Analysis of Predictability Improvement due to the Enhancement of Observation Error for the GK-2A Infrared Channels in Data Assimilation

The All-Sky Radiance (ASR) data from geostationary satellites are important for improving initial conditions in numerical modeling through data assimilation, as it provides dense spatio-temporal atmospheric information over a wide area. Accurately applying the error information inherent in observations is essential for enhancing its effectiveness of satellite data assimilation. In this study, we calculated an observation error model for the ten infrared radiation channels of the Advanced Meteorological Imager (AMI) on the GEO-KOMPSAT-2A (GK-2A) for the summer season using the standard deviation of the brightness temperature observation minus background (O-B) as a function of the cloud impact parameter (Ca). The normalized brightness temperature of O-B probability density function is scaled such that it more closely approximates a normal distribution. For data assimilation experiments, we used the Community Radiative Transfer Model (CRTM) as the satellite observation operator and applied the 3-dimensional variational data assimilation method of the Weather Research and Forecasting Model Data Assimilation. When applying the adjusted observation error model for summer precipitation cases in the Korean peninsula, both the analysis and forecast fields improved compared to a prescribed constant error value. The best rainfall forecast performance was observed in the linear model, which followed the normal distribution better than the high-order regression observation error model. This is thought to be due to the observation error in the linear model saturates more gradually, allowing for consideration of a wider variability of Ca, i.e., a more detailed spatial distribution of cloud impact. Meanwhile, the assimilation results of Clear-Sky Radiance (CSR), excluding cloud area information, were compared to analyze the additional effects of cloud-precipitation area information during ASR assimilation. Further, we plan to assimilate both the water vapor channel ASR and the surface-sensitive channel CSR to improve the cloud detection algorithm, quality control, and refine surface parameter estimates for enhanced predictability.

Key words: GK-2A infrared channels, data assimilation, observation error, precipitation forecast

※ This research was supported by the National Research Foundation of Korea (No. 2022R1A2C1012361) funded by the Ministry of Science and Technology, and also was funded by the Korea Meteorological Administration Research and Development Program under Grant RS-2023-00237740. Additional support was provided by the BK21 FOUR project funded by the Ministry of Education.