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Aerosol particles affect the Earth’s radiative balance and represent one of the largest uncertainties in climate research. The removal of clouds is the first step and critical in the aerosol retrievals. However, the cloud detection is still challenging. Here, a novel simplified cloud detection algorithm (SCDA) is proposed to identify the cloud and clear-sky over land and based on a simplified radiative transfer model (RTM). The fewer input bands, dynamic thresholds, and only one parameter to be modified are the main advantages of the algorithm, which can be applied to different satellite sensors. In this article, we apply SCDA to the Himawari-8 data in 2016 for preliminary analysis. The detection results are validated using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical feature mask (VFM) data and the National Centers for Environmental Information (NCEI) ground-based observation data. We also compare the results with the Himawari-8 cloud products from the Japan Aerospace Exploration Agency (JAXA). Compared with CALIPSO VFM data and NCEI ground-based observation data, the correct rate of SCDA cloud detection result is 86.08% and 79.86%, which are higher than that of Himawari-8 cloud products (85.71% and 78.89%). The correct rate of SCDA clear-sky detection result is 88.33% and 87.85%, which are close to the correct rate of Himawari-8 clear-sky products (90.54% and 88.63%). The overall performance of the SCDA is comparable to that of the threshold method for JAXA Himawari-8 cloud products. Therefore, the SCDA can provide accurate cloud mask with only one threshold to be modified and few input parameters. [ABSTRACT FROM AUTHOR]

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To promote the intelligent and accurate management of river basins, especially large basins which involve many catchments, it is highly required to develop a useful platform to effectively coordinate arithmetic resources and data, and simultaneously help to make decisions based on the real-time calculation. In this study, a multi-centre cloud platform architecture called 3L4C was constructed, which includes a Cloud-edge-terminal Layer (3L), data centre, model centre, control centre, and customer-service centre (4C). Data fusion technology and an air-land-water coupled model were constructed. Based on HTML5, JavaScript, and Java, an integrated water environment management platform was created and applied to the Three Gorges Reservoir Basin, China. The platform was tested and successfully used for automatic water quality prediction, water environment pollution analysis and control, early warning of abnormal water quality, and emergency water pollution incident evaluation. This platform quickly and accurately forecasts and perfectly displays past, present and future state of the water environment, and offers beneficial support for management decisions in various water environment departments. • A cloud platform architecture for environmental management decision-making. • Three Gorges Reservoir Basin Water Environment Management Platform. • Integrate water environment data from multiple sources. [ABSTRACT FROM AUTHOR]

In the era of big data astronomy, next generation telescopes and large sky surveys produce data sets at the TB or even PB level. Due to their large data volumes, these astronomical data sets are extremely difficult to transfer and analyze using personal computers or small clusters. In order to offer better access to data, data centers now generally provide online science platforms that enable analysis close to the data. The Chinese Virtual Observatory (China-VO) is one of the member projects in the International Virtual Observatory Alliance and it is dedicated to providing a research and education environment where globally distributed astronomy archives are simple to find, access, and interoperate. In this study, we summarize highlights of the work conducted at the China-VO, as well the experiences and lessons learned during the full life-cycle management of astronomical data. Finally, We discuss the challenges and future trends for astronomical science platforms. [ABSTRACT FROM AUTHOR]