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Takemasa Miyoshi

Takemasa Miyoshi

RIKEN
Japan

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Environmental Sciences 50 5022 4811 87 84 287 8719

Takemasa Miyoshi publications per year

The chart shows the history of publications by Takemasa Miyoshi between 2004 and 2026, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Takemasa Miyoshi published across 23 years, from 2004 to 2026, averaging 16.4 papers a year. Output peaked at 39 publications in 2022. 29 of the 378 publications appeared in the last two years.

No. of publications
10 20 30
Bar chart. Horizontal axis: year, 2004 to 2026. Vertical axis: number of publications, 0 to 39. Peak 39 publications in 2022. 2004: 2 publications 2005: 1 publication 2006: 7 publications 2007: 9 publications 2008: 1 publication 2009: 8 publications 2010: 13 publications 2011: 14 publications 2012: 15 publications 2013: 15 publications 2014: 12 publications 2015: 15 publications 2016: 26 publications 2017: 35 publications 2018: 25 publications 2019: 29 publications 2020: 23 publications 2021: 21 publications 2022: 39 publications 2023: 25 publications 2024: 14 publications 2025: 25 publications 2026: 4 publications
2004 2026

378 publications in total across all disciplines

View publications per year as a table
Takemasa Miyoshi: publications per year, 2004 to 2026
Year Publications
2004 2
2005 1
2006 7
2007 9
2008 1
2009 8
2010 13
2011 14
2012 15
2013 15
2014 12
2015 15
2016 26
2017 35
2018 25
2019 29
2020 23
2021 21
2022 39
2023 25
2024 14
2025 25
2026 4
Total 378
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Takemasa Miyoshi publication distribution in Environmental Sciences in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Environmental Sciences in 2026. The highlighted bar marks where Takemasa Miyoshi sits on this spectrum.

No. of scientists
200 400 600
Bar chart with 66 bars. Horizontal axis: publications, 41–50 to 690+. Vertical axis: number of scientists, 0 to 617. Most scientists, 617, have 121–130 publications. The last bar groups every scientist with 690 publications or more. The highlighted bar, 281–290 publications, is where this scientist sits. 41–50 publications: 21 scientists 51–60 publications: 62 scientists 61–70 publications: 133 scientists 71–80 publications: 257 scientists 81–90 publications: 361 scientists 91–100 publications: 440 scientists 101–110 publications: 492 scientists 111–120 publications: 541 scientists 121–130 publications: 617 scientists 131–140 publications: 544 scientists 141–150 publications: 541 scientists 151–160 publications: 539 scientists 161–170 publications: 444 scientists 171–180 publications: 444 scientists 181–190 publications: 400 scientists 191–200 publications: 377 scientists 201–210 publications: 318 scientists 211–220 publications: 282 scientists 221–230 publications: 263 scientists 231–240 publications: 220 scientists 241–250 publications: 217 scientists 251–260 publications: 180 scientists 261–270 publications: 181 scientists 271–280 publications: 155 scientists 281–290 publications: 130 scientists 291–300 publications: 127 scientists 301–310 publications: 130 scientists 311–320 publications: 85 scientists 321–330 publications: 106 scientists 331–340 publications: 80 scientists 341–350 publications: 83 scientists 351–360 publications: 75 scientists 361–370 publications: 69 scientists 371–380 publications: 52 scientists 381–390 publications: 54 scientists 391–400 publications: 56 scientists 401–410 publications: 44 scientists 411–420 publications: 40 scientists 421–430 publications: 36 scientists 431–440 publications: 25 scientists 441–450 publications: 25 scientists 451–460 publications: 32 scientists 461–470 publications: 29 scientists 471–480 publications: 21 scientists 481–490 publications: 26 scientists 491–500 publications: 26 scientists 501–510 publications: 17 scientists 511–520 publications: 19 scientists 521–530 publications: 15 scientists 531–540 publications: 22 scientists 541–550 publications: 12 scientists 551–560 publications: 15 scientists 561–570 publications: 11 scientists 571–580 publications: 19 scientists 581–590 publications: 9 scientists 591–600 publications: 9 scientists 601–610 publications: 7 scientists 611–620 publications: 11 scientists 621–630 publications: 5 scientists 631–640 publications: 5 scientists 641–650 publications: 6 scientists 651–660 publications: 3 scientists 661–670 publications: 3 scientists 671–680 publications: 4 scientists 681–689 publications: 4 scientists 690+ publications: 100 scientists
41–50 publications 690+

This scientist: 287 publications — 84th percentile

84% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 690 publications or more.

View publications distribution as a table
Number of Environmental Sciences scientists by publication count, Research.com 2026 ranking edition. Based on 9,676 ranked scientists.
Publications Scientists This scientist
41–50 21
51–60 62
61–70 133
71–80 257
81–90 361
91–100 440
101–110 492
111–120 541
121–130 617
131–140 544
141–150 541
151–160 539
161–170 444
171–180 444
181–190 400
191–200 377
201–210 318
211–220 282
221–230 263
231–240 220
241–250 217
251–260 180
261–270 181
271–280 155
281–290 130 287
291–300 127
301–310 130
311–320 85
321–330 106
331–340 80
341–350 83
351–360 75
361–370 69
371–380 52
381–390 54
391–400 56
401–410 44
411–420 40
421–430 36
431–440 25
441–450 25
451–460 32
461–470 29
471–480 21
481–490 26
491–500 26
501–510 17
511–520 19
521–530 15
531–540 22
541–550 12
551–560 15
561–570 11
571–580 19
581–590 9
591–600 9
601–610 7
611–620 11
621–630 5
631–640 5
641–650 6
651–660 3
661–670 3
671–680 4
681–689 4
690+ 100
Download as CSV

Takemasa Miyoshi D-index placement in Environmental Sciences in 2026

The chart shows the D-index (discipline H-index) distribution of Environmental Sciences scientists ranked by Research.com in 2026. The highlighted bar marks where Takemasa Miyoshi sits on this spectrum.

No. of scientists
100 200 300
Bar chart with 97 bars. Horizontal axis: D-Index, 30 to 126+. Vertical axis: number of scientists, 0 to 348. Most scientists, 348, have 45 D-Index. The last bar groups every scientist with 126 D-Index or more. The highlighted bar, 50 D-Index, is where this scientist sits. 30 D-Index: 12 scientists 31 D-Index: 26 scientists 32 D-Index: 51 scientists 33 D-Index: 88 scientists 34 D-Index: 123 scientists 35 D-Index: 163 scientists 36 D-Index: 206 scientists 37 D-Index: 267 scientists 38 D-Index: 265 scientists 39 D-Index: 275 scientists 40 D-Index: 321 scientists 41 D-Index: 343 scientists 42 D-Index: 305 scientists 43 D-Index: 336 scientists 44 D-Index: 330 scientists 45 D-Index: 348 scientists 46 D-Index: 291 scientists 47 D-Index: 275 scientists 48 D-Index: 272 scientists 49 D-Index: 273 scientists 50 D-Index: 263 scientists 51 D-Index: 232 scientists 52 D-Index: 266 scientists 53 D-Index: 217 scientists 54 D-Index: 198 scientists 55 D-Index: 177 scientists 56 D-Index: 202 scientists 57 D-Index: 204 scientists 58 D-Index: 166 scientists 59 D-Index: 177 scientists 60 D-Index: 166 scientists 61 D-Index: 152 scientists 62 D-Index: 143 scientists 63 D-Index: 150 scientists 64 D-Index: 124 scientists 65 D-Index: 119 scientists 66 D-Index: 120 scientists 67 D-Index: 118 scientists 68 D-Index: 82 scientists 69 D-Index: 98 scientists 70 D-Index: 94 scientists 71 D-Index: 105 scientists 72 D-Index: 74 scientists 73 D-Index: 84 scientists 74 D-Index: 70 scientists 75 D-Index: 67 scientists 76 D-Index: 78 scientists 77 D-Index: 60 scientists 78 D-Index: 59 scientists 79 D-Index: 52 scientists 80 D-Index: 47 scientists 81 D-Index: 38 scientists 82 D-Index: 48 scientists 83 D-Index: 42 scientists 84 D-Index: 42 scientists 85 D-Index: 43 scientists 86 D-Index: 29 scientists 87 D-Index: 37 scientists 88 D-Index: 29 scientists 89 D-Index: 30 scientists 90 D-Index: 34 scientists 91 D-Index: 20 scientists 92 D-Index: 22 scientists 93 D-Index: 17 scientists 94 D-Index: 19 scientists 95 D-Index: 24 scientists 96 D-Index: 21 scientists 97 D-Index: 20 scientists 98 D-Index: 24 scientists 99 D-Index: 17 scientists 100 D-Index: 17 scientists 101 D-Index: 21 scientists 102 D-Index: 25 scientists 103 D-Index: 18 scientists 104 D-Index: 26 scientists 105 D-Index: 20 scientists 106 D-Index: 15 scientists 107 D-Index: 10 scientists 108 D-Index: 13 scientists 109 D-Index: 15 scientists 110 D-Index: 12 scientists 111 D-Index: 8 scientists 112 D-Index: 7 scientists 113 D-Index: 9 scientists 114 D-Index: 6 scientists 115 D-Index: 12 scientists 116 D-Index: 7 scientists 117 D-Index: 8 scientists 118 D-Index: 3 scientists 119 D-Index: 5 scientists 120 D-Index: 7 scientists 121 D-Index: 2 scientists 122 D-Index: 4 scientists 123 D-Index: 8 scientists 124 D-Index: 7 scientists 125 D-Index: 9 scientists 126+ D-Index: 92 scientists
30 D-Index 126+

This scientist: 50 D-Index — 50th percentile

50% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 126 D-Index or more.

View D-Index distribution as a table
Number of Environmental Sciences scientists by D-index, Research.com 2026 ranking edition. Based on 9,676 ranked scientists.
D-Index Scientists This scientist
30 12
31 26
32 51
33 88
34 123
35 163
36 206
37 267
38 265
39 275
40 321
41 343
42 305
43 336
44 330
45 348
46 291
47 275
48 272
49 273
50 263 50
51 232
52 266
53 217
54 198
55 177
56 202
57 204
58 166
59 177
60 166
61 152
62 143
63 150
64 124
65 119
66 120
67 118
68 82
69 98
70 94
71 105
72 74
73 84
74 70
75 67
76 78
77 60
78 59
79 52
80 47
81 38
82 48
83 42
84 42
85 43
86 29
87 37
88 29
89 30
90 34
91 20
92 22
93 17
94 19
95 24
96 21
97 20
98 24
99 17
100 17
101 21
102 25
103 18
104 26
105 20
106 15
107 10
108 13
109 15
110 12
111 8
112 7
113 9
114 6
115 12
116 7
117 8
118 3
119 5
120 7
121 2
122 4
123 8
124 7
125 9
126+ 92
Download as CSV

Overview

Takemasa Miyoshi is a researcher affiliated with RIKEN in Japan, specializing in Earth and Planetary Sciences with a focus on Atmospheric Science and Global and Planetary Change. Their work spans Environmental Science and subfields such as Oceanography, Astronomy and Astrophysics, and Environmental Engineering.

The main topics of their research include:

  • Meteorological Phenomena and Simulations
  • Climate variability and models
  • Precipitation Measurement and Analysis
  • Tropical and Extratropical Cyclones Research
  • Atmospheric and Environmental Gas Dynamics
  • Oceanographic and Atmospheric Processes
  • Flood Risk Assessment and Management

Miyoshi's publication record includes numerous contributions to prominent venues in atmospheric and climate science. Frequent publication venues for their work are:

  • Quarterly Journal of the Royal Meteorological Society
  • SOLA
  • Nonlinear Processes in Geophysics
  • Monthly Weather Review
  • Journal of Geophysical Research Atmospheres

Their recent papers demonstrate a range of topics and collaborations:

  • "A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation" (2020), published in Monthly Weather Review
  • "Machine learning-based tsunami inundation prediction derived from offshore observations" (2022), published in Nature Communications
  • "A convective-scale 1,000-member ensemble simulation and potential applications" (2020), published in Quarterly Journal of the Royal Meteorological Society
  • "Distributions and convergence of forecast variables in a 1,000-member convection-permitting ensemble" (2022), published in Quarterly Journal of the Royal Meteorological Society
  • "Data Assimilation for Climate Research: Model Parameter Estimation of Large-Scale Condensation Scheme" (2020), published in Journal of Geophysical Research Atmospheres

Frequent collaborators of Takemasa Miyoshi include:

  • Shunji Kotsuki
  • Koji Terasaki
  • Takumi Honda
  • Shigenori Otsuka
  • Yasumitsu Maejima

Best Publications

  • The Non-hydrostatic Icosahedral Atmospheric Model: description and development

    Masaki Satoh;Masaki Satoh;Hirofumi Tomita;Hisashi Yashiro;Hiroaki Miura;Hiroaki Miura

  • 4-D-Var or ensemble Kalman filter?

    Eufenia Kalnay;Hong Li;Takemasa Miyoshi;Shu Chih Yang

  • Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 Resolution

    Takemasa Miyoshi;Shozo Yamane

  • The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter

    Takemasa Miyoshi

  • Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter

    Hong Li;Eugenia Kalnay;Takemasa Miyoshi

  • Balance and Ensemble Kalman Filter Localization Techniques

    Steven J. Greybush;Eugenia Kalnay;Takemasa Miyoshi;Kayo Ide

  • Data assimilation of CALIPSO aerosol observations

    T. T. Sekiyama;T. Y. Tanaka;A. Shimizu;T. Miyoshi;T. Miyoshi

  • Modeling Sustainability: Population, Inequality, Consumption, and Bidirectional Coupling of the Earth and Human Systems

    Safa Motesharrei;Jorge Rivas;Eugenia Kalnay;Ghassem R. Asrar

  • Assimilating All-Sky Himawari-8 Satellite Infrared Radiances: A Case of Typhoon Soudelor (2015)

    Takumi Honda;Takemasa Miyoshi;Guo-Yuan Lien;Seiya Nishizawa

  • Ensemble Kalman Filter and 4D-Var Intercomparison with the Japanese Operational Global Analysis and Prediction System

    Takemasa Miyoshi;Yoshiaki Sato;Takashi Kadowaki

  • “Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation

    Ji-Sun Kang;Eugenia Kalnay;Junjie Liu;Inez Fung

  • Estimating and Correcting Global Weather Model Error

    Christopher M. Danforth;Eugenia Kalnay;Takemasa Miyoshi

  • Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review

    Juan Jose Ruiz;Manuel Arturo Pulido;Takemasa Miyoshi

  • Localizing the Error Covariance by Physical Distances within a Local Ensemble Transform Kalman Filter (LETKF)

    Takemasa Miyoshi;Shozo Yamane;Shozo Yamane;Takeshi Enomoto

  • A review of innovation-based methods to jointly estimate model and observation error covariance matrices in ensemble data assimilation

    Pierre Tandeo;Pierre Ailliot;Marc Bocquet;Alberto Carrassi;Alberto Carrassi

  • Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model

    N. A.J. Schutgens;T. Miyoshi;Toshihiko Takemura;T. Nakajima

  • “Big Data Assimilation” Revolutionizing Severe Weather Prediction

    Takemasa Miyoshi;Masaru Kunii;Juan Ruiz;Guo-Yuan Lien

  • The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations

    Takemasa Miyoshi;Masaru Kunii

  • Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation

    Travis C. Sluka;Stephen G. Penny;Eugenia Kalnay;Takemasa Miyoshi

  • The 10,240‐member ensemble Kalman filtering with an intermediate AGCM

    Takemasa Miyoshi;Takemasa Miyoshi;Keiichi Kondo;Toshiyuki Imamura

  • Accounting for Model Errors in Ensemble Data Assimilation

    Hong Li;Eugenia Kalnay;Takemasa Miyoshi;Christopher M. Danforth

  • Estimation of surface carbon fluxes with an advanced data assimilation methodology

    Ji-Sun Kang;Eugenia Kalnay;Takemasa Miyoshi;Junjie Liu

  • A simpler formulation of forecast sensitivity to observations: application to ensemble Kalman filters

    Eugenia Kalnay;Yoichiro Ota;Takemasa Miyoshi;Junjie Liu

  • Effective assimilation of global precipitation: simulation experiments

    Guo-Yuan Lien;Eugenia Kalnay;Takemasa Miyoshi

  • Ensemble-based observation impact estimates using the NCEP GFS

    Yoichiro Ota;John C. Derber;Eugenia Kalnay;Takemasa Miyoshi

Frequent Co-Authors

Eugenia Kalnay
Eugenia Kalnay University of Maryland, College Park
Masaki Satoh
Masaki Satoh University of Tokyo
Tomoo Ushio
Tomoo Ushio Osaka University
Ross N. Hoffman
Ross N. Hoffman University of Maryland, College Park
Yutaka Ishikawa
Yutaka Ishikawa University of Tokyo
Kei Yoshimura
Kei Yoshimura University of Tokyo
Inez Y. Fung
Inez Y. Fung University of California, Berkeley
Toshihiko Takemura
Toshihiko Takemura Kyushu University
R. John Wilson
R. John Wilson Geophysical Fluid Dynamics Laboratory
Marc Bocquet
Marc Bocquet École des Ponts ParisTech

External Links

Personal website of Takemasa Miyoshi Google Scholar page

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Related Online Degrees & Career Pathways

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For those aiming to blend environmental knowledge with social impact, dsw programs offer valuable insights into community health and policy development. They are increasingly affordable and accessible online, making them a strategic choice for enhancing interdisciplinary expertise.

Lastly, if flexibility and cost are priorities, students might consider a low cost online general studies degree. This degree provides broad foundational skills that complement specialized environmental training, helping graduates adapt to diverse career opportunities.

Best Scientists Citing Takemasa Miyoshi

Masaki Satoh

Masaki Satoh

University of Tokyo

Publications: 96

Marc Bocquet

Marc Bocquet

École des Ponts ParisTech

Publications: 51

Eugenia Kalnay

Eugenia Kalnay

University of Maryland, College Park

Publications: 46

Jeffrey L. Anderson

Jeffrey L. Anderson

National Center for Atmospheric Research

Publications: 41

Fuqing Zhang

Fuqing Zhang

Pennsylvania State University

Publications: 28

Teruyuki Nakajima

Teruyuki Nakajima

University of Tokyo

Publications: 27

Akira Noda

Akira Noda

Japan Agency for Marine-Earth Science and Technology

Publications: 21

Jeffrey S. Reid

Jeffrey S. Reid

United States Naval Research Laboratory

Publications: 19

Kentaroh Suzuki

Kentaroh Suzuki

University of Tokyo

Publications: 18

Ibrahim Hoteit

Ibrahim Hoteit

King Abdullah University of Science and Technology

Publications: 18

Zhengyu Liu

Zhengyu Liu

The Ohio State University

Publications: 17

Keiya Yumimoto

Keiya Yumimoto

Kyushu University

Publications: 16

Jianglong Zhang

Jianglong Zhang

University of North Dakota

Publications: 16

Sebastian Reich

Sebastian Reich

University of Potsdam

Publications: 15

Laurent Bertino

Laurent Bertino

Bjerknes Centre for Climate Research

Publications: 14

Guangyu Shi

Guangyu Shi

Chinese Academy of Sciences

Publications: 14

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