Mio Matsueda

2.5k total citations · 1 hit paper
45 papers, 1.8k citations indexed

About

Mio Matsueda is a scholar working on Atmospheric Science, Global and Planetary Change and Oceanography. According to data from OpenAlex, Mio Matsueda has authored 45 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Atmospheric Science, 39 papers in Global and Planetary Change and 3 papers in Oceanography. Recurrent topics in Mio Matsueda's work include Climate variability and models (39 papers), Meteorological Phenomena and Simulations (30 papers) and Atmospheric and Environmental Gas Dynamics (18 papers). Mio Matsueda is often cited by papers focused on Climate variability and models (39 papers), Meteorological Phenomena and Simulations (30 papers) and Atmospheric and Environmental Gas Dynamics (18 papers). Mio Matsueda collaborates with scholars based in Japan, United Kingdom and United States. Mio Matsueda's co-authors include Ryo Mizuta, Hirokazu Endo, Shoji Kusunoki, H. L. Tanaka, T. N. Palmer, Seiji Yukimoto, Masato Sugi, Hiroyuki Murakami, Hiromasa Yoshimura and Tomoaki Ose and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, Journal of Climate and Geophysical Research Letters.

In The Last Decade

Mio Matsueda

44 papers receiving 1.7k citations

Hit Papers

Climate Simulations Using MRI-AGCM3.2 with 20-km Grid 2012 2026 2016 2021 2012 100 200 300 400

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mio Matsueda Japan 19 1.6k 1.6k 285 114 89 45 1.8k
Shinji Kadokura Japan 9 1.4k 0.9× 1.5k 0.9× 419 1.5× 108 0.9× 85 1.0× 19 1.7k
Arindam Chakraborty India 25 1.2k 0.8× 1.2k 0.7× 240 0.8× 53 0.5× 91 1.0× 80 1.4k
Mischa Croci‐Maspoli Switzerland 23 2.1k 1.3× 2.1k 1.3× 315 1.1× 87 0.8× 66 0.7× 32 2.4k
Romain Roehrig France 24 1.7k 1.1× 1.6k 1.0× 347 1.2× 52 0.5× 85 1.0× 66 1.9k
Xin Qu United States 22 1.8k 1.2× 1.9k 1.2× 217 0.8× 83 0.7× 61 0.7× 40 2.3k
Rajib Chattopadhyay India 25 1.7k 1.0× 1.5k 1.0× 425 1.5× 108 0.9× 174 2.0× 88 1.9k
Sajani Surendran India 12 1.6k 1.0× 1.5k 0.9× 266 0.9× 193 1.7× 222 2.5× 27 1.9k
Timothy E. LaRow United States 12 1.3k 0.8× 1.3k 0.8× 364 1.3× 165 1.4× 193 2.2× 14 1.6k
Kaz Higuchi Canada 23 1.4k 0.9× 1.2k 0.8× 211 0.7× 93 0.8× 128 1.4× 75 1.7k
Virginie Guémas France 26 1.8k 1.1× 1.8k 1.2× 633 2.2× 52 0.5× 81 0.9× 66 2.2k

Countries citing papers authored by Mio Matsueda

Since Specialization
Citations

This map shows the geographic impact of Mio Matsueda's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mio Matsueda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mio Matsueda more than expected).

Fields of papers citing papers by Mio Matsueda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mio Matsueda. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mio Matsueda. The network helps show where Mio Matsueda may publish in the future.

Co-authorship network of co-authors of Mio Matsueda

This figure shows the co-authorship network connecting the top 25 collaborators of Mio Matsueda. A scholar is included among the top collaborators of Mio Matsueda based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mio Matsueda. Mio Matsueda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Nakanowatari, Takuya, et al.. (2022). Ensemble forecast experiments of summertime sea ice in the Arctic Ocean using the TOPAZ4 ice-ocean data assimilation system. Environmental Research. 209. 112769–112769. 3 indexed citations
2.
Matsueda, Mio, et al.. (2019). Assessing the Predictability of Heavy Rainfall Events in Japan in Early July 2018 on Medium-Range Timescales. SOLA. 15A(0). 19–24. 4 indexed citations
3.
Matsueda, Mio, et al.. (2019). Skill of medium-range reforecast for summertime extraordinary Arctic Cyclones in 1986–2016. Polar Science. 20. 107–116. 13 indexed citations
4.
Vannière, Benoît, Marie‐Estelle Demory, Pier Luigi Vidale, et al.. (2018). Multi-model evaluation of the sensitivity of the global energy budget and hydrological cycle to resolution. Climate Dynamics. 52(11). 6817–6846. 65 indexed citations
5.
Nakanowatari, Takuya, Jun Inoue, Kazutoshi Sato, et al.. (2018). Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice–ocean data assimilation system. ˜The œcryosphere. 12(6). 2005–2020. 15 indexed citations
6.
Nakanowatari, Takuya, Jun Inoue, Kazutoshi Sato, et al.. (2018). Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea: Importance of dynamical and thermodynamic melting processes. Biogeosciences (European Geosciences Union). 1 indexed citations
7.
Matsueda, Mio, et al.. (2018). Predictability of the 2012 Great Arctic Cyclone on medium-range timescales. Polar Science. 15. 13–23. 23 indexed citations
8.
Matsueda, Mio & T. N. Palmer. (2017). Predictability of winter Pacific weather regimes and its connections with MJO on medium-range timescales. EGU General Assembly Conference Abstracts. 9149. 1 indexed citations
9.
Matsueda, Mio, et al.. (2016). Wintertime East Asian Flow Patterns and Their Predictability on Medium-Range Timescales. SOLA. 12(0). 121–126. 8 indexed citations
10.
Swinbank, Richard, Piers Buchanan, Lizzie S. R. Froude, et al.. (2015). The TIGGE Project and Its Achievements. Bulletin of the American Meteorological Society. 97(1). 49–67. 196 indexed citations
11.
Mizuta, Ryo, Hiromasa Yoshimura, Hiroyuki Murakami, et al.. (2012). Climate Simulations Using MRI-AGCM3.2 with 20-km Grid. Journal of the Meteorological Society of Japan Ser II. 90A(0). 233–258. 419 indexed citations breakdown →
12.
Matsueda, Mio & T. N. Palmer. (2010). Impact of horizontal resolution on simulations of summertime Euro-Atlantic blocking. 1 indexed citations
13.
Matsueda, Mio. (2009). Blocking Predictability in Operational Medium-Range Ensemble Forecasts. SOLA. 5. 113–116. 40 indexed citations
14.
Matsueda, Mio & H. L. Tanaka. (2008). Can MCGE Outperform the ECMWF Ensemble?. SOLA. 4. 77–80. 21 indexed citations
15.
Matsueda, Mio, et al.. (2007). Daily Forecast Skill of Multi-Center Grand Ensemble. SOLA. 3. 29–32. 12 indexed citations
16.
Matsueda, Mio, et al.. (2006). Multi-Center Grand Ensemble using Three Operational Ensemble Forecasts. SOLA. 2. 33–36. 10 indexed citations
17.
Tanaka, H. L. & Mio Matsueda. (2005). Arctic Oscillation Analyzed as a Singular Eigenmode of the Global Atmosphere. Journal of the Meteorological Society of Japan Ser II. 83(4). 611–619. 8 indexed citations
18.
Matsueda, Mio & H. L. Tanaka. (2005). EOF and SVD Analyses of the Low-Frequency Variability of the Barotropic Component of the Atmosphere. Journal of the Meteorological Society of Japan Ser II. 83(4). 517–529. 1 indexed citations
19.
Tanaka, H. L. & Mio Matsueda. (2004). Analysis of Recent Extreme Events Measured by the Barotropic Component of the Atmosphere. Journal of the Meteorological Society of Japan Ser II. 82(5). 1281–1299. 5 indexed citations
20.
Adams, Beverley J., et al.. (2004). MCEER/NHRAIC Response to Hurricane Charley: Collection of Satellite-Reference Building Damage Information in the Aftermath of Hurricane Charley. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact

Rankless by CCL
2026