Assessing the Impact of Assimilating All-Sky Window-Channel Infrared Radiances from GOES-ABI And Himawari-AHI on Analyses and Forecasts

Recent progress towards the assimilation of cloud-affect infrared radiances has shown improvements when assimilating infrared water vapor bands. However, due to uncertainties in the observation error distributions, forward operators, and cloud representation in the models, infrared window channels remain a challenge to the scientific community. In this study, we examine the assimilation of infrared window channel 13 from GOES-ABI and Himawari-AHI sensors over the ocean, using the Model for Prediction Across Scales – Atmosphere (MPAS-A) coupled with the Joint Effort for Data assimilation Integration (JEDI). The Community Radiative Transfer Model (CRTM) interfaced in JEDI-UFO is used as a cloudy radiance observation operator for the channel 13's assimilation and forecast evaluation. Two experiments are conducted using the hybrid-3DEnVar method of MPAS-JEDI with a global quasi-uniform 30km grid. The benchmark experiment assimilated “conventional” observations (radiosondes, aircraft, surface pressure, satellite AMVs, and GNSS radio occultation observations) and clear-sky radiances from AMSU-A and MHS. The sensitivity to ABI and AHI’s channel 13 is analyzed by assimilating these data in addition to the observations in the benchmark experiment. Cloud analyses and forecasts are verified in terms of the observed and simulated brightness temperatures at different wavelengths of ABI and AHI. The impact on temperature and humidity forecasts up to 7 days is also evaluated. This study provides guidance in understanding situation-dependent errors in the forecasts and for the future research direction of assimilating infrared window channels in all-weather conditions.