Information about moisture distribution and transportation in the preconvection environment is very important for nowcasting and forecasting severe weather events. The Advanced Himawari Imager (AHI) onboard the Japanese Himawari‐8/‐9 provides high temporal and spatial resolution moisture information useful for weather monitoring and forecasting. Algorithms have been developed for three‐layered precipitable water (LPW: surface to 0.9, 0.9–0.7, and 0.7–0.3 in sigma vertical coordinate) retrievals from AHI infrared band radiances using a Geostationary Operational Environmental Satellite‐R series algorithm working group algorithm. The LPW products from AHI have been validated with in situ measurements. An important application of the AHI LPW product is to improve local severe storm forecasts through assimilating high temporal and spatial resolution moisture information into regional‐ and storm‐scale numerical weather prediction (NWP) models. Assimilation techniques and approaches have been developed; the impact on precipitation forecasts for local severe storm over land from the assimilation of LPWs from AHI shows improvement on heavy precipitation forecasts over those from the assimilation of conventional data. Comparisons between AHI infrared band radiance assimilation and LPW assimilation show overall similar or comparable impact on precipitation forecast. The approaches for assimilating LPW can be applied to the assimilation of data from other advanced imagers such as the Advanced Baseline Imager onboard the U.S. next generation of Geostationary Operational Environmental Satellites‐R series, the Advanced Geosynchronous Radiation Imager onboard the Chinese FengYun‐4 series, and the Flexible Combined Imager onboard the upcoming European Meteosat Third Generation.

Strapdown inertial navigation system/celestial navigation system (SINS/CNS) integrated navigation is a fully autonomous and high precision method, which has been widely used to improve the hitting accuracy and quick reaction capability of near-Earth flight vehicles. The installation errors between SINS and star sensors have been one of the main factors that restrict the actual accuracy of SINS/CNS. In this paper, an integration algorithm based on the star vector observations is derived considering the star sensor installation error. Then, the star sensor installation error is accurately estimated based on Kalman Filtering (KF). Meanwhile, a local observability analysis is performed on the rank of observability matrix obtained via linearization observation equation, and the observable conditions are presented and validated. The number of star vectors should be greater than or equal to 2, and the times of posture adjustment also should be greater than or equal to 2. Simulations indicate that the star sensor installation error could be readily observable based on the maneuvering condition; moreover, the attitude errors of SINS are less than 7 arc-seconds. This analysis method and conclusion are useful in the ballistic trajectory design of near-Earth flight vehicles.

No abstract

No abstract

No abstract