Japan Meteorological Research Articles

Non-iterative equivalent linearization method for generating seismic fragility curve of equipment

In the seismic PRA, tremendous computational and human resources are required to evaluate the seismic fragility curves of facilities in a nuclear building, so the development of a simple evaluation method has been desired. In this study, we propose and apply a non-iterative equivalent linearization method for evaluating the real capacity (median value of fragility curve) and propose a method that can easily evaluation seismic fragility curves. Then, the method was tested using 6 equivalent linearization models for equipment installed on the ground surface and comparing the results with those obtained using nonlinear analysis. The validation was done by using 37 ground motion records observed by Japan Meteorological Agency. As a result, the equivalent linearization method proposed by Liu et al. (2015) and Watabe et al (1985) can obtain relatively stable results. In particular, the equivalent linearization method of Liu et al. was found to be independent of parameter changes.

Estimation of Seasonal Snow Mass Balance all over Japan Using a High-Resolution Atmosphere-Snow Model Chain

Despite the importance of seasonal snow in Japan, quantitative information on seasonal snow mass in the whole area is lacking. To understand the current mass balance of seasonal snow cover, we develop a method to quantify the mass balance of seasonal snow all over Japan using the Japan Meteorological Agency's operational regional atmospheric model, Local Forecast Model (LFM), as well as the snow physics model, Snow Metamorphism and Albedo Process (SMAP). Our model simulations using the LFM-SMAP model chain show that the seasonal snow water equivalent (SWE) evolution is mostly controlled by snowfall and runoff. It is estimated that the seasonal peak area-integrated SWE in Japan reaches 42.2 Gt on average during the 2017-2022 winters, and 60.1 Gt (+43% with respect to the average) and 22.0 Gt (−48%) during relatively heavy (2017-2018) and relatively light (2019-2020) snow years, respectively. The ratios between seasonal peak area-integrated SWE and winter-accumulated (November to February) snowfall amounts for the heavy, average, and light snow years are 83%, 74%, and 59%, respectively.

A New Application Method of Radar/Raingauge-Analyzed Precipitation Amounts for Long-term Statistical Analyses of Localized Heavy Rainfall Areas

We propose a new application method in which radar/raingauge-analyzed precipitation amounts (RAP) produced by the Japan Meteorological Agency are spatially converted into 5km-resolution data, in addition to a three-hourly accumulation procedure, in order to statistically analyze localized heavy rainfall areas (HRAs) for a long period. A long-term trend and homogeneity in the appearance frequency of RAP with 5km-resolution converted by several methods, including the conventional method, are statistically evaluated in comparison with rain-gauge observations. The results indicate that the following application method is the most suitable to represent long-term variations in the appearance frequency of HRAs; (1) the converted value of RAP from 1 km to 5 km resolution is set to the 90th percentile value in 30 segments with 1km-resolution included in a grid with 5km-resolution, and (2) this spatial conversion is conducted after accumulating original RAP with 1km-resolution for three hours. Statistical analyses were performed for the appearance frequency of HRAs extracted from 5km-resolution RAP that were produced by the new application method, which indicates that the number of HRAs of the linear-stationary type could be increased without compromising the characteristics of HRAs extracted by the conventional method.

A time series image prediction method combining a CNN and LSTM and its application in typhoon track prediction.

Typhoon forecasting has always been a vital function of the meteorological department. Accurate typhoon forecasts can provide a priori information for the relevant meteorological departments and help make more scientific decisions to reduce the losses caused by typhoons. However, current mainstream typhoon forecast methods are very challenging and expensive due to the complexity of typhoon motion and the scarcity of ocean observation stations. In this paper, we propose a typhoon track prediction model, DeepTyphoon, which integrates convolutional neural networks and long short-term memory (LSTM). To establish the relationship between the satellite image and the typhoon center, we mark the typhoon center on the satellite image. Then, we use hybrid dilated convolution to extract the cloud features of the typhoon from satellite images and use LSTM to predict these features. Finally, we detect the location of the typhoon according to the predictive markers in the output image. Experiments are conducted using 13, 400 satellite images of time series of the Northwest Pacific from 1980 to 2020 and 8420 satellite images of time series of the Southwest Pacific released by the Japan Meteorological Agency. From the experimentation, the mean average error of the 6-hour typhoon prediction result is 64.17 km, which shows that the DeepTyphoon prediction model significantly outperforms existing deep learning approaches. It achieves successful typhoon track prediction based on satellite images.

Analysis of Intense Rainfalls over Nagasaki Area on 23, July 1982 using Precipitation Data of Japan Meteorological Agency and Other Organizations

Analysis of Intense Rainfalls over Nagasaki Area on 23, July 1982 using Precipitation Data of Japan Meteorological Agency and Other Organizations

A Probabilistic Forecast Experiment and Its Verification for Relative Humidity and Leaf Wetness with Downscaling Data of One-Week Ensemble Prediction System of the Japan Meteorological Agency

A Probabilistic Forecast Experiment and Its Verification for Relative Humidity and Leaf Wetness with Downscaling Data of One-Week Ensemble Prediction System of the Japan Meteorological Agency

北西太平洋の台風通過に伴って生じる気象庁準リアルタイム版全球海面水温解析のバイアス

The near-real-time merged satellite and in-situ data global daily sea surface temperature (SST) of the Japan Meteorological Agency (hereinafter abbreviated as R-MGD) is subjected to filtering out short-time-scale fluctuations from observations prior to the analysis time. Therefore, the rapid SST change due to the passage of tropical cyclones (TCs) is thought to cause biases. Here, the biases in the R-MGD with respect to in-situ observations were quantified along the passage of TCs in the western North Pacific. First, we examined a case study on the approach of three successive TCs in August–September 2020. The R-MGD had positive biases of > 2°C just after the passage of three TCs, and negative biases were observed after one week of the last TC's passage. The comparison of the R-MGD with a moored buoy indicates that the biases can be explained by short-term fluctuations filtered out and the SST prior to the analysis time in R-MGD analysis. Second, the composite analysis from May 2015–October 2020 indicates that the statistically significant biases at the observation points ranged between −1 days and +4 days for positive biases and between +7 days and +14 days for negative biases relative to the time of the closest approach of a TC within 500 km. The positive SST bias is largely associated with cold subsurface water and intense TCs, being pronounced in the mid-latitude, except around the Kuroshio and Kuroshio extension regions. The assimilation of in-situ observations recorded within 72 h prior to the R-MGD analysis time through additional optimal interpolation alleviates these biases because this process redeems short-time-scale fluctuations. The impact on TC forecasts and the validity of the optimal interpolation experiment against the independent observations were also investigated.

Positive Cloud-to-Ground Lightning Characteristics in the Eyewall of Typhoon Faxai (2019) Observed by Tokyo Lightning Mapping Array

Although a number of studies have been conducted of the lightning activity in hurricanes and typhoons, little information has been obtained on the 3-dimensional structure of the lightning, or how it is related to the precipitation structures within the storms. Here we utilize observational data from the 3-D Tokyo Lightning Mapping Array (Tokyo LMA), a Japan Meteorological Agency C-band Doppler radar, and the Japanese Lightning Detection Network (JLDN) to conduct a study of the lightning activity during Typhoon Faxai (2019) in comparison with the storm's precipitation structure. This is done for the dissipating stage of the typhoon, when the eyewall was well within the range of the instruments and undergoing a surge in lightning activity. Of particular interest in the surge was the occurrence of numerous positive cloud-to-ground (+CG) lightning flashes. Detailed study of the Tokyo LMA and JLDN data show that, out of 52 flashes during the surge, 29 flashes or 56 % produced positive strokes to ground, an unheard-of number considering that, from the lightning and 3-D radar structures, the storm appeared to be normally-electrified, and under such circumstances would produce negative rather than positive strokes to ground. It also focuses attention on the question of how +CGs are produced in tropical cyclones in the first place. Based on a lack of −CG strokes and the LMA observations showing that the +CG strokes are produced mid-way or toward the end of normal-polarity intracloud (IC) flashes, it appears that the dissipating storm cells have a depleted or horizontally-sheared mid-level negative charge, such that an IC flash propagating into and through upper positive storm charge effectively funnels a steadily increasing amount of positive charge into the mid-level initiation region, eventually causing the positive breakdown of the IC flash to turn downward toward ground, producing a +CG stroke.

Impact of seasonal and meteorological factors on the incidence of adhesive small bowel obstruction: A large-scale study using a national inpatient database.

AimWhether seasonal and meteorological factors affect the incidence of adhesive small bowel obstruction (ASBO) remains unclear. This study aimed to clarify the impacts of seasonal and meteorological factors on the occurrence of ASBO.MethodsClinical data of patients with ASBO were acquired from 42 national university hospitals in Japan, using a national inpatient database, between April 2012 and March 2020. Meteorological data were obtained from the Japan Meteorological Agency. The number of monthly admissions of patients with ASBO was compared between each of the 12 months. Daily weather variables were investigated to clarify their association with ASBO patient admissions on a total of 119 802 days (Formula for calculation: study period [2922 days] ×41 cities).ResultsOverall, 4985 patients with ASBO were admitted. The number of admissions in June was smaller than that in October, November, and December (39 vs 63.5, P = .002, 39 vs 65, P = .004, and 39 vs 59.5, P = .002, respectively). Logistic regression analysis revealed that January, October, November, and December were associated with increased risk of admission compared to June (odds ratio [OR], 1.264; P = .001; OR, 1.454; P < .001; OR, 1.408; P < .001; OR, 1.330; P < .001), respectively. Regarding the weather variables, higher barometric pressure and lower humidity were associated with increased risk of admission (OR, 1.011; P < .001 and OR, 0.995; P < .001), respectively.ConclusionThe incidence of ASBO is susceptible to barometric pressure and humidity and varies monthly. These results can contribute to the prevention, early detection, and immediate and appropriate management of ASBO.

Classification of volcanic tremors and earthquakes based on seismic correlation: application at Sakurajima volcano, Japan

SUMMARY Classification of volcanic tremors and earthquakes is an important part of volcano monitoring. Conventional classification technique relies on visual characterization of the amplitudes, frequency, or duration of seismic signals. In some cases, such classification is difficult to be maintained continuously, especially during high eruptive activities. In the present study, we develop a classification technique based on analyses of seismic wave correlation. The technique is based on a measure of spatial coherence using the distribution width of eigenvalues from the seismic covariance matrix that is referred to as ‘spectral width’. We use the spectral width characteristics to extract the frequencies where the seismic signals are the most spatially coherent. We analyse 6-month continuous seismic data at six seismic stations deployed on Sakurajima volcano, Japan. The classification is performed every 10 min to recognize volcanic tremor and B-type earthquakes, both of which show unclear onsets and are dominant at around 1–4 Hz. Their frequencies of high spatial coherence are different from each other, providing a basis for automatic classification of both types of seismic events. Our classification results show that an increase in seismic activity, particularly volcanic tremors, occurred during high eruptive activity and is well matched with the events that are routinely determined by the Japan Meteorological Agency (JMA) based on visual examination of the data. We discuss the volcanic activities by combining our classification results with those from detections and location methods based on seismic correlation. Most of the source locations of volcanic tremor and B-type earthquakes are distributed at the same region with depths of less than 4 km beneath the active craters, which are almost consistent with the source locations of explosion earthquakes and B-type earthquakes determined from P-wave onsets in the previous studies. Also, the distribution of horizontal locations is spatially elongated at shallow depths above a dyke that is inferred to extend from Aira caldera at a depth of 7–9 km. We obtain deep and shallow sources for the tremor that occurred before and after an explosive eruption, respectively. We also obtain shallow sources beneath the active crater during a continuous tremor that accompanies increasing eruptive activity. The source locations of the classified volcanic tremor and B-type earthquakes may represent the movement of magma and gas from the shallow magma chamber at 5 km depth to the active craters.

Historical data of geoelectric field observations in Japan

AbstractThe Kakioka Magnetic Observatory (KMO) of the Japan Meteorological Agency (JMA) made geoelectric field observations at Kakioka, Memambetsu and Kanoya from 1932, 1949 and 1948, respectively, until February 2021, when observations at all three sites were terminated. The digital numerical data are available on the KMO website, although not all observation periods are covered. In this article, we introduce the available datasets and summarize the metadata regarding the observations, such as the electrodes, recording system, maintenance methods and data processing.

Sensitivity of different physics schemes using WRF model in Typhoon Damrey (2017) over the Indochina region

To reduce the tropical cyclone impact of lives and economics, the precise forecast of the event is required. The typhoon Damrey (2017), which caused ravaging of the strong wind, heavy precipitation, flash flood and storm surge over the Indochina region, was simulated by Weather Research and forecasting (WRF) model. The simulated duration was 8 days starting from 31 October 2017 00 00 UTC to 8 November 2017 00 00 UTC. The NCEP 6-hour global FNL (final analysis) data at 1-degree resolution is provided for initial condition. The WRF model was run in a single domain of 20 km horizontal resolution bounded 0 to 20 N and 96°E to 124°E. The different physics schemes, which are the microphysics schemes, the planetary boundary layer schemes and cumulus parameterization schemes, were emphasized to examine the suitable schemes in tropical cyclone simulation over the Indochina region. To evaluate the reliability of the simulation of tropical cyclone, the track-position is correlated with the Japan Meteorological Agency (JMA) observation. The results show that the typhoon simulation forced by Belts-Millers-Janjic cumulus, WSM6 microphysics was suitable for simulating of typhoon Damrey.

Detection of low-frequency earthquakes by the matched filter technique using the product of mutual information and correlation coefficient

The matched filter technique is often used to detect microearthquakes such as deep low-frequency (DLF) earthquakes. It compares correlation coefficients (CC) between waveforms of template earthquakes and the observed data. Conventionally, the sum of CC at multiple seismic stations is used as an index to detect the DLF earthquakes. A major disadvantage of the conventional method is drastically reduced detection accuracy when there are too few seismic stations. The new matched filter method proposed in this study can accurately detect microearthquakes using only a single station. It adopts mutual information (MI) in addition to CC to measure the similarity between the template and target waveforms. The method uses the product of MI and CC (MICC) as an index to detect DLF earthquakes. This index shows a distinct peak corresponding to an earthquake signal in a synthetic data set consisting of artificial noise and the waveform of a DLF earthquake. Application of this single-station method to field observations of Kirishima volcano, one of the most active volcanoes in Japan, detected a total of 354 events from the data in December 2010, whereas the catalog of the Japan Meteorological Agency shows only two. Of the detected events, 314 (89%) are likely DLF earthquakes and other events may be false detections. Most of the false detections correspond to surface-wave arrivals from teleseismic events. The catalog of DLF earthquakes constructed here shows similar temporal behavior to that found by the conventional matched filter method using the sum of the CC of the six stations near the volcano. These results suggest that the proposed method can greatly contribute to the accurate cataloging of DLF earthquakes using only a single seismic station.Graphical

Temporal variations of net Kuroshio transport based on a repeated hydrographic section along 137°E

Temporal variations of net Kuroshio transport are examined for 1972–2018 based on a repeated hydrographic section along 137°E, which is maintained by the Japan Meteorological Agency. The net Kuroshio transport obtained by integration of geostrophic current velocity relative to 1000 dbar depth fluctuates on inter-annual and decadal timescales. The predominant timescale of the net Kuroshio transport changes with time; the inter-annual variation is pronounced in 1972–1990 and 2000–2018, and the decadal variation is detected only before 2000. We find that a winter wind stress curl variation in the central North Pacific which reflects meridional movements of the Aleutian Low and intensity fluctuations of the North Pacific subtropical high on an inter-annual timescale and intensity fluctuations of the Aleutian Low on a decadal timescale, causes the net Kuroshio transport variation. In addition to the inter-annual and decadal variations, we further pointed out a bi-decadal-scale variation of the net Kuroshio transport and its possible link to the Aleutian Low intensity fluctuation. Moreover, our results indicate that during large net Kuroshio transport, sea surface temperature around the Kuroshio and Kuroshio Extension region tends to increase, resulting in vigorous upward sensible and latent heat release.

DAGDU: center location of tropical cyclones using Deviation Angle Gradient Distribution Uniformity from infrared satellite images

ABSTRACT The tropical cyclone (TC) is a strong weather system that forms on the surface of the ocean. It is important to locate the TC centre when monitoring and forecasting. Infrared geostationary satellite images are reliable sources of TC location due to their high temporal resolution and wide regional coverage. A novel method for locating the centre of tropical cyclones by deviation angle gradient distribution uniformity is proposed in this paper. A region of interest is first intercepted from an infrared image with a spatial resolution of 5 km per pixel derived from the China FY-2 C and FY-2E geostationary satellites. The Bezier histogram segmentation method and K-means clustering segmentation method are then used to obtain the binary image in which the infrared brightness temperature changes drastically. Next, Hough transformation is utilized to reduce the search range of the centre location of the tropical cyclone. Finally, the deviation angle gradient distribution uniformity matrix is calculated from each pixel in the detection area as the reference centre, and the maximum value location of the matrix is considered to be the corresponding tropical cyclone centre. The methodology of this study could achieve good performance compared with the best track data of the China Meteorological Administration (CMA), the Japan Meteorological Agency (JMA), and the United States Joint Typhoon Warning Center (JTWC).

The skill assessment of ENSO prediction issued by JMA ensemble prediction system and CFSv2

Indonesian climate is strongly affected by El Niño-Southern Oscillation (ENSO) as one of climate-driven factor. ENSO prediction during the upcoming months or year is crucial for the government in order to design the further strategic policy. Besides producing its own ENSO prediction, BMKG also regularly releases the status and ENSO prediction collected from other climate centers, such as Japan Meteorological Agency (JMA) and National Oceanic and Atmospheric Administration (NOAA). However, the skill of these products is not well known yet. The aim of this study is to conduct a simple assessment on the skill of JMA Ensemble Prediction System (EPS) and NOAA Climate Forecast System version 2 (CFSv2) ENSO prediction using World Meteorological Organization (WMO) Standard Verification System for Long Range Forecast (SVS-LRF) method. Both ENSO prediction results also compared each other using Student's t-test. The ENSO predictions data were obtained from the ENSO JMA and ENSO NCEP forecast archive files, while observed Nino 3.4 were calculated from Centennial in situ Observation-Based Estimates (COBE) Sea Surface Temperature Anomaly (SSTA). Both ENSO prediction issued by JMA and NCEP has a good skill on 1 to 3 months lead time, indicated by high correlation coefficient and positive value of Mean Square Skill Score (MSSS). However, the skill of both skills significantly reduced for May-August target month. Further careful interpretation is needed for ENSO prediction issued on this mentioned period.

Improvement of the Cloud Microphysics Scheme of the Mesoscale Model at the Japan Meteorological Agency Using Spaceborne Radar and Microwave Imager of the Global Precipitation Measurement as Reference

Abstract In this study, the single-moment 6-class bulk cloud microphysics scheme used in the operational numerical weather prediction system at the Japan Meteorological Agency was improved using the observations of the Global Precipitation Measurement (GPM) core satellite as reference values. The original cloud microphysics scheme has the following biases: underestimation of cloud ice compared to the brightness temperature of the GPM Microwave Imager (GMI) and underestimation of the lower-troposphere rain compared to the reflectivity of GPM Dual-frequency Precipitation Radar (DPR). Furthermore, validation of the dual-frequency rate of reflectivity revealed that the dominant particles in the solid phase of simulation were graupel and deviated from DPR observation. The causes of these issues were investigated using a single-column kinematic model. The underestimation of cloud ice was caused by a high ice-to-snow conversion rate, and the underestimation of precipitation in the lower layers was caused by an excessive number of small-diameter rain particles. The parameterization of microphysics scheme is improved to eliminate the biases in the single-column model. In the forecast obtained using the improved scheme, the underestimation of cloud ice and rain is reduced. Consequently, the prediction errors of hydrometeors were reduced against the GPM satellite observations, and the atmospheric profiles and precipitation forecasts were improved.

A Constraint Method for Supersaturation in a Variational Data Assimilation System

Abstract The reproducibility of precipitation in the early stages of forecasts, often called a spindown or spinup problem, has been a significant issue in numerical weather prediction. This problem is caused by moisture imbalance in the analysis data, and in the case of the Japan Meteorological Agency’s (JMA’s) mesoscale data assimilation system, JNoVA, we found that the imbalance stems from the existence of unrealistic supersaturated states in the minimal solution of the cost function in JNoVA. Based on the theory of constrained optimization problems, we implemented an exterior penalty function method for the mixing ratio within JNoVA to suppress unrealistic supersaturated states. The advantage of this method is the simplicity of its theory and implementation. The results of twin data assimilation cycle experiments conducted for the heavy rain event of July 2018 over Japan show that—with the new method—unrealistic supersaturated states are reduced successfully, negative temperature bias to the observations is alleviated, and a sharper distribution of the mixing ratio is obtained. These changes help to initiate the development of convection at the proper location and improve the fractions skill score (FSS) of precipitation in the early stages of the forecast. From these results, we conclude that the initial shock caused by moisture imbalance is mitigated by implementing the penalty function method, and the new moisture balance has a positive impact on the reproducibility of precipitation in the early stages of forecasts.

Comparative Analysis on Wave Field Using Wave Measurement and Wave Analysis Data from JMA-MSM

Kim, H.-J.; Cho, W.C., and Moon, Y.-H., 2021. Comparative analysis on wave field using wave measurement and wave analysis data from JMA-MSM. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 181–185. Coconut Creek (Florida), ISSN 0749-0208. Waves are the most important factor in the numerical analysis of coastal hydrodynamics. Wave conditions in coastal areas can be determined in on-site wave measurements or wave numerical analyses. Because the acquisition of spatial wave measurement data in a specific coastal area is limited, the spatial wave data are generally acquired with accurate numerical analyses. In this study, the wave field in a coastal area in South Korea was comparatively analyzed with wave measurements and numerical analyses of wind data acquired with the JMA (Japan Meteorological Agency) Meso-Scale Model (MSM). The high-resolution meteorological data were used for the numerical analysis, and the wave measurement data provided by the Wave Information Network of Korea (WINK) were used for the comparative analysis. Results showed the root mean square errors of 0.3-0.4m for all wave prediction in the East Sea in 2018 and 0.4-0.5m for typhoon KONG-REY in 2018. Therefore, it was concluded that the wave numerical simulations based on the wave data from JMA-MSM overall reproduced the wave field accurately.

Developments of the Nationwide Earthquake Early Warning System in Japan After the 2011 Mw9.0 Tohoku-Oki Earthquake

In Japan, the nationwide earthquake early warning (EEW) system has been being operated by the Japan Meteorological Agency (JMA) since 2007, disseminating information on imminent strong ground motion to the general public and advanced technical users. In the beginning of the operation, the system ran based mainly on standard source-based algorithms with a point-source location estimate and ground motion prediction equation. The point-source algorithms successfully provided ground motion predictions with high accuracy during the initial operation; however, the 2011 Mw9.0 Tohoku-Oki earthquake and the subsequent intense aftershock and triggered earthquake activities underscored the weaknesses of the source-based approach. In this paper, we summarize major system developments after the Tohoku-Oki event to overcome the limits of the standard point-source algorithms and to enhance the EEW performance further. In addition, we evaluate how the system performance was influenced by the updates. One of significant improvements in the JMA EEW system was the implementation of two new ground motion prediction methods: the integrated particle filter (IPF) and propagation of local undamped motion (PLUM) algorithms. IPF is a robust point-source algorithm based on the Bayesian inference, and PLUM is a wavefield-based algorithm that predicts ground motions directly from observed shakings. Another notable update was the incorporation of new observation facilities including S-net, a large-scale ocean bottom seismometer network deployed along the Japan and Kuril trenches. The prediction accuracy and warning issuance performance analysis for the updated JMA EEW system showed that IPF improved the source-based ground motion prediction accuracy and reduced the risk of issuing overpredicted warnings. PLUM made the system less likely to underpredict strong ground motions and improved the warning issuance timeliness. The detection time analysis for the S-net incorporation suggested that S-net enabled the system to issue the first EEW report earlier than before the S-net incorporation for earthquakes around the Japan and Kuril trenches. Those findings indicate that the JMA EEW system has made substantial progress both on software and hardware aspects over the 10 years after the Tohoku-Oki earthquake.