The Advanced Himawari Imager (AHI) on board Himawari-8 provides continuous high-resolution observations of severe weather phenomena in space and time, providing a good data source for the rapidly updated cycling data assimilation in numerical weather prediction (NWP). In this study, the all-sky AHI water vapor infrared radiances were rapidly assimilated using a dual-resolution hybrid EnVar method to introduce the flow-dependent ensemble information with a reduced ensemble cost in a case study. Considering the greatly increased computational costs for all-sky radiance data assimilation, the sensitivity of the all-sky radiance data assimilation to the resolution of ensemble error covariance as well as the cycling frequency using dual-resolution EnVar was investigated. The impact of all-sky radiance data assimilation using the dual-resolution hybrid EnVar was then compared to the clear-sky radiance data assimilation.

From the case study, it was found that the dual-resolution hybrid EnVar and the full-resolution hybrid EnVar generally performed comparable for the all-sky assimilation of the AHI water vapor radiances in this study. Both show great advantages over 3DVar in analyses and forecasts with different leading time for model state variables. However, the former cost much fewer computational resources with only a tiny loss of the accuracy. For the assimilation frequency of all-sky radiances using the dual-resolution hybrid EnVar, it was found that the half-hourly and hourly test both obtain the better results than the other two (namely the 10-minutely test and 3-hourly test). The former two have the comparable performances in rainfall forecast although the half-hourly test is better. Finally, in the dual-resolution hybrid EnVar framework, the all-sky AHI radiance assimilation impact was studied versus the clear-sky radiances as well as the conventional observations in a case study. The results present a generally positive improvement of the rainfall forecast with the larger rainfall thresholds, caused by the assimilation of the cloud-influenced AHI water vapor radiances.

Himawari-8 上的高级 Himawari 成像仪 (AHI) 提供了对恶劣天气现象在空间和时间上的连续高分辨率观测，为数值天气预报 (NWP) 中快速更新的循环数据同化提供了良好的数据源。在这项研究中，使用双分辨率混合 EnVar 方法快速同化全天 AHI 水蒸气红外辐射，在案例研究中引入与流量相关的集合信息，降低集合成本。考虑到全天辐射数据同化计算成本大大增加，研究了全天辐射数据同化对集合误差协方差分辨率以及双分辨率EnVar循环频率的敏感性。

从案例研究中发现，双分辨率混合 EnVar 和全分辨率混合 EnVar 在本研究中对 AHI 水汽辐射的全天空同化表现一般相当。在模型状态变量的不同前置时间的分析和预测中，两者都显示出优于 3DVar 的巨大优势。然而，前者消耗的计算资源少得多，精度损失很小。对于使用双分辨率混合 EnVar 的全天辐射同化频率，发现半小时和小时测试都比其他两个（即 10 分钟测试和 3 小时测试）获得更好的结果. 前两者在降雨预报方面的表现相当，但半小时测试更好。最后，在双分辨率混合 EnVar 框架中，在一个案例研究中，研究了全天 AHI 辐射同化影响与晴空辐射以及常规观测的对比。结果表明，由于受云影响的 AHI 水汽辐射的同化，降雨阈值较大，降雨预报总体呈正向改善。