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This study investigates how the balance between wind and mass is treated in data assimilation, and how it affects the quality of the model states in an analysis–forecast cycle. This is done in terms of the dependence of balance on latitude and on the type of variable. The impact of the nonlinear balance equation is compared with regressed wind–mass balance in the 3‐Dimensional Variational data assimilation (3D‐Var) system of the Korea Institute of Atmospheric Prediction Systems (KIAPS). Its impact is significantly positive in temperature rather than in wind, despite the two‐way influence of the cross‐correlation between wind and mass, in terms of the root‐mean‐square difference (RMSD) of 6 h forecasts against the ERA‐Interim reanalysis data. This temperature effect is observed in the southern hemispheric (SH) polar jet of the mid‐troposphere, the SH midlatitudinal jet, and the mid/lower stratosphere in the Tropics, where there is strong zonal mean flow. Although the zonal wind forecast was harmed by application of the nonlinear balance, the temporal consistency of the damage is relatively weak compared to the improvement by the nonlinear balance in the temperature forecasts. In the SH midlatitudinal jet and the mid/lower stratosphere in the Tropics, the nonlinear balance equation, including the advection term, improves the quality of temperature RMSDs in the analysis–forecast cycle by imposing the proper balance in the initial conditions. However, in the SH polar jet of the mid‐troposphere, where the observation density is relatively low, the nonlinear balance equation achieves the same effect by reducing the analysis error (i.e. generating initial conditions more accurately). The nonlinear balance equation contributes to robustly improving the model states of the analysis–forecast cycles, depending on the dynamical activity and the observation density of the corresponding regions.

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The Korean Integrated Model (KIM) and hybrid data assimilation system were extended to assimilate all‐sky radiance from microwave satellite sensors. Initially, a radiative transfer model for the TIROS Operational Vertical Sounder (TOVS), called RTTOV‐SCATT (version 11.3), was implemented to assimilate the Microwave Humidity Sounder (MHS) 183 GHz channels over the ocean. Cloud and precipitation parameters are not directly assimilated into our system, but temperature and humidity profiles are improved in the all‐sky assimilation. In the cycled analysis and forecast experiments, an assimilation of the MHS in a cloudy region shows globally substantial benefits: the coverage of the MHS radiance data is increased by 23–28% in the all‐sky assimilation. It is demonstrated that RTTOV‐SCATT well describes the brightness temperature (TB) on selected cloudy pixels over the Tropics and the Southern Hemisphere, with potential for linking the TB innovation to the increment for the hydrometeor variable. As a result, a reduction of 1.11% in the specific humidity root‐mean‐square error occurs in the 6‐hr forecast field as compared to the European Centre for Medium‐Range Weather Forecasts (ECMWF)'s Integrated Forecasting System (IFS) analysis. Even though the all‐sky MHS assimilation's impact on the Northern Hemisphere midlatitudes is not remarkable, it nonetheless produces an improved humidity analysis increment for a heavy rainfall case over East Asia.

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In this study, the spatial error correlations in the Atmospheric Motion Vectors (AMVs) derived from the geostationary satellite imagery over East Asia are investigated. Good characterization of systematic errors in observations is essential in order to extract the maximum amount of information from the observations during the assimilation process. The spatial structure of AMV error correlations was identified based on datasets of AMVs collocated with sonde observations for July 2015. Results of AMVs from water vapour (WV; 6.7 µm) and infrared (IR; 10.8 µm) channels of the Korean geostationary Communication, Ocean and Meteorological Satellite (COMS) and the Multifunction Transport Satellite‐2 (MTSAT) are presented. For both channels, the length scales of the spatial error correlations in the MTSAT AMVs are longer than those of the COMS AMVs. For MTSAT (COMS) AMVs, a longer (shorter) length‐scale yields more (less) inflated observation error variance, and a larger (smaller) inflation factor for the diagonal form of the observation error covariance matrix is required to obtain the best quality of analysis.

The Korean Integrated Model (KIM) forecast system, based on a hybrid fourdimensional ensemble-variational method, was extended to assimilate Horizontal Line-Of-Sight (HLOS) wind observations from the Atmospheric Laser Doppler Instrument (ALADIN) on board the Atmospheric Dynamic Mission Aeolus satellite. In a global cycling experiment, assimilation of Aeolus HLOS wind observations led to reductions in the average root-mean-square error of 0.8 and 0.5% for the zonal and meridional wind analyses when compared against European Center for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) analyses. Even though the observed variable is wind, there was also an overall beneficial impact on analyses of the mass variables. In the Southern Hemisphere (SH), the reduced analysis errors led to forecast skill improvements out to 72 h. In contrast, in the Northern Hemisphere (NH) there was relatively little reduction of analysis errors, but wind forecasts were nevertheless improved, and these positive impacts lasted longerout to 120 h rather than 72 h. Experiments suggest that the relatively poor long-range performance in the SH high latitudes was due to problems with the mass increments derived from Aeolus wind increments via the ensemble-based part of the hybrid background error covariance (B), which eventually led to adverse effects on the wind variables as forecasts progressed in the SH. This study shows that it is necessary to estimate the ensemble B in the Antarctic region with its high elevation more accurately in order to effectively use the Aeolus observation information.