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Fengyun meteorological satellites have undergone a series of significant developments over the past 50 years. Two generations, four types, and 21 Fengyun satellites have been developed and launched, with 9 currently operational in orbit. The data obtained from Fengyun satellites is employed in a multitude of applications, including weather forecasting, meteorological disaster prevention and reduction, climate change, global environmental monitoring, and space weather. These data products and services are made available to the global community, resulting in tangible social and economic benefits. In 2023, two Fengyun meteorological satellites were successfully launched. This report presents an overview of the two recently launched Fengyun satellites and currently in orbit Fengyun satellites, including an evaluation of their remote sensing instruments since 2022. Additionally, it addresses the subject of Fengyun satellite data archiving, data services, application services, international cooperation, and supporting activities. Furthermore, the development prospects have been outlined.

Using meteorological satellite data and the reanalysis dataset from European Centre for Medium Range Weather Forecasts(ERA5) and others, based on the accuracy evaluation of FY-3D/VASS temperature in North America, the climatic background, development and evolution of North America winter storm Uri in 2021, the triggering effects of polar vortex activities on Uri and the characteristics of atmospheric environment causing extreme low temperature and snowfall are studied. The results show that the average absolute error of FY-3D/VASS temperature at 100, 400 hPa and 850 hPa are 1.14℃, 1.44℃ and 2.63℃ respectively. It shows that FY-3D/VASS temperature can meet the demand of global extreme cold event monitoring, and it is an important data source in regions where conventional meteorological observation is insufficient. FY-3D/VASS temperature analysis shows that in February 2021, when winter storm Uri is active, the temperature in the western part of the key area of polar cold air activity in North America (50°-150°W, 50°-80°N) is 4-8℃ lower than climate mean. The cold air intensity is the strongest in the first ten days of February, and the maximum percentage of temperature anomaly is about 70%. Under the guidance of the warm high ridge in the northeast Pacific, the polar vortex intensifies and extends southward, and the cold air breaks out southward during the vertical rotation of the horizontal trough on the west side of the polar vortex center. The abnormal southward extension of the upper troposphere potential vorticity in high latitude provides upper-level dynamic forcing for the formation of Uri. The cold air in the low layer extends southward and intersects with the warm and humid airflow transported northward over the Gulf of Mexico along the west side of the subtropical high in southern United States. They trigger the low-level vortex of storm Uri and the rapid development of cloud system. The cloud causing strong snowfall of storm Uri shows convective characteristics, with the cloud top brightness temperature reaching lower than -40℃ and even lower than -52℃ in some areas. Lightning is also active in the storm cloud system which causes heavy snow.

Severe convection is a disastrous mesoscale weather system. The early detection of such systems is very important for saving peoples' lives and properties. Previous studies address the issue mainly based on thresholding methods, which are not robust and accurate enough. In this paper, we propose a novel semantic segmentation method (Dugs-UNet) to solve the problem. Our method is based on the well-known U-Net framework. As convective clouds mimic fluids, its detection faces two important challenges. First, the shape and boundary features of clouds need to be carefully exploited. Second, the positive and negative samples for convection detection are very imbalanced. To address the two challenges, our method was carefully developed. Regarding the importance of the shape and boundary features for convective target detection, we introduce a shape stream module to extract these features. Also, a data-dependent upsample operation is adopted in the decoder of U-Net to effectively utilize the features. This is one of our contributions. To address the imbalance issue for convective target detection, the a focal loss function is employed to train our method, which is another contribution. Experimental results of 2018 Fengyun-4A satellite observations in China demonstrate the effectiveness of the proposed method. Compared to conventional thresholding-based methods and deep semantic segmentation algorithms such as SegNet, PSPNet, DeepLav-v3+ and U-Net, the proposed approach performs the best. [ABSTRACT FROM AUTHOR]

Based on atmospheric motion vectors (AMVs) derived from the Fengyun-4 meteorological satellite (FY-4), in this paper, integrated multi-satellite retrievals for GPM precipitation and reanalysis datasets and the vertical distribution characteristics of FY-4 AMVs, their application in the identification of the South Asian high (SAH) anticyclone and their application in the real-time monitoring of rainstorm disasters are studied. The results show that the AMVs' vertical distribution characteristics are different across regions and seasons. AMVs from 150 to 350 hPa can be chosen as the upper troposphere wind (the total number accounts for about 77.2% on average). The center and shape of the upper tropospheric anticyclone obtained from AMVs are close to or slightly southward compared with those of the SAH at 200 hPa obtained from the ERA5 geopotential height. The SAH ridge line identified using the upper troposphere AMV zonal wind (the zonal wind is equal to zero) is slightly southward by about 1–2 degrees of latitude from that identified using ERA5 at 200 hPa but with a similar seasonal advance. The upper troposphere AMV can be used to monitor the location of the SAH and the evolution of its ridge line. The abnormally strong precipitation in South China is related to the location of the SAH and its ridge line. When the precipitation is abnormally strong/weak, the upper troposphere AMV deviation airflow shows divergence/convergence. During the "Dragon Boat Water" period in South China in 2022, strong precipitation occurred in the strong westerly winds or divergent flow on the northeast side of the upper troposphere anticyclone obtained from AMVs, and the precipitation intensity was the strongest when the divergence reached its peak, but this is not shown clearly in the EAR5 dataset. [ABSTRACT FROM AUTHOR]