Matsueda Mio

写真a

Title

Associate Professor

Current Affiliation Organization 【 display / non-display

  • Duty   University of the Ryukyus   Faculty of Science   Associate Professor  

University 【 display / non-display

  • 1998.04
    -
    2002.03

    University of Tsukuba   First Cluster of College   Graduated

Graduate School 【 display / non-display

  • 2002.04
    -
    2007.05

    University of Tsukuba  Life and Environmental Sciences  Master's Course  Accomplished credits for doctoral program

Academic degree 【 display / non-display

  • University of Tsukuba -  Doctor (Science)

External Career 【 display / non-display

  • 2007.06
    -
    2010.03

    AESTO  

  • 2010.04
    -
    2012.02

    Japan Agency for Marine-Earth Science and Technology  

  • 2012.02
    -
    2014.02

    University of Oxford, Department of Physics, Atmospheric, Oceanic and Planetary Physics  

  • 2014.04
    -
    2024.03

    University of Tsukuba  

  • 2014.09
    -
    2018.03

    University of Oxford,Department of Physics, Atmospheric, Oceanic and Planetary Physics  

Affiliated academic organizations 【 display / non-display

  • 2012.07
    -
    Now
     

    Royal Meteorological Society 

  • 2017.08
    -
    Now
     

    World Meteorological Organization (WMO)    Commission on Atmospheric Sciences, Working Group on Predictability, Dynamics and Ensemble Forecasting (PDEF)/member

Research Interests 【 display / non-display

  • 異常気象、数値予報、アンサンブル予報、予測可能性

Research Areas 【 display / non-display

  • Natural Science / Atmospheric and hydrospheric sciences

Published Papers 【 display / non-display

  • Flowering-date forecast of cherry blossom in Tokyo using seasonal ensemble forecasts

    YAMAKI Toshinori, ASAGA Yuzuki, MATSUEDA Mio

    Climate in Biosphere ( The Society of Agricultural Meteorology of Japan )  24 ( 0 ) 36 - 42   2024.07 [ Peer Review Accepted ]

    Type of publication: Research paper (scientific journal)

     View Summary

    <p> This study assessed the flowering-date forecast skill of cherry blossom in Tokyo from 2018 to 2023 using seasonal ensemble forecasts from three numerical weather prediction centers: the Deutscher Wetterdienst, the European Centre for Medium-Range Weather Forecasts, and the Météo-France. First, the optimal seven parameters used in the flowering-date estimation model, developed by Maruoka and Itoh (2009), were determined for Tokyo, based on the period from 1994 to 2017, during which the estimation bias was ±1.91 days. Then, flowering dates were predicted using bias-corrected seasonal ensemble forecast of 2 m temperature as a model input. The root-mean-square errors for the flowering-date forecasts initialized on 1st January, February, and March, averaged over all ensemble members, were about ±8.0 days, ±6.2 days, and ±2.3 days, respectively. The best- or worst-performing center is dependent on the specific cases. The grand ensemble forecast, comprising all forecasts from all single-center ensembles, showed better performance in predicting flowering dates of cherry blossoms than the single-center ensemble forecasts alone. These results suggest that the grand ensemble approach at seasonal timescales holds potential for predicting of the growth of flowers and fruits.</p>

  • Prediction skill and practical predictability depending on the initial atmospheric states in S2S forecasts

    Matsueda, Mio

    Journal of the Atmospheric Sciences ( American Meteorological Society )    2023.04 [ Peer Review Accepted ]

    Type of publication: Research paper (scientific journal)

     View Summary

    Abstract The hypothesis that predictability depends on the atmospheric state in the planetary-scale low-frequency variability in boreal winter was examined. We first computed six typical weather patterns from 500-hPa geopotential height anomalies in the Northern Hemisphere using self-organising map (SOM) and k-clustering analysis. Next, using 11 models from the subseasonal-to-seasonal (S2S) operational and reforecast archive, we computed each model’s climatology as a function of lead time to evaluate model bias. Although the forecast bias depends on the model, it is consistently the largest when the forecast begins from the atmospheric state with a blocking-like pattern in the eastern North Pacific. Moreover, the ensemble-forecast spread based on S2S multi-model forecast data was compared with empirically estimated Fokker-Planck equation (FPE) parameters based on reanalysis data. The multi-model mean ensemble-forecast spread was correlated with the diffusion tensor norm; they are large for the cases when the atmospheric state started from a cluster with a blocking-like pattern. As the multi-model mean is expected to substantially reduce model biases and may approximate the predictability inherent in nature, we can summarise that the atmospheric state corresponding to the cluster was less predictable than others.

  • 世界の現業モデルによる平成30年7月豪雨の中期予測可能性

    松枝, 未遠, 松信匠

    気象研究ノート   ( 246 ) 107 - 116   2022.10

    Type of publication: Research paper (scientific journal)

  • Skill of Medium-Range Forecast Models Using the Same Initial Conditions

    Magnusson, L, Ackerley, D, Bouteloup, Y, Chen, J.-H, Doyle, J, Earnshaw, P, Kwon, Y. C, Köhler, M, Lang, S. T. K, Lim, Y.-J, Matsueda, Mio, Matsunobu, T, McTaggart-Cowan, R, Reinecke, A, Yamaguchi, M, Zhou, L

    BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY ( AMER METEOROLOGICAL SOC )  103 ( 9 ) E2050 - E2068   2022.09 [ Peer Review Accepted ]

    Type of publication: Research paper (scientific journal)

     View Summary

    In the Different Models, Same Initial Conditions (DIMOSIC) project, forecasts from different global medium-range forecast models have been created based on the same initial conditions. The dataset consists of 10-day deterministic forecasts from seven models and includes 122 forecast dates spanning one calendar year. All forecasts are initialized from the same ECMWF operational analyses to minimize the differences due to initialization. The models are run at or near their respective operational resolutions to explore similarities and differences between operational global forecast models. The main aims of this study are 1) to evaluate the forecast skill and how it depends on model formulation, 2) to assess systematic differences and errors at short lead times, 3) to compare multimodel ensemble spread to model uncertainty schemes, and 4) to identify models that generate similar solutions. Our results show that all models in this study are capable of producing high-quality forecasts given a high-quality analysis. But at the same time, we find a large variety in model biases, both in terms of temperature errors and precipitation. We are able to identify models whose forecasts are more similar to each other than they are to those of other systems, due to the use of similar model physics packages. However, in terms of multimodel ensemble spread, our results also demonstrate that forecast sensitivities to different model formulations still are substantial. We therefore believe that the diversity in model design that stems from parallel development efforts at global modeling centers around the world remains valuable for future progress in the numerical weather prediction community.

  • Ensemble forecast experiments of summertime sea ice in the Arctic Ocean using the TOPAZ4 ice-ocean data assimilation system

    Nakanowatari, T, Xie, J, Bertino, L, Matsueda, Mio, Yamagami, A, Inoue, J

    Environmental Research ( Elsevier BV )  209   112769 - 112769   2022.01 [ Peer Review Accepted ]

    Type of publication: Research paper (scientific journal)

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Other Papers 【 display / non-display

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Presentations 【 display / non-display

  • 2018年台風24号(TRAMI)の進路の予測可能性

    仲尾次晴空, 松枝, 未遠

    日本地球惑星科学連合2023年大会  2023.05  -  2023.05 

  • Forecast skill of the large MJO case in March 2015

    中澤哲夫, 松枝, 未遠

    日本気象学会2023年度春季大会  2023.05  -  2023.05 

  • 季節アンサンブル予報による東京のサクラ開花予測

    八巻俊則, 浅賀結月, 田中拓海, 松枝, 未遠

    日本農業気象学会  2023.03  -  2023.03 

  • 北太平洋偏西風レジームの予測可能性と航空分野での利用可能性に関する研究

    田中拓海, 松枝, 未遠

    第17回航空気象研究会  2023.02  -  2023.02 

  • 2021年2月テキサス寒波の予測可能性

    八巻俊則, 松枝, 未遠

    異常気象研究会2022・第10回観測システム・予測可能性研究連絡会  2022.12  -  2022.12 

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Work 【 display / non-display

  • The Coperinicus Museum

    Mio, Matsueda 

    2021.06
     
     
     

  • The S2S Museum

    Mio Matsueda 

    1900.01
     
     
     

  • The TIGGE Museum

    Mio Matsueda 

    1900.01
     
     
     

Academic Awards 【 display / non-display

  • Syono award

    2016.10   Meteorological Society of Japan  

  • Certificate of Appreciation

    2014.11   World Meteorological Organization   Contribution to the THORPEX project

    Winner: Mio Matsueda

Grant-in-Aid for Scientific Research 【 display / non-display

  • Weather regimes in Europe and Asia: Subseasonal Predictability (WEASP)

    University of Tsukuba–DAAD Partnership Program

    Project Year: 2019.04  -  2021.03 

    Investigator(s): Christian Grams 

    Direct: 2,206,000 (YEN)  Overheads: 0 (YEN)  Total: 2,206,000 (YEN)

  • New theory of Geostrophic turbulence by Rossby wave breaking and saturation

    Grant-in-Aid for Scientific Research(C)

    Project Year: 2017.04  -  2020.03 

    Investigator(s): Tanaka Hiroshi 

    Direct: 3,400,000 (YEN)  Overheads: 4,420,000 (YEN)  Total: 1,020,000 (YEN)

     View Summary

    Global scale atmospheric energy spectrum is known to obey the -3 power law of the wavenumber. The energy spectrum has been explained by the inertial subrange theory of 2 dimensional geostrophic trubulence. However, our normal mode energetics analysis shows that the spectrum is created by Rossby wave breaking and saturation rather than the inertial subrange theory. According to this new theory, the energy spectrum obeys E=mc2 relation. Here, E is energy, c is phase speed of Rossby wave, and m is mass of the atmosphere in unit area. In this study we propose Rossby wave breaking and saturation theory, as a new theory to explain the global scale energy spectrum of the atmosphere.

  • Predictability of weather regime-related atmospheric phenomena

    Grant-in-Aid for Young Scientists(B)

    Project Year: 2016.04  -  2019.03 

    Investigator(s): MATSUEDA MIO 

    Direct: 2,700,000 (YEN)  Overheads: 3,510,000 (YEN)  Total: 810,000 (YEN)

     View Summary

    In the study, there are three focused topics: 1. predictability of winter Euro-Atlantic (EA) regimes, 2. predictability of regime-related heatwave in EA summer, and 3. predictability of severe weather events occurred during the research period (e.g. the 2018 Western Japan Heavy Rainfall). Regarding the winter regimes, the most interesting result is that the longer the NAO- events persist, the higher the skill of forecasts initialised on NAO-. The skill dependency on regime duration is less clearly observed for the other regimes. Regarding the regime-related heatwave, 6 of 8 detected EA summer regimes are related to well-known heatwaves. The UK-France heatwave regime was least predictable. Regarding the predictability of the 2018 western Japan heavy rainfall which was highly predicted by NCEP operational forecast, joint analysis using operational forecasts and ensemble simulation with NCEP initial conditions and an ECMWF model revealed why ECMWF had lower skill for the event.

  • Arctic Challenge for Sustainability Project (ArCS)

    Project Year: 2015.09  -  2020.03 

    Direct: 23,290,000 (YEN)  Overheads: 0 (YEN)  Total: 23,290,000 (YEN)

  • On predictability of weather regimes and prediction of their forecast skills

    Grant-in-Aid for Research Activity start-up

    Project Year: 2014.09  -  2016.03 

    Investigator(s): MATSUEDA MIO 

    Direct: 1,700,000 (YEN)  Overheads: 2,210,000 (YEN)  Total: 510,000 (YEN)

     View Summary

    This study have assessed predictability of weather regime (persistent and/or recurring large-scale atmospheric flow pattern) on medium-range timescale and have tried to find some clues to know forecast skills of regimes in advance. My findings are that (1) predictability of regimes strongly depends on regions, (2) there are some preferred circuits of regimes in some regions, and (3) the Madden-Julian Oscillation can be a metric to know forecast skills of regimes in advance.

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SDGs 【 display / non-display

  • 気象予測、気候変動予測