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.

Climate Cloud Model Forecast Verification-an Engineering Perspective

The processes relevant to the verification of cloud forecasts generated by climate models are discussed from an engineering perspective. These processes include an assessment of cloud product requirements to be evaluated, the creation of a verification test plan including procedures and data to be analyzed, the development of independent sources of validation or truth datasets, and the quantitative comparisons between the cloud forecast products and the truth data needed to establish model performance. The engineering perspective means minimal effort is focused on assessing the veracity of the physics contained in the cloud forecast model, rather emphasis is upon evaluating the results produced by it. It is postulated that these procedures are critical to improve the reliability of climate model predictions. The World Meteorological Organization has stated accuracy requirements for cloud products created from satellite observations, through the Global Climate Observing System (GSOC) program; however, no similar requirements have been defined for cloud forecast products. A statement of accuracy requirements is urgently needed. Meanwhile, it is assumed herein that cloud observation and cloud forecast requirements are identical. The assessment of model performance exploits high quality, manually-generated cloud truth products created from remotely-sensed satellite data which serve as truth data. Results show clouds under-specified in reanalysis cloud datasets created for use to initialize climate models but an over-specification of clouds by the cloud forecast model, in short-range predictions. This system level analysis demonstrates the need to improve the accuracy of cloud forecasts, especially lower-level water clouds which are responsible for most of the uncertainty in climate model predictions.

Creating Truth Data to Quantify the Accuracy of Cloud Forecasts from Numerical Weather Prediction and Climate Models

Clouds are critical in mechanisms that impact climate sensitivity studies, air quality and solar energy forecasts, and a host of aerodrome flight and safety operations. However, cloud forecast accuracies are seldom described in performance statistics provided with most numerical weather prediction (NWP) and climate models. A possible explanation for this apparent omission involves the difficulty in developing cloud ground truth databases for the verification of large-scale numerical simulations. Therefore, the process of developing highly accurate cloud cover fraction truth data from manually generated cloud/no-cloud analyses of multispectral satellite imagery is the focus of this article. The procedures exploit the phenomenology to maximize cloud signatures in a variety of remotely sensed satellite spectral bands in order to create accurate binary cloud/no-cloud analyses. These manual analyses become cloud cover fraction truth after being mapped to the grids of the target datasets. The process is demonstrated by examining all clouds in a NAM dataset along with a 24 h WRF cloud forecast field generated from them. Quantitative comparisons with the cloud truth data for the case study show that clouds in the NAM data are under-specified while the WRF model greatly over-predicts them. It is concluded that highly accurate cloud cover truth data are valuable for assessing cloud model input and output datasets and their creation requires the collection of satellite imagery in a minimum set of spectral bands. It is advocated that these remote sensing requirements be considered for inclusion into the designs of future environmental satellite systems.