The formation of the stable layer below about 2 × 10⁶ Pa pressure level (about 20 km altitude) of the atmosphere of Venus detected by in situ observations is investigated by the use of a radiative-convective equilibrium model. We demonstrate that, assuming mixing ratio profiles of absorbers to be at the upper limits of the observed ranges for H₂O and SO₂ and the lower limit for CO, a stable layer forms as a radiative-convective equilibrium state, but its stability is lower than the observed one. Also, increasing the continuum absorption coefficient of CO₂ and/or H₂O, which are not well constrained observationally or experimentally, results in the formation of a stable layer whose stability is comparable to the observed one. These results suggest a practical method to form the stable layer in the dynamical models of the Venus atmosphere. Further, these results indicate that the important targets of future observations and laboratory measurements are to obtain more precise profiles of the mixing ratios of H₂O, CO, and SO₂ in the Venus atmosphere, and to determine the continuum absorption coefficients of those.

The effect of vertical discretization methods and vertical resolution on Quasi-Biennial Oscillation (QBO)-like oscillations that can occur in mechanistic General Circulation Models (dry GCMs) is investigated. Two models are compared. One model uses the spectral method in the horizontal direction but the finite difference method in the vertical direction (VFD model), while the other is a three-dimensional spectral model that uses the spectral method for discretization in both the horizontal and vertical directions (3DS model). Both models include horizontal hyperdiffusion, simple Newtonian cooling and Rayleigh friction, but as they are dry models, they do not include the effects of moist convection, and no explicit vertical diffusion is used, following a previous study. Long-term numerical integrations of these models show that the 3DS model does not generate QBO-like oscillations at the vertical resolution settings used. On the other hand, the VFD model generates QBO-like oscillations at low vertical resolution, but no QBO-like oscillations at higher vertical resolution. Wavenumber-frequency spectral analyses of wave disturbances show that, in the VFD model, the amplitude of the waves at the sigma-level near the central altitude of the QBO-like oscillations is highly dependent on the vertical resolution of the model. Analyses of the wave contribution to the vertical momentum fluxes and additional numerical experiments show that in the higher vertical resolution setting, steady eastward zonal winds form above the altitude corresponding to the tropopause, and these zonal winds suppress the upward propagation of eastward moving waves. Transformed Eulerian mean analyses are also done for the results of the VFD models to investigate the contribution of the residual circulation and the wave-mean-flow interaction to the QBO-like oscillation.

Research on particle filters has been progressing with the aim of applying them to high-dimensional systems, but alleviation of problems with ensemble Kalman filters (EnKFs) in nonlinear or non-Gaussian data assimilation is also an important issue. It is known that the deterministic EnKF is less robust than the stochastic EnKF in strongly nonlinear regimes. We prove that if the observation operator is linear the analysis ensemble perturbations of the local ensemble transform Kalman filter (LETKF) are uniform contractions of the forecast ensemble perturbations in observation space in each direction of the eigenvectors of a forecast error covariance matrix. This property approximately holds for a weakly nonlinear observation operator. These results imply that if the forecast ensemble is strongly non-Gaussian the analysis ensemble of the LETKF is also strongly non-Gaussian, and that strong non-Gaussianity therefore tends to persist in high-frequency assimilation cycles, leading to the degradation of analysis accuracy in nonlinear data assimilation. A hybrid EnKF that combines the LETKF and the stochastic EnKF is proposed to mitigate non-Gaussianity in nonlinear data assimilation with small additional computational cost. The performance of the hybrid EnKF is investigated through data assimilation experiments using a 40-variable Lorenz-96 model. Results indicate that the hybrid EnKF significantly improves analysis accuracy in high-frequency data assimilation with a nonlinear observation operator. The positive impact of the hybrid EnKF increases with the increase of the ensemble size.

What controls the variability of daily precipitation averaged over the tropics? Are these the most numerous precipitation rates or the most intense ones? And do they relate to a specific cloud type? This work addresses these questions using precipitation from the one-year simulation of the global-coupled storm-resolving ICOsahedral Non-hydrostatic model run in its Sapphire configuration (ICON-Sapphire) and observations. Moreover, we develop a framework to analyze the precipitation variability based on the area covered by and the mean intensity of different groups of precipitation rates. Our framework shows that 60 % of the precipitation variability is explained by precipitation rates between 20 mm d⁻¹ and 70 mm d⁻¹, but those precipitation rates only explain 46 % of the mean precipitation in the tropics. The decomposition of the precipitation variability into the area fraction and mean intensity of a set of precipitation rates shows that this variability is explained by changes in the area fraction covered by precipitation rates between 20 mm d⁻¹ and 70 mm d⁻¹, not by changes in the mean intensity. These changes in the area fraction result from changes in the area covered by congestus clouds, not by cumulonimbus or shallow clouds, even though congesti and cumulonimbi contribute equally to the mean tropical precipitation.

Overall, ICON-Sapphire reproduces the probability density function of precipitation rates and the control of specific precipitation rates on the tropical mean precipitation and variability compared to observations.

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 (GPH) 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.

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 (JMA) 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 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 with subsequent intensity changes from the SAR observation time. 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.