Kayo Ide

7.2k total citations · 2 hit papers
101 papers, 5.3k citations indexed

About

Kayo Ide is a scholar working on Atmospheric Science, Global and Planetary Change and Oceanography. According to data from OpenAlex, Kayo Ide has authored 101 papers receiving a total of 5.3k indexed citations (citations by other indexed papers that have themselves been cited), including 81 papers in Atmospheric Science, 69 papers in Global and Planetary Change and 33 papers in Oceanography. Recurrent topics in Kayo Ide's work include Meteorological Phenomena and Simulations (68 papers), Climate variability and models (52 papers) and Oceanographic and Atmospheric Processes (29 papers). Kayo Ide is often cited by papers focused on Meteorological Phenomena and Simulations (68 papers), Climate variability and models (52 papers) and Oceanographic and Atmospheric Processes (29 papers). Kayo Ide collaborates with scholars based in United States, France and United Kingdom. Kayo Ide's co-authors include Michael Ghil, Dmitri Kondrashov, Philippe Courtier, Andrew C. Lorenc, Michael D. Dettinger, F. Váradi, Ying Tian, M. R. Allen, Michael Mann and Pascal Yiou and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and SHILAP Revista de lepidopterología.

In The Last Decade

Kayo Ide

93 papers receiving 5.1k citations

Hit Papers

ADVANCED SPECTRAL METHODS FOR CLIMATIC TIME SERIES 1997 2026 2006 2016 2002 1997 500 1000 1.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kayo Ide United States 32 3.1k 2.8k 1.5k 427 404 101 5.3k
Dmitri Kondrashov United States 26 1.5k 0.5× 1.5k 0.6× 854 0.6× 196 0.5× 280 0.7× 57 3.6k
James B. Elsner United States 44 4.2k 1.4× 4.2k 1.5× 1.7k 1.2× 552 1.3× 459 1.1× 178 7.2k
Robert F. Cahalan United States 32 5.0k 1.6× 5.8k 2.1× 1.3k 0.9× 366 0.9× 198 0.5× 99 7.0k
Klaus Fraedrich Germany 53 5.9k 1.9× 7.6k 2.7× 1.4k 1.0× 715 1.7× 624 1.5× 322 9.6k
Gerald R. North United States 44 7.2k 2.3× 6.7k 2.4× 2.1k 1.4× 813 1.9× 429 1.1× 164 9.6k
Olivier Talagrand France 28 3.6k 1.2× 3.0k 1.1× 1.1k 0.7× 687 1.6× 289 0.7× 74 6.1k
Eric J. Kostelich United States 31 2.4k 0.8× 2.2k 0.8× 523 0.4× 524 1.2× 1.4k 3.6× 81 5.2k
Tapio Schneider United States 53 7.5k 2.4× 7.4k 2.7× 2.3k 1.5× 410 1.0× 214 0.5× 160 10.5k
Brian F. Farrell United States 41 3.4k 1.1× 3.6k 1.3× 1.6k 1.1× 635 1.5× 474 1.2× 107 6.6k
G. Boffetta Italy 41 1.0k 0.3× 960 0.3× 637 0.4× 668 1.6× 1.1k 2.8× 147 6.1k

Countries citing papers authored by Kayo Ide

Since Specialization
Citations

This map shows the geographic impact of Kayo Ide'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 Kayo Ide with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kayo Ide more than expected).

Fields of papers citing papers by Kayo Ide

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kayo Ide. 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 Kayo Ide. The network helps show where Kayo Ide may publish in the future.

Co-authorship network of co-authors of Kayo Ide

This figure shows the co-authorship network connecting the top 25 collaborators of Kayo Ide. A scholar is included among the top collaborators of Kayo Ide 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 Kayo Ide. Kayo Ide 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
2.
Ide, Kayo, et al.. (2024). Missing values imputation in ocean buoy time series data. Ocean Engineering. 318. 120145–120145. 1 indexed citations
3.
Hoffman, Ross N., et al.. (2024). Assimilating atmospheric motion vector winds using a feature track correction observation operator. Quarterly Journal of the Royal Meteorological Society. 150(765). 5074–5093.
4.
Barnet, C., et al.. (2023). Evaluating the Value of CrIS Shortwave-Infrared Channels in Atmospheric-Sounding Retrievals. Remote Sensing. 15(3). 547–547. 6 indexed citations
5.
6.
Ide, Kayo, et al.. (2023). Simulations of modulated plane waves using weakly compressible smoothed particle hydrodynamics. Engineering With Computers. 40(3). 1831–1856. 1 indexed citations
7.
Pedatella, N. M., J. G. Anderson, Kayo Ide, et al.. (2023). Development of Data Assimilation Systems for the Ionosphere, Thermosphere, and Mesosphere. elib (German Aerospace Center).
8.
Garrett, Kevin, et al.. (2022). A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote Sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15. 8819–8833. 15 indexed citations
9.
Ide, Kayo, et al.. (2022). Exploiting Aeolus level-2b winds to better characterize atmospheric motion vector bias and uncertainty. Atmospheric measurement techniques. 15(9). 2719–2743. 4 indexed citations
10.
Liu, Hui, et al.. (2022). A statistically optimal analysis of systematic differences between Aeolus horizontal line-of-sight winds and NOAA's Global Forecast System. Atmospheric measurement techniques. 15(13). 3925–3940. 3 indexed citations
12.
Sharma, A. S., et al.. (2016). Brief Communication: Breeding vectors in the phase space reconstructed from time series data. Nonlinear processes in geophysics. 23(3). 137–141. 2 indexed citations
13.
Matsuo, Tomoko, T. J. Fuller‐Rowell, R. A. Akmaev, et al.. (2014). Predictability and Ensemble Modeling of the Space-Atmosphere Interaction Region. 2014 AGU Fall Meeting. 2014.
14.
Ide, Kayo & Stephen Wiggins. (2014). Transport induced by mean-eddy interaction: I. Theory, and relation to Lagrangian lobe dynamics. Communications in Nonlinear Science and Numerical Simulation. 20(2). 516–535. 6 indexed citations
15.
Greybush, Steven J., Eugenia Kalnay, Takemasa Miyoshi, et al.. (2011). Martian Atmosphere Data Assimilation of TES and MCS Retrievals. 34–37. 4 indexed citations
16.
Hoffman, Matthew J., Steven J. Greybush, Eugenia Kalnay, et al.. (2010). Ensemble Kalman Filter Data Assimilation of TES Retrievals. DPS. 1 indexed citations
17.
Kuznetsov, L., et al.. (2003). A method for direct assimilation of Lagrangian data. EGS - AGU - EUG Joint Assembly. 4837.
18.
Ide, Kayo, Philippe Courtier, Michael Ghil, & Andrew C. Lorenc. (1997). Unified Notation for Data Assimilation : Operational, Sequential and Variational (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice). Journal of the Meteorological Society of Japan Ser II. 75(1B). 181–189. 658 indexed citations breakdown →
19.
Smyth, Padhraic, et al.. (1997). Detecting atmospheric regimes using cross-validated clustering. Knowledge Discovery and Data Mining. 19(19). 61–66. 2 indexed citations
20.
Weeks, Eric R., Yudong Tian, Jeffrey S. Urbach, et al.. (1997). Transitions Between Blocked and Zonal Flows in a Rotating Annulus with Topography. Science. 278(5343). 1598–1601. 74 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.

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