range of sample data. This is due to the fluctuating characteristics of the irradiation value received by solar PV.

Deep learning methods for intra-day cloudiness prediction using geostationary satellite images in a solar forecasting framework

Accurate solar resource forecasting remains a challenge. Electricity grid applications require both days-ahead and intra-day prediction. Satellite-based methods are known to be the best option for hourly intra-day solar forecasts up to some hours ahead. An adapted Deep Learning (DL) method has been recently reported to outperform the traditional Cloud Motion Vectors (CMV) strategy. This article analyzes the utilization of a well-documented computer vision DL architecture, the U-Net in various forms, for the satellite Earth albedo forecast problem (cloudiness), a straightforward proxy for solar irradiance forecast. It is shown that the U-Net performs better than advanced and optimized CMV techniques and previous art IrradianceNet, setting it at the state-of-the-art. The tests are done over the Pampa Húmeda region of southeast South America, an area in which challenging cloud conditions are frequent. The data for this study are GOES-16 visible channel images. These images present a finer spatial (≃1 km/pixel) and temporal (10 min) resolution than previously explored data sources for solar forecasting. Moreover, the image size used here is ×4 bigger (1024 × 1024 pixels) and the predictions reach further into the future (5 h) than in previous works. The analysis includes several ablation studies, involving different architectures, optimization objectives, inputs, and network sizes. The U-Net is optimized for direct and differential image prediction, being the latter a better-performing option. More notably, the U-Net models are shown to be able to predict cloud extinction, something that has been a barrier for CMV methods.

Longitudinal Velocity Estimation of Driverless Vehicle by Fusing LiDAR and Inertial Measurement Unit

Aiming at the problem that, under certain extreme conditions, relying on tire force or tire angular velocity to represent the longitudinal velocity of the unmanned vehicle will fail, this paper proposes a longitudinal velocity estimation method that fuses LiDAR and inertial measurement unit (IMU). First, in order to improve the accuracy of LiDAR odometry, IMU information is introduced in the process of eliminating point cloud motion distortion. Then, the statistical characteristic of the system noise is tracked by an adaptive noise estimator, which reduces the model error and suppresses the filtering divergence, thereby improving the robustness and filtering accuracy of the algorithm. Next, in order to further improve the estimation accuracy of longitudinal velocity, time-series analysis is used to predict longitudinal acceleration, which improves the accuracy of the prediction step in the unscented Kalman filter (UKF). Finally, the feasibility of the estimation method is verified by simulation experiments and real-vehicle experiments. In the simulation experiments, medium- and high-velocity conditions are tested. In high-velocity conditions (0–30 m/s), the average error is 1.573 m/s; in the experiment, the average error is 0.113 m/s.

Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data

Abstract. This work introduces a model for all-sky-image-based cloud and direct irradiance nowcasting (MACIN), which predicts direct normal irradiance (DNI) for solar energy applications based on hemispheric sky images from two all-sky imagers (ASIs). With a synthetic setup based on simulated cloud scenes, the model and its components are validated in depth. We train a convolutional neural network on real ASI images to identify clouds. Cloud masks are generated for the synthetic ASI images with this network. Cloud height and motion are derived using sparse matching. In contrast to other studies, all derived cloud information, from both ASIs and multiple time steps, is combined into an optimal model state using techniques from data assimilation. This state is advected to predict future cloud positions and compute DNI for lead times of up to 20 min. For the cloud masks derived from the ASI images, we found a pixel accuracy of 94.66 % compared to the references available in the synthetic setup. The relative error of derived cloud-base heights is 4 % and cloud motion error is in the range of ±0.1ms-1. For the DNI nowcasts, we found an improvement over persistence for lead times larger than 1 min. Using the synthetic setup, we computed a DNI reference for a point and also an area of 500 m×500 m. Errors for area nowcasts as required, e.g., for photovoltaic plants, are smaller compared with errors for point nowcasts. Overall, the novel ASI nowcasting model and its components proved to work within the synthetic setup.

Data-Driven Minute-Ahead Forecast of PV Generation with Adjacent PV Sector Information

This paper proposes and validates a data-driven minute-ahead forecast model for photovoltaic (PV) generation, which is essential for real-time micro-grid scheduling. Unlike day-ahead PV forecasts that heavily rely on weather forecast information, our proposed model does not require such data as it operates in an ultra-short-term time domain. Instead, the model leverages the generation data of the target PV sector and its adjacent sectors to capture short-term factors that affect electricity generation, such as the movement of clouds. The proposed model employs a long short-term memory (LSTM) network to process the data. By conducting experiments with real PV site data, we demonstrate that the information from adjacent PV sectors improves the accuracy of minute-ahead PV generation forecasts by 3.66% in the mean squared error index and 1.19% in the mean absolute error index compared to the model without adjacent sector information.

Vision Transformer-Based Photovoltaic Prediction Model

Sensing the cloud movement information has always been a difficult problem in photovoltaic (PV) prediction. The information used by current PV prediction methods makes it challenging to accurately perceive cloud movements. The obstruction of the sun by clouds will lead to a significant decrease in actual PV power generation. The PV prediction network model cannot respond in time, resulting in a significant decrease in prediction accuracy. In order to overcome this problem, this paper develops a visual transformer model for PV prediction, in which the target PV sensor information and the surrounding PV sensor auxiliary information are used as input data. By using the auxiliary information of the surrounding PV sensors and the spatial location information, our model can sense the movement of the cloud in advance. The experimental results confirm the effectiveness and superiority of our model.

Image feature learning combined with attention‐based spectral representation for spatio‐temporal photovoltaic power prediction

AbstractClean energy is a major trend. The importance of photovoltaic power generation is also growing. Photovoltaic power generation is mainly affected by the weather. It is full of uncertainties. Previous work has relied chiefly on historical photovoltaics data for time series forecasts. However, unforeseen weather conditions can sometimes skew. Consequently, a spatial‐temporal‐meteorological‐long short‐term memory prediction model (STM‐LSTM) is proposed to compensate for the shortage of photovoltaic prediction models for uncertainties. This model can simultaneously process satellite image data, historical meteorological data, and historical power generation data. In this way, historical patterns and meteorological change information are extracted to improve the accuracy of photovoltaic prediction. STM‐LSTM processes raw satellite data to obtain cloud image data. It can extract cloud motion information using the dense optical flow method. First, the cloud images are processed to extract cloud position information. By adaptive attentive learning of images in different bands, a better representation for subsequent tasks can be obtained. Second, it is important to process historical meteorological data to learn meteorological change patterns. Last but not least, the historical photovoltaic power generation sequences are combined to obtain the final photovoltaic prediction results. After a series of experimental validation, the performance of the proposed STM‐LSTM model has a good improvement compared with the baseline model.

Detection of clouds in multiple wind velocity fields using ground-based infrared sky images

To improve the performance of an intra-hour global solar irradiance forecasting algorithm, it is important to detect multiple layers of clouds. Horizontal atmospheric wind shear causes wind velocity fields to have different directions and speeds. In images of clouds acquired using ground-based sky imagers, clouds may be moving in different wind layers. The information provided by a solar forecasting algorithm is necessary to optimize and schedule the solar generation resources and storage devices in a smart grid. This investigation studies the performance of unsupervised learning techniques when detecting the number of cloud layers in infrared sky images. The images are acquired using an innovative infrared sky imager mounted on a solar tracker. Different mixture models are used to infer the distribution of the cloud features. Multiple Bayesian metrics and a sequential hidden Markov model are implemented to find the optimal number of clusters in the mixture models, and their performances are compared. The motion vectors are computed using a probabilistic implementation of the Lucas-Kanade algorithm. The correlations between the cloud motion vectors and temperatures are analyzed to discover the method that leads to the most accurate results. The findings point that a sequential hidden Markov model outperforms the detection accuracy of standard Bayesian model selection metrics.

Capture of the free-floating planets and primordial black holes into protostellar clouds

The capture of the free-floating planets and primordial black holes into a collapsing protostellar cloud is considered. Although the last stage of rapid contraction leading to the star formation lasts for a relatively short time ∼105 years, during this time there is a strong change in the gravitational potential created by the movement of the entire cloud’s mass (∼M⊙). As a result, the probability of capturing an object into a contracting cloud is comparable to the probability of capturing into an already formed planetary system. Taking into account the collapse of the cloud increases by 17% the full probability of the planets capture at the orbits with large semi-axis a<103 au. Capture in the cloud can explain the wide inclined orbit of the supposed 9th planet in the solar system. At the same time, the probability of primordial black holes capturing from the galactic halo into a contracting cloud is extremely small.

Numerical Evaluation of Wind Speed Influence on Accident Toxic Spill Consequences Scales

This study aims to evaluate numerically the influence of wind speed on scales of environmental harmful consequences caused by accidentally spilled toxic liquid evaporated from the surface of a free-form outlined spill spot. A coupled problem of the gas-dynamic movement of a toxic air-mixture cloud in the surface layer of the atmosphere under the influence of wind and a negative toxic inhalation impact on a human in an accident zone is solved by means of mathematical modelling and computer experiment. Physical processes of toxic liquid evaporation from the spill spot, formation of a mixture of toxic gas with the incoming air, and further dispersion of a hazardous gaseous chemical in the atmosphere under various wind speed conditions are investigated. A three-dimensional non-stationary mathematical model of the turbulent movement of a gas-air mixture is used for obtaining distribution of relative mass concentration of toxic gas impurities in time and space. The model takes into account the complex terrain, compressibility of the gas flow, three-dimensional and non-stationary nature of actual physical processes, different toxic properties of chemical substances, and arbitrary contour shape of the toxic spill spot. A probabilistic harmful impact model based on using a modernized probit analysis method is used to obtain fields of the conditional probability of a fatal human injury resulting from toxic gas inhalation. This model extracts relative mass concentration of toxic gas that could cause negative impact on humans at any control point during calculation time step exposition, collects integral toxic dose values from the multicomponent gas mixture dynamics model, calculates a value of the probit function for the corresponding toxic inhalation dose dangerous factor, and automatically assesses the human fatal injury conditional probability using partial cubic Hermitian spline. This technique allows environmental safety experts assessing the scale of considered type technogenic accident consequences numerically depending on wind speed conditions and elaborating the means to mitigate them to acceptable levels.

Ultraviolet Raman spectroscopy for remote detection of chlorine gas

As a primary material frequently used in industry, chlorine is relatively easy to obtain and available even in large quantities. Despite its high toxicity, molecular chlorine is readily available since it is an essential educt in the chemical industry. Over the past decades, numerous accidents involving injured and dead victims have occurred. Furthermore, it was already misused as a warfare agent at the beginning of the last century with still reported attacks. Early detection, localization, and monitoring of sources and cloud movements are essential for protecting stationary facilities, mobile operations, and the public. In contrast to most chemical hazardous materials, where it is possible to detect them by vibrational spectroscopic methods (e.g., passive hyper-spectral absorption technologies in the infrared), halogens are inactive to infrared absorption. Raman-based technologies rely on changes in the polarizability of the molecule and provide vibrational-spectroscopic access to such diatomic molecules and therefore close the gap in infrared detection capabilities. Here we present a straightforward approach for a standoff Raman detector in a backscattering configuration. This paper uses a simplified model to discuss optimum excitation wavelengths in achievable detection ranges. We validate the model by spontaneous (vibrational) Raman spectroscopic measurements between 20 and 60 m standoff distance. We also briefly discuss detection performances and technical and physical aspects as prospects of system design.

The Movement of GPS Positioning Discrepancy Clouds at a Mid-Latitude Region in March 2015

The geomagnetic storm on 17 March 2015 had a strong impact on the global navigation satellite systems (GNSS) positioning results in many GNSS Continuously Operating Reference Stations (CORS) in Europe. The analysis of global positioning system (GPS) observations in Latvian CORS stations discovered a strong impact of this space weather event over the whole country. The impact appeared as a moving cloud of positioning discrepancies across the country. However, the analysis of the days before 17 March revealed other smaller duration ionospheric scintillation events. The objective was to analyze the GPS positioning discrepancy cloud movement, total electron content (TEC), and rate of change of the TEC index (ROTI) relationships, as well as discrepancy statistics. The area of analysis on 16–18 March was increased by including the EGNOS ground-based Ranging and Integrity Monitoring Stations (RIMS): GVLA and GVLB, LAPA and LAPB, and WRSA and WRSB. The conclusion of the study is that each “shot” after 90 s gives a completely new cloud with a new impacted station subset, its configuration, and completely irregular discrepancy values.

Break and Splice: A Statistical Method for Non‐Rigid Point Cloud Registration

Abstract3D object matching and registration on point clouds are widely used in computer vision. However, most existing point cloud registration methods have limitations in handling non‐rigid point sets or topology changes (e.g. connections and separations). As a result, critical characteristics such as large inter‐frame motions of the point clouds may not be accurately captured. This paper proposes a statistical algorithm for non‐rigid point sets registration, addressing the challenge of handling topology changes without the need to estimate correspondence. The algorithm uses a novel Break and Splice framework to treat the non‐rigid registration challenges as a reproduction process and a Dirichlet Process Gaussian Mixture Model (DPGMM) to cluster a pair of point sets. Labels are assigned to the source point set with an iterative classification procedure, and the source is registered to the target with the same labels using the Bayesian Coherent Point Drift (BCPD) method. The results demonstrate that the proposed approach achieves lower registration errors and efficiently registers point sets undergoing topology changes and large inter‐frame motions. The proposed approach is evaluated on several data sets using various qualitative and quantitative metrics. The results demonstrate that the Break and Splice framework outperforms state‐of‐the‐art methods, achieving an average error reduction of about 60% and a registration time reduction of about 57.8%.

Optimizing cloud motion estimation on the edge with phase correlation and optical flow

Abstract. Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.