The new-generation Himawari-8 geostationary meteorological satellite of the Japan Meteorological Agency (JMA) started operation in July 2015 after the completion of in-orbit testing and checking of the overall system. Himawari-8 features the new Advanced Himawari Imager (AHI), which has 16 bands and double the spatial resolution of its MTSAT-series predecessor satellites . Full-disk imagery is obtained every 10 minutes, and regional observation at 2.5-minute intervals is also conducted. These significant improvements are expected to bring unprecedented levels of performance in nowcasting services and short-range weather forecasting systems. To leverage the full potential of the advanced imager, high precision in navigation and radiometric calibration is essential. This is estimated in off-line processes such as pattern matching for navigation and the Global Space-based Inter-Calibration System (GSICS) for radiometric calibration.
On 9 March 2016, JMA updated its ground processing system, including the image navigation and registration (INR) module, for further quality improvement. This update covered improvement of the band-to-band co-registration process for infrared bands, improvement of the resampling process, and implementation of a coherent noise reduction process. Results from the off-line processes showed that the update had improved Himawari Standard Data (HSD), which is Himawari-8/AHI L1B-equivalent data.
The next-generation geostationary meteorological satellite of the Japan Meteorological Agency (JMA), Himawari-8, entered operation on 7 July 2015. Himawari-8 features the new 16-band Advanced Himawari Imager (AHI), whose spatial resolution and observation frequency are improved over those of its predecessor MTSAT-series satellites. These improvements will bring unprecedented levels of performance in nowcasting services and short-range weather forecasting systems. In view of the essential nature of navigation and radiometric calibration in fully leveraging the imager’s potential, this study reports on the current status of navigation and calibration for the AHI. Image navigation is accurate to within 1 km, and band-to-band co-registration has also been validated. Infrared-band calibration is accurate to within 0.2 K with no significant diurnal variation, and is being validated using an approach developed under the GSICS project. Validation approaches are currently being tested for the visible and near-infrared bands. In this study, two of such approaches were compared and found to produce largely consistent results.
Spatial cross-talk has been discovered in the visible channel data of the Multi-functional Transport Satellite (MTSAT)-1R. The slight image blurring is attributed to an imperfection in the mirror surface caused either by flawed polishing or a dust contaminant. An image processing methodology is described that employs a two-dimensional deconvolution routine to recover the original undistorted MTSAT-1R data counts. The methodology assumes that the dispersed portion of the signal is small and distributed randomly around the optical axis, which allows the image blurring to be described by a point spread function (PSF) based on the Gaussian profile. The PSF is described by 4 parameters, which are solved using a maximum likelihood estimator using coincident collocated MTSAT-2 images as truth. A subpixel image matching technique is used to align the MTSAT-2 pixels into the MTSAT-1R projection and to correct for navigation errors and cloud displacement due to the time and viewing geometry differences between the two satellite observations. An optimal set of the PSF parameters is derived by an iterative routine based on the 4-dimensional Powell’s conjugate direction method that minimizes the difference between PSF-corrected MTSAT-1R and collocated MTSAT-2 images. This iterative approach is computationally intensive and was optimized analytically as well as by coding in assembly language incorporating parallel processing. The PSF parameters were found to be consistent over the 5-days of available daytime coincident MTSAT-1R and MTSAT-2 images, and can easily be applied to the MTSAT-1R imager pixel level counts to restore the original quality of the entire MTSAT-1R record.