Recent studies proposed leading averaged coupled covariance (LACC) as an effective strongly coupled data assimilation (SCDA) method to improve the coupled state estimation over weakly coupled data assimilation (WCDA) in a coupled general circulation model (CGCM). This SCDA method, however, has been previously evaluated only in the perfect model scenario. Here, as a further step toward evaluating LACC for real world data assimilation, LACC is evaluated for the assimilation of reanalysis data in a CGCM. Several criteria are used to evaluate LACC against the benchmark WCDA. It is shown that despite significant model bias, LACC can improve the coupled state estimation over WCDA. Compared to WCDA, LACC increases the globally averaged anomaly correlation coefficients (ACCs) of sea surface temperature (SST) by 0.036 and atmosphere temperature at the bottom level (Ts) by 0.058. However, there also exist regions where WCDA outperforms LACC. Although the reduction in the anomaly root-mean-square error (RMSE) is not as consistently clear as the increase in ACC, LACC can largely correct the biased model climatology.

Geosynchronous satellite observation images have the advantages of a wide observation range and high temporal resolution, which are critical for understanding atmospheric motion and change patterns. The realization of geosynchronous satellite observation image prediction will provide significant support for short-term forecasting, including precipitation forecasting. Here, this paper proposes a deep learning method for predicting satellite observation images that can perform the task of predicting satellite observation sequences. In the study of predicting the observed images for Band 9 of the FY-4A satellite, the average mean square error of the network’s 2-h prediction is 4.77 Kelvin. The network’s predictive performance is the best among multiple deep learning models. We also used the model to predict Bands 10–14 of the FY-4A satellite and combined the multi-band prediction results. To test the application potential of the network prediction performance, we ran a precipitation area detection task on the multi-band prediction results. After 2 h of prediction, the detection results from satellite infrared images still achieved an accuracy of 0.855.

Clouds are closely related to precipitation, as their type, microphysical characteristics, and dynamic properties determine the intensity, duration, and form of rainfall. While geostationary satellites offer continuous cloud-top observations, they cannot capture the full three-dimensional structure of clouds, limiting the accuracy of precipitation forecasting based on geostationary satellite data. However, cloud–precipitation relationships contain valuable physical information that can be leveraged to improve forecasting performance. To further enhance the precision of satellite precipitation forecasting, this study proposes a multi-channel satellite precipitation forecasting method that integrates cloud classification products. The method combines precipitation-prior information from Himawari-8 satellite cloud classification products with multi-channel satellite observations to generate precipitation forecasts for the next four hours. This approach further exploits the potential of satellite observations in precipitation forecasting. Experimental results show that integrating cloud classification products improves the Critical Success Index by 8.0%, improves the Correlation Coefficient by 5.8%, and reduces the Mean Squared Error by 3.0%, but increases the MAE by 4.5%. It is proven that this method can effectively improve the accuracy of multi-channel satellite precipitation forecasting.

The 2019 Antarctic sudden stratospheric warming (SSW) is well captured by the specified dynamics Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension (SD-WACCM-X). This SSW is dominated by a strong quasi-stationary planetary wave with zonal wavenumber 1 (SPW1) activity, and nonmigrating tides show great variations. The nonlinear interactions between SPW1 and diurnal, semidiurnal and terdiurnal migrating tides triggered by this SSW also have significant impacts on the variabilities of corresponding nonmigrating tides. This is clearly proven by the fact that the variations of the secondary nonmigrating tides, generated by the nonlinear interaction, show higher correlation during this SSW than those during the non-SSW period. Meanwhile, the SPW1 dominates the nonlinear interactions with diurnal, semidiurnal and terdiurnal migrating tides, and the corresponding secondary nonmigrating tides show concurrent increases with SPW1. In the ionosphere, the nonmigrating tidal oscillations exhibit consistent temporal variabilities with those shown in the neutral atmosphere, which demonstrates the neutral–ion coupling through nonmigrating tides and that nonmigrating tides are significant sources for the short-term ionospheric variability during this SSW event. Specifically, the enhancement of the ionospheric longitudinal wavenumber 4 structure coincides with the increase of the eastward-propagating diurnal tide with zonal wavenumber 3 (DE3), semidiurnal tide with zonal wavenumber 2 (SE2) and terdiurnal tide with zonal wavenumber 1 (TE1). Also, DE3 dominates the influence of nonmigrating tides on the ionospheric longitudinal wavenumber 4 structure during this SSW.

In order to overcome the shortcomings of traditional spectral filtering method which cannot reflect the local characteristics of the spatial scales, a more efficient noise filtering technique in ensemble data assimilation is needed. In this paper, by introducing the improved threshold value and constraint parameter, a constrained wavelet threshold de-noising method (CWTD-NM) is designed to filter out the noise swamped in different scales. An ideal experiments group is implemented based on the two-dimensional barotropic vorticity equation. The best wavelet basis function (Db11) and the optimal decomposition level (five) are chosen firstly. The numerical results show that, since the wavelet coefficients are constrained by parameter α, the CWTD-NM can produce better filtering results with the smallest root mean square error (RMSE). Moreover, the filtering accuracy of 10 ensemble samples’ variances of the CWTD-NM is equivalent to the result estimated directly from 80 ensemble samples, while the running time is only about 1/7 of the latter. It also shows that Peak Signal to Noise Ratio (PSNR) performs well, and has a good corresponding relationship to the minimum RMSE.