High-resolution Geostationary Satellite Research Articles

Impact of Assimilating Advanced Himawari Imager Channel 16 Data on Precipitation Prediction over the Haihe River Basin

Assimilation of high-resolution geostationary satellite data is of great value for precise precipitation prediction in regional basins. The operational geostationary satellite imager carried by the Himawari-8 satellite, Advanced Himawari Imager (AHI), has two additional water vapor channels and four other channels compared with its predecessor, MTSAT-2. However, due to the uncertainty in surface parameters, AHI surface-sensitive channels are usually not assimilated over land, except for the three water vapor channels. Previous research showed that the brightness temperature of AHI channel 16 is much more sensitive to the lower-tropospheric temperature than to surface emissivity, which is similar to the three water vapor channels 8–10. As a follow-up work, this paper evaluates the effectiveness of assimilating brightness temperature observations over land from both the three AHI water vapor channels and channel 16 to improve watershed precipitation forecasting through both case analysis (in the Haihe River basin, China) and batch tests. It is found that assimilating AHI channel 16 can improve the upstream near-surface atmospheric temperature forecast, which in turn affects the development of downstream weather systems. The precipitation forecasting test results indicate that adding the terrestrial observations of channel 16 to the assimilation of AHI data can improve short-term precipitation forecasting in the basin.

Seamless maps of major elements of the Moon: Results from high-resolution geostationary satellite

Major elements such as Fe, Ti, Mg, Al, Ca and Si play very important roles in understanding the origin and evolution of the Moon. Previous maps of these major elements derived from orbital data are based on mosaic images or low-resolution gamma-ray data. The hue variations and gaps among orbital boundaries in the mosaic images are not conducive to geological studies. This paper aims to produce seamless and homogenous distribution maps of major elements using the single-exposure image of the whole lunar disk obtained by China’s high-resolution geostationary satellite, Gaofen-4, with a spatial resolution of ∼ 500 m. The elemental contents of soil samples returned by Apollo and Luna missions are regarded as ground truth, and are correlated with the reflectance of the sampling sites extracted from Gaofen-4 data. The final distribution maps of these major oxides are generated with the statistical regression model. With these products, the average contents and proportions of the major elements for maria and highlands were estimated and compared. The results showed that SiO2 and TiO2 have the highest and lowest fractions in mare and highland areas, respectively. Moreover, the relative concentrations of these elements could serve as indicators of geologic processes, e.g., the obviously asymmetric distributions of Al2O3, CaO and SiO2 around Tycho crater may suggest that Tycho crater was formed by an oblique impact from the southwest direction.

Effects of high-resolution geostationary satellite imagery on the predictability of tropical thunderstorms over Southeast Asia

Abstract. Tropical thunderstorms cause significant damage to property and lives, and a strong research interest exists in the advances and improvement of thunderstorm predictability by satellite observations. Using high-resolution (2 km and 10 min) imagery from the geostationary satellite, Himawari-8, recently launched over Southeast Asia, we examined the earliest possible time for the prediction of thunderstorms as compared to the potential of low-resolution (4 km and 30 min) imagery of the former satellite. We compared the lead times of high- and low-resolution imageries of 60 tropical thunderstorms that occurred in August 2017. These thunderstorms were identified by the decreasing trend in the 10.45 µm brightness temperature (BT11) by over 5 K per 10 min for the high-resolution imagery and 15 K per 30 min for the low-resolution imagery. The lead time was then calculated over the time from the initial state to the mature state of the thunderstorm, based on the time series of a minimum BT11 of thunderstorm pixels. The lead time was found to be 90–180 min for the high-resolution imagery, whereas it was only 60 min (if detectable) for the low-resolution imagery. These results indicate that high-resolution imagery is essential for substantial disaster mitigation owing to its ability to raise an alarm more than 2 h ahead of the mature state of a tropical thunderstorm.

Top Cloud Motion Field of Typhoon Megi-2016 Revealed by GF-4 Images

Gaofen-4 (GF-4) is the first high-resolution geostationary satellite of China, launched on December 29, 2015. Its visible-near infrared optical sensor is capable of imaging the earth at 50 m resolution, covering a 512 km $\times512$ km area with a minimum imaging interval of 20 s. More than 300 GF-4 images, taken in four sequences with imaging rates of 36 and 69 s per scene, captured the development of Typhoon Megi-2016—from its peak in the afternoon of September 26, 2016 to its dispersion two days later on September 28. These consecutive images recorded the motion field of the typhoon’s top clouds nearly continuously. By using advanced image matching technology, the motion has been estimated for every pixel, at subpixel accuracy from image pairs with 179 and 206 s intervals. The process has generated time series of atmospheric motion vector (AMV) fields at 50 m spatial resolution for the whole imaged area. It is the first time, to our knowledge, that such high-resolution and nearly continuous AMV data of a typhoon system have been produced. The data provide accurate measurements of the typhoon top cloud motion speed and direction, and reveal quantitative details of the motion field spatiotemporal evolution at high altitude. The finding that the high altitude top cloud motion speed is significantly lower than that recorded at low altitude below the planetary boundary layer is of scientific value and further exploitation of the motion data can lead to a better understanding of typhoon dynamics and thus improve cyclone modeling.