World's Best Scientists 2026 revealed!
Takemasa Miyoshi

Takemasa Miyoshi

D-Index & Metrics

Environmental Sciences

D-Index
50
Citations
8719
World Ranking
5022
National Ranking
87

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.

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 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.

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.

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.

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

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students interested in expanding their expertise beyond a Bachelor’s in Environmental Sciences, exploring related online degree programs can open new career pathways. Advanced credentials like an online EdD program can be particularly valuable for those pursuing leadership roles in academia or public policy. Many programs offer flexible options such as online edd programs no dissertation, allowing students to focus on practical applications without the lengthy dissertation process.

Additionally, transitioning from a teaching-based degree to a doctorate is simplified through online eds to edd bridge program options. These pathways enable educators and environmental professionals to deepen their research and leadership skills without starting from scratch.

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

Trending Scientists