Monday, 29 January 2024
Hall E (The Baltimore Convention Center)
In the age of the Global Navigation Satellite System (GNSS), the latest generation of Low Earth orbit (LEO) Radio Occultation (RO) satellites, such as Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), receive signals across multiple bands from various GNSS systems including the Global Positioning System (GPS), GLONASS, and Galileo. This trend is set to expand with the rise of RO sensors through initiatives like NOAA's Commercial Weather Data for the RO (CWD RO) program, which aims to explore commercial Small Satellite-based RO data in Numerical Weather Prediction (NWP). With the increasing number of advanced GNSS RO sensors, there are pressing needs for swift inter-calibration, validation, and quality assurance of Small Satellite-based multi-RO sensors.
This paper introduces an efficient algorithm for predicting occurrences of RO limb-sounding events between GNSS transmitters and LEO RO receivers. The algorithm uses the Simplified orbital Perturbation Model (SGP4) with Two-Line Element (TLE) inputs to predict the positions and velocities of LEO and GNSS satellites. By establishing a functional relationship between RO profile impact height and direct height learned from previous RO sounding events, the algorithm solves coupled equations at each instance, predicting time, location (longitude and latitude), and occurrence of rising or setting RO limb sounding events. The prediction method was validated by comparing predicted RO events with COSMIC-2 RO observations and assessing the accuracy of the predicted ground tracks, RO event counts, and RO event distribution relative to antenna view angle. The applications of the prediction tool span RO mission planning, pre-scheduling radiosonde launches for collocated RO limb sounding to support RO sensor and radiosonde inter-calibration, and aiding inter-calibration among multiple RO sensors using simultaneous radio occultation (SRO) method. In this study, we also demonstrate how the prediction tool facilitates the projection of temporal and spatial RO occurrences and signal-to-noise ratio (SNR) distributions for upcoming missions, accommodating different receiver antenna designs (single patch antenna vs. beam steering phased array antenna) and incorporating transmitters like GPS, GLONASS, Galileo, BeiDou, and QZSS. Our RO prediction tool effectively addresses the demands of planning, inter-calibration, and quality monitoring for increasing RO missions.
This paper introduces an efficient algorithm for predicting occurrences of RO limb-sounding events between GNSS transmitters and LEO RO receivers. The algorithm uses the Simplified orbital Perturbation Model (SGP4) with Two-Line Element (TLE) inputs to predict the positions and velocities of LEO and GNSS satellites. By establishing a functional relationship between RO profile impact height and direct height learned from previous RO sounding events, the algorithm solves coupled equations at each instance, predicting time, location (longitude and latitude), and occurrence of rising or setting RO limb sounding events. The prediction method was validated by comparing predicted RO events with COSMIC-2 RO observations and assessing the accuracy of the predicted ground tracks, RO event counts, and RO event distribution relative to antenna view angle. The applications of the prediction tool span RO mission planning, pre-scheduling radiosonde launches for collocated RO limb sounding to support RO sensor and radiosonde inter-calibration, and aiding inter-calibration among multiple RO sensors using simultaneous radio occultation (SRO) method. In this study, we also demonstrate how the prediction tool facilitates the projection of temporal and spatial RO occurrences and signal-to-noise ratio (SNR) distributions for upcoming missions, accommodating different receiver antenna designs (single patch antenna vs. beam steering phased array antenna) and incorporating transmitters like GPS, GLONASS, Galileo, BeiDou, and QZSS. Our RO prediction tool effectively addresses the demands of planning, inter-calibration, and quality monitoring for increasing RO missions.
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