The state-of-the-art progress in cloud detection, identification, and tracking approaches: a systematic review

A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too

Cloud tomography applied to sky images: a virtual testbed

Two tomographic techniques are applied to two simulated sets of sky images with different cloud fraction. The Algebraic Reconstruction Technique (ART) is applied to optical depth maps from sky images to reconstruct 3-D cloud extinction coefficients without considering multiple scattering effects. Reconstruction accuracy is explored for different products, including surface irradiance and extinction coefficients, and as a function of the number of available sky imagers and setup distance. Increasing the number of imagers improves the accuracy of the 3-D reconstruction: for surface irradiance, the error decreases significantly up to four imagers at which point the improvements become marginal. But using nine imagers gives more robust results in practical situations in which the circumsolar region of images has to be excluded due to poor cloud detection. The ideal distance between imagers was also explored: for a cloud height of 1 km, increasing distance up to 3 km (the domain length) improved the 3-D reconstruction. An iterative reconstruction technique that iteratively updated the source function improved the results of the ART by minimizing the error between input red radiance images and reconstructed red radiance simulations. For the best case of a nine-imager deployment, the ART and iterative method resulted in 53.4% and 33.6% relative mean absolute error for the extinction coefficients, respectively.The authors acknowledge funding from the California Energy Commission EPIC program. Felipe Mejia was supported by the National Science Foundation Graduate Research Fellowship under Grant No. (DGE-1144086). In addition, Íñigo de la Parra has been partially supported by the Spanish State Research Agency (AEI) and FEDER-UE under grants DPI2016-80641-R and DPI2016-80642-R

Cloud base height estimates from sky imagery and a network of pyranometers

Cloud base height (CBH) is an important parameter for physics-based high resolution solar radiation modeling. In sky imager-based forecasts, a ceilometer or stereographic setup is needed to derive the CBH; otherwise erroneous CBHs lead to incorrect physical cloud velocity and incorrect projection of cloud shadows, causing solar power forecast errors due to incorrect shadow positions and timing of shadowing events. In this paper, two methods to estimate cloud base height from a single sky imager and distributed ground solar irradiance measurements are proposed. The first method (Time Series Correlation, denoted as “TSC”) is based upon the correlation between ground-observed global horizontal irradiance (GHI) time series and a modeled GHI time series generated from a sequence of sky images geo-rectified to a candidate set of CBH. The estimated CBH is taken as the candidate that produces the highest correlation coefficient. The second method (Geometric Cloud Shadow Edge, denoted as “GCSE”) integrates a numerical ramp detection method for ground-observed GHI time series with solar and cloud geometry applied to cloud edges in a sky image. CBH are benchmarked against a collocated ceilometer and stereographically estimated CBH from two sky imagers for 15 min median-filtered CBHs. Over 30 days covering all seasons, the TSC method performs similarly to the GCSE method with nRMSD of 18.9% versus 20.8%. A key limitation of both proposed methods is the requirement of sufficient variation in GHI to enable reliable correlation and ramp detection. The advantage of the two proposed methods is that they can be applied when measurements from only a single sky imager and pyranometers are available