All‐sky infrared radiance assimilation of a geostationary satellite in the Japan Meteorological Agency's global system

Abstract

AbstractAll‐sky assimilation of infrared (IR) radiances has been developed for water vapor bands of the geostationary satellite Himawari‐8 in the operational global data assimilation system. Cloud‐dependent quality control, bias correction, and observation error modeling are essential developments to effectively utilize the all‐sky radiances (ASRs). ASR assimilation increases the assimilated number of observations by 2.8 times and improves the coverage relative to the traditional clear‐sky radiance (CSR) assimilation. The additional observations better alleviate model dry biases in the middle and upper tropospheric humidity. ASR assimilation brings statistically significant improvements in the background (first guess) in humidity, temperature, and wind over the CSR assimilation. It also better improves short‐range forecasts of the middle and upper tropospheric temperature and humidity up to day 3 in the Tropics. A mixed impact in the stratospheric temperature is under investigation. The impacts of various aspects of the ASR assimilation configuration are evaluated with sensitivity assimilation experiments. The interband correlation and cloud‐dependent standard deviation of the observation error are crucial, whereas the cloud dependency of the correlation is not so important. Although ASRs at a single band were assimilated in many previous studies targeting severe weather using research‐based regional assimilation systems due to decreasing independent information in the presence of clouds, they are distinctly inferior to not only ASRs at multiple bands but also CSRs at multiple bands in a global data assimilation system that contains fewer cloud‐affected scenes. The cloud‐dependent bias correction predictors are essential in the presence of observation‐minus‐background bias that increases with cloud effects.