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Stereo motion vectors (SMVs) from the Multiangle Imaging SpectroRadiometer (MISR) were evaluated against Meteosat‐9 cloud‐motion vectors (CMVs) over a one‐year period. In general, SMVs had weaker westerlies and southerlies than CMVs at all latitudes and levels. The E‐W wind comparison showed small vertical variations with a mean difference of −0.4 m s−1, −1 m s−1, −0.7 m s−1 and corresponding rmsd of 2.4 m s−1, 3.8 m s−1, 3.5 m s−1for low‐, mid‐, and high‐level clouds, respectively. The N‐S wind discrepancies were larger and steadily increased with altitude, having a mean difference of −0.8 m s−1, −2.9 m s−1, −4.4 m s−1 and rmsd of 3.5 m s−1, 6.9 m s−1, 9.5 m s−1at low, mid, and high levels. The best overall agreement was found in marine stratocumulus off Namibia, while differences were larger in the Tropics and convective clouds. The SMVs were typically assigned to higher altitudes than CMVs. Attributing each observed height difference to MISR and/or Meteosat‐9 retrieval biases will require further research; nevertheless, we already identified a few regions and cloud types where CMV height assignment seemed to be the one in error. In thin mid‐ and high‐level clouds over Africa and Arabia as well as in broken marine boundary layer clouds the 10.8‐μm brightness temperature‐based heights were often biased low due to radiance contributions from the warm surface. Contrarily, low‐level CMVs in the South Atlantic were frequently assigned to mid levels by the CO2‐slicing method in multilayer situations. We also noticed an apparent cross‐swath dependence in SMVs, whereby retrievals were less accurate on the eastern side of the MISR swath than on the western side. This artifact was traced back to sub‐pixel MISR co‐registration errors, which introduced cross‐swath biases in E‐W wind, N‐S wind, and height of 0.6 m s−1, 2.6 m s−1, and 210 m. Key Points More accurate MISR cloud‐motion vector heights in broken stratocumulus Detection of slight cross‐swath bias in MISR cloud‐motion vectors Good agreement in stratocumulus but larger differences in tropics

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.

Solar forecasting is essential for optimizing the integration of solar photovoltaic energy into a power grid. This study presents solar forecasting models based on satellite imagery. The cloud motion vector (CMV) model is the most popular satellite-image-based solar forecasting model. However, it assumes constant cloud states, and its accuracy is, thus, influenced by changes in local weather characteristics. To overcome this limitation, satellite images are used to provide spatial data for a new spatiotemporal optimized model for solar forecasting. Four satellite-image-based solar forecasting models (a persistence model, CMV, and two proposed models that use clear-sky index change) are evaluated. The error distributions of the models and their spatial characteristics over the test area are analyzed. All models exhibited different performances according to the forecast horizon and location. Spatiotemporal optimization of the best model is then conducted using best-model maps, and our results show that the skill score of the optimized model is 21% better than the previous CMV model. It is, thus, considered to be appropriate for use in short-term forecasting over large areas. The results of this study are expected to promote the use of spatial data in solar forecasting models, which could improve their accuracy and provide various insights for the planning and operation of photovoltaic plants.

The worldwide growing development of PV capacity requires an accurate forecast for a safer and cheaper PV grid penetration. Solar energy variability mainly depends on cloud cover evolution. Thus, relationships between weather variables and forecast uncertainties may be quantified to optimize forecast use. An intraday solar energy forecast algorithm using satellite images is fully described and validated over three years in the Paris (France) area. For all tested horizons (up to 6 h), the method shows a positive forecast skill score compared to persistence (up to 15%) and numerical weather predictions (between 20% and 40%). Different variables, such as the clear-sky index (Kc), solar zenith angle (SZA), surrounding cloud pattern observed by satellites and northern Atlantic weather regimes have been tested as predictors for this forecast method. Results highlighted an increasing absolute error with a decreasing SZA and Kc. Root mean square error (RMSE) is significantly affected by the mean and the standard deviation of the observed Kc in a 10 km surrounding area. The highest (respectively, lowest) errors occur at the Atlantic Ridge (respectively, Scandinavian Blocking) regime. The differences of relative RMSE between these two regimes are from 8% to 10% in summer and from 18% to 30% depending on the time horizon. These results can help solar energy users to anticipate—at the forecast start time and up to several days in advance—the uncertainties of the intraday forecast. The results can be used as inputs for other solar energy forecast methods.

This study focuses on the use of cloud motion vectors (CMV) and fast radiative transfer models (FRTM) in the prospect of forecasting downwelling surface solar irradiation (DSSI). Using near-real-time cloud optical thickness (COT) data derived from multispectral images from the spinning enhanced visible and infrared imager (SEVIRI) onboard the Meteosat second generation (MSG) satellite, we introduce a novel short-term forecasting system (3 h ahead) that is capable of calculating solar energy in large-scale (1.5 million-pixel area covering Europe and North Africa) and in high spatial (5 km over nadir) and temporal resolution (15 min intervals). For the operational implementation of such a big data computing architecture (20 million simulations in less than a minute), we exploit a synergy of high-performance computing and deterministic image processing technologies (dense optical flow estimation). The impact of clouds forecasting uncertainty on DSSI was quantified in terms of cloud modification factor (CMF), for all-sky and clear sky conditions, for more generalized results. The forecast accuracy was evaluated against the real COT and CMF images under different cloud movement patterns, and the correlation was found to range from 0.9 to 0.5 for 15 min and 3 h ahead, respectively. The CMV forecast variability revealed an overall DSSI uncertainty in the range 18–34% under consecutive alternations of cloud presence, highlighting the ability of the proposed system to follow the cloud movement in opposition to the baseline persistent forecasting, which considers the effects of topocentric sun path on DSSI but keeps the clouds in “fixed” positions, and which presented an overall uncertainty of 30–43%. The proposed system aims to support the distributed solar plant energy production management, as well as electricity handling entities and smart grid operations.

Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index (kc) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the kc and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric.

With increasing use of photovoltaic (PV) power generation by utilities and their residential customers, the need for accurate intra-hour and day-ahead solar irradiance forecasting has become critical. This paper details the development of a low cost all-sky imaging system and an intra-hour cloud motion prediction methodology that produces minutes-ahead irradiance forecasts. The SkyImager is designed around a Raspberry Pi single board computer (SBC) with a fully programmable, high resolution Pi Camera, housed in a durable all-weather enclosure. Our software is written in Python 2.7 and utilizes the open source computer vision package OpenCV. The SkyImager can be configured for different operational environments and network designs, from a standalone edge computing model to a fully integrated node in a distributed, cloud-computing based micro-grid. Preliminary results are presented using the imager on site at the National Renewable Energy Laboratory (NREL) in Golden, CO, USA during the fall of 2015 under a variety of cloud conditions.

The increasing use of renewable energies as a source of electricity has led to a fundamental transition of the power supply system. The integration of fluctuating weather-dependent energy sources into the grid already has a major impact on its load flows. As a result, the interest in forecasting wind and solar radiation with a sufficient accuracy over short time periods (<4 h) has grown. In this study, the short-term forecast of the effective cloud albedo based on optical flow estimation methods is investigated. The optical flow method utilized here is TV-L1 from the open source library OpenCV. This method uses a multi-scale approach to capture cloud motions on various spatial scales. After the clouds are displaced, the solar surface radiation will be calculated with SPECMAGIC NOW, which computes the global irradiation spectrally resolved from satellite imagery. Due to the high temporal and spatial resolution of satellite measurements, the effective cloud albedo and thus solar radiation can be forecasted from 5 min up to 4 h with a resolution of 0.05°. The validation results of this method are very promising, and the RMSE of the 30-min, 60-min, 90-min and 120-min forecast equals 10.47%, 14.28%, 16.87% and 18.83%, respectively. The paper gives a brief description of the method for the short-term forecast of the effective cloud albedo. Subsequently, evaluation results will be presented and discussed.

The Compact Midwave Imaging System (CMIS) is a wide field of view, multi-angle, multi-spectral push frame imager that relies on the forward motion of the satellite to create a two-dimensional (2D) image swath. An airborne demonstration of CMIS was successfully completed in January-February 2021 on the NASA Langley Research Center Gulfstream III. The primary objective of the four-flight campaign was to demonstrate the capability of this unique instrument to perform stereo observations of clouds and other particulates (e.g. smoke) in the atmosphere. It is shown that the midwave infrared (MWIR) spectral bands of CMIS provide a unique 24/7 capability with high resolution for accurate stereo sensing.The instrument relies on new focal plane array (FPA) technology, which provides excellent sensitivity at much warmer detector temperatures than traditional technologies. This capability enabled a compact, low-cost design that can provide atmospheric motion vectors and cloud heights to support requirements for atmospheric winds in the 2017-2027 Earth Science Decadal Survey. Applications include day/night observations of the planetary boundary layer, severe weather, and wildfires. A comparison with current space-based earth science instruments demonstrates that the SWIR/MWIR multi-spectral capability of CMIS is competitive with larger, more expensive instrumentation. Imagery obtained over a controlled burn and operating nuclear power plant demonstrated the sensitivity of the instrument to temperature variations. The system relies on a mature stereoscopic imaging technique applied to the same scene from two independent platforms to unambiguously retrieve atmospheric motion vectors (AMVs) with accurate height assignment.This capability has been successfully applied to geostationary and low-earth orbit satellites to achieve excellent accuracy. When applied to a ground-point validation case, the accuracy for the CMIS aircraft observations was 20 m and 0.3 m/s for cloud heights and motion vectors, respectively. This result was confirmed by a detailed error analysis with analytical and covariance models. The results for CMIS cases with under flights of Aeolus, CALIPSO and Aqua provided a good validation of expected accuracies. The paper also showed the feasibility of accommodating CMIS on CubeSats to enable multiple instruments to be flown in a leader-follower mode.

The aim of this chapter is to describe the use of satellite-derived surface solar irradiance (SSI) from the continuous optical image acquisition of geostationary meteorological satellites to produce solar power forecasting, typically for intraday time horizons from 0 to 6 h ahead. Different approaches and products of satellite-based SSI retrieval used for solar power estimations are first presented. Then two families of satellite-based solar forecasting method are described: The one considering only the temporal variability of SSI observed in quasi real-time by the satellite for a single location or an aggregated region. The one exploiting the ability of the satellite to observe the correlated spatial and temporal variability of SSI (cloud motion vectors or statistical approaches). The different possibilities of using in situ SSI or solar power monitoring in combination with satellite are also presented.