Using OSSEs to Evaluate the Impacts of Geostationary Infrared Sounders

• Published in: Journal of atmospheric and oceanic technology 2022-12, Vol.39 (12), p.1903-1918
• Main Authors: McGrath-Spangler, Erica L., McCarty, Will, Privé, N. C., Moradi, Isaac, Karpowicz, Bryan M., McCorkel6, Joel
• Format: Article
• Language: English
• Subjects: Assimilation; Atmosphere; Data assimilation; Earth Resources And Remote Sensing; Error analysis; Error reduction; Evaluation; Geosynchronous orbits; Impact analysis; Meteorological observations; Meteorological satellites; Numerical prediction; Numerical weather forecasting; Polar orbiting satellites; Predictions; Satellite observation; Satellites; Temporal resolution; Tropical environments; Vertical profiling; Weather; Weather forecasting; Wind; Wind fields
• ISSN: 0739-0572; 1520-0426
• DOI: 10.1175/JTECH-D-22-0033.1

An observing system simulation experiment (OSSE) was performed to assess the impact of assimilating hyperspectral infrared (IR) radiances from geostationary orbit on numerical weather prediction, with a focus on the proposed sounder on board the Geostationary Extended Observations (GeoXO) program’s central satellite. Infrared sounders on a geostationary platform would fill several gaps left by IR sounders on polar-orbiting satellites, and the increased temporal resolution would allow the observation of weather phenomena evolution. The framework for this OSSE was the Global Modeling and Assimilation Office (GMAO) OSSE system, which includes a full suite of meteorological observations. The experiment additionally assimilated four identical IR sounders from geostationary orbit to create a “ring” of vertical profiling observations. Based on the experimentation, assimilation of the IR sounders provided a beneficial impact on the analyzed mass and wind fields, particularly in the tropics, and produced an error reduction in the initial 24–48 h of the subsequent forecasts. Specific attention was paid to the impact of the GeoXO Sounder (GXS) over the contiguous United States (CONUS) as this is a region that is well-observed and as such difficult to improve. The forecast sensitivity to observation impact (FSOI) metric, computed across all four synoptic times over the CONUS, reveals that the GXS had the largest impact on the 24-h forecast error of the assimilated hyperspectral infrared satellite radiances as measured using a moist energy error norm. Based on this analysis, the proposed GXS has the potential to improve numerical weather prediction globally and over the CONUS.