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This study investigates the responses and mechanisms of East Asian winter monsoon (EAWM) and East Asian summer monsoon (EASM) to weakened Atlantic meridional overturning circulation (AMOC) through three perturbation experiments with different intensities of freshwater hosing in the North Atlantic, using the Flexible Global Ocean – Atmosphere – Land System Model, Grid‐point Version 2. These experiments show that a subtle weakening of AMOC does not significantly influence either the EAWM or the EASM. When AMOC weakens more substantially, northerly wind anomalies emerge in East Asia throughout the year, strengthening the EAWM and weakening the EASM. The northerly wind anomalies result from an anomalous westward sea level pressure (SLP) gradient, namely positive SLP anomalies across Eurasia and negative anomalies in the western North Pacific (WNP). In winter, negative WNP SLP anomalies are found in mid‐high latitudes. They are caused by anomalous stationary wave activity, which is associated with the Aleutian Iceland seesaw teleconnection. In summer, negative WNP SLP anomalies are present in the subtropics, and are linked with an anomalous cyclonic circulation over the north of the Philippian Sea. This cyclonic circulation anomaly is induced by the anomalous warm water in the subsurface of the South China Sea and Philippian Sea through the Gill mechanism.

To make better use of microwave radiance observations for data assimilation, removal of radiances contaminated by hydrometeor particles is one of the most important steps. Generally, all observations below the middle troposphere are eliminated before the analysis when precipitation is present. However, the altitude of the cloud top varies; when the weighting function peak height of a channel is higher than the altitude of the cloud top, observations are not affected by the absorption or scattering of cloud particles. Thus, the radiative transfer calculation can be performed under a clear sky scenario. In this paper, a dynamic channel selection (DCS) method was developed to determine the radiance observations unaffected by clouds under cloudy conditions in assimilation. First, the sensitivity of cloud liquid water (CLW) profiles to radiance from the microwave temperature sounding frequencies was analyzed. It was found that the impact of CLW on transmittance can be neglected where the cloud top height is below the weighting function peak height. Second, three lookup tables were devised through analysis of the impact of cloud fraction and cloud top height on radiance, which is the basis of the DCS method. The unified cloud top height of the Microwave Temperature Sounder (MWTS)-2 fields of view (FOVs) can be calculated by remapping the cloud mask and cloud top height data from the Medium Resolution Spectral Imager-2 (MERSI-2). Observations from various channels may be removed or retained based on real-time dynamic unified cloud top height data. Twelve-hour and long-term time-series brightness temperature simulation experiments both showed that an increase in the amount of observations used for data assimilation of more than 300% can be achieved by application of DCS, but this had no effect on the amount of error. Through DCS, areas of strong precipitation can be accurately identified and removed, and more observations above cloud top height can be included in the data assimilation. The application of DCS to data assimilation will greatly improve the data utilization rate, and therefore allow for more accurate characterization of upper atmospheric circulation.

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The Clear Sky Radiance (CSR) product has been widely used instead of Level 1 (L1) geostationary imager data in data assimilation for numerical weather prediction due to its many advantages concerning superobservation methodology. In this study, CSR was produced in two water vapor channels (channels 9 and channel 10, with wavelengths at 5.8–6.7 μm and 6.9–7.3 μm) of the Advanced Geostationary Radiation Imager aboard Fengyun 4A. The root mean square error (RMSE) between CSR observations and backgrounds was used as a quality flag and was predicted by cloud cover, standard deviation (STD), surface type, and elevation of a CSR field of view (FOV). Then, a centesimal scoring system based on the predicted RMSE was set to a CSR FOV that indicates its percentile point in the quality distribution of the whole FOV. Validations of the scoring system demonstrated that the biases of the predicted RMSE were small for all FOVs and that the score was consistent with the predicted RMSE, especially for FOVs with high scores. We suggest using this score for quality control (QC) to replace the QC of cloud cover, STD, and elevation of CSR, and we propose 40 points as the QC threshold for the two channels, above which the predicted RMSE of a CSR is superior to the RMSE of averaged clear-sky L1 data.