This study summarizes the discussions on typhoons or hurricanes modification in Japan and the United States (US) from the 1940s to the present, based on a survey of past literature and interviews with relevant personnel. Research on hurricane modification began approximately 80 years ago with Project Cirrus (1947–1952) and Project Stormfury (1962–1983) run by the US government. This project was initiated following a US proposal to Japan in 1965, which aimed to conduct field experiments using cloud seeding techniques for typhoons over the western North Pacific. The proposal sparked in-depth discussions in both academia and the National Diet of Japan. In 1971, the typhoon committee conditionally approved the field experiment in the western North Pacific, but ultimately, the typhoon field experiment was not conducted. This paper identifies the factors that led to the decision not to proceed with the typhoon field experiment despite significant progress, as well as the reasons underlying the decline of typhoon modification and general weather modification research in Japan from that period onward.

Surrogate downscaling is one of the most promising applications of deep learning techniques in meteorology. Sekiyama et al. (2023), a companion paper to this study, employed a super-resolution surrogate downscaling (SRSD) scheme to construct 1-km gridded wind fields from 5-km gridded operational weather forecasts. The SRSD model functions at a much lower computational load than physics-based weather forecast models do to downscale wind fields. This study presents a dispersion simulation, in which fluid dynamics are physics-based but driven by the SRSD’s wind fields, reproducing air pollution plumes over complex terrain near Tokyo. The purpose of this study is to demonstrate the accuracy of not only the SRSD’s wind fields but also the dispersion simulation driven by the SRSD’s wind fields. The SRSD’s wind-driven dispersion model (1-km grid) yielded better statistical scores than a lower-resolution physics-based model (5-km grid). In the snapshots of air pollution plumes, the SRSD’s wind-driven dispersion reproduced reasonable distributions in physics, such as horizontally diverted and blocked plumes around steep terrain and highland areas, better than the lower-resolution physics-based model did. Although a perfect surrogate of higher-resolution physics-based dispersion models cannot be achieved, our strategy can support air pollution dispersion simulations considering the overwhelming difference in the wind downscaling forecast speed between the SRSD and physics-based schemes. This strategy must be beneficial for environmental emergency responses.

The Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) with top at 0.01 hPa (high-top) is investigated focusing on the semiannual oscillation (SAO) in the tropical middle atmosphere, together with the other high-top reanalyses, the fifth generation ECMWF atmospheric reanalysis of the global climate (ERA5) and the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), and the Microwave Limb Sounder (MLS) and the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) satellite data. By removing the annual component and using the SAO component alone in the SABER data spanning the recent two decades, the seasonal cycle of the mesospheric SAO (MSAO) at 0.01 hPa is found to have significantly larger first cycle than the second cycle in a year with the largest easterly wind in boreal spring. The seasonal cycle of the stratospheric SAO (SSAO) at 1 hPa shows commonly in both satellite data that the easterly wind amplitude in boreal winter is double as large as that in boreal summer, while the westerly wind amplitudes in boreal spring and autumn are nearly the same. The two satellite data exhibit that the MSAO amplitude has significant and negative trend, about −5 m s⁻¹ decade⁻¹ and −7 m s⁻¹ decade⁻¹ at 0.01 hPa in MLS and SABER, respectively. JRA-3Q reproduces well the seasonal cycle of the SAO, i.e., the calendar-locked downward propagation of the SAO from 0.01 hPa to 10 hPa with clear separation between the MSAO and SSAO, despite the MSAO being substantially underestimated compared to the satellite observations. The SSAO amplitude at 1 hPa is significantly increasing in JRA-3Q over about three decades from 1970s to 2000s, and it exhibits slight decreasing trend over the recent two decades from 2000s. Before 1970s the SSAO wavelet spectra are less concentrated around 6 months and the wavelet spectra around the annual component are significantly larger than those after 1970s in JRA-3Q and ERA5. None of the reanalyses show any hint of the MSAO significant and negative trend at 0.01 hPa.

Tropical cyclones (TCs) are a threat to coastal regions in countries and areas situated in the tropics to, at times, mid-latitudes, and their threat is expected to escalate due to factors like global warming and urbanization. This emphasizes imperative need that warnings based on accurate and reliable forecasts be delivered to those who need them in order to prevent or mitigate TC impacts effectively. While conventional Numerical Weather Prediction (NWP) models have traditionally dominated TC forecasting at short to medium range lead times (i.e., up to two weeks), the emergence of Artificial Intelligence (AI) models, i.e., Machine Learning (ML) models trained on global reanalysis, has raised the possibility of such models competing and thus supplementing NWP models. Here, we examine the potential of ML models in operational TC forecasting, comparing them with conventional NWP models. The ML model used in this study is Pangu-Weather and TC forecasts by this ML model are compared with those from the operational global NWP model at the Japan Meteorological Agency, especially focusing on the track. All 64 named TCs for a period of 2021 to 2023 in the western North Pacific basin are verified. Results indicate that the ML forecasts exhibit smaller position errors compared to the NWP model, alleviate the westward bias around Japan, and retain its forecast accuracy for TCs with unusual paths, offering potential operational utility. Another benefit would be the ability to deliver forecast results to forecasters quicker than before, since the ML model’s forecast takes less than a minute. Meanwhile, challenges such as forecast bust cases and TC intensity, which are also present in NWP models, persist. A proposed way to utilize ML models at current operational systems would be to add ML-based track forecasts as one independent member of consensus forecasts.