Impact‑Based Forecasting (IBF) has attracted international attention as an approach to enhance disaster risk management by linking weather forecast information to concrete societal actions. This review examines the implementation of IBF in Japan, focusing on the Real‑time Risk Map provided by the Japan Meteorological Agency, and systematically organizes its design philosophy, technical structure, operational framework, and role in decision support. Japan is characterized by steep topography and numerous short rivers with steep gradients, under which rain‑related disasters such as landslides, inundation, and flash floods tend to occur rapidly and in a highly localized manner. Under these conditions, the Real‑time Risk Map evaluates disaster risk by transforming precipitation into indices that represent the progression of physical processes such as subsurface infiltration, storage, and runoff, and by combining these indices with criteria that reflect past disaster records and regional characteristics. As a result, risk information with high temporal update frequency and spatial resolution is visualized on maps. Rather than explicitly quantifying hazard, exposure, and vulnerability as separate components, the Real‑time Risk Map is designed as information to support decision‑making, including evacuation judgment, and is operated in coordination with warnings, advisories, and the nationally shared Alert Levels. From an IBF perspective, this review organizes the technical structure and decision‑support functions of the Real‑time Risk Map and clarifies its positioning through comparison with international IBF developments. It further illustrates the role of the Real‑time Risk Map in Japan’s IBF practice through evaluations based on concrete case studies, and discusses remaining challenges, including the treatment of forecast uncertainty.
PREVENIR—Forecast and Warning of Flash Flood Events is an Argentina-Japan cooperation project for five years from 2022 that aims to develop an early warning system for heavy rainfall and urban floods. The current operational numerical weather prediction system of the Argentine National Meteorological Service consists of deterministic and probabilistic forecasts at 4 km resolution. Since PREVENIR aims at more accurate and timely precipitation forecast, we developed a 2 km resolution 5-min update data assimilation system with the Local Ensemble Transform Kalman Filter (LETKF) that assimilates observation data from an automated weather station network and C-band Doppler weather radars. Independent LETKF systems have been coupled with the regional models’ Weather Research and Forecasting (WRF) and Scalable Computing for Advanced Library and Environment (SCALE), for two target basins: a mountainous region of the Suquía Villa Paez in Córdoba Province and a flat region of the Sarandí Santo Domingo in Buenos Aires. This study investigates the performance of the two systems in two extreme rain cases. We show that the forecasted precipitation benefits from the high-resolution rapid-update process, which improves its location and intensity with respect to coarser forecasting resources available in the region and also non-data assimilation systems. The results suggest that the prototypes could offer several advantages for hydrological applications, representing a potentially valuable tool for the region.
According to Niino et al. (1997), more than half of tornadoes in Japan between 1961 and 1993 moved toward the northeast quadrant. Since their data was subjective assessments incorporating damage surveys and field reports, however, the resulting directions of tornado movements were biased toward 8 directions (e.g., N, NE, E) out of 16 (e.g., N, NNE, NE, ENE). Using latitudes and longitudes of the starting and end points of tornado damage paths in the database of gusty winds of the Japan Meteorological Agency, our study objectively examined the statistics of the direction of tornado movement and its relation with the large-scale wind field. The analysis revealed that approximately 70
We analyzed atmospheric waves generated by the eruption of Hunga Tonga–Hunga Ha’apai on January 15, 2022 using high-frequency (2.5-min) infrared observations from Himawari-8 over Japan region. To isolate short-period atmospheric disturbances, we applied a second-order temporal derivative to the brightness temperature, followed by spatial bandpass filtering. A data stacking technique was then used to enhance the effective temporal resolution and reconstruct one-dimensional waveforms. Three distinct wave modes were identified. Lamb modes were clearly detected in all infrared channels from Band 7 to Band 16. The Pekeris mode appeared behind the Lamb mode in Band 12, and in the Band 12 − Band 13 brightness-temperature difference, consistent with its vertical-mode structure, but was weak or absent in the mid-tropospheric water-vapor channels. A coherent short-period internal-gravity-wave train (IG-train) was detected behind the Pekeris mode in the second-derivative field across all channels, and most prominently in Band 12 and in the Band 12 − Band 13 difference. The IG-train had a period range of 10–15 min and a phase speed of 197.9 ± 9.65 m s− 1, overlapping with the typical phase speed of oceanic long waves in the Pacific. Comparison with ground-based observations shows that the passage of the IG-train corresponded closely to enhancements in surface-pressure oscillations and to peak tidal disturbances associated with the volcanic meteotsunamis along Japan’s Pacific coast. These results provide observational evidence that the IG-train—consistent with the GR3 internal-gravity-wave mode detected by Mizutani and Yomogida (2023)—served as the dominant atmospheric forcing responsible for the largest meteotsunami amplitudes recorded during the Tonga event.
The National Centre for Medium Range Weather Forecasting (NCMRWF) receives NOAA-21 Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS) data through the European Organisation for the Exploitation of Meteorological Satellites’ data dissemination system (EUMETCast). Necessary modifications were made to the NCMRWF Unified Model (NCUM) assimilation and forecasting system to assimilate NOAA-21 data, as well as similar data from S-NPP and NOAA-20. As an initial step, prior to the operational use of NOAA-21 data, background and analysis innovations from ATMS and CrIS observations were computed and compared with those from S-NPP and NOAA-20 satellites. The NOAA-21 ATMS and CrIS innovations were found to be comparable in magnitude to those from Suomi National Polar Orbiting Partnership satellite (S-NPP) and NOAA-20. Upon confirming the quality of NOAA-21 data using the Observation Processing System, and the impact of the data on the assimilation and simulation of two monsoon deep depression (DD) events over east India in September 2024 was evaluated by performing Observing System Experiments. Two sets of experiments were conducted: a control run, in which all observations except NOAA-21 data were assimilated to produce analysis files; and an experimental run, in which all observations were assimilated, including NOAA-21 ATMS and CrIS observations. The experiments focused on two recent DD events over east India, occurring during 8–11th September and 14–20th September 2024. Results indicate that assimilating NOAA-21 observations alongside the existing dataset led to distinct, positive improvements in the analyses and forecasts of both DD events. A maximum up to 6
This study analyzed long-term data from the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) and the Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) Core Observatory to determine inconsistencies in observation characteristics and irregular statistics related to precipitation intensity. Reference statistics were employed to quantify the effects of three observational gaps: the low-level precipitation profile (LPP) and shallow precipitation deficiency (SPD), both arising from the incidence angle dependence, and the weak precipitation deficiency (WPD), attributable to limited sensor sensitivity. The findings revealed that the LPP, the SPD, and the WPD contributed to considerable underestimation of precipitation, particularly at large incidence angles, in high-latitude areas, low-precipitation zones, and mountainous regions. Adjusting for these gaps increased overall TRMM PR precipitation estimates by approximately 12
This study proposes a novel formulation of a three-dimensional spectral model for the primitive equations, where the spectral method is applied in both the horizontal and vertical directions. We utilize scaled Laguerre functions as the vertical basis and introduce a scaling parameter that enables flexible control over the distribution of vertical grid points. We demonstrate that the optimal setting of this parameter allows the model top to be placed at significantly higher altitudes while maintaining adequate grid spacing in the upper atmosphere, thereby addressing a practical limitation of previous three-dimensional spectral models. The proposed formulation is implemented as a numerical model and validated through several standard atmospheric benchmark experiments. Comparative experiments reveal that the numerical error of the three-dimensional spectral models converges significantly faster than that of a conventional vertical finite-difference model, exhibiting the rapid error reduction characteristic of spectral methods when the vertical degrees of freedom are increased. It is also demonstrated that the computational speed of the three-dimensional spectral model becomes comparable to that of the finite-difference model, provided that the vertical spectral transform subroutines are appropriately optimized. Furthermore, we investigate the properties of gravity wave propagation within the framework of the proposed discretization, using linearized two-dimensional and nonlinear three-dimensional forms of the primitive equations. This confirms that the proposed discretization method can represent upward-propagating waves more accurately than the previous three-dimensional spectral model and the conventional vertical finite-difference model.
Repeated false alarms for adverse weather events may reduce people’s willingness to take appropriate actions, such as evacuation. Therefore, understanding how false alarms influence perceptions and actions is essential for building effective early warning systems (EWS). We aimed to examine perceptions, emotions, and actions regarding false flood warnings in Japan. Specifically, we investigated (1) people’s definition of hits; (2) emotional responses toward false alarms; (3) the effect of the false alarm ratio (FAR) on the perceived FAR (pFAR) and the heterogeneity of this effect according to participants’ definition of hits; and (4) the effect of pFAR on evacuation action and the heterogeneity of this effect according to the emotional responses toward false alarms. We used newly constructed municipality-level FAR data and questionnaire data collected from residents of the Kyushu Region (n = 997). The results showed that participants tended to consider a warning as a hit when the river water reached a hazardous water level or when an overflow or levee breach occurred. Furthermore, false alarms were associated with decreased negative emotions (i.e., sadness and anger) and increased positive emotions (i.e., being pleased and at ease). We found a non-significant relationship between FAR and pFAR, which was maintained regardless of the participants’ hit definitions. However, pFAR had a significantly negative effect on the probability of evacuation, and this negative effect was weaker among those who experienced positive emotions toward false alarms. These findings suggest that effective EWS require risk communication strategies that consider emotional responses to false alarms.
Hybrid gain data assimilation offers a flexible and practical alternative to traditional hybrid covariance data assimilation by combining gain matrices from ensemble-based Kalman filter (EnKF) and variational methods. This study is the first to compare two update scenarios of hybrid gain data assimilation, namely the sequential and parallel update scenarios, within an operational global weather prediction system, the Taiwan Global Forecast System (TGFS). By correcting the EnKF analysis with 3DVAR adjustments, the sequential update scenario with an optimal hybridization weight demonstrates advantages in the Northern Hemisphere and tropics during the boreal summer, but performs worse in the Southern Hemisphere compared to the parallel update scenario. In contrast, the parallel update scenario produces hybrid analysis by averaging the EnKF and 3DVAR analyses, exhibiting greater robustness to weight variations than the sequential one. In addition, the introduction of non-affine hybridization weights, which fix the EnKF weight at one, is found to be beneficial for the parallel update scenario even with a limited ensemble size. Overall, this study demonstrates the promising performance and flexible experimentation with the hybrid gain data assimilation algorithm in an operational system, and it further contributes practical insights into update scenario selection and hybridization weight configuration.
With permission from the Ministry of Defense, Japan, we analyzed daily precipitation and related meteorological variables observed on Iwoto Island in the Ogasawara (Bonin) Islands from January 1971 to December 2024. Monthly precipitation records for Iwoto Island in the Global Historical Climatology Network (GHCN) version 4 are nearly identical with those for Chichijima Island (approximately 270 km to the north) during the overlapping period of 1951–1960 in GHCN version 2, the latter being duplicates. Iwoto Island exhibits a distinct precipitation peak in July–August, reflecting the strong influence of the Western North Pacific Monsoon, whereas Chichijima Island is more affected by mid-latitude weather systems. Although previous studies have projected decreasing precipitation in parts of the subtropics including the Ogasawara Islands, observed annual precipitation across monitored islands in the study area shows a shift from a decreasing to an increasing trend around the year 2000. To investigate the relationship between annual precipitation variability on Iwoto Island and El Niño–Southern Oscillation (ENSO), we divided the analysis period into two stages: Stage 1 (1971–1997) and Stage 2 (1998–2024), and conducted equivalent analyses for Chichijima and Minami-torishima Islands. ENSO-related precipitation variability intensified on Iwoto Island during Stage 2, accompanied by higher mean precipitation amount than in Stage 1. In contrast, ENSO significantly influenced precipitation on Minami-torishima Island during Stage 1 but weakened in Stage 2, suggesting a westward shift of the ENSO-related precipitation systems. No statistically significant relationship between ENSO and precipitation was found for Chichijima Island.
This study discusses the computational advantages of high-order dynamical cores over a conventional low-order dynamical core in atmospheric turbulent simulations. Implicit and explicit large-eddy simulations (LES) of dry Rayleigh convection were performed using two dynamical cores: one based on a high-order discontinuous Galerkin method (DGM) and the other on a totally second-order conventional finite-volume method (FVM) with advection schemes of various orders. The effective resolution and numerical energy accumulation, derived from kinetic energy spectra, were used to evaluate the physical performance of both dynamical cores. In the implicit LES experiment, we confirmed that the high-order DGM with a polynomial order of p=11 achieved a finer effective resolution than the FVM with third-order and seventh-order upwind schemes (UD3 and UD7). In contrast, in the explicit LES experiment, the effective resolution was largely determined by the subgrid-scale turbulence model, thereby reducing the relative advantage of high-order schemes. Our cost metrics, which combine the physical performance and computational resource usage, demonstrate that the DGM with p=7,11 and the FVM with UD7 can achieve lower overall computational costs than the FVM with UD3 in both implicit and explicit LES experiments. Although the high-order DGM requires larger computational resources due to a stricter stability limit, its overall computational costs are reduced in the implicit LES experiment because of its finer effective resolution, as well as high computational efficiency associated with superior data locality and smaller inter-node communication overhead. On the other hand, to fully exploit the advantages of high-order schemes in explicit LES, it is necessary to redesign the turbulence model so that the filter length becomes consistent with the inherent effective resolution of the dynamical cores, and to relax the stricter stability limit for the DGM.
One of the major uncertainties in atmospheric modeling is parametric uncertainty. It is important to infer appropriate parameters in various parameterizations from observation. Despite previous efforts on the calibration of parameters in atmospheric models, there is no existing work that comprehensively calibrates parameters using geostationary satellite observations although they are of paramount importance to monitor tropical cyclones. In this study, we estimate the posterior distribution of parameters of a meso-scale atmospheric model using brightness temperature observations from a geostationary satellite toward the improvement of the simulation of tropical cyclones. With the aid of an image-processing inspired evaluation index and machine-learning-based surrogate models, we developed a method to calibrate model parameters of a meso-scale atmospheric model by geostationary satellite observations. We found that parameters in cloud microphysics and boundary layer schemes could be efficiently estimated by geostationary infrared satellite observation. The estimated posterior distribution of parameters not only improves the accuracy of the prediction of satellite images but also partly reduces errors in the prediction of tropical cyclone intensity. We demonstrate the potential of adjusting multiple parameters based on satellite data and its implications of model development to improve the accuracy of the simulation of tropical cyclones. Although we did not consider uncertainty in initial conditions, this work would be extended to meso-scale ensemble forecasting systems which explicitly consider uncertainty in both initial conditions and model parameters.
Understanding the processes of carbon dioxide (CO2) sources and sinks at various scales is required for climate mitigation action, and carbon isotopes (13/14C) are ideal markers of exchange processes between reservoirs. However, the lack of joint simulation of CO2 and δ13C using a 3-dimensional atmospheric chemistry-transport model (ACTM) has limited our understanding of the long-term trends and drivers of the inter-decadal and inter-annual variations of the global carbon cycle covering the period of rapid industrialization and land-use change, i.e., the 1940s to present. We simulated atmospheric CO2 and δ13C for the period 1948 − 2021, using fossil-fuel emissions (GridFED inventory), oceanic fluxes (LENS model), and two cases of land-biosphere fluxes (VISIT and LENS models) in a newly developed MIROC4-ACTM framework. The combined-flux MIROC4-ACTM simulations are analysed by comparing with (1) a merged precise ice core and firn-air reconstructions (1948–1980; deseasonalised) and (2) direct measurements by flask air sampling from the Scripps Institution of Oceanography (SIO) network (1958 − 2021 for CO2 and 1977 − 2021 for δ13C). Simulated Seasonal Cycle Amplitudes (SCAs) of both CO2 and δ13C (using VISIT and LENS land flux combinations—GVL and GLL, respectively) have increased over 1950s − 2010s at Northern Hemisphere (NH) mid-to-high latitudes and remained relatively unchanged in the Southern Hemisphere (SH). These patterns are supported by atmospheric observations of CO2, caused by an intensified terrestrial carbon exchange in the NH. At the NH sites, observed and modeled δ13C SCA shows only weak changes since the 1980s (i.e., in recent decades), in agreement with a recent study. The model inter-site gradients in both CO2 and δ13C have increased since the 1960s, mainly due to increased fossil fuel emissions. The overall consistencies of GVL and GLL simulations with observed CO2 and δ13C highlights recent advances in our understanding of global carbon cycle processes, but differences persist at inter-decadal and inter-annual timescales, which must be better understood for future projection of carbon-climate feedback.
To classify and understand the causes of heat waves in western Japan, this study investigated atmospheric circulation patterns during August from 1992–2021, using reanalysis data. First, we defined heat-wave days using daily surface air temperature and identified 108 days out of 930, each having a spatial scale larger than a few hundred kilometers. We then applied empirical orthogonal function (EOF) analysis to the 850-hPa geopotential height for those 108 heat-wave days. The leading three EOF modes explained over 60
This study developed the integration of the Spectral Radiation-Transport Model for Aerosol Species (SPRINTARS) aerosol module into Scalable Computing for Advanced Library and Environment (SCALE), a regional meteorological and climate model which can simulate three-dimensional aerosol distribution with extremely high spatiotemporal resolution capable of resolving cloud physics. SPRINTARS calculates aerosol transport processes as well as aerosol-radiation and aerosol-cloud interactions. The integrated model called SCALE-SPRINTARS treats the processes of the major tropospheric aerosols: sulfate, black carbon, organic matter, soil dust, and sea salt. Simulated aerosol distributions by the SCALE-SPRINTARS throughout the year targeting the Asian region were generally consistent with in-situ, satellite and reanalysis data. The SCALE-SPRINTARS is expected to serve as a fundamental tool for quantitatively assessing the impact of aerosols on local phenomena such as tropical cyclones and thunderstorms, as well as for reproducing and predicting aerosol distributions at ultra-high resolution, anticipating future advancements in computer power. Furthermore, its capability for Large Eddy Simulation-scale simulations, including aerosol-cloud interactions, brings it closer to realistically representing cloud and precipitation processes in meteorological and climate models.
Obonai-dashi are local winds that blow into the Obonai District located on the western base of the Ou Mountains in Japan. Residents believe the Obonai-dashi provide agricultural benefits. This study statistically clarified the climatological features of the Obonai-dashi and discussed possible reasons for their perceived benefits. Our statistical analysis of 377 Obonai-dashi events between 2010 and 2022 revealed that they occurred frequently from April to September. Four synoptic weather patterns causing the Obonai-dashi were identified: cyclones on the Sea of Japan (23.7
To elucidate dynamics of significant downward propagation (SDP) events of zonal wavenumber 1 (WN1) planetary waves from the stratosphere after sudden stratospheric warming (SSW) events and their influence on the extratropical troposphere, a case study of an SDP event in March 2023 and composite analyses for SDP events with and without SSW using JRA-3Q reanalysis are conducted. The March 2023 event is characterized by the equatorward propagation of enhanced WN1 components in the troposphere, which follows the WN1 downward propagation after the SSW. Exceptionally warm anomalies in East Asia including Japan in early March are associated with the equatorward propagation. A statistical investigation on the timing of all SDP and SSW events reveals that an SDP event is significantly more likely to occur after an SSW. The composite of the SDP event with SSW is characterized by stratospheric easterlies, in contrast to that without SSW, in which westerlies prevail in the stratosphere. Downward propagated WN1 components in the troposphere propagate equatorward and produce temperature anomalies in the extratropics, depending on the WN1 phase at high latitudes for both SDP events. When the WN1 ridge is positioned around the date line in SDP events with SSW, cold anomalies tend to cover East Asia including Japan. The WN1 component during the March 2023 event has the largest amplitude among SDP events with SSW and a ridge location around 90°W, which is far apart from the rest. These peculiar characteristics of the WN1 component would contribute to the extraordinary warm anomalies near Japan.
Tibetan Plateau (TP)-originated mesoscale convective systems (TP_MCSs) played a crucial role in driving record-breaking rainfall during the abnormal 2020 Mei-Yu season (MY2020), though their anomalous background environment and quantitative precipitation contributions remained unclear. Using FY-2G TBB, GPM-IMERG precipitation, and ERA5 reanalysis data, we partially address this knowledge gap, revealing that: (i) TP_MCSs exhibited abnormal activity during MY2020, 15
To explicitly represent short-term atmosphere-ocean interactions while adequately simulating long-term regional-scale climate, climate simulations from the mid-20th century to the present were performed using the Meteorological Research Institute Earth System Model version 2 (MRI-ESM2), with the atmosphere of 60 km horizontal resolution and assimilating ocean observation. A monthly objective analysis of the ocean temperature and salinity were assimilated with a relaxation time of 10 days. Short time-scale atmosphere-ocean interactions, such as precipitation variations that lag behind sea-surface temperature (SST) variations, and SST decreases due to the passage of intense tropical cyclones, are represented in a manner similar to a fully coupled model. In addition, the performance of the model in reproducing climate over long time scales is comparable to our previous atmosphere-only simulations with prescribed observed SSTs, both over the global scale and over East Asia. The overestimation of intense tropical cyclones at mid-latitudes, which was seen in the atmosphere-only simulations, has been improved. The seasonal progression of the East Asian monsoon is also well simulated. These results are expected to contribute to reducing the uncertainty in the assessment of global warming impact, especially for extreme events in the Asian region.
