Japan Meteorological Research Articles

Assessing global ensemble systems’ forecasts of tropical cyclone genesis in differing environmental flow regimes in the western North Pacific

The forecast probability of tropical cyclone (TC) genesis in the western North Pacific from 2017 to 2020 was investigated using global ensembles from the Japan Meteorological Agency (JMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), the U.S. National Centers for Environmental Prediction (NCEP), and the Met Office in the United Kingdom (UKMO). The time of TC genesis was defined as the time the TCs were first recorded in the best-track data (Case 1) and as the time they reached the intensity of a Tropical Storm (Case 2). The results in Case 1 showed that differences between the forecast probability based on each global ensemble were large, even for a 1-day forecast, and that mean probability were from 18 % to 74 %. The forecasts based on the NCEP had a large frequency bias and overpredicted TC genesis events. The results indicated that the representation of genesis events differed greatly between global ensembles. The effectiveness of multiple ensembles was investigated. The results from the threat score and the false alarm ratio indicated that multiple ensembles had skillful forecasts. When the forecast probability was examined for environmental patterns of synoptic low-level flow, the mean 5-day forecast probability was highest for the pattern in the confluence region. The results also showed that the forecast probability was much larger in Case 2 than in Case 1. In all global ensembles, the mean probability with a lead time of up to 1-week was below 10 % for both Case 1 and 2. This result indicates that even with today's operational forecasting systems, it is difficult to regularly predict TC genesis events with a 1-week lead time with high confidence. These results provide a better understanding of TC genesis forecast products in each global ensemble and will be useful information when multiple-ensemble products are created.

Extension of all‐sky radiance assimilation to hyperspectral infrared sounders

AbstractThis study accomplished the all‐sky infrared (IR) radiance assimilation of the hyperspectral IR sounders of the IR atmospheric sounding interferometer in the Japan Meteorological Agency's global system. Essential assimilation procedures, including cloud‐dependent quality control, bias correction, and observation‐error modeling, were directly adapted from the all‐sky assimilation of geostationary satellite imagers in our previous study, without any sophisticated modifications. Data assimilation experiments conducted in different seasons demonstrated that, compared with clear‐sky radiance assimilation, the all‐sky radiance assimilation of three channels sensitive to mid and upper tropospheric water vapor yielded significant forecast improvements in not only humidity but also temperature, wind, and geopotential height. These improvements originated from more than twofold increments in the number of globally assimilated observations under the all‐sky approach and better observation coverages. Increasing the number of assimilated channels to nine further amplified these improvements. The incorporation of an upper tropospheric channel mitigated upper tropospheric humidity biases that were originally exacerbated during three‐channel assimilation. These results underscore the broad applicability of the all‐sky assimilation approach to various IR instruments.

Association between heat exposure and Kawasaki disease: A time-stratified case-crossover study

Nationwide studies investigating the association between daily mean temperature and Kawasaki disease are lacking. This study aimed to examine the association between ambient temperature and Kawasaki disease by utilizing daily data from nationwide administrative claims databases. The daily number of Kawasaki disease patients younger than 15 years old, who were hospitalized from 2011 to 2022, was extracted from the nationwide administrative claims database of hospitalizations in Japan. Daily mean temperature data and relative humidity data were obtained from the Japan Meteorological Agency. Since the exposure of interest includes heat, hospitalizations during the five warmest months (May through September) were used for analysis. A time-stratified case-crossover study with conditional quasi-Poisson regression analysis was used to estimate the relative risk (RR) of weather exposure for Kawasaki disease hospitalization with a lag of 0–5 days by prefecture. Relative humidity was included in the model simultaneously to control for its potential confounding effect. Random-effects meta-analysis was used to estimate pooled RRs. There was a total of 48,784 cases of Kawasaki disease hospitalization during the study period, of which 87.9% were under 5 years of age. Exposure to high daily mean temperatures was associated with an increased risk of hospitalization for Kawasaki disease. Specifically, exposure to extreme high daily mean temperatures (99th percentile high temperature) was associated with higher risk of hospitalization by 33% (RR 1.33, 95% confidence interval (CI):1.08, 1.65). Similar results were obtained from sensitivity analysis. Future research should elucidate the mechanisms by which high temperature is associated with hospitalization for Kawasaki disease.

Case study of high waves in the South Pacific generated by Tropical Cyclone Harold in 2020

This study highlighted a high wave case by severe tropical cyclone Harold and conducted a simulation with a newly developed wave forecasting system for the South Pacific based on the Japan Meteorological Agency third generation wave model (JMA MRI-III) using the National Center for Environment Prediction Global Forecast System (GFS) winds. Harold was a very intense tropical cyclone (TC) and very high waves up to 10 m affected parts of Vanuatu and Fiji. The model results were reasonable and verified against observations of orbital satellites and a wave buoy at Komave in Fiji. The statistical verifications were carefully analysed. The Root Mean Squared Error (RSME), Scatter Index (SI), Bias and R2 are all showing very impressive results. The new wave forecasting system is the first high resolution operational model at Fiji Meteorological Service (FMS), which covers the whole Fiji area. The system will provide guidance to FMS in preparing marine alerts and warning better and more confidence in providing the marine forecast accurately.

The Evolution Process between the Earthquake Swarm Beneath the Noto Peninsula, Central Japan and the 2024 M 7.6 Noto Hanto Earthquake Sequence

Several physical mechanisms of earthquake nucleation, such as pre-slip, cascade triggering, aseismic slip, and fluid-driven models, have been proposed. However, it is still not clear which model plays the most important role in driving foreshocks and mainshock nucleation for given cases. In this study, we focus on the relationship between an intensive earthquake swarm that started beneath the Noto Peninsula in Central Japan since November 2020 and the nucleation of the 2024 M7.6 Noto Hanto earthquake. We relocate earthquakes listed in the standard Japan Meteorological Agency (JMA) catalog since 2018 with the double-different relocation method. Relocated seismicity revealed that the 2024 M7.6 mainshock likely ruptured a thrust fault above a parallel fault where the M6.5 Suzu earthquake occurred in May 2023. We find possible along-strike and along-dip expansion of seismicity in the first few months at the beginning of the swarm sequence, while no obvious migration pattern in the last few days before the M7.6 mainshock was observed. Several smaller events occurred in between the M5.5 and M4.6 foreshocks that occurred about 4 and 2 minutes before the M7.6 mainshock. The Coulomb stress changes from the M5.5 foreshock were negative at the hypocenter of the M7.6 mainshock, which is inconsistent with a simple cascade triggering model. Moreover, an M5.9 foreshock was identified in the JMA catalog 14 s before the mainshock. Results from back-projection of high-frequency teleseismic P waves show a prolonged initial rupture process near the mainshock hypocenter lasting for ∼25 seconds, before propagating bi-laterally outward. Our results suggest a complex evolution process linking the earthquake swarm to nucleation of the M7.6 mainshock at a region of complex structures associated with the bend of a mapped large-scale reverse fault. A combination of fluid migration, aseismic slip and elastic stress triggering likely work in concert to drive both the prolonged earthquake swarm and the nucleation of the M7.6 mainshock.

Hourly estimation of water reaching the ground surface in snow-covered regions of Japan using mesoscale meteorological data with the heat balance method

In snow-covered regions, landslides, including debris flows, frequently result from snowmelt and cause significant hazards. Because these snowmelt-induced landslides are triggered by increasing groundwater level and pore-water pressure acting on the slip surface due to infiltration of snowmelt, sometimes mixed with rainwater, accurate estimation of water reaching the ground surface (abbreviated MR in this study) on an hourly basis is essential for developing countermeasures such as an early warning system. This study introduces a model for estimating hourly MR, as well as snow water equivalent and snow depth, using mesoscale meteorological data provided by the Japan Meteorological Agency. The model considers four processes: precipitation, snow accumulation and densification, snowmelt, and infiltration. In the snowmelt process, snowmelt is estimated using the heat balance method. We applied the model to a snow observation station in Niigata Prefecture, central Japan, where MR, snow water equivalent, and snow depth were observed during the winter seasons of 2017–18 through 2020–21. Comparisons of observed and calculated values demonstrated that the model successfully reproduced the observations, regardless of the amount of snowfall in individual winter seasons. We propose that this model can estimate hourly MR, snow water equivalent, and snow depth at any location in snow-covered regions where on-site meteorological and snow observations are unavailable.

Focal mechanics and disaster characteristics of the 2024 M 7.6 Noto Peninsula Earthquake, Japan

On January 1, 2024, a devastating M 7.6 earthquake struck the Noto Peninsula, Ishikawa Prefecture, Japan, resulting in significant casualties and property damage. Utilizing information from the first six days after the earthquake, this article analyzes the seismic source characteristics, disaster situation, and emergency response of this earthquake. The results show: 1) The earthquake rupture was of the thrust type, with aftershock distribution showing a north-east-oriented belt-like feature of 150 km. 2) Global Navigation Satellite System (GNSS) and Interferometric synthetic aperture radar (InSAR), observations detected significant westward to north-westward co-seismic displacement near the epicenter, with the maximum horizontal displacement reaching 1.2 m and the vertical uplift displacement reaching 4 m. A two-segment fault inversion model fits the observational data well. 3) Near the epicenter, large Peak Ground Velocity (PGV) and Peak Ground Acceleration (PGA) were observed, with the maxima reaching 145 cm/s and 2681 gal, respectively, and the intensity reached the highest level 7 on the Japanese (Japan Meteorological Agency, JMA) intensity standard, which is higher than level 10 of the United States Geological Survey (USGS) Modified Mercalli Intensity (MMI) standard. 4) The observation of the very rare multiple strong pulse-like ground motion (PLGM) waveform poses a topic worthy of research in the field of earthquake engineering. 5) As of January 7, the earthquake had left 128 deaths and 560 injuries in Ishikawa Prefecture, with 1305 buildings completely or partially destroyed, and had triggered a chain of disasters including tsunamis, fires, slope failures, and road damage. Finally, this paper summarizes the emergency rescue, information dissemination, and other disaster response and management measures taken in response to this earthquake. This work provides a reference case for carrying out effective responses, and offers lessons for handling similar events in the future.

A hybrid reanalysis-forecast meteorological forcing data for advancing climate adaptation in agriculture

Climate variability in the growing season is well suited for testing adaptation measures. Adaptation to adverse events, such as heatwaves and droughts, increases the capacity of players in agri-food systems, not only producers but also transporters and food manufacturers, to prepare for production disruptions due to seasonal extremes and climate change. Climate impact models (e.g., crop models) can be used to develop adaptation responses. To run these models, historical records and climate forecasts need to be combined as a single daily time series. We introduce the daily 0.5° global hybrid reanalysis-forecast meteorological forcing dataset from 2010 to 2021. The dataset consists of the Japanese 55-yr Reanalysis (JRA55) and the Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System version 2 (JMA/MRI-CPS2) 5-member ensemble forecast. Both are bias-corrected using the Delta method and integrated with a baseline climatology derived from the Environmental Research and Technology Development Fund’s Strategic Research 14 Meteorological Forcing Dataset (S14FD). The dataset is called JCDS (JRA55-CPS2-Delta-S14FD) and offers a framework for monitoring and forecasting applications towards adaptation.

An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based On Ensemble Weather Forecast Data

AbstractIn ensemble weather forecast, tropical cyclone (TC) tracks sometimes group together into trajectories parting away from each other. The goal of this study is to propose an objective method, based on a robust clustering approach, to detect such separation scenarios in the Japan Meteorological Agency Meso‐scale Ensemble Prediction System (MEPS) for three TCs: “Dolphin” (2020), “Nepartak” (2021), and “Meari” (2022). Taking advantage of the independence of the density‐based spatial clustering of applications with noise algorithm to the prior choice of the number of clusters, we first describe an objective way to calculate the aggregation distance, by searching the most frequent Euclidean distance between all the tracks. The clustering is then applied to the forecasted tracks, for each initialization time of MEPS (every 6 hr). Separation scenarios exist when the number of clusters is greater than one.

Effects of Storm Waves Caused by Typhoon Bolaven (1215) on Korean Coast: A Comparative Analysis with Deepwater Design Waves

This paper employs the third-generation simulating waves nearshore (SWAN) ocean wave model to estimate and analyze storm waves induced by Typhoon Bolaven, focusing on its impact along the west coast and Jeju Island of Korea. Utilizing reanalyzed meteorological data from the Japan Meteorological Agency meso scale model (JMA-MSM), the study simulated storm waves from Typhoon Bolaven, which maintained its intensity up to high latitudes as it approached the Korean Peninsula in 2012. Validation of the SWAN model against observed wave data demonstrated a strong correlation, particularly in regions where wind speeds exceeded 20 m/s and wave heights surpassed 5 m. Results indicate significant storm wave heights across Jeju Island and Korea's west and southwest seas, with coastal grid points near islands recording storm wave heights exceeding 90% of the 50-year return period design wave heights. Notably, specific grid points near islands in the northern West Sea and southwest Jeju Island estimated storm wave heights at 90.22% and 91.48% of the design values, respectively. The paper highlights the increased uncertainty and vulnerability in coastal disaster predictions due to event-driven typhoons and emphasizes the need for enhanced accuracy and speed in typhoon wave predictions amid the escalating climate crisis.

Using wet-bulb globe temperature meters to examine the effect of heat on various tennis court surfaces

In this study, we evaluated the thermal environments of different tennis courts using wet-bulb globe temperature (WBGT) meters. WBGT meters were installed in an outdoor hard court, sand-filled artificial grass court, and clay court (a softball field), and measurements were taken hourly from 9:00 to 17:00 on weekdays from June 1 to September 21, 2022. The results were compared with data from different courts and the nearest Japan Meteorological Agency station (JMA WBGT) based on the Japan Sports Association’s guidelines for exercise to prevent heat stroke. The median WBGT on each court was significantly higher for hard courts at the “Warning” (25 ≤ JMA WBGT < 28) level or above, sand-filled artificial grass courts at the “Severe Warning” (28 ≤ JMA WBGT < 31) level or above, and clay courts at the “Danger” (31 ≤ JMA WBGT) level than the JMA WBGT. Compared with the JMA WBGT, hard and sand-filled artificial grass courts are played on under particularly hot conditions. The results of this study could indicate to tournament organizers and coaches the importance of measuring the WBGT on each court surface from an early stage to prevent heat-related incidents.

Retrieval of hourly aerosol single scattering albedo over land using geostationary satellite data

The single scattering albedo (SSA) of aerosol particles is one of the key variables that determine aerosol radiative forcing. Herein, an Algorithm for the retrieval of Single scattering albedo over Land (ASL) is proposed for application to full-disk data from the advanced Himawari imager (AHI) sensor flying on board the Himawari-8 satellite. In this algorithm, an atmospheric radiative transfer model known as the USM (the top of the atmosphere reflectance as the sum of Un-scattered, Single-scattered, and Multiple-scattered components) is used to calculate the SSA instead of predetermining the aerosol model; the USM is constrained by the surface bidirectional reflectance distribution function shape and aerosol optical depth (AOD) in the retrieval process. Combining two consecutive observations and a 2 * 2 pixel window, the optimal estimation algorithm is adopted to obtain the optimal solution for the aerosol SSA. These SSA results are evaluated by comparing with aerosol robotic network (AERONET) data. Linear regression shows that SSAASL = 0.60*SSSAERONET + 0.38, with a correlation coefficient (0.7284), mean absolute error (0.0319), mean bias error (0.00324), root mean square error (0.0427), and ~80.11% of the ASL SSA data within an uncertainty of ±0.05 of the AERONET data. A comparison of the ASL SSA products with collocated Himawari-8 SSA products (Version 03, officially released by the Japan Meteorological Agency (JMA), referred to herein as JMA SSA) shows that the accuracy of the ASL SSA is better than that of the JMA SSA products. For the SSA retrieval in large AODs (>0.4), the validation metrics vs. AERONET data are better.

Exploring the factors influencing the first singing date of a cicada, Graptopsaltria nigrofuscata: How will it be affected by climate change?

Abstract Climate change affects various scales of biotic interaction through phenological shifts. The emergence phenology of cicadas is ecologically important because these insects have large effects on the ecosystem as herbivores, as food resources, and in nutrient flux from subterranean resources. However, little is known about the weather factors affecting their emergence patterns in the field because their nymphal stages grow underground for several years. Here, we used long‐term observation data on the first singing date (i.e. the first emergence of male individuals) of Graptopsaltria nigrofuscata, recorded by the Japan Meteorological Agency and citizen scientists throughout Japan, to (1) explore the most influential weather factors across a variety of time spans on the first singing date of G. nigrofuscata and (2) determine whether the temporal trend of the first singing date could be explained by temporal weather trends caused by climate change by using a state space model. Our results indicated that higher temperatures from midsummer to early winter in the previous year are bringing the cicadas' emergence forward, and the annual increase in temperature is causing the advancement of emergence patterns. Other weather factors, such as precipitation and humidity, did not have a strong effect. Our findings suggest that increased growth rates at the nymphal stage due to warming in the previous year influence cicada emergence timing. To understand the mechanisms of how rising temperatures are advancing cicada emergence, we need an approach based on the physiology and ecology of their nymphs.

Intercomparison of aerosol optical depths from four reanalyses and their multi-reanalysis consensus

Abstract. The emergence of aerosol reanalyses in recent years has facilitated a comprehensive and systematic evaluation of aerosol optical depth (AOD) trends and attribution over multi-decadal timescales. Notable multi-year aerosol reanalyses currently available include NAAPS-RA from the US Naval Research Laboratory, the NASA MERRA-2, JRAero from the Japan Meteorological Agency (JMA), and CAMSRA from Copernicus/ECMWF. These aerosol reanalyses are based on differing underlying meteorology models, representations of aerosol processes, as well as data assimilation methods and treatment of AOD observations. This study presents the basic verification characteristics of these four reanalyses versus both AERONET and MODIS retrievals in monthly AOD properties and identifies the strength of each reanalysis and the regions where divergence and challenges are prominent. Regions with high pollution and often mixed fine-mode and coarse-mode aerosol environments, such as South Asia, East Asia, Southeast Asia, and the Maritime Continent, pose significant challenges, as indicated by higher monthly AOD root mean square error. Moreover, regions that are distant from major aerosol source areas, including the polar regions and remote oceans, exhibit large relative differences in speciated AODs and fine-mode versus coarse-mode AODs among the four reanalyses. To ensure consistency across the globe, a multi-reanalysis consensus (MRC, i.e., ensemble mean) approach was developed similarly to the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME). Like the ICAP-MME, while the MRC does not consistently rank first among the reanalyses for individual regions, it performs well by ranking first or second globally in AOD correlation and RMSE, making it a suitable candidate for climate studies that require robust and consistent assessments.

Plateau-linear reaction norm model analysis of number born alive in purebred Landrace pigs using meteorological data in Japan.

We performed a plateau-linear reaction norm model (RNM) analysis of number born alive (NBA) in purebred Landrace pigs, where breeding value changes according to maximum temperature at mating day, using public meteorological observation data in Japan. We analysed 52,668 NBA records obtained from 10,320 Landrace sows. Pedigree data contained 99,201 animals. Off-farm daily temperature data at the nearest weather station from each of the farms were downloaded from the Japan Meteorological Agency website. A plateau-linear RNM analysis based on daily maximum temperature on mating day (threshold temperature of 16.6°C) was performed. The percentage of the records with daily maximum temperatures at mating days of ≤16.6, ≥25.0, ≥30.0 and ≥35.0°C were 34.3%, 33.6%, 14.0% and 0.8%, respectively. The value of Akaike's information criterion for the plateau-linear RNM was lower than that for a simple repeatability model (RM). With the plateau-linear RNM, estimated value of heritability ranged from 0.14 to 0.15, while that from the RM analysis was 0.15. Additive genetic correlation between intercept and slope terms was estimated to be -0.52 from the plateau-linear RNM analysis. Estimated additive genetic correlations were >0.9 between NBA at different temperatures ranging from 16.6 to 37.6°C. For the 10,320 sows, average values of prediction reliability of the intercept and slope terms for breeding values in the plateau-linear RNM were 0.47 and 0.16, respectively. Increasing weight for slope term in linear selection index could bring positive genetic gain in the slope part, but prediction accuracy would decrease. Our results imply that genetically improving heat tolerance in sows reared in Japan focusing on NBA using RNM is possible, while RNM is more complex to implement and interpret. Therefore, further study should be encouraged to make genetic improvement for heat tolerance in sows more efficient.

Machine learning ground motion models for a critical site with long-term earthquake records

Ground motion models derived using data from widespread stations and earthquakes induce significant aleatory variability when applied to predict ground motions for a single site. This issue can be even worse when the sites of interest show complex site response characteristics and do not have enough similar sites in the ground motion model development dataset. This work provides a machine-learning-based ground motion modeling framework for sites with numerous earthquake recordings. The Onahama Port Array, a liquefiable site in Japan that has recorded more than 1200 earthquakes in the last 25 years, was selected as a case study. The gradient boosting method was employed to predict response spectra with periods from 0.01 to 10 s for earthquakes with Japan Meteorological Agency (JMA) magnitude ranging from 2.4 to 9.0. Different predictor combinations were tested, and two gradient boosting models were recommended: the basic model that requires earthquake magnitude and epicentral distance as inputs, and the optimal model that requires three additional inputs, focal depth, azimuth, and rainfall. The basic and optimal gradient boosting models have the root mean square error of 0.004 and 0.001 g, average r2 of 5-fold cross validation of 0.915 and 0.983, respectively. The results shed light on seismic hazard assessment for critical sites with long-term earthquake recordings.

Shaking table tests of a one-quarter scale model of concrete hollow block masonry houses retrofitted with fiber-reinforced paint

Unreinforced masonry (URM) buildings are prone to significant damage when subjected to ground motion. Some strengthening methods have been proposed to increase the seismic capacity. However, the widespread adoption of these methods faces various challenges, including economic constraints experienced by common people in developing countries, the complexity of implementation, efficiency, and seismic safety of each technique. This paper introduces a new retrofitting method of fiber-reinforced paint using fiberglass as the primary reinforcing material. The advantage of this technique lies in its simplicity and ease of application, with the added benefit of using the paint to improve the appearance of the house. Two 1:4 scale concrete hollow block (CHB) masonry houses were constructed to represent unreinforced masonry and retrofitted masonry structures using fiber-reinforced paint (FR-Paint). The shaking table test results indicate that the retrofitted house model showed improvements of up to 18 times in deformation capacity and up to 13 times in energy dissipation compared to the non-retrofitted house model. FR-Paint has a robust performance even in high input motion at a seismic intensity JMA of 7 (Japan Meteorological Agency). This confirms that this retrofitting method has a high earthquake-resistant performance.

Water Vapor Lidar Observation and Data Assimilation for a Moist Low-Level Jet Triggering a Mesoscale Convective System

Abstract We conducted field observations using two water vapor Raman lidars (RLs) in Kyushu, Japan, to clarify the characteristics of a moist low-level jet (MLLJ), which plays a fundamental role in the formation and maintenance of mesoscale convective systems (MCSs). The two RLs observed the inside and outside of an MLLJ, providing moisture to an MCS with local heavy precipitation on 9 July 2021. Our observations revealed that the MLLJ contained large amounts of moisture below the convective mixing layer height of 1.6 km. The large amount of moisture in the MLLJ might be intensified by low-level convergences and/or water vapor buoyancy facilitated by strong horizontal wind. We conducted four data assimilation experiments: CNTL that assimilated Japan Meteorological Agency operational observation data and three other experiments that ingested the lidar-derived vertical moisture profiles as well as the operational observation data. The experiments assimilating lidar-derived vertical moisture profiles caused intensification and southwestward extensions of the low-level convergence zone, resulting in local heavy precipitation at lower latitudes in experiments assimilating lidar-derived moisture profiles than in CNTL. All three experiments ingesting vertical moisture profiles generally produced better 9-h precipitation forecasts than CNTL, implying that the assimilation of vertical moisture profiles could be well suited for numerical weather prediction of local heavy precipitation. Moreover, the experiment assimilating both of the two RL sites’ data reproduced better forecast fields than experiments assimilating a single RL site’s data, implying that data assimilation of vertical moisture profiles at multiple RL sites enables us to improve initial conditions compared to a single RL site. Significance Statement Moist low-level jets (MLLJs) are moisture-rich airflows in the low-level atmosphere that play an important role in developing mesoscale convective systems and local heavy rainfall. To better understand the mechanisms affecting the development of local heavy rainfall events and to improve our ability to forecast them, studying the moisture structures in MLLJs is important. We succeeded in observing an MLLJ in western Japan using water vapor Raman lidars (RLs), which obtained vertical moisture profiles, and revealed details of vertical moisture structures in the MLLJ. We also performed data assimilation experiments to examine the impact of assimilating vertical moisture profiles observed by the RLs. The results showed that the assimilation of the moisture data improved the forecasting of local heavy rainfall.

Multi-model seismic susceptibility assessment of the 1950 great Assam earthquake in the Eastern Himalayan front

The seismic susceptibility and mitigation management is paramount concern in tectonically active area like Northeastern India. This area has been devastated innumerably during the 1950 Assam great earthquake. The present study area falls in the foreland basin (Brahmaputra Valley) of Eastern Himalaya. This region is seismically vulnerable due to the tectonic complexity caused by the convergence of the Eurasian, Indian, and Burmese plates. In such, an area optimal disaster management and preparedness is necessary to define the non-linear character of seismic susceptibility, where population and unscientific urbanization have increased manifold. Therefore, for the present study, various multi-criteria decision making (MCDM) methods such as analytical hierarchy process (AHP), fuzzy-AHP (FAHP), and maximum entropy technique (MaxEnt) have been used for determining the seismic susceptibility, by assigning weightage to nine controlling factors such as: predominant frequency (f0), geology (G), vulnerability index (K), peak amplification (A0), liquefaction potential (LP), groundwater condition (WT), shear wave velocity (Vs30), peak ground acceleration (PGA), and land use/land cover (LU). The MaxEnt model exhibits the highest accuracy (87.5%) when the performance of the models was compared using the receiver operating characteristic curve (ROC) and area under the curve (AUC) value. Further, overlay analysis of best seismic susceptibility model using MaxEnt and PGV-based Japan Meteorological Agency (JMA) intensity shows that 40% the study area is in the very high and high seismic risk zone. In tectonically active areas, this kind of integration work is essential to improves the mitigation strategy and aids urban planners in designing earthquake-resistant buildings.

Implementation and evaluation of a spectral cumulus parametrization for simulating tropical cyclones in JMA‐GSM

AbstractA spectral cumulus parametrization (spectral scheme) developed using a cloud‐resolving model simulation was implemented in Global Spectral Model of the Japan Meteorological Agency (JMA‐GSM, GSM hereafter). The performance of the spectral scheme in terms of mean state, variability, and tropical cyclone (TC) properties was evaluated using atmosphere‐only model experiments by comparing results of the original convection scheme (based on Arakawa–Schubert scheme, AS scheme) and the spectral scheme. Parameter tunings were first conducted for the spectral scheme, through which the climatological errors of the cloud cover in the Southern Ocean and temperature were greatly improved. The spectral scheme showed comparable climatological errors to the AS scheme in the increased resolutions. The spectral scheme greatly improved tropical variability, that is, response to El Niño/Southern Oscillation and Madden–Julian Oscillation. Analyses on TC statistical data revealed that the spectral scheme improved TC properties in terms of genesis frequency, intensity, and the response of TC to tropical variability. Specifically, significant improvements were seen in the Northern Hemisphere. Further analyses on the TC structure revealed that higher TC intensity was sustained for longer times by the spectral scheme than by the AS scheme. The reason for this is that the spectral scheme better simulates convective heating by considering coexistence of different cloud types with parametrized vertically varying entrainment rates, especially from low to mid‐altitudes. The convective heating leads to a sharper vertical‐radial circulation in the TC structure, causing stronger incoming moisture flux toward the TC centre, and resulting in a stronger TC intensity. Consequently, the spectral scheme can simulate a comparable mean state, better variability, and better TC properties compared with the original scheme by simulating a more detailed convective structure. Therefore, it has a potential to increase the TC forecast skill for operational GSM.