Thank You to Our Peer Reviewers in 2025

Enrico Camporeale; Raffaele Marino; Thomas Berger; Yangkang Chen; Doris Folini; Geoffrey Fox; Cédric M. John; Xiaofeng Li; Donald Lucas; Daniel O’Malley; Steve Tobias; Markus Reichstein; John Rundle; Chaopeng Shen; Renata Wentzcovitch; Yixin Wen

doi:10.1029/2026JH001360

Thank You to Our Peer Reviewers in 2025

Enrico Camporeale
, Raffaele Marino
, Thomas Berger
, Yangkang Chen
, Doris Folini
, Geoffrey Fox
, Cédric M. John
, Xiaofeng Li
, Donald Lucas
, Daniel O’Malley
, Steve Tobias
, Markus Reichstein
, John Rundle
, Chaopeng Shen
, Renata Wentzcovitch
, Yixin Wen

University of Colorado Boulder
Queen Mary University of London
École centrale de Lyon
National Center for Atmospheric Research
University of Texas at Austin
Swiss Federal Institute of Technology Zurich
University of Virginia School of Engineering and Applied Science
CAS - Institute of Oceanology
Lawrence Livermore National Laboratory
Los Alamos National Laboratory
University of Edinburgh
Max Planck Institute for Biogeochemistry
University of California at Davis
Pennsylvania State University
Columbia University
University of Florida

Research output: Contribution to journal › Editorial

Abstract

On behalf of AGU, the scientific community, and the editorial team of Journal of Geophysical Research: Machine Learning and Computation, we extend our sincere gratitude to the reviewers who dedicated their time and expertise to evaluating manuscripts for us in 2025. Scientific research can now be communicated in various ways, yet peer review remains the cornerstone of scholarly publishing. We deeply appreciate the reviewers who devoted hours to reading and providing insightful feedback. The high quality of our published papers is a testament to their commitment to this vital community service.

Original language	English
Article number	e2026JH001360
Journal	Journal of Geophysical Research: Machine Learning and Computation
Volume	3
Issue number	2
DOIs	https://doi.org/10.1029/2026JH001360
State	Published - Apr 2026
Externally published	Yes

Keywords

editorial
peer review

Access to Document

10.1029/2026JH001360

Cite this

Camporeale, E., Marino, R., Berger, T., Chen, Y., Folini, D., Fox, G., John, C. M., Li, X., Lucas, D., O’Malley, D., Tobias, S., Reichstein, M., Rundle, J., Shen, C., Wentzcovitch, R., & Wen, Y. (2026). Thank You to Our Peer Reviewers in 2025. Journal of Geophysical Research: Machine Learning and Computation, 3(2), Article e2026JH001360. https://doi.org/10.1029/2026JH001360

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title = "Thank You to Our Peer Reviewers in 2025",

abstract = "On behalf of AGU, the scientific community, and the editorial team of Journal of Geophysical Research: Machine Learning and Computation, we extend our sincere gratitude to the reviewers who dedicated their time and expertise to evaluating manuscripts for us in 2025. Scientific research can now be communicated in various ways, yet peer review remains the cornerstone of scholarly publishing. We deeply appreciate the reviewers who devoted hours to reading and providing insightful feedback. The high quality of our published papers is a testament to their commitment to this vital community service.",

keywords = "editorial, peer review",

author = "Enrico Camporeale and Raffaele Marino and Thomas Berger and Yangkang Chen and Doris Folini and Geoffrey Fox and John, \{C{\'e}dric M.\} and Xiaofeng Li and Donald Lucas and Daniel O{\textquoteright}Malley and Steve Tobias and Markus Reichstein and John Rundle and Chaopeng Shen and Renata Wentzcovitch and Yixin Wen",

note = "Publisher Copyright: {\textcopyright} 2026. The Author(s). Journal of Geophysical Research: Machine Learning and Computation published by Wiley Periodicals LLC on behalf of American Geophysical Union.",

year = "2026",

month = apr,

doi = "10.1029/2026JH001360",

language = "English",

volume = "3",

journal = "Journal of Geophysical Research: Machine Learning and Computation",

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T1 - Thank You to Our Peer Reviewers in 2025

AU - Camporeale, Enrico

AU - Marino, Raffaele

AU - Berger, Thomas

AU - Chen, Yangkang

AU - Folini, Doris

AU - Fox, Geoffrey

AU - John, Cédric M.

AU - Li, Xiaofeng

AU - Lucas, Donald

AU - O’Malley, Daniel

AU - Tobias, Steve

AU - Reichstein, Markus

AU - Rundle, John

AU - Shen, Chaopeng

AU - Wentzcovitch, Renata

AU - Wen, Yixin

PY - 2026/4

Y1 - 2026/4

N2 - On behalf of AGU, the scientific community, and the editorial team of Journal of Geophysical Research: Machine Learning and Computation, we extend our sincere gratitude to the reviewers who dedicated their time and expertise to evaluating manuscripts for us in 2025. Scientific research can now be communicated in various ways, yet peer review remains the cornerstone of scholarly publishing. We deeply appreciate the reviewers who devoted hours to reading and providing insightful feedback. The high quality of our published papers is a testament to their commitment to this vital community service.

AB - On behalf of AGU, the scientific community, and the editorial team of Journal of Geophysical Research: Machine Learning and Computation, we extend our sincere gratitude to the reviewers who dedicated their time and expertise to evaluating manuscripts for us in 2025. Scientific research can now be communicated in various ways, yet peer review remains the cornerstone of scholarly publishing. We deeply appreciate the reviewers who devoted hours to reading and providing insightful feedback. The high quality of our published papers is a testament to their commitment to this vital community service.

KW - editorial

KW - peer review

UR - https://www.scopus.com/pages/publications/105034758078

U2 - 10.1029/2026JH001360

DO - 10.1029/2026JH001360

M3 - Editorial

AN - SCOPUS:105034758078

SN - 2993-5210

VL - 3

JO - Journal of Geophysical Research: Machine Learning and Computation

JF - Journal of Geophysical Research: Machine Learning and Computation

IS - 2

M1 - e2026JH001360

ER -

Thank You to Our Peer Reviewers in 2025

Abstract

Keywords

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