Assimilation of All-Sky Infrared Radiances from Himawari-8 and Impacts of Moisture and Hydrometer Initialization on Convection-Permitting Tropical Cyclone Prediction

Abstract

This study explores the impacts of assimilating all-sky infrared satellite radiances from Himawari-8, a new-generation geostationary satellite that shares similar remote sensing technology with the U.S. geostationary satellite GOES-16, for convection-permitting initialization and prediction of tropical cyclones with an ensemble Kalman filter (EnKF). This case studies the rapid intensification stages of Supertyphoon Soudelor (2015), one of the most intense tropical cyclones ever observed by Himawari-8. It is found that hourly cycling assimilation of the infrared radiance improves not only the estimate of the initial intensity, but also the spatial distribution of essential convective activity associated with the incipient tropical cyclone vortex. Deterministic convection-permitting forecasts initialized from the EnKF analyses are capable of simulating the early development of Soudelor, which demonstrates encouraging prospects for future improvement in tropical cyclone prediction through assimilating all-sky radiances from geostationary satellites such as Himawari-8 and GOES-16. A series of forecast sensitivity experiments are designed to systematically explore the impacts of moisture updates in the data assimilation cycles on the development and prediction of Soudelor. It is found that the assimilation of the brightness temperatures contributes not only to better constraining moist convection within the inner-core region, but also to developing a more resilient initial vortex, both of which are necessary to properly capture the rapid intensification process of tropical cyclones.

Development and Evaluation of an Ensemble‐Based Data Assimilation System for Regional Reanalysis Over the Tibetan Plateau and Surrounding Regions

“…Despite the WRF‐EnKF experiments showing an encouraging performance in specific humidity, precipitation, and diurnal variation of rain rate over the TP by assimilating only conventional observations, the WRF‐EnKF reanalyses of temperature and wind may need further improvement compared to ERA5, which assimilated much more observations including radiances from NOAA‐18/NOAA‐19, METOP‐A/METOP‐B, and other satellites. Ongoing research seeks to further improve the prototype regional WRF‐EnKF system through direct assimilation of all‐sky radiances (Minamide & Zhang, , ; Zhang et al, ). Moreover, a larger domain covering the whole Tibetan Plateau and/or higher‐resolution convection‐permitting data assimilation (Ying & Zhang, ) will be considered for the WRF‐EnKF reanalysis in the future study, as well as the use of more advanced assimilation techniques including En3DVar and En4DVar (Zhang et al, ; Zhang & Zhang, ).…”

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

“…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.…”

“…aNote though that there has been significant progress recently with all‐sky assimilation of IR radiances (e.g. Geer et al ., ; Minamide and Zhang, ).…”

“…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.…”

“…Zhang et al . () and Minamide and Zhang (; ) for instance showed that, in their EnKF, inflating observation error variances in regions where the innovation is significantly larger than the ensemble spread improves the assimilation of all‐sky satellite IR data, and Migliorini and Candy () showed that inflating observation error variances in regions of large liquid water path similarly improves the assimilation of all‐sky MW data. In a recent study, Bishop () argues the need for state‐dependent observation error variances for observations of variables that have physical bounds.…”