GNSS-R Data Assimilation Efforts at Spire Global: From Ocean Winds to DDM’s
In an effort to explore the benefit of NASA Cyclone Global Navigation Satellite System (CYGNSS)/Delay-Doppler Map (DDM) data for improving global/operational numerical weather analysis and forecasting, we included a new assimilation capability in the Joint Effort for Data Assimilation Integration (JEDI) infrastructure. We utilized and adapted the DDM forward operator described in Huang et al., 2021, in which CYGNSS/DDM power is regarded as one of the lowest forms–level 1a–of calibrated observables in global navigation satellite system reflectometry. This type of data is generally regarded as more beneficial for improving data assimilation and forecasting skill, as compared to the level 2 wind speed retrieval.
For our project, we utilized the Unified Forecast System Finite-Volume on a Cubed-Sphere (UFS FV3) global NWP model and conducted global observational data impact assessment experiments in operational-like conditions, during the spring of 2022. In previous studies, CYGNSS level 1 or level 2 data have been used during active tropical cyclone seasons in the Tropics, and in regional NWP. In our case, the global model application enabled us to investigate the CYGNSS/DDM data impact in the subtropical and midlatitude regions, which were not evaluated in previous CYGNSS data impact studies. In this presentation, we will explain the specifics of the CYGNSS/DDM observation operator implementation in JEDI and present preliminary results on data assimilation and forecast skill improvements.
Reference:
- Huang et al., “A Forward Model for Data Assimilation of GNSS Ocean Reflectometry Delay-Doppler Maps,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 3, pp. 2643-2656, March 2021, doi: 10.1109/TGRS.2020.3002801.
