Numerical simulations of convective boundary layers (CBLs) based on the stability categories of the Pasquill–Gifford (PG) chart were conducted using a large-eddy simulation (LES) model. We studied the simulation settings for different combinations of wind speeds and sensible heat fluxes that could quantitatively generate turbulences satisfying the requirement of each stability category. Additionally, we determined appropriate CBL depths for desired turbulence generation and verified the effectiveness of the velocity scale (w*) in turbulence generation simulations. This study identified wind speed and sensible heat flux combinations that could generate turbulences under the atmospheric stability categories B (unstable), C (weakly unstable), and D (neutral). However, the results did not follow the scaling law based on w* in category D when the CBL depth was 600 m. A shallower CBL (300 m depth) should be set in category D to generate turbulence under the scaling law based on w*. In category B, the transient layer disappeared due to active thermals in the CBL when the depth was 300 m. These results indicated that we should set appropriate CBL depths in addition to horizontal wind speeds and sensible heat fluxes to generate desired and scalable turbulences based on the PG chart.

The diurnal variation of boundary layer wind speed in Japan was statistically investigated using wind profiler data for 2002-2013. The analysis was made for 17 stations where the diurnal variation in surface air temperature had an amplitude of 2°C or more. It was found that the wind speed in the upper boundary layer (985 m above the surface) had a minimum in early afternoon in all seasons, in agreement with the general understanding that winds in the daytime boundary layer are reduced due to enhanced frictional force. However, the wind speed in the lower boundary layer (394 m) had a maximum in the afternoon at some stations in contrast to the general feature mentioned above. This afternoon maximum is more conspicuous in summer than in winter and in southern Japan than in northern Japan, and appears to be due to thermally driven local circulations. Nevertheless, the ratio of wind speeds at 394 m with respect to surface had a minimum during the daytime in all stations and seasons, indicating the daytime reduction of surface-relative boundary layer wind speed as a common feature over land.

Soil physical properties are critical to the energy and water balance between land and atmosphere interactions. Accurate soil data inputs could improve the simulations in land surface models and numerical weather models. However, further efforts are required to access the impact of soil data changes on global long-term simulations for climate system models. The Flexible Global Ocean-Atmosphere-Land System Model: Grid-Point Version 3 (FGOALS-g3) in an Atmospheric Model Intercomparison Project (AMIP) - style configuration with two different soil texture datasets is employed to investigate the role of soil texture in the long-term simulations of hydrological and related atmospheric variables. The results show that the difference in sand and clay content between the two datasets is slight in the global mean but exhibits regional heterogeneity. Updating soil texture data considerably reduced the deviation of global annual mean surface soil moisture, with significant improvements occurring in regions with the most remarkable changes in sandy soil content. However, there is almost no improvement in runoff, precipitation, and temperature on the global annual mean scale due to the complexity of the impact factor. Simulations of long-term soil moisture would be enhanced with more accurate data on soil texture.

This study aimed to generalize the understanding of the dependence of convective clouds on the environment. We conducted a massive parameter sweep experiment on convective cloud environments using the warm bubble method with 15600 different profiles to examine how the rainfall characteristics of convective clouds change in response to environmental changes. The experiment showed that an increase in the conditional instability resulted in a significant increase in the total rainfall amount by several orders of magnitude, even when the precipitable water was almost identical. Vertical wind shear either enhanced or suppressed convective rainfall, depending on the degree of conditional instability. The threshold of conditional instability at which the effect of vertical shear switched from suppression to enhancement lowered as the magnitude of vertical shear increased. Regarding the depth of the shear layer, the effect of vertical shear became more significant as the depth increased from 3 km to 6 km. The drastic change in the rainfall amount reflects a shift in the mode of convective cloud development. When the conditional instability was large, vertical shear changed the convective cloud development from the “decay mode” to the “growth mode” in some environments, resulting in a significant increase in the rainfall amount.

During typhoons, direct, reliable experimental observations of the atmosphere and sea surface are difficult. The target storm for the present experiment in 2023 was typhoon Khanun, a Category-4 storm. Two wave gliders, used as autonomous surface vehicles, were improved after assessing problems encountered during a 2022 storm. These improvements reduced equipment vibration and sensor damage on the wave gliders and resulted in uninterrupted data acquisition. Changing atmospheric and oceanographic phenomena were continuously observed before and after passage of the typhoon on both sides of the typhoon's course, and inside the storm zone. Meteorometers were mounted redundantly to evaluate sensors with different specifications and to assess the reliability of acquired data. Data collected at the sea surface during typhoons should enhance understanding of interactions between the atmosphere and ocean.

“Senjo-kousuitai” is a quasi-stationary band-shaped precipitation system (QSBPSs). Its frequency of occurrence over Japan during the 2023 rainy season was higher than usual, especially in Kyushu. This paper evaluates the impact of historical anthropogenic global warming and natural variability on the frequency of QSBPSs using risk-based event attribution based on a 100-ensemble regional climate simulation with 5-km grid spacing. In the historical ensemble experiments, the frequency of QSBPSs during the 2023 rainy season exceeded that expected in a typical year. The re-analysis and the ensemble experiment showed a westward extension of the Pacific subtropical high that led to an enhanced water vapor flux over Kyushu, indicating that this synoptic condition was forced by the global distribution of sea surface temperature during the 2023 rainy season. A comparison between historical and non-warming experiments demonstrated that historical anthropogenic global warming increases the occurrence probability of QSBPSs. The rising temperature results in a higher frequency of inflow of large amounts of water vapor, which facilitates the development of QSBPSs. In addition, the decrease in atmospheric stability at low levels, caused by the increase in sea surface temperature, is likely to contribute to an increasing probability of QSBPSs.

We have developed a compact ground-based microwave radiometer (MWR) for estimating water vapor. The MWR observes radio wave intensity at frequencies between 17.9 and 26.4 GHz across 34 channels and estimates precipitable water vapor (PWV) and the profile of water vapor density using machine learning methods. Data from the Global Navigation Satellite System (GNSS) and radiosonde (SONDE) collected at the Meteorological Research Institute of the Japan Meteorological Agency were used to train and evaluate the machine learning models. Data from June 2021 to March 2022 were used for training, and data from April 2022 to March 2023 were used for evaluation. As a result, the maximum root-mean-square errors (RMSEs) of MWR-derived PWV compared to GNSS-derived PWV and MWR-derived water vapor density compared to SONDE at the lowest layer of the atmosphere were 2.7 mm and 2.4 g m⁻³, respectively. Analysis of the error characteristics of water vapor estimation showed that both PWV and water vapor density profiles had errors in the presence of cloud water, as determined by infrared radiometer, and high accuracy in the absence of cloud water. The estimation accuracy was also affected by fog and water vapor inversion layer.

This study reproduced the meteorological conditions, including typhoon movements near Japan and wind changes over Tokyo, during the 1923 Great Kanto Earthquake, using a numerical simulation model (Weather Research and Forecasting v4.3) and the first European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the 20th century (ERA-20C). The reproduced meteorological conditions coincided in many respects with weather analysis maps and observations produced by the Central Meteorological Observatory. Strong southerly winds around noon on the day of the earthquake were associated with a typhoon on the coast of the Sea of Japan and appear to have had a significant impact on the spread of fires immediately after the earthquake. However, the strong evening and nighttime winds observed at the Tokyo station are likely to have been local phenomena associated with the fire spread, which caused severe damage in Tokyo after the earthquake.

On February 5, 2024, an extratropical cyclone passing the south coast of Japan brought 8 cm of snow to Tokyo, while in Tsukuba, located about 50 km from Tokyo, the snow cover was 0 cm. We investigated the mechanism generating the inhomogeneous distribution of snow cover using a numerical weather model with 1 km grid spacings. Our numerical simulation shows that a coastal front is located on the eastern coast of the Kanto Plain. On the other hand, a relatively warm area broadly spreads from the leeward side of mountainous areas including Mt. Tsukuba. A strong downward flow related to a gravity wave caused by the mountainous area brings adiabatic warming on the leeward side of the mountains. This adiabatic heating accelerates snowfall melting at the low troposphere, and the melting layer is higher on the leeward side of the mountains. Also, the adiabatic heating reduces relative humidity and decreases total precipitation amount. As a result, there is much less snowfall on the leeward side of the mountainous areas, including Tsukuba observational station, as compared with neighboring areas. A numerical simulation with 5 km grid spacings cannot simulate the local-scale snowfall distribution around the mountainous areas.