Using the Regional Ocean Modeling System and the conditional nonlinear optimal perturbation (CNOP) approach, this study explored a CNOP‐based adaptive observation system design with regard to improving the prediction of one seasonal upstream Kuroshio transport (UKT) reduction event. For comparison, investigations were also conducted using a set of random observation systems built within a sensitive area inferred from previous studies and a conventional observation system built at 18°N. For these observation systems, observation networks constructed using different numbers of observation sites and observation distances (i.e., the shortest distances between any two observation sites) were evaluated by performing observing system simulation experiments. Results showed that most assimilation experiments can improve the UKT prediction, indicating the importance of initial conditions. For all observation systems, large numbers of observation sites generally led to higher prediction benefits, whereas the effects of observation distances differed significantly. Overall, the CNOP‐based observation system exhibited the best performance. Optimal CNOP‐based observation networks were established using six or eight observation sites and observation distances of 140 or 165 km, which produced a mean prediction improvement of 43.1%. This preliminary work is expected to provide guidance on future observations in the upstream Kuroshio region and to help realistic UKT predictions.

In this study, we develop an advanced hybrid forecasting system for short‐term (0–120 hr) typhoon predictions, seamlessly integrating the FuXi machine‐learning model with the physics‐based Shanghai Typhoon Model (SHTM). By employing spectral nudging, the hybrid FuXi‐SHTM model leverages FuXi's robust large‐scale forecasting capabilities alongside SHTM's mesoscale strengths, significantly enhancing track, intensity, and precipitation predictions for super typhoons Yagi (2024) and Krathon (2024). To further improve the forecasting capability for extreme typhoons, the Conditional Nonlinear Optimal Perturbation method is employed for the first time to identify sensitive regions for the hybrid model. Despite being constrained by FuXi's large‐scale forecast fields, the dense assimilation of satellite observations within these sensitive regions can further enhance typhoon forecasts. This study emphasizes the synergy between data‐driven strategies and established physical modeling, which can inspire further depth in understanding of extreme typhoon events.

The Madden‐Julian Oscillation (MJO) is a dominant intraseasonal variability originating in the tropics and influencing global weather and climate. Despite this dominance, we still lack reliable precursor signals triggering nonlinear initiation of primary MJO events. Here for the first time, we deal with this question from the point of view of nonlinearity, with successful implementation of conditional nonlinear optimal perturbation (CNOP). Our results show that relative to random perturbations, CNOPs of moisture have the most potential to trigger the strongest primary MJO events more than 15–20 days in advance from the given non‐MJO reference states. The CNOPs manifest as a moist equator and aggregate in the lower troposphere, with stronger signals over the western Indian Ocean than over the eastern one. These CNOPs can also capture the observational moist precursor conditions of primary MJO events. This work also implies the necessity of utilizing nonlinear optimal moist initialization in subseasonal predictions.

An extreme cold surge during 21–25 January 2016 resulted in extensive damage to eastern Asian countries. Synoptic diagnoses show that the southward movement of the north Siberian anomalous anticyclone to the midlatitudes was the key process for the cold surge. Operational forecasts from the European Centre for Medium‐Range Weather Forecasts show that this event could be captured in the control forecast started on 12 January. Further diagnosis shows that the control forecast started on 11 January missed it, possibly due to uncertainties in the Arctic because the north Siberian anomalous anticyclone is not strong enough due to the erroneous eddy vorticity forcing (EVF) induced by the Arctic initial conditions uncertainty. Therefore, the north Siberian anomalous anticyclone cannot move southward to midlatitude when another anomalous anticyclone comes from the Caspian Sea. Finally, numerical experiments are conducted with the Open Integrated Forecasting System model to verify the role of the Arctic initial conditions. Numerical results show that integration with the ERA5 initial conditions could capture the cold surge in the forecast starting on 12 January, while integration with the ERA‐Interim initial conditions could not. Sensitivity experiments with modified Arctic initial conditions show an improved cold surge forecast skill. This improvement may be due to the north Siberian anomalous anticyclone, which is sufficiently strong to move southward when the anomalous anticyclone comes from the Caspian Sea, with accurate EVF induced by the improved initial conditions in the Arctic. This result highlights the importance of the Arctic initial conditions in this eastern Asian cold surge forecast.