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

Abstract

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. KEYWORDS all-sky infrared radiance, data assimilation, Himawari-8 1 Q J R Meteorol Soc. 2019;145:745-766.wileyonlinelibrary.com/journal/qj

Assimilating Every‐10‐minute Himawari‐8 Infrared Radiances to Improve Convective Predictability

“…Correcting biases of all‐sky radiances is challenging because the substantial and complicated biases are produced from both observations and models. This important issue was investigated in recent studies (e.g., Okamoto et al, ). Removing the biases between model forecast and observations would contribute to effectively assimilating satellite radiances and improving the analysis in the operational systems (e.g., Kazumori, ; Otkin et al, ).…”

“…Correcting biases of all-sky radiances is challenging because the substantial and complicated biases are produced from both observations and models. This important issue was investigated in recent studies (e.g., Okamoto et al, 2019).…”

Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales

“…Studies include those of Minamide and Zhang () and Okamoto et al . () for IR data, and Zhu et al . () and Migliorini and Candy () for MW data.…”

“…All‐sky MW data are considered easier to use than all‐sky IR data due to the smoother properties of the MW radiative transfer model output between cloudy and clear scenes (Bauer et al ., ), so these data were the first to be treated directly (Bauer et al ., ; Zhu et al ., ; Migliorini and Candy, ). Recently though, satellite IR data have also been assimilated directly (Zhang et al ., ; Minamide and Zhang, ; Okamoto et al ., ), which is discussed further in Sections 6.5, 6.8 and 6.9.…”

“…IR measurements are generally difficult to interpret for cloudy scenes (e.g. Pavelin et al ., ), although this situation is improving rapidly (Zhang et al ., ; Minamide and Zhang, ; Okamoto et al ., ). MW instruments are divided similarly: sounders such as AMSU‐A/B, ATMS, MHS, SAPHIR and MWHS‐2 (capable of measuring tropospheric WV), imagers such as AMSR‐2 and GMI (using MW window channels providing information on total column water vapour, and cloud and precipitation quantities), and combined imager/sounders such as the SSMIS instruments.…”