Mesoscale convective systems (MCSes) occasionally develop over the East China Sea (ECS) in the Baiu frontal zone under both the atmospheric and oceanic influence. The factors that determine their predictability have not been fully understood yet. This study investigates the uncertainties affecting two MCSes observed by research vessels on 19 June 2022 using regional ensemble simulations. These MCSes have contrasting features: the first was triggered by an atmospheric mesoscale disturbance, while the second was induced by the boundary layer destabilization over the warm Kuroshio current.

 The first MCS shows high variability in the synoptic-scale uncertainties detected by the breeding ensemble. The best-performing member successfully represents the strong meso-β-scale cyclone and the frontal structure with deep moist layers. The ensemble simulations are less skillful for the second MCS than the first. The enhanced surface turbulent heat flux in the SST frontal zone is found to be significantly correlated to the precipitation due to the second MCS despite the cold bias of SST that is commonly imposed on all members. The dense upper-air information from the vessels significantly improves the representation of the sharp frontal structure associated with the first MCS, but has little impact on the second MCS probably due to the underestimation of the boundary layer moistening. This case study indicates that the predictability of MCSes over the ECS depends on their development mechanisms, and that the incorporation of uncertainties in both the atmosphere and ocean are important for the ensemble forecasting of these MCSes.

 The Statistical Hurricane Intensity Prediction Scheme (SHIPS) is a multiple linear regression model for predicting tropical cyclone (TC) intensity. It has been widely used in operational centers because of forecast stability, high accuracy, easy interpretation, and low computational cost. The Japan Meteorological Agency version of SHIPS is called the Typhoon Intensity Forecasting scheme based on SHIPS (TIFS) and predicts both maximum wind speed and central pressure. Although the addition of new predictors to SHIPS and TIFS has improved its accuracy, predicting TC intensity with a single regression model has limitations. In this study, a new TIFS-based forecasting scheme is developed using data from 2000 to 2021, in which three TIFS regression models corresponding to the intensifying, steady-state, and weakening stages of TCs are introduced and in which the weighted mean of the three TIFS forecasts based on random forest (RF) decision trees is computed as a final intensity forecast. Compared to the conventional TIFS model, the new scheme (TIFS-RF) has better accuracy with improvement rates of up to 12 % at forecast times from 1 to 4 days. The improvement is particularly significant for steady-state TCs, tropical depressions, and TCs undergoing extratropical transition within five days. The accuracy of TIFS-RF forecasts is generally better than that of conventional TIFS forecasts for rapidly intensifying TCs, but much worse for rapidly weakening TCs. This study also confirms that a consensus forecast of the TIFS-RF and Hurricane Weather Research and Forecasting (HWRF) models can overcome the weaknesses of each model used alone.

 Spaceborne synthetic aperture radar (SAR) for measuring high winds is expected to reduce uncertainties in tropical cyclone (TC) intensity and structure estimation, yet the consistency of SAR observed winds equivalent to a 1-min sustained wind speed with the conventionally estimated 10-min maximum wind speed (Vmax10) remains to be assessed. This study compares SAR wind observations with western North Pacific best track estimates from the Japan Meteorological Agency (JMA) and the Joint Typhoon Warning Center (JTWC). Because SAR wind observations have a bias dependent on SAR incidence angle, a first order corrective term is proposed and used to correct SAR-derived maximum wind (SAR Vmax) tentatively. After this correction, conversion of SAR Vmax into SAR Vmax10 with Dvorak conversion tables revealed a mean difference between SAR Vmax10 and JMA Vmax10 (ΔVmax10) of −0.1 m s⁻¹ and a mean absolute difference of 4.8 m s⁻¹. ΔVmax10 is found to be correlated with current intensities and future intensity changes. Also, comparison of the JMA best track 50-kt wind radius (R50) with SAR wind speeds suggests that R50 is systematically underestimated. Aside from the SAR wind limitations, possible reasons for the observed discrepancies between SAR wind observations and best track estimates include biases in the Dvorak analysis and conventional surface wind products. Further accumulation of SAR wind observations with appropriate bias correction in the future is expected to contribute to a comprehensive evaluation and improvement of conventional Vmax estimation methods, which could also be useful to verify TC intensity forecasts.

 The dynamical characteristics of the zonal wavenumber 1 (s = 1) Rossby-gravity (RG) wave are examined using recently available reanalysis data for the whole neutral atmosphere over 16 years. An isolated peak is detected in the two-dimensional zonal wavenumber-frequency spectra that likely corresponds to the theoretically-expected s = 1 RG mode at heights of z = 30, 50, 65, and 80 km. The wave period of the spectral peak is approximately 1.3 days, which is close to one day. The s = 1 RG wave is successfully extracted using a band-pass filter after removing the diurnal tide with quite large amplitudes. The s = 1 RG wave exhibits a characteristic seasonal variation: the geopotential height amplitudes are largest in the winter hemisphere in the stratosphere and lower mesosphere while enhancement is observed in both the winter and summer hemispheres in the upper mesosphere. Phase structures are examined in detail for a strong case. The horizontal phase structure at each height is consistent with the normal mode theory. The vertical phase structure is approximately barotropic from the lower stratosphere to the upper mesosphere at 30°N and 30°S where the amplitudes are large.