In this study, we investigate the rate at which the Silk Road pattern (SRP) with Rossby wave breaking (RWB) near the Asian jet exit causes the Pacific–Japan (PJ) pattern in boreal summer. In this study, the SRP case is detected using the two principal components of upper-tropospheric meridional winds over Eurasia and characterized by the presence of an upper-level anticyclonic anomaly over the Yellow Sea or near Japan. They are further classified into cases with and without RWBs.

In the SRP case with RWB, the upper-level anticyclonic anomaly near the Asian jet exit has a more extended shape in the zonal direction and larger amplitude than in the case without RWB. In the composite, a wave train associated with the SRP appears over Eurasia, which is accompanied by the RWB near the Asian jet exit. The occurrence of RWB is associated with strong deceleration and diffluence in the basic state there. The RWB promotes enhanced convection on its southern side due to the intrusion of upper-level high potential vorticity toward the southwest, resulting in the formation of the PJ pattern. The excited PJ pattern in the composite has a dipole structure with cyclonic anomalies to the south and anticyclonic anomalies to the north. Approximately 60–70 % of the SRP case with RWB is accompanied by the PJ patterns.

Conversely, in the case of the SRP without RWB, the composite represents a wave train structure over Eurasia but indicates neither enhanced convection south of the RWB nor PJ patterns. Approximately 40–50 % of the SRP case without RWBs is accompanied by the PJ patterns. Hence, the presence of RWBs increases the percentage of the formation of positive PJ patterns by a factor of 1.2–1.7, indicating that the RWB plays an important role in the excitation of PJ patterns.

The turnabout of air temperature anomalies over East Asia between the first and second halves of winter 2020/21 was examined from a teleconnection perspective with regionally different convective heating anomalies over the Indo-western Pacific sector. In the first half of winter 2020/21, the air temperature over East Asia was lower than normal, accompanied by a pair of anticyclonic and cyclonic anomalies in the upper troposphere southeast of the Tibetan Plateau and north of Japan, respectively. This dipole pattern is newly referred to as the Southeast Asia–Japan (SAJ) pattern in this study, indicating the propagation of Rossby waves caused by enhanced tropical convection over the eastern Indian Ocean toward the South China Sea. In the second half of winter 2020/21, the enhanced convection shifted eastward to the Philippine Sea. The subsequent anticyclonic anomaly changed its position to the south of Japan, which was similar to the western Pacific (WP)-like teleconnection pattern, causing warmer conditions over East Asia. The composite analysis indicated that the anomalous anticyclone over the southeastern Tibetan Plateau corresponding to the SAJ pattern emerged simultaneously with intensification of convection over the South China Sea. Half of the cases of the WP-like pattern have been accompanied by enhanced convection over the Philippine Sea. The different circulation patterns were reproduced by prescribing the heat source over the South China Sea and Philippine Sea to the linear baroclinic model. Moreover, the vorticity budget analysis suggested that the presence of upper-tropospheric convergence of winds to the southeast of the Tibetan Plateau seen in the climatology is conceivable for the in situ localized anomalous circulation constituting the SAJ pattern due to vortex stretching effects.

A set of line-by-line and correlated k-distribution radiation models are developed aiming for applications in simulations and examinations of Venus and Mars-like planetary atmospheres. Our line-by-line model is validated by comparing the results with observations and those of previous studies under conditions of Venus, and present and possible early Mars. The radiation fields calculated by our line-by-line model agree well with observed profiles and are within the acceptable range from those presented in previous studies. The results obtained by our line-by-line model are then processed to generate a series of parameters for our correlated k-distribution model. It is confirmed that the radiation fields calculated with those sets of parameters by our correlated k-distribution model sufficiently agree with those by our line-by-line model for the atmospheres with a wide range of surface pressure. By the use of our correlated k-distribution model implemented with those sets of parameters, we evaluate the radiation field for Venus and calculate radiative-convective equilibrium profiles for Venus and Mars. The obtained vertical thermal structures for Venus are qualitatively consistent with observations, and the behaviors of surface pressure and surface temperature for Mars are similar to those reported by previous studies. Those results demonstrate that our models including the procedure for generating tables of radiation parameters are applicable to examine climates of CO₂ dominant atmospheres in our solar and exoplanetary systems.

In drylands, the dry vegetation coverage affects dust occurrence by modulating threshold friction velocity (or wind speed) for dust emission. However, research into quantifying the effect of dry vegetation coverage on dust occurrence is scarce. This study investigated the spatial and temporal variations of dust occurrence and three definitions of strong wind frequency over the Gobi Desert and surrounding regions in March and April, months when dust occurrence is frequent, during 2001–2021. We evaluated the effects of variations in dry vegetation on dust occurrence using the threat scores of forecasted dust occurrences for each strong wind definition. Our results indicated that dry vegetation, which was derived from the MODIS Soil Tillage Index, affects dust occurrence more significantly in April than in March. In March, land surface parameters, such as soil freeze–thaw and snow cover, in addition to dry vegetation coverage, should be considered to explain dust variations in that month. However, using the threshold wind speed estimated from dry vegetation coverage improved the prediction accuracy of dust occurrence in April. Therefore, we propose that the dry vegetation coverage is a key factor controlling dust occurrence variations in April. The findings indicate that estimation of dry vegetation coverage should be applied to dust models.

The observation operator (OO) is essential in data assimilation (DA) to derive the model equivalent of observations from the model variables. In the satellite DA, the OO for satellite microwave brightness temperature (BT) is usually based on the radiative transfer model (RTM) with a bias correction procedure. To explore the possibility to obtain OO without using physically based RTM, this study applied machine learning (ML) as OO (MLOO) to assimilate BT from Advanced Microwave Sounding Unit-A (AMSU-A) channels 6 and 7 over oceans and channel 8 over both land and oceans under clear-sky conditions. We used a reference system, consisting of the nonhydrostatic icosahedral atmospheric model (NICAM) and the local ensemble transform Kalman filter (LETKF). The radiative transfer for TOVS (RTTOV) was implemented in the system as OO, combined with a separate bias correction procedure (RTTOV-OO). The DA experiment was performed for 1 month to assimilate conventional observations and BT using the reference system. Model forecasts from the experiment were paired with observations for training the ML models to obtain ML-OO. In addition, three DA experiments were conducted, which revealed that DA of the conventional observations and BT using ML-OO was slightly inferior, compared to that of RTTOV-OO, but it was better than the assimilation based on only conventional observations. Moreover, ML-OO treated bias internally, thereby simplifying the overall system framework. The proposed ML-OO has limitations due to (1) the inability to treat bias realistically when a significant change is present in the satellite characteristics, (2) inapplicability for many channels, (3) deteriorated performance, compared with that of RTTOV-OO with respect to accuracy and computational speed, and (4) physically based RTM is still used to train the ML-OO. Future studies can alleviate these drawbacks, thereby improving the proposed ML-OO.