Journal of the Meteorological Society of Japan. Ser. II
Online ISSN : 2186-9057
Print ISSN : 0026-1165
ISSN-L : 0026-1165
Current issue
Displaying 1-9 of 9 articles from this issue
Article: Special Edition on the Frontier of Atmospheric Science with High-Performance Computing
  • Sho YOKOTA, Takahiro BANNO, Masanori OIGAWA, Ginga AKIMOTO, Kohei KAWA ...
    2024 Volume 102 Issue 2 Pages 129-150
    Published: 2024
    Released on J-STAGE: March 08, 2024
    Advance online publication: December 05, 2023
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    This study hybridizes the background error covariance (BEC) of the hourly atmospheric three-dimensional variational data assimilation (3DVar) in Local Analysis (LA) operated at Japan Meteorological Agency using the flow-dependent BEC derived from the singular vector-based Mesoscale Ensemble Prediction System (MEPS) and the static BEC. The impact of introducing the hybrid BEC into the 3DVar is examined, along with its sensitivities to various factors like the ensemble size that is augmented by using lagged ensemble forecasts, the weight given to the ensemble-based component of BEC, the localization scales, and the use (or not) of the cross-variable correlation. This hybrid 3DVar system can be operated with small additional computational cost because it has no coupling with another ensemble data assimilation system. In sensitivity experiments, this hybrid 3DVar is shown to yield smaller forecast root mean square errors than the pure 3DVar, especially for surface variables. Moreover, the hybrid 3DVar shows a better equitable threat score for strong precipitation. These improvements were greater in the experiments with larger ensemble sizes that were increased by using lagged ensemble forecasts because of the reduced sampling errors in the ensemble-based BEC. These results were sensitive to the weight given to the ensemble-based BEC and the horizontal localization scale, whose optimal values were found to be approximately 0.5 and 100 km, respectively. The longer vertical correlation scale and the cross-variable correlation were also found important to create dynamically-balanced analysis, which is especially true for heavy rain cases.

Article
  • Aina OTSUBO, Ahoro ADACHI
    2024 Volume 102 Issue 2 Pages 151-165
    Published: 2024
    Released on J-STAGE: February 22, 2024
    Advance online publication: December 27, 2023
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    Dual-polarization radar often detects columnar regions of enhanced differential reflectivity (ZDR) extending vertically above the environmental 0 °C level. Indicative of supercooled liquid drops and wet ice particles lofted by strong updrafts, these ZDR columns are increasingly understood to be of use in predicting extreme rainfall. With the aim of achieving practical application of ZDR column measurements, this paper focuses on the relationship between the height of ZDR columns and rainfall intensity near the ground.

    All the data on ZDR columns analyzed in this study was collected from weather radar stations in Japan. The height of each column and rainfall rates at low levels were analyzed using an automated algorithm. A regression analysis result reveals peak column height to be positively correlated with maximum rainfall rate near ground level, and that rainfall intensity on the ground is likely to exceed 50 mm h−1 when radar identifies a ZDR column. Furthermore, extreme rainfall with an intensity of 180 mm h−1 or more is likely associated with a column over 3 km tall from the 0 °C level. These findings suggest that surveillance of ZDR columns can contribute to the reliability of very short-range forecasts or nowcasts as well as assist with the issue of early warnings of extreme rainfall and flash floods.

Article: Special Edition on Research on Extreme Weather Events that Occurred around East Asia in 2017-2021
  • Daichi TOYOOKA, Takuya KAWABATA, Hiroshi L. TANAKA
    2024 Volume 102 Issue 2 Pages 167-183
    Published: 2024
    Released on J-STAGE: March 13, 2024
    Advance online publication: December 28, 2023
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    In this study, we investigated how the prediction of the record-breaking heavy rainfall event that occurred in western Japan in July 2018 was affected by the initial conditions. The most sensitive region was identified and its impact on the verification region was described through ensemble forecasting. Backward trajectory and ensemble sensitivity analyses were conducted to determine the origin of the air mass that reached western Japan, leading to the event. The results consistently indicate that a moist air mass near the Ryukyu Islands, which lies windward of the affected area, was transported by the Western Pacific Subtropical High in the lower troposphere. Observation system experiments were conducted to confirm the importance of windward information, and the resulting statistical verification showed degradation for precipitation forecasts that did not include windward observations. Furthermore, windspeed overestimation in the poor forecast resulted in the precipitation zone being pushed northward, and the weakened convergence led to weaker precipitation than that observed during the actual event.

  • Junshi ITO, Hiroshi NIINO, Eigo TOCHIMOTO
    2024 Volume 102 Issue 2 Pages 185-208
    Published: 2024
    Released on J-STAGE: March 09, 2024
    Advance online publication: January 10, 2024
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    Supplementary material

    A tornado hit Nobeoka city on the southeast coast of Kyushu Island, Japan on 22 September 2019 when Typhoon Tapah was located about 500 km to the southwest of Kyushu Island and moving northeastward. Triply-nested numerical simulations are performed to reproduce the typhoon, a parent storm, and associated tornadoes. The simulation with the coarsest resolution reasonably reproduces Typhoon Tapah and associated outer rainbands at several 500 km east of its center, where the environment around the rainband is found to be favorable for mini-supercells. The simulation with the finest resolution reproduces a train of mini-supercells and associated tornadoes. The mini-supercells have typical structures that cause tornadoes associated with tropical cyclones. The minimum central pressure of the strongest tornado is 945 hPa. The time evolution of the simulated tornadoes is very fast: significant transitions of vortex structure occur within 1 minute before the tornado attains its peak strength. Most of the circulation of the tornado is derived from rear-flank downdrafts. Three tornadoes occur sequentially in association with non-occluding mesocyclogenesis, where a new tornado develops in the northwest of the old one.

Article: Special Edition on Heavy Rainfall and Snowfall, and Moisture Transport
  • Masayoshi ISHII, Hirotaka KAMAHORI, Hisayuki KUBOTA, Masumi ZAIKI, Ryo ...
    2024 Volume 102 Issue 2 Pages 209-240
    Published: 2024
    Released on J-STAGE: March 09, 2024
    Advance online publication: January 17, 2024
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    Supplementary material

    A historical atmospheric reanalysis from 1850 to 2015 was performed using an atmospheric general circulation model assimilating surface pressure observations archived in international databases, with perturbed observational sea surface temperatures as a lower boundary condition. Posterior spread during data assimilation provides quantitative information on the uncertainty in the historical reanalysis. The reanalysis reproduces the evolution of the three-dimensional atmosphere close to those of the operational centers. Newly archived surface pressure observations greatly reduced the uncertainties in the present reanalysis over East Asia in the early 20th century. A scheme for assimilating tropical cyclone tracks and intensities was developed. The scheme was superior to the present several reanalyses in reproducing the intensity close to the observations and the positions. The reanalysis provides possible images of atmospheric circulations before reanalyses with full-scale observations become available, and opportunities for investigating extreme events that occurred before World War II. Incorporating dynamical downscaling with a regional model that includes detailed topography and sophisticated physics is an application of historical reanalysis to reveal the details of past extreme events. Some examples of past heavy rainfall events in Japan are shown using a downscaling experiment, together with dense rainfall observations over the Japanese islands.

Article
  • Gen WANG, Wei HAN, Song YUAN, Jing WANG, Ruo-Ying YIN, Song YE, Feng X ...
    2024 Volume 102 Issue 2 Pages 241-264
    Published: 2024
    Released on J-STAGE: March 01, 2024
    Advance online publication: January 16, 2024
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    The temperature profile is an important parameter of the atmospheric thermal state in atmospheric monitoring and weather forecasting. The hyperspectral infrared sounder of a geostationary satellite provides abundant spectral information and can retrieve the temperature profile. Based on the mediumwave channel data (independent variable and model input data) of FY-4A/GIIRS (geosynchronous interferometric infrared sounder) and ERA5 reanalysis data (dependent variable and model output data), the atmospheric temperature profile is retrieved by generalized ensemble learning. Firstly, the feature variables of the model are constructed. Because there are many GIIRS channels, a two-step feature selection method is adopted: step 1—establish a blacklist of GIIRS channels; step 2—select feature variables by using the method of importance permutation. Secondly, they are integrated based on optimizing and adjusting the hyperparameters of three basic machine learning models (Random Forest, XGBoost and LightGBM). Generalized ensemble learning nonlinear convex optimization is used to optimize the weight of each basic model. Finally, based on high-frequency GIIRS observations of Typhoon Lekima and Typhoon Higos, testing and method evaluation of the temperature profile retrievals are carried out. The results show that LightGBM achieves the best retrieval result among the three basic models, followed by Random Forest and finally XGBoost. The root-mean-square error of the whole temperature profile in the training dataset of generalized ensemble learning is less than 0.3 K, while that of the testing dataset is less than 1.4 K, and that between 150 hPa and 925 hPa is less than 1 K. The retrieval results correlate well with the radiosonde temperature profile. The performance of generalized ensemble learning is better than the performances of the three basic models, but it depends on the retrieval results of LightGBM. In the Lekima experimental case, compared to other channels selected for temperature retrieval models, the importance of mediumwave channels 9 and 307 of GIIRS ranks first and second, respectively. The method in this paper provides a new solution and technical support for retrieving atmospheric parameters from hyperspectral and other satellite data.

  • Tomoaki OSE, Hirokazu ENDO, Toshiyuki NAKAEGAWA
    2024 Volume 102 Issue 2 Pages 265-283
    Published: 2024
    Released on J-STAGE: March 07, 2024
    Advance online publication: January 17, 2024
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    Supplementary material

    Recent year-to-year and long-term climate variabilities during 1980 – 2020 were investigated using the Japanese 55-year reanalysis dataset (JRA-55) to assess the robustness of and uncertainties in future sea-level pressure (SLP) patterns for summertime East Asia due to global warming, which were obtained in a previous study by an inter-model empirical orthogonal function (EOF) analysis of the multi-model future projections in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). One major finding is that the future robust SLP pattern emerges with a significant trend in the recent long-term variability consistent with the CMIP6 future projection. A few of the future uncertain patterns also display significant trends recently, but against the future projection means. The year-to-year variability of the patterns tends to make the polarized extreme summer SLP variations through the superposition with the long-term trends.

    The second EOF pattern reflects low- and high-SLP anomalies in northern and southern East Asia, respectively, which is a robust future SLP pattern as its future appearance is predicted by almost all CMIP6 models. While the pattern appears in the summer following an El Niño winter, the significant trend in the recent long-term variability is created similarly to the CMIP6 future projection by recent warming over northern continents and seas.

    The other EOFs are the uncertain future SLP patterns as the future polarities depend on the CMIP6 projection model. The first and third patterns represent a strengthened high-pressure anomaly and a weakened southerly wind pattern over East Asia, respectively. They show small linear trends in the magnitude consistent with the small future changes. The high-ranked patterns display long-term decreases against each future ensemble mean. The trends in the uncertain patterns are attributed to the weak and reverse surface warming distribution over the tropical oceans in the recent climate change compared with the future change.

  • Shoji KUSUNOKI, Tosiyuki NAKAEGAWA, Ryo MIZUTA
    2024 Volume 102 Issue 2 Pages 285-308
    Published: 2024
    Released on J-STAGE: March 07, 2024
    Advance online publication: January 17, 2024
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    Supplementary material

    The performance of the Meteorological Research Institute-Atmospheric General Circulation model version 3.2 (MRI-AGCM3.2) in simulating precipitation is compared with that of global atmospheric models registered to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The Atmospheric Model Intercomparison Project (AMIP) experiments simulated by 36 Atmospheric General Circulation Model (AGCM)s and the High Resolution Model Intercomparison Project (HighResMIP) highresSST-present experiments simulated by 23 AGCMs were analyzed. Simulations by MRI-AGCM3.2S (20-km grid size) and MRI-AGCM3.2H (60-km grid size) are included as a part of the HighResMIP highresSST-present experiments. MRI-AGCM3.2S has the highest horizontal resolution of all 59 AGCMs. As for the global distribution of seasonal and annual average precipitation, monthly precipitation over East Asia, and the seasonal march of rainy zone over Japan, MRI-AGCM3.2 models perform better than or equal to CMIP6 AMIP AGCMs and HighResMIP AGCMs. HighResMIP AGCMs (average grid size 78 km) perform better than CMIP6 AMIP AGCMs (180 km) in simulating seasonal and annual precipitation over the globe, and summer (June to August) precipitation over East Asia. MRI-AGCM3.2 models perform better than or equal to CMIP6 AMIP AGCMs and HighResMIP AGCMs in simulating extreme precipitation events over the globe. Correlation analysis between grid size and model performance using all 59 models revealed that higher horizontal resolution models are better than lower resolution models in simulating the global distribution of seasonal and annual precipitation and the global distribution of intense precipitation, and the local distribution of summer precipitation over East Asia.

  • Fumie MURATA, Toru TERAO, Yusuke YAMANE, Azusa FUKUSHIMA, Masashi KIGU ...
    2024 Volume 102 Issue 2 Pages 309-329
    Published: 2024
    Released on J-STAGE: March 19, 2024
    Advance online publication: January 17, 2024
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    Supplementary material

    Near-surface rain rate datasets derived from the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) and Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) and near-surface raindrop size distribution (DSD) parameters derived from the GPM DPR were validated using 43 tipping-bucket rain gauges installed over the northeastern Indian subcontinent and two Parsivel2 disdrometers installed on the Meghalaya Plateau, India. Both TRMM PR version 7 and version 8 products significantly underestimated the rainfall over the Indian subcontinent during the monsoon season (June–September). The GPM DPR version 06A product also significantly underestimated the rainfall at stations on the Meghalaya Plateau, India. The heavy rainfall area (HRA) of the Meghalaya Plateau in the TRMM PR climatology showed lighter rainfall on the plateau, whereas heavier rainfall was detected in adjacent valleys. Intense surface rainfall over the HRA may be detectable, because such intense rainfalls tended to occur from deeper convections, which were less affected by the ground clutter interferences. A comparison of the statistical features of the DSD parameters between the disdrometers and GPM DPR retrievals around the Meghalaya Plateau confirmed that an adequate assumption of the adjustment factor ϵ is important for improving the DSD parameters in GPM DPR retrievals.

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