Geosynchronous meteorological satellites, operating at an altitude of 35800 km above the equator, frequently capture images of the Earth disk. The successive image derives atmospheric motion vectors. They have been monitoring various weather systems continuously and are indispensable tools for precise weather forecasting. Our study shows image navigation and atmospheric motion vector algorithms for FY geosynchronous meteorological satellites.

The geosynchronous satellite takes Earth observation pixel by pixel. The observation pixels are assembled to form images. The image assembling contains two major components: image registration and image navigation. Image registration refers to the process of ensuring that each pixel within an image is correctly aligned with its nominal Earth location within a specified accuracy, which measures pointing stability. Image navigation involves determining the location of each pixel within an image in terms of Earth latitude and longitude, which measures absolute pointing accuracy. Both image registration and navigation are critical steps in image assembling, impacting all subsequent data processing procedures and product quality. Due to the satellite’s considerable distance from Earth, the accuracy of attitude determination significantly affects image navigation quality. Precise image navigation requires accurate measurement of the position and the attitude of the satellite at any observation time. The FY-2 satellite has a spin-stabilized attitude. The Earth position within the image is used to determine the attitude of the satellite. The time series of the satellite orientation relative to the centerline of the Earth disk provides information on the attitude parameter in the north-south direction. The angle between the sun and the Earth serves as a reference for aligning the Earth observation pixels position in the scan line together with the attitude parameter in the east-west direction. The solution to the image navigation model requires the parameters to be well-defined, measured, transformed, and applied within appropriate coordinate systems while maintaining correct astronomical relationships. The FY-4 satellite, on the other hand, is three-axis stabilized. The additional moving equipment causes uneven shifts of the satellite. Moreover, the satellite is heated at the side facing the sun which makes uneven temperature distribution in the spacecraft with diurnal variation. Both factors affect the orientation of the observation vectors. Thus, the operation of the image registration and navigation for FY-4 rely on the interactions between the satellite and the ground system more closely. The star positions are used to determine the attitude of the satellite. Using previous observation, the ground system estimates future positions of the stars and possible observation vector orientation errors caused by uneven heating. Those parameters are transmitted to the satellite, which then adjusts its attitude to maintain stability and compensate for observation vector deviations. Tracing clouds and other features in the successive images provides an estimation of the scene’s displacements, which represent atmospheric motion vectors. The height of the wind vector is determined with a physical method. For opaque clouds, the infrared window brightness temperature reflects the upwelling radiation energy from the cloud. The cloud level is identified at the height where the feature brightness temperature fits the forecast model temperature. For semi-transparent clouds, a part of the energy is from the cloud, the other part is from the background under the cloud. Since the semi-transparent status is only related to cloud density, not related to the observation wavelengths. In the cloudy region, there is a linear relationship between observations from the window and absorption channels. By using observations from both the window and absorption channels, the portions of upwelling radiation energy from the cloud and from the background are well estimated. This approach needs the locations of both the cloudy and the cloud-free pixels, and the upwelling energy from those locations. Based on the moving status of the pixels during the feature tracing stage, the cloudy and the cloud-free pixels are well separated. The upwelling radiation energy from the under cloud background is estimated with data from the nearest cloud-free pixels. This algorithm provides a more accurate estimation of semi-transparent cloud heights.

By using algorithms introduced in our study achieve more accurate observation, navigation, and wind derivation. For both FY-2 and FY-4, all the parameters are produced automatically and routinely without any manual operation. The accuracy of image navigation reaches pixel level. The image navigation accuracy approaches pixel level. The accuracy and distribution of the atmospheric vectors are also improved. Meteorological satellite data processing involves a long chain including many steps simulating the radiation transmission process from the observation objective to the sensor. A deep understanding and the precise expression of the real situation in the data processing algorithm ensure better product quality.