Substantial progress has been made in understanding the role of snow cover (SC) within the climate system. Previous studies have focused predominantly on average SC conditions across specific months or seasons. However, the atmospheric impacts of occasional SC, which last only a few days, have been largely overlooked. Here, we demonstrate that the sudden appearance of occasional SC in East Asia, despite its short duration, induces an immediate local cooling effect on the atmosphere, extending from the near surface to the lower troposphere. This cooling effect is driven by diabatic heating caused by the snow albedo effect. Numerical experiments indicate that 24% of the maximum intensity of extreme temperature drops associated with SC events is driven by SC amplification. Furthermore, in the absence of this feedback, the extremity of these temperature drops can be mitigated. This study underscores the critical role of occasional SC in shaping cold extremes and aims to highlight the need for greater attention to occasional SC phenomena in atmospheric research.

In July 2023, persistent rainfall associated with the Baiu front caused record-breaking rainfall and flooding on the Sea of Japan side of the Tohoku region, where heavy Baiu rainfall is usually less common. Using a risk-based event attribution approach with a 100-member climate simulation at 5-km grid spacing, our analysis revealed that anthropogenic global warming substantially increased the likelihood of such an extreme rainfall event under the July 2023 conditions. At the Akita weather station, the probability of a 72-h rainfall event similar to that observed in 2023 increased from approximately 0.4% in a non-warming climate to 7.2% in the historical climate. This indicates that anthropogenic warming increased the probability of a 2023-like extreme rainfall event by approximately 18 times. As observed, the ensemble experiment showed that westward extension of the western Pacific subtropical high facilitated moisture inflow toward the Sea of Japan side of the Tohoku region, and that this extension was forced primarily by the global sea surface temperature pattern. Under this synoptic condition, increase in lower-atmosphere equivalent potential temperature driven by global warming enhanced the latent instability, contributing to the persistent heavy rainfall similar to that observed during the 2023 event.

Numerical simulations were conducted to examine the influence of unusually high sea surface temperatures (SSTs) in the southern part of the Sea of Japan (SOJ) on heavy rainfall that occurred in Noto, Japan, on 21 September 2024. During this period, SSTs in the southern SOJ were significantly higher as compared to climatological normals. In a simulation in which local SSTs west of the Noto Peninsula were substituted with the climatological normals from 1991 to 2020, the maximum and area-averaged values of the 48-hour accumulated precipitation were reduced by approximately 28% and 14%, respectively, as compared with the control simulation. Backward trajectory analyses showed that boundary-layer air parcels entering the heavy rainfall region were enriched with substantial water vapor from the warm sea west of the Noto Peninsula. In addition, these parcels reached the heavy rainfall region without losing heat to the warm sea. These features indicate that the sensible and latent heat fluxes from the warm sea not only increase the water vapor content but also enhance low-level convective instability, leading to a significant increase in precipitation.

This study proposes introducing convex optimization to find initial perturbations of atmospheric states to realize specified changes in subsequent weather. In the proposed method, we formulate and solve an inverse problem to find effective perturbations to atmospheric variables so that controlled variables satisfy specified changes at a specified time. The proposed method first constructs a sensitivity matrix of controlled variables, such as accumulated precipitation, to the initial atmospheric variables, such as temperature and humidity, through sensitivity analysis using a numerical weather prediction (NWP) model. Then a convex optimization problem is formulated to achieve various control specifications involving not only quadratic functions but also absolute values and maximum values of the controlled variables and initial atmospheric variables in the cost function and constraints. The proposed method was validated through a benchmark warm bubble experiment using the NWP model. The experiments showed that the identified perturbations successfully realized specified spatial distributions of accumulated precipitation.

The Japan Meteorological Agency reported that a record strengthening of the upper-level high and the North Pacific subtropical high (NPSH) at the surface in late June 2022 was the primary cause of the unprecedented heat wave in Japan from late June to early July 2022. Using observational data and a linear baroclinic model (LBM), this study explains the mechanism behind the NPSH strengthening associated with the negative Indian Ocean dipole (IOD) occurring at that time. A lower-level anticyclonic circulation anomaly to the south of Japan did not appear in the LBM experiment for June with a negative-IOD-related heat source over the eastern Indian Ocean. However, it appeared in another LBM experiment where the heat source was located over the northern Indian Ocean. From mid to late June 2022, enhanced convection over the southeastern Indian Ocean gradually expanded toward the northern Indian Ocean, accompanied by the synchronous development of a low-level anticyclonic circulation anomaly south of Japan. Similar phenomena occurred in the same period in previous years, indicating that the negative IOD played a key role of the strengthening the NPSH around late June 2022.

East Asia experiences a distinctive summer rainfall period, referred to as Meiyu in China and Baiu in Japan. The precipitation during the Meiyu-Baiu season is a critical component of summer rainfall, significantly impacting agriculture and economic development. While both Southeast China (SEC) and Japan (JPN) encounter the Meiyu-Baiu season from June to July, the structure of the Meiyu-Baiu Front (MBF) differs between these regions. Utilizing daily ERA5 reanalysis data and a method that defines the MBF based on equivalent potential temperature, we analyzed precipitation patterns and atmospheric conditions during the East Asian monsoon season. The MBF demonstrates a distinct northward progression in SEC from early June to late July, whereas in JPN, the front moves northward from early June to mid-July, stagnating thereafter. Meiyu-Baiu precipitation correlates strongly with the position of the MBF during early June to mid-July, with two major precipitation centers identified in both SEC and JPN. Atmospheric conditions associated with the MBF exhibit clear regional and temporal variability. Compared to JPN, SEC features a stronger meridional humidity gradient, more pronounced upward motion, and a smaller meridional temperature gradient on the southern side of the MBF.

This study introduces a novel framework, Standardized Anomalies Quantile Regression Forests (SA-QRF), which integrates nonlinear predictor selection via quantile random forests (QRF) into the standardized anomaly model output statistics (SAMOS) method. Unlike the traditional Boosting-based approach (SA-Boosting), QRF effectively captures nonlinear interactions between predictors and forecast targets while quantifying predictor importance. This strategy avoids overfitting and highlights key variables influencing forecast accuracy. Using ECMWF fine-grid and ensemble forecast data (2019-2020) for Beijing-Tianjin-Hebei Province in China, SA-QRF is evaluated against SA-Boosting for forecasts of 2 m temperature, 2 m relative humidity, and 10 m wind speed. Results demonstrate that SA-QRF achieves skill levels 10 comparable to SA-Boosting in continuous ranked probability skill scores. The spatial continuous ranked probability score comparison with SA-Boosting shows that SA-QRF outperforms by covering 7% more stations in the spatial forecasts of relative humidity and 9% more stations in the spatial forecasts of wind speed. In addition, these two methods effectively mitigate underdispersion in probabilistic forecasts, as evidenced by visual examination of the probability integral transform plots, and enhance deterministic forecast performance by 15%, 31%, and 34%, respectively. These findings validate the QRF can complement and optimize SAMOS, leveraging its nonlinear strengths to achieve better performance.

The mesoscale heavy rainfall, “Senjo-Kousuitai” (SK), often causes severe disasters. This study utilizes a regional reanalysis dataset for Japan (RRJ-Conv; hourly data from 1976 to 2020 with horizontal resolution of 5 km) to extract and analyze SK events across Japan and its surrounding area. By applying an objective extraction method, we identified 6760 SK events. They are mostly oriented southwest–northeast or west–east and more frequently appear between June and October and in the morning. These characteristics of their occurrences agree with those in observations. While RRJ-Conv does not reproduce all observed SK events, the vast number of SK samples is useful for statistical analysis of their characteristics and environmental conditions. The present study demonstrates that the SK orientations have the strongest correlation with wind directions at 600 hPa with a slight clockwise deviation from the wind direction at this altitude. The environmental winds exhibit veering, whose magnitude decreases after onsets of SK.