Meteorological satellites have become an indispensable tool for weather and land observation. Traditionally, geostationary (GEO) satellites have been used in operational meteorological services due to their high temporal resolution, while polar-orbiting satellites, with their high spatial resolution, are applied more to monitor environmental change and natural disasters. The development of China’s next-generation geostationary meteorological satellites (the FY-4 series) represents an exciting expansion of Chinese non-meteorological remote sensing capabilities. The first satellite (FY-4A) of the FY-4 series was launched on 11 December 2016. The Advanced Geosynchronous Radiation Imager (AGRI) on board FY-4A has 14 spectral bands (increased from the 5 bands of FY-2) that are quantized with 12 bits per pixel (up from 10 bits for FY-2) and sampled at 1 km at nadir in the visible (VIS), 2 km in the near-infrared (NIR), and 4 km in the remaining IR spectral bands (compared with 1.25 km for VIS, no NIR, and 5 km for IR of FY-2). In later satellites in the FY-4A series, the AGRI channel number will be gradually increased from 14 to 18 with IR spatial resolution of 2 km, and the full-disk temporal resolution will be enhanced from 15 to 5 min. With their improved spectral, spatial, and temporal resolution properties, the FY-4 series will gradually approach low earth orbiting (LEO) sensors in spatial and spectral resolution, which will offer greater opportunity and capability for observing small objects and rapid changes in land, ocean, and atmosphere. This review paper provides an introduction to the Chinese FY-4 observation capabilities, a comparison of FY-4 with other new-generation GEO and LEO weather satellites, and associated non-meteorological applications. A series of typical examples based on recent and on-going operational work in National Satellite Meteorological Center of China Meteorological Administration (NSMC/CMA ) that use FY-4A data for non-meteorological applications are demonstrated and discussed, including (i) aerosol monitoring, (ii) dust monitoring, (iii) volcanic ash detection and aviation applications, (iv) fire detection and dynamical evaluation, (v) water body detection, and (vi) floating algae monitoring. The paper concludes with a synthesis of these application areas and the challenges that CMA has to address for future research, technological innovation, and in-depth applications.

With the rapid development of the cellular network, network planning is increasingly important. Generating large-scale urban cellular traffic contributes to network planning via simulating the behaviors of the planned network. Existing methods fail in simulating the long-term temporal behaviors of cellular traffic while cannot model the influences of the urban environment on the cellular networks. We propose a knowledge-enhanced GAN with multi-periodic patterns to generate large-scale cellular traffic based on the urban environment. First, we design a GAN model to simulate the multi-periodic patterns and long-term aperiodic temporal dynamics of cellular traffic via learning the daily patterns, weekly patterns, and residual traffic between long-term traffic and periodic patterns step by step. Then, we leverage urban knowledge to enhance traffic generation via constructing a knowledge graph containing multiple factors affecting cellular traffic in the surrounding urban environment. Finally, we evaluate our model on a real cellular traffic dataset. Our proposed model outperforms three state-of-art generation models by over 32.77%, and the urban knowledge enhancement improves the performance of our model by 4.71%. Moreover, our model achieves good generalization and robustness in generating traffic for urban cellular networks without training data in the surrounding areas.

Two back-propagation artificial neural network retrieval models have been developed for obtaining the dust aerosol optical depth (AOD) and dust-top height (DTH), respectively, from Atmospheric InfraRed Sounder (AIRS) brightness temperature (BT) measurements over Taklimakan Desert area. China Aerosol Remote Sensing Network (CARSNET) measurements at Tazhong station were used for dust AOD validation. Results show that the correlation coefficient of dust AODs between AIRS and CARSNET reaches 0.88 with a deviation of −0.21, which is the same correlation coefficient as the AIRS dust AOD and the Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product. In the AIRS DTH retrieval model, there is an option to include the collocated MODIS deep blue (DB) AOD as additional input for daytime retrieval; the independent dust heights from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used for AIRS DTH validation, and results show that the DTHs derived from the combined AIRS BT measurements and MODIS DB AOD product have better accuracy than those from AIRS BT measurements alone. The correlation coefficient of DTHs between AIRS and independent CALIOP dust heights is 0.79 with a standard deviation of 0.41 km when MODIS DB AOD product is included in the retrieval model. A series of case studies from different seasons were examined to demonstrate the feasibility of retrieving dust parameters from AIRS and potential applications. The method and approaches can be applied to process measurements from advanced infrared (IR) sounder and high-resolution imager onboard the same platform.