Comparison of assimilating all‐sky and clear‐sky infrared radiances from Himawari‐8 in a mesoscale system

This study examines the advantages of infrared all-sky radiance (ASR) assimilation over traditional clear-sky radiance (CSR) assimilation using a mesoscale LETKF data assimilation system. To effectively assimilate ASR data from the Himawari-8 geostationary satellite, a cloud-dependent quality-control procedure and an observation error model were developed. A single humidity band to be assimilated and thinning distance were determined based on observation error statistics. The operational pre-processing and parameter settings, such as observation errors and an adaptive bias correction for CSR, were incorporated into the LETKF data assimilation system. A comparison of the impacts of assimilating ASRs and CSRs was accomplished using single-cycle and 10-day cycle assimilation experiments. Study results revealed that ASR assimilation provided a higher degree of improvement in the first-guess fit for conventional observations and satellite retrievals with respect to temperature, moisture and wind. Furthermore, ASR assimilation displayed a more stable improvement in the prediction of a severe rainfall event because it has more universal data coverage than CSR. Adaptive bias correction schemes with two different sets of predictors for ASRs were tested and revealed the difficulty in extracting additional information for the assimilation of no-bias corrected ASR due to complicated bias factors. This was in contrast to the CSR assimilation, where bias correction had a positive impact.