Scientific Online Letters on the Atmosphere (SOLA) is a fully Open Access journal under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License. The journal aims to publish peer-reviewed short scientific papers in the atmospheric and related interdisciplinary sciences. SOLA, derived from the Japanese word for "sky," was first published in 2004.
We are pleased to publish 56 papers in Volume 20, 2024, including the Special Edition of Advances in Studies of Torrential-Rain-Producing Quasi-Stationary Band-Shaped Precipitating Systems, or "Senjo-Kousuitai" and the Special Edition of the Frontier of Atmospheric Science with High Performance Computing. The Special Edition on Recent Advances in the Global Energy and Water Cycle Exchanges (GEWEX) Sciences will call for papers from January 2025.
The Editorial Committee of SOLA presents the SOLA Award to one or two outstanding papers published each year. The SOLA Award for 2023 was presented to Kanada and Nishii (2023) and Suzuki et al. (2023). The SOLA Award for 2024 will be announced shortly, by the end of January 2025.
SOLA will transition to Springer for publication in 2026. The submission deadline for the current ScholarOne system is 1600 JST on 29 August 2025. After September 2025, manuscripts should be submitted to Springer’s SNAPP system, and, upon acceptance, they will be published by Springer. Moreover, the deadline for acceptance of manuscripts under review in ScholarOne for publication on J-STAGE is 20 November 2025. Manuscripts accepted after this date will be published by Springer. Please note that the Article Processing Charge differs depending on whether the article is published on J-STAGE or by Springer.
We hope that SOLA becomes one of your preferred platforms for disseminating your research achievements. SOLA welcomes submissions from the international community in meteorology, atmospheric sciences, and related fields.

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The Editorial Committee of Scientific Online Letters on the Atmosphere (SOLA) presents the SOLA Award to one or two outstanding papers published each year. We are pleased to announce that The SOLA Award in 2024 will be given to the paper by Dr. Yohei Sawada, entitled "Ensemble Kalman Filter Meets Model Predictive Control in Chaotic Systems" (Sawada 2024).

Sawada (2024):
Data assimilation originated from control theory and plays an important role in the prediction of various Earth sciences, including meteorology. In recent years, attention has shifted from simple prediction to weather control, aiming to mitigate the impact of meteorological disasters by modifying the atmospheric states based on predictions. This study proposes a new method for weather control based on the ensemble Kalman filter (EnKF), one of the major data assimilation methods. The new method leverages the similarity between the cost functions minimized in EnKF and model predictive control. The proposed method obtains appropriate perturbations for control by assimilating the target state in model predictive control as pseudo-observations. This study evaluates the new method alongside conventional approaches using a simple dynamical system, demonstrating the new method’s superior ability to control state variables and its sensitivity to tunable parameters. The greatest advantage of the new method lies in its compatibility with EnKF, paving the way for extensions and optimizations using existing EnKF and applications in control problems in other fields. The editorial committee highly evaluates the significance of this study and therefore presents "The SOLA Award".

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This study investigates the influence of aerosol optical depth (AOD) on warm cloud microphysics in three distinct regions: East Asia (EA), the Equatorial Atlantic Ocean (EqAO), and Peru (PR), using Cloud Frequency by Optical Depth Diagram (CFODD) analysis. Satellite data from Aqua/MODIS and CloudSat/CPR during the period 2006-2014 were utilized to compare cloud properties under low and high AOD conditions. In EA, high AOD levels are associated with enhanced radar reflectivity at smaller cloud droplet radius (CDR), particularly within the 12-15 μm range. This indicates an increased droplet numbers and greater reflectivity variability within the same CDR interval. The observed enhancement in radar reflectivity at the cloud base under high AOD suggests an intensified collision-coalescence processes, consistent with the Anti-Twomey effect. A similar, though weaker, pattern is observed over the EqAO, indicating a reduced aerosol impact. In contrast, the PR region, exhibits minimal AOD variability, leading to limited aerosol effect and relatively stable cloud structures. These regional differences highlight the importance of considering both Twomey and Anti-Twomey effects to improve the representation of aerosol–cloud interactions (ACI) representation in climate models.

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The accurate quantification of tropical cyclone (TC) activity in the western North Pacific (WNP) requires a robust metric able to capture frequency and intensity variation. This study uses average accumulated cyclone energy (AACE) to assess TC variability under extreme scenarios and El Niño–Southern Oscillation (ENSO) phases. AACE measures interannual variability more comprehensively than traditional metrics by incorporating the average annual intensity contributions of TCs. In this study, AACE effectively captured ENSO-driven shifts, revealing TC energy concentration in the eastern and western WNP during El Niño and La Niña years, respectively. Additionally, AACE is less influenced by annual variations in TC frequency while preserving the contributions of both strong and weak storms. Its ability to capture spatial and intensity variations makes it a valuable metric for analyzing TC variability. These findings highlight the advantages of AACE for climate projections and risk assessments, offering novel insights into the ENSO-driven modulation of TC activity in the WNP.

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This study demonstrates that a transformer-based neural operator (TNO) can perform zero-shot super-resolution of two-dimensional temperature fields near the ground in urban areas. During training, super-resolution is performed from a horizontal resolution of 100 m to 20 m, while during testing, it is performed from 100 m to a finer resolution of 5 m. This setting is referred to as zero-shot, since no data with the target 5 m resolution are included in the training dataset. The 20 m and 5 m resolution data were independently obtained by dynamically downscaling the 100 m data using a physics-based micrometeorology model that resolves buildings. Compared to a convolutional neural network, the TNO more accurately reproduces temperature distributions at 5 m resolution and reduces test errors by approximately 33%. Furthermore, the TNO successfully performs zero-shot super-resolution even when trained with unstructured data, in which grid points are randomly arranged. These results suggest that the TNO recognizes building shapes independently of grid point locations and adaptively infers the temperature fields induced by buildings.

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A statistical study was conducted on weekday-holiday differences in atmospheric pressure in the central part of Tokyo and Osaka, and on surface wind fields in surrounding areas. The analysis for pressure was based on 33-year data from stations of the Japan Meteorological Agency (JMA). The analysis of winds was based on 44-year data from the Automated Meteorological Data Acquisition System (AMeDAS) of the JMA and 28-year data from the Air Pollution Monitoring System (APMS) of the Tokyo Metropolis. It was found that the pressure in central Tokyo was higher during the daytime on holidays than on weekdays, by approximately 0.045 hPa at 1500 JST. The daytime surface winds had a divergent anomaly within several tens of kilometers of the city center, corresponding to a wind speed anomaly of the order of 0.1 m s⁻¹. Similarly, a pressure anomaly of approximately 0.015 hPa was found in the afternoon on holidays in central Osaka, as well as a divergent anomaly in surface winds in the surrounding area.

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This is a case study of March 2023 when Japan experienced significant warming, along with its prediction using the atmospheric reanalysis dataset and Japan Meteorological Agency's Global Ensemble Prediction System (GEPS). A major sudden stratospheric warming event occurred in mid-February 2023 with a shift of the center of the stratospheric polar vortex toward northern Eurasia. In conjunction with the stratospheric polar vortex shift, the tropospheric westerly jet over Japan shifted poleward, leading to significant warming in March 2023. Regression analyses were conducted using the GEPS ensemble forecast initialized on 15 February 2023, which predicted the significant warming phenomenon near Japan. The regressed anomalies of geopotential height from Japan to the mid-latitude North Pacific showed that the strengthening of the displaced stratospheric polar vortex over Siberia was related to the poleward shift of the westerly jet near Japan. This finding suggests that when the stratospheric polar vortex shifts toward Siberia, the subsequent significant warming near Japan can be predicted with reliability. The geographical position of the stratospheric polar vortex is likely to be a key predictor to improve the forecast skill of the weather conditions near Japan in early spring.

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In summer 2024, record-high temperatures were observed over Japan, where the area-averaged summer-mean temperature was tied highest with 2023. The extreme heatwave in July was attributable primarily to the poleward-deflected upper-tropospheric subtropical jet (STJ). The low-level North Pacific Subtropical High (NPSH) intensified to the immediate south of Japan under the remote influence of enhanced cumulus convection over the northern Indian Ocean. Persistent anomalous descent and increased solar radiation associated with the NPSH contributed to the record-high temperatures in the southern portion of Japan. In August, the persistent heatwave, particularly over western Japan, was attributable to the poleward-deflected STJ and enhanced convection associated with a lower-tropospheric cyclonic gyre to the southeast of Japan where several typhoons sequentially formed. Meanwhile, heavy rainfall over northern Japan in late July was attributable primarily to developed convective systems organized just to the south the Baiu front under the intensified moist westerly airflow to the north of the markedly-extended NPSH over western Japan. Other factors that could contribute to the extreme heatwave and heavy rainfall in 2024 summer include extreme warmth of the surrounding ocean, global warming, and extremely high zonal-mean temperatures in the midlatitude Northern Hemisphere troposphere during a post El Niño summer.

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Sudden localized heavy rainfall events, capable of disrupting daily life and damaging infrastructure, are becoming more frequent. Their nowcasting (very short-term forecast) requires higher spatiotemporal (4D) resolution than conventional radars, and effective 4D methods to extrapolate the vertical development of convective systems. This study evaluates the performance of a new system that generates 10-minute lead-time precipitation nowcasts in real time, which are used by a publicly available smartphone application to issue heavy rainfall warnings. Dense 4D observations from new Multi-Parameter Phased Array Weather Radars (MP-PAWR) in Saitama, Osaka, and Kobe (Japan) are extrapolated using an Artificial Neural Network (ANN4D), which has demonstrated high performance in forecasting the sudden onset of precipitation in Saitama, prior to 2020. The study demonstrates that, despite using the same ANN4D instance, the system generates reliable nowcasts, generalizes well to new locations and years, and that performance is enhanced by a post-ANN4D procedure for mitigating false rainfall predictions. ANN4D outperforms a 4D Eulerian model (TREC4D) in predicting convective rainfall onset, while TREC4D is more efficient for well-developed rainfall. The study identifies minor issues, like the need to expand ANN4D's vertical range, and highlights the next major step: integrating ANN4D with TREC4D to exploit their complementarity.

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Based on a cloud resolving model (CRM) simulation, cumulus parameterization (CP) problem for gray zone is revisited. In the CRM simulation of deep cumulus convections in the tropical Pacific, we can see many intermittent kilo-meter scale ascending updraft bubbles (thermals). If we gather these updraft bubbles in a 20 km grid area into one place, they can be represented by a single hypothetical large plume: equivalent plume (EP). The total updraft in the grid is equivalent to the updraft of EP. It is shown that the mean temperature of EP is almost the same as the environment temperature. As the EP is saturated, the mean specific humidity of EP is the same as the saturation specific humidity at the environment temperature. The EP shows that “moist buoyancy” is essential for cumulus convections. With the mean temperature and mean specific humidity of EP, we can calculate the moist static energy and the entrainment rate for EP. Using this entrainment rate and the updraft mass flux at cloud base which is proportional to fractional convective cloud area, we can calculate the updraft mass flux necessary for a CP scheme for gray zone.

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The drainage flow in a valley was observed with meteorological sensors attached on compact drones. We first developed a wind measurement system by mounting an ultrasonic anemometer on a drone and evaluated its performance by comparing the data with those from wind measurements on a meteorological tower and on a pole. With this system, the vertical structure of drainage flow was observed through multiple observation campaigns. By compiling 35 profiles in total, the statistical features of their vertical profiles in temperature, humidity and horizontal winds have been derived. It is found that the flow is characterized by a strong temperature inversion layer with positive humidity anomaly and that the wind profile takes a parabolic shape, with the wind speed taking its maximum around at the center of the layer. This shape seems to agree with the previous findings from measurements at a relatively deep valley while differing from those on a simple plane terrain.

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The Sea of Japan (SOJ) has exhibited significant sea surface temperature (SST) warming in early winter, particularly in the East Korea Bay (EKB) and the subpolar oceanic front (SF). This study examined the thermodynamic impact of SST anomalies over the EKB and SF regions on the Japan-Sea polar-airmass convergence zone (JPCZ) using high-resolution numerical experiments. Results revealed that the local warm SOJ–SST anomalies played two contradictory roles in modulating the JPCZ. The anomalously warm EKB warmed the atmospheric boundary layer over the downstream region (JPCZ area) where the JPCZ prevailed, thereby decreasing sea level pressure (SLP) through hydrostatic equilibrium. The SLP decrease facilitated low-level wind convergence, intensifying the JPCZ. Enhanced moisture supplies from the sea surface due to strong winds also contributed to the JPCZ precipitation through the dominance of moisture flux convergence. In contrast, the extremely warm SF induced an anomalous surface low to the north of the JPCZ area through boundary-layer warming. Such thermodynamic changes strengthened low-level wind convergence over the SF, whereas they interrupted monsoonal winds that flowed into the JPCZ area, thereby inhibiting the JPCZ precipitation. These findings emphasize that monitoring of the local SOJ–SST warming is crucial for the accurate prediction of the JPCZ.

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The Nonhydrostatic Icosahedral Atmospheric Model (NICAM)–Local Ensemble Transform Kalman Filter (LETKF) JAXA Research Analysis (NEXRA), a weather research and analysis system integrating NICAM with the LETKF, has been updated. NEXRA combines an atmospheric data assimilation system (NICAM–LETKF) and a five-day deterministic weather forecast model using NICAM. This study compares the previous system version, NEXRA2, with the updated version, NEXRA3. NEXRA3 features a higher-resolution NICAM–LETKF with a 56 km mesh for assimilation and an updated version of the 14 km mesh NICAM with improved physical schemes and optimized source code. We performed a statistical comparison by analyzing one year of data assimilation cycles and conducting five-day deterministic forecasts under both summer and winter conditions. The results demonstrate that NEXRA3 outperforms NEXRA2 in both the NICAM–LETKF analysis and five-day forecasting, particularly in terms of precipitation forecast accuracy. These findings show that the implementation of several model improvements, which have proven highly effective in previous developments, also improve both the data assimilation and forecasting cycles.

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Subseasonal forecasting for extreme precipitation represents a critical yet challenging frontier in weather prediction, particularly in regions like Vietnam, where monsoons, tropical cyclones, and diverse topography complicate the precipitation patterns. This study explores the integration of machine learning techniques—Random Forest (RF) and Extreme Gradient Boosting (XGB)—into model output statistics to enhance subseasonal extreme rainfall forecasts across Vietnam's seven climatic regions. ECMWF S2S hindcast data for the Madden-Julian Oscillation, monsoon indices, and soil moisture are used to predict rainfall extremes. The models are trained over 2001-2014 and evaluated over 2015-2023 against observational data. Evaluation metrics, including probability of detection, false alarm ratio, critical success index, and Brier skill score, highlight the superior performance of RF and XGB over raw ECMWF forecasts, particularly in North West, North East, Red River Plain, Central North and Central South regions. However, challenges remain in the Central Highland and South regions, where both deterministic and probabilistic skills are weaker. Overall, this study underscores the potential of machine learning to address regional and temporal variability in extreme rainfall prediction, offering a transformative tool for disaster preparedness in Vietnam.

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Estimating the radius of maximum wind (RMW) with high accuracy is important for tropical cyclone (TC) wind-field monitoring. Recent advances in spaceborne synthetic aperture radar (SAR) have made it possible to observe detailed inner-core wind speeds of TCs. To leverage this opportunity, two methods are developed to estimate the RMW from 89-GHz passive microwave satellite data, with SAR observations used as ground truth data. The first method, for TCs with an eyewall ring, is based on the relationship between the RMW and the radius of the azimuthally averaged 89-GHz brightness temperature (89TB) at −40°C. After objective exclusion of cases with concentric eyewalls or no ring structure, this method achieved a mean absolute error (MAE) of less than 5 km. When the first method cannot be used, the second method estimates the RMW as the radius where the azimuthally averaged 89TB radial gradient is at its most negative (Rgrad). After exclusion of cases with a small 89TB radial gradient or a large Rgrad and high-latitude cases, the second method yielded an MAE on the order of 20 km. By using a combination of the two methods, a 72% chance of estimation among the available microwave satellite data samples was achieved.

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Using a large-ensemble climate simulation, this study examines how global warming influences temperature extremes associated with the Ural anticyclonic height anomaly. A comparison of 4K global warming and non-warming experiments exhibits an increased frequency of Ural blocking events. These events produce a dipole temperature anomaly pattern, with pronounced warming in the Eurasian Arctic coast and cooling in the Eurasian mid-latitudes. This meridionally displaced warm and cold temperature anomalies arise from the anticyclonic height anomaly in the Ural region acting upon the climatological northwest-southeast isotherms. Under 4K warming, the frequency distribution of near-surface temperature anomaly reveals suppression of extreme warm anomalies in the Arctic coast while cold extremes in the mid-latitudes remain robust. This regional contrast stems from the zonally symmetric amplification of Arctic warming interacting with the tilted climatological isotherms in the Ural region. Enhanced high-latitude warming reduces the meridional temperature gradient in the Arctic coast, leading to weaker temperature advection associated with the Ural anticyclonic height anomaly which suppresses the warm extremes. In comparison, the mid-latitude temperature gradient remains less affected, preserving the frequency distribution of the cold anomalies.

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Japan Meteorological Agency's Himawari-8 and -9 geostationary meteorological satellites are scheduled to continue operation until around 2029. As the Himawari series cover a wide variety of uninterrupted domestic and international observations, Japan Meteorological Agency (JMA) began the building of the new Himawari-10 satellite in March 2023 toward launch in Japan Fiscal Year (JFY) 2028 and operation in JFY2029, in accordance with the Basic Plan on Space Policy approved by the Cabinet of Japan on 13 June, 2023. The Himawari-10 initiative remains dedicated to ongoing development of weather observation and forecasting capability with new hyperspectral infrared sounder data to support issues such as disaster risk reduction in East Asia and the Western Pacific. This paper overviews the Himawari-10 project and onboard observation equipment.

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The microphysical properties of fog off the southeast coast of Hokkaido and the east coast of the Sanriku region in Japan are estimated using data from the geostationary satellites Himawari-8/9. A retrieval algorithm for estimating cloud microphysical properties is applied to the Himawari-8/9 data, and an approach that calculates the cloud base height is introduced to distinguish between fog and low cloud. This approach captures 70% of the fog events reported at the Kushiro site in Hokkaido with 78% precision rate, 38% miss rate, and 29% overlook rate. Using this approach, 9 years of climatology for the optical thickness, effective radius, and liquid water path of fog are derived over the wide target areas. The estimated effective radius over the land agrees qualitatively with ground-based measurements of the microphysical properties of fog despite the limited availability of such data in the literature. The results also show that the microphysical properties of fog vary around the coastline between sea and land.

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To diagnose stratospheric transport, the mean age of air, the transit time of an air parcel from the troposphere to the stratosphere, has been estimated from measurements of trace gases, typically CO₂ and SF₆. Here we present a new set of halocarbon measurements on air samples collected at 15-35 km above Japan in 2020 using a balloon-borne cryogenic sampler. The mean ages of air were estimated based on a variety of trace gases combined with reference time series at the surface. The mean ages of air from HFC-23 and HFC-227ea showed general agreement to those derived from CO₂ and SF₆, which varied from < 2 yr below 20 km to almost constant values of ∼5 yr above ∼25 km altitude. In addition to conservation of the tracer in the stratosphere, measurement precision was found to be decisive to precisely estimate the mean age of air. We examined the age of air estimated from multiple tracers for future investigation towards improving assessment of the long-term trend in stratospheric circulation.

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An RGB hexagram, a new visualization and multivariate analysis method capable of representing mixtures of up to three components, was developed. This method was applied as a case study to analyze the mixed-phase cloud characteristics in numerical simulations of an isolated convective cloud containing mixed-phase areas. For this analysis, liquid water, graupel, and ice mass were selected as the three components corresponding to Red, Green, and Blue of the RGB hexagram to investigate the cloud microphysical characteristics. During the developing stage of the cloud before precipitation onset, simulation results represented the active positive and negative charge separation of graupel and graupel growth. Visualization using RGB values defined by the RGB hexagram effectively illustrated the vertical cross-sectional characteristics of mixed-phase clouds in this stage. Furthermore, the analysis demonstrated differences in charge separation properties between areas dominated by graupel growth and those with active charge separation. This application case suggests that visualization and analysis with the RGB hexagram, through appropriate variable selection, can contribute to the understanding of meteorological phenomena.

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Daily rainfall, minimum, and maximum temperatures from 60 Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) synoptic stations from 1951-2022 were homogenized using the Climatol Package in which time series breakpoints were found in three stations for rainfall, 48 for maximum temperature, and 53 for minimum temperature, respectively. The identified breakpoints were validated using the station metadata gathered from a series of PAGASA workshop-dialogues on data homogenization and station profiling. Only 25% of the breakpoints for rainfall coincided with station metadata, 36%, and 28% for maximum and minimum temperatures, respectively. Instrument issues and station relocations caused most inhomogeneities, while breakpoints due to natural causes, such as major volcanic activities, were disregarded in the analysis. The homogenized data have closely similar mean values and a lower standard deviation than the observed, especially the temperature data. The Mann-Kendall test revealed a warming trend for annual maximum (0.03°C/decade; p-value < 0.01) and minimum temperature (0.08°C/decade; p-value < 0.01), while homogenized rainfall has no significant trend (11.87 mm/decade; p-value = 0.097) from 1951-2022. This study produced long-term, quality-controlled, and homogenized data for climate change research and decision-making. The homogenized rainfall, maximum and minimum temperatures provide a better representation of the Philippine climate.

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This study investigates the dependency of the charge structure in a summer convective cloud on the horizontal resolution of a bulk lightning model coupled with a meteorological model. For simulations involving a bulk lightning model with fine grid resolution, a multigrid solver is applied to the model, and the simulation results show that the charge separation numerically converges when the resolution reaches 200 m. The reason for this convergence is that with finer resolution, the strong vertical updraft actively transports liquid water and water vapor to higher altitudes, generating an environment conducive to riming, with sufficient supercooled water, transported by vertical updraft and produced from water vapor by condensation, present at altitudes of 9-11 km. Consequently, with higher resolution, the graupel mixing ratio increases at these altitudes, and this graupel formation leads to increased charge separation, which results in a higher lightning frequency. These results suggest that simulations with a resolution of 200 m or finer are necessary to simulate the lightning frequency accurately in numerical models coupled with a bulk lightning model.

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Extreme and intense atmospheric phenomena occur in complex urban areas, but stationary ground-based meteorological observations cannot capture the detailed meteorological conditions across urban blocks. Vehicle-based mobile observation provides a method to collect high spatiotemporal data in cities. We developed a small IoT observation system to mount vehicles easily and measure air temperature, humidity, pressure, wind speed and direction, together with local time and location. To evaluate this system, we conducted a case study of mobile observation in Tokyo almost daily for one and a half months. Mobile observation data had occasional missing and erroneous, so data extraction and processing were applied based on weather conditions and vehicle's speed. Using the quality-controlled data, we confirmed that given the mobile observation was within 2 km of the fixed-point observation, air temperature and humidity from mobile observation was highly correlated with reliable fixed-point observation (RMSD < 1°C and < 0.34 g kg⁻¹). Thus, the mobile observation system potentially provides those datasets comparable to conventional ground meteorological datasets. The spatial distributions of air temperature and humidity exhibited distinctive changes between urban blocks, influenced by land use and urban characteristics. This system enables long-term, extensive vehicle-based observations and aids in detecting extreme atmospheric phenomena in urban areas.

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Atmospheric hydrogen peroxide (H₂O₂) was intensively measured in the Tokyo metropolitan area (Kazo City, Saitama Prefecture and Shinjuku-ku, Tokyo), Japan in the summer of 2022 and 2023. In particular, the temperatures in the summer of 2023 were very high. H₂O₂ and ozone (O₃) concentrations were generally higher in Kazo City than in Shinjuku-ku, and the concentrations were particularly higher during the period of typical summer atmospheric pressure pattern when Photochemical Oxidant Warning was issued in Saitama Prefecture. Sufficiently high concentrations of H₂O₂ exceeding 3-4 ppb (30-minute values) were occasionally measured, which have hardly been seen in Toyama Prefecture, where the air environment is relatively clean in Japan. The oxidation capacity of SO₂ seems to be high in summer in the Tokyo metropolitan area. In Kazo City, H₂O₂ was highly positively correlated with O₃. When the atmospheric pressure pattern was typical for summer, both O₃ and H₂O₂ reached their maximums at around 15 JST in Kazo City; however, in Shinjuku-ku, the peak time of O₃ was in the early afternoon (around 13 JST), while the highest concentration of H₂O₂ was seen at around 15 JST when NO_X became significantly low.

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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.

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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.

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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.

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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.

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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.

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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.

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This study introduces a novel framework, Standardized Anomaly Quantile Regression Forests (SA-QRF), which integrates nonlinear predictor selection via quantile regression 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 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.

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Typhoons AMPIL and SHANSHAN (2024) slowed down as they approached and moved parallel to the Kuroshio Current. Both typhoons reached peak intensity at relatively high latitudes while they were moving northward. Various atmospheric and oceanic datasets were used to investigate atmospheric and oceanic thermodynamic similarities and differences between AMPIL and SHANSHAN. In addition, numerical simulations and sensitivity experiments for high sea surface temperature (SST) in the Kuroshio Current region were performed with a nonhydrostatic atmosphere model and an atmosphere-wave-ocean coupled model to understand related intensification mechanism. The 26°C isotherm was relatively deep and the upper-ocean heat content was relatively high where AMPIL and SHANSHAN intensified. Despite the different atmospheric environments, such as the vertical wind shear and the relative humidity at the 600-hPa altitude, AMPIL and SHANSHAN were able to develop and maintain the maximum intensity at relatively high latitudes during when the TCs moved slowly near the Kuroshio Current region. The results of the sensitivity experiments suggest that high SSTs in the Kuroshio Current region possibly contribute to the development and maintenance of the maximum intensity of AMPIL and SHANSHAN through high latent heat fluxes outside the radius of maximum wind speed and the inner-core axisymmetrization.

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In 2017/18 winter, the Siberian High intensified significantly, leading to a severe cold winter in central Eurasia. Here, we apply the event attribution methods using two types of historical large-ensemble simulations from an atmospheric general circulation model to quantify the influence of human activities on this event. The 2017/18 winter was dominated by a circulation regime known as the Warm-Arctic Cold-Eurasia (WACE) pattern, and both observation and model showed high WACE indices. However, the models exhibited a bias characterized by weak magnitudes of the cold Eurasian anomalies associated with the WACE, attributed to an underrepresentation of externally driven components. Anthropogenic climate change has increased the probability of the positive WACE regime year by year, significantly enhancing its occurrence in the 2017/18 winter. However, influences of this modulation of WACE occurrence did not significantly alter the probability of cold events in central Eurasia due to the model bias characterized by the muted cold lobe associated with the WACE. While a simple bias correction resolved this issue, it was demonstrated that the presence or absence of such corrections led to vastly different attribution results. Elucidating the mechanisms behind the WACE and accurately representing them in models is essential for more reliable attribution.

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 To estimate global frozen precipitation particle characteristics, this study developed a method that utilizes Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) observations. This method estimates the volume-weighted mean diameter before melting (D_m) and the number concentration (N_w) of frozen precipitation particles for a given particle model from the DPR reflectivity factors (Z_e). The likelihood of this particle model is estimated using the difference between the brightness temperatures at 89 and 166 GHz calculated from this D_m and N_w (TB_c) and the GMI observation (TB_o) as a measure.

 Particle models representing snowflake, aggregate, strongly rimed aggregate, and graupel (sphere) were selected from existing scattering databases. Under idealized conditions, the TB_c computed from DPR Z_e was highly dependent on the particle model. In the OLYMPEX (3 December 2015) case, the TB_c calculated from the observed DPR Z_e also depended on the particle model. The most likely particle models were spherical particles and strong rimed aggregates south of the Olympic Mountains and aggregates to the north. The D_m values for the most likely particle models had a smaller bias than the D_m values for each particle model when compared with the OLYMPEX airborne observations.

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The quantitative relationship between the observed snowfall and PM_2.5 concentration data collected at Niigata Prefecture, Japan, was examined. The PM_2.5 concentration decreased as snowfall increased. A composite analysis revealed that the PM_2.5 concentration decreased by 27.33% from 1 h before to 1 h after the onset of snowfall, which is statistically significant. The average PM_2.5 concentration for all snowfall events decreased over a total of three hours, from 2 h before to 1 h after the onset of snowfall. The average PM_2.5 scavenging rate for all snowfall events was 6.15 × 10⁻⁶ s⁻¹, which was much higher than that for rainfall events. The scavenging rates were positive at relative humidity (RH) between 50% and 75% and above 95%. It is suggested that the deposition processes of wet scavenging are effective, both in reducing PM_2.5 concentration during snowfall and in the RH dependence of scavenging rates.

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Tropospheric ozone (O₃) is an air pollutant and greenhouse gas and is still a problem in East Asia. O₃ is formed via secondary photochemistry from nitrogen oxide (NO_x) and volatile organic compound (VOC) precursors. Because of the increase in anthropogenic emissions globally, changes in O₃ concentration over the Northern Hemisphere have been a concern. In this study, using measurements at three remote sites conducted by the Japan Meteorological Agency (JMA), the 30-year (1990s to 2020) long-term trend of background O₃ concentration was analyzed. The results of the fitted curve revealed that background O₃ concentration has changed by within ±0.3 ppbv/year over 30 years. The increasing trend at Ryori (39.03°N, 141.82°E) peaked in 2009 and then decreased, agreeing well with other reports in the mid-latitude zone (40°N-55°N). In contrast, the present study revealed continuous decreasing trends at Minamitorishima (24.28°N, 153.98°E) and Yonagunijima (24.47°N, 123.02°E) at lower latitudes. In addition, the analysis for 2020 clarified that the frequency density shifted to a lower O₃ concentration compared with the dataset for the 1990s to 2019 at all three remote sites in Japan. This suggested that the emission reduction resulting from the COVID-19 pandemic could be a factor causing the decreasing background O₃ concentration.

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This study evaluated the potential for reducing storm surges in experiments designed to artificially increase the surface drag coefficient (Cd) at the entrance of Tokyo Bay. Using atmospheric and storm surge models, we varied Cd within the range 0.005-0.025 and the area of increased friction. The results showed that increased friction at the entrance to Tokyo Bay led to decreased wind speeds, significantly reducing storm surge height, particularly at Tokyo. Under the maximum friction increase, the storm surge height at Tokyo decreased by up to 24%. The storm surge reduction was most effective when Cd was increased to 0.010, with further increases in Cd or the area of friction yielding diminishing wind speed and storm surge. The findings suggest a novel approach, similar to traditional coastal windbreak forests, for mitigating meteorological and oceanographic disasters, with potential applications for protecting urban areas within bays from storm surges.

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This study aims to evaluate the impact of lateral boundary conditions, land surface model schemes, and soil conditions on the simulations during extreme heat events in Metro Manila, Philippines. Extreme heat events are simulated using a numerical model representing urban land use and anthropogenic heat flux and compared with observation data. The simulations of near-surface temperature are more sensitive to the land surface model used than initial and boundary conditions. Increasing soil temperature by a factor of 1.10 showed improvements in nighttime temperatures, but overestimated temperature with higher factors. Default temperature simulations are typically underestimated, but soil temperature modifications helped improve bias and error, particularly benefiting Noah-MP simulations at night. The contrast between daytime and nighttime urban heat island intensity was more pronounced in FNL simulations compared to ERA5. This study highlights the value of accurate temperature simulations in understanding and planning for urban heat island effects in Metro Manila.

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The response of snow cloud bands to the increase in air temperatures and sea surface temperatures (SSTs) over the Sea of Japan was investigated. We focused on a typical snowfall event in Japan by intense cloud bands around the Japan-Sea Polar-Airmass Convergence Zone (JPCZ) on 25-26 December 2021. After confirming that a regional atmospheric model fairly reproduced the mesoscale system in the event, we conducted two sensitivity experiments with air temperatures or SSTs imposed as boundary values uniformly increasing by 4 K. The results revealed that, in the model experiment with higher SST, more water vapor is supplied to the planetary boundary layer, which encouraged the higher clouds along the convergence zone. The experiment identified the dominance of the transversal mode (T-mode) of cloud bands in the east of the zone. This is presumably because of the cloud advection from the top near the JPCZ and vertical shear response to local SST increase over the Sea of Japan away from the JPCZ. In contrast, the experiment with higher air temperatures exhibited wider areas dominated by the longitudinal-mode (L-mode) cloud bands over the Sea of Japan.

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Long-term trends in the frequency of “Violent Typhoons”, which were defined as tropical cyclones with a 10-min maximum sustained wind speed (MSWS) of 54 m s⁻¹ or higher in the western North Pacific, were investigated during 1977-2021. Four datasets were used: the Regional Specialized Meteorological Center (RSMC) Tokyo best-track data, RSMC-Tokyo Dvorak reanalysis data, and two datasets from the Joint Typhoon Warning Center (JTWC) best-track data. One JTWC dataset includes the 10-min MSWS converted from the 1-min MSWS using a factor of 0.88 throughout the periods, and the other includes the 10-min MSWS converted using empirical equations before 1987 and Dvorak conversion tables afterwards. The results showed that the statistical significance of the long-term trend was not found in all datasets for the same analysis period. After 1987, the 10-year average number of Violent Typhoons differed substantially between the RSMC-Tokyo dataset and the JTWC dataset converted using 0.88. However, the Dvorak reanalysis data and the JTWC dataset with the other conversion method were closer to the RSMC-Tokyo dataset. Given the variability among the datasets analyzed, it was not possible in this study to find any robust long-term trends of Violent Typhoons.

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Indonesia, as a tropical region and prone to frequent thunderstorms, is faced with potential risks to both human life and infrastructure. The analysis of a dataset containing observations of thunderstorms enabled us to identify variations in the vertical air temperature profiles between days experiencing thunderstorms and those characterized by no significant weather conditions (Nosig) within the layer between 1000 and 150 hPa. Examination of the relative humidity in the middle–troposphere during the thunderstorm days exhibited elevated moist layers in compared to the Nosig days across all the investigated regions. By employing stability indices that reflect the atmospheric conditions favorable for thunderstorm development, in conjunction with gridded reanalysis data and logistic regression methodologies, we ascertained that among the numerous convective stability parameters scrutinized, precipitable water (PW), K index (KI), and relative humidity in the middle troposphere (RH_Middle) emerged as the most effective parameters in distinguishing between environmental conditions conducive to thunderstorms and those devoid of significant weather phenomena. Assessment utilizing receiver operating characteristic curves illustrated that the optimal normalized thresholds for PW, KI, and RH_Middle were 0.67, 0.86, and 0.62, respectively.

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Quantum computers have attracted much attention in recent years. This is because the development of the actual quantum machine is accelerating. Research on how to use quantum computers is active in the fields such as quantum chemistry and machine learning, where vast amounts of computation are required. However, in weather and climate simulations, less research has been done despite similar computational demands. In this study, a quantum computing algorithm is applied to a problem of the atmospheric science. The effectiveness of the proposed algorithm is evaluated using a quantum simulator. The results show that it can achieve the same simulations as a conventional algorithm designed for classical computers. More specifically, the stochastically fluctuating behavior of a multi-cloud model was obtained using classical Monte Carlo method, and comparable results are also achieved by utilizing probabilistic outputs of computed quantum states. Our results show that quantum computers have a potential to be useful for the atmospheric and oceanic science, in which stochasticity is widely inherent.

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 Local wind systems, including land and sea breezes, are significant factors influencing temperature distribution over the Kanto Plain. In this study, the diurnal variation patterns of these local wind systems on sunny summer days were classified into five categories based on the divergence field using dense surface observation data. Furthermore, the characteristics of temperature distribution and pressure fields, as well as recent changes in their frequency were examined.

 Each wind category was closely related to the pressure gradient around Japan, revealing differences in temperature distribution based on the wind pattern. In categories with a large northward pressure gradient, a North Pacific subtropical high south of Japan was strong, and the surface wind systems were dominated by southerly winds, causing sea breeze fronts to penetrate quickly. In these categories, notably high daytime temperatures were observed in the eastern Kanto region, where the easterly sea breeze was weaker than average. The frequency of these categories has increased in recent years, likely corresponding to the more frequent appearance of the pressure pattern where the subtropical high extends south of Japan.

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Over the Northwest Pacific off Hokkaido, low clouds such as stratocumulus, stratus, and fog form frequently in summer. Despite the scientific and socioeconomic importance of these low clouds, our understanding of their reproducibility in weather prediction models under different synoptic circumstances is still lacking. This study assesses the ability of low-cloud representation and prediction in the Japan Meteorological Agency Meso-Scale Model (MSM) mainly in June and July 2020-2022, focusing particularly on synoptic fluctuations of near-surface temperature advection and low-cloud fraction. The low-cloud fraction predicted from the preceding day corresponds quite well with that in the analysis. However, the low-cloud fraction in the analysis is not highly correlated with satellite observations on daily timescales, while the mean low-cloud fraction agrees with the observations. The MSM sometimes simulates unrealistic fog within the near-surface stable layer associated with warm advection, resulting in a positive cloud fraction bias and a negative downward shortwave radiation bias. Under both warm and cold advection, the MSM may also underestimate the low-cloud fraction, although it still reproduces the horizontal distribution of low clouds rather consistently with satellite observations.

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In this study, we conducted large-eddy simulations of turbulent flows and plume dispersion over idealized two-dimensional double steep hills. In the simulations, we investigated the distribution patterns of the mean plume concentrations, considering various distances between the hills and emission sources. Our objective was to provide information on the area of influence of local hilly terrains on plume dispersion from the viewpoint of accuracy, i.e., determining if the conventional Gaussian plume model can accurately predict plume concentrations. The result showed that the clockwise circulation was dominant in the area between the windward and leeward hills (valley) when the valley width was less than 10 times the hill height (H). This circulation makes the flow close to the stack remain in the valley, resulting in the higher concentrations in the valley than in wider-valley (> 10H valley) cases. The effect of the leeward hill on the flow field was negligible when the valley width was greater than 10H. In the area beyond 20H from the crest of the windward hill, estimated plume spreads for all cases were similar, indicating that the area of influence of the hills was approximately 20H.

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The Weather Research and Forecasting model version 4.3.3 with an eddy-diffusivity mass-flux (EDMF) scheme, which is a blend of the Mellor–Yamada–Nakanishi–Niino scheme including a partial condensation scheme and a mass-flux scheme, is used to forecast a quasi-stationary band-shaped precipitation event that occurred over the Hokuriku region on 12-13 July 2023. However, the original model does not satisfactorily reproduce the heavy precipitation over the region at a horizontal resolution of 5 km. Our companion papers suggested that improving the parameterization of the buoyancy production of turbulent kinetic energy (TKE) is necessary for such a coarse-resolution model to accurately simulate the transition from shallow to deep convection that triggers heavy precipitation. In this study, its parameterization in the EDMF scheme is modified to divide it into two contributions from the partial condensation and mass-flux schemes. The results show that although the modified model predicts the band-shaped distribution of precipitation with a westward bias, it simulates the timing and intensity of heavy precipitation reasonably well. This study presents the modified parameterization of the TKE buoyancy production and demonstrates that a modification of its parameterization can improve the prediction of heavy precipitation.

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In this study, we introduced a new convection scheme in an atmosphere–ocean-coupled model called Coupled general circulation model For the Earth Simulator (CFES). A 150-year CFES simulation shows that the SST bias is almost comparable to that of the simulation with the previous (Emanuel) convection scheme in CFES. The new convection scheme clearly improved the representation of tropical cyclone (TC) frequency over the western North Pacific (WNP) in terms of both climatology and interannual co-variability with El Niño-Southern Oscillation (ENSO). The TC-ENSO relationship is seasonally different between JJA and SON. The clear difference in ENSO-related precursor for TC variability over WNP between JJA and SON is simulated in CFES with the new convection scheme. From the lagged correlation, the new convection scheme CFES simulation reproduces tropical tropospheric cooling (warming) and its impact on the TC variability after La Niña (El Niño) (i.e., the Indian Ocean capacitor effect) in JJA and the Matsuno-Gill like pattern during El Niño in SON.

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