NOAA STAR 1D-var Retrieval Algorithm to Process Radio Occultation Data
Presenter:
Stanislav Kireev
Global Science and Technology, Inc.
NOAA/STAR
Poster
Temperature and water vapor play a crucial role in weather and climate. Accurate global water vapor and temperature estimates, particularly in the middle and lower troposphere (LT), are extremely important for understanding the physics of convective cloud systems, precipitation, the hydrological cycle, and the energy balance of the Earth. Global Positioning System (GPS) Radio Occultation (RO) is the first technique that can provide a high vertical resolution all-weather refractivity profile, which is a function of pressure, temperature and moisture. Launched in 2006, the Formosa Satellite Mission 3–Constellation Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT-3/COSMIC) has demonstrated the great value of RO data in ionosphere, climate, meteorological research, and operational weather forecasting. A COSMIC follow-up mission, COSMIC-2 is scheduled to be launched in June 2019. In preparation for COSMIC-2, NOAA/NESDIS/STAR RO team is developing one-dimensional variational (1D-var) retrieval algorithm to derive temperature and water vapor profiles in the troposphere from RO refractivity. In this presentation, we detail the 1D-var method and analyze retrieved tropospheric temperature and water vapor profiles. COSMIC refractivity obtained from UCAR were used as inputs while the operational COSMIC-2 data are not available. How the first guess profiles, the observational error covariance matrix, and background covariance matrix are specified will be explained. Averaging kernels and the impacts of using different first guess profiles (NCEP Forecast vs NCEP Analysis) on the retrieval results are also estimated. The 1D-var water vapor results are also compared with the directly derived water vapor profiles when the first guess temperature profiles and COSMIC refractivity are used as the known variables. The accuracy of retrievals is validated by means of monthly averaged bias and root mean square differences against UCAR/CDAAC retrievals and independently by comparison with collocated RAOB data. We also compared the derived COSMIC total precipitable water vapor (TPWV) with collocated TPWV derived from ground-based GPS measurements.
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