Peter Kuma

794 total citations · 1 hit paper
18 papers, 437 citations indexed

About

Peter Kuma is a scholar working on Global and Planetary Change, Atmospheric Science and Environmental Engineering. According to data from OpenAlex, Peter Kuma has authored 18 papers receiving a total of 437 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Global and Planetary Change, 15 papers in Atmospheric Science and 3 papers in Environmental Engineering. Recurrent topics in Peter Kuma's work include Atmospheric aerosols and clouds (13 papers), Meteorological Phenomena and Simulations (9 papers) and Atmospheric chemistry and aerosols (6 papers). Peter Kuma is often cited by papers focused on Atmospheric aerosols and clouds (13 papers), Meteorological Phenomena and Simulations (9 papers) and Atmospheric chemistry and aerosols (6 papers). Peter Kuma collaborates with scholars based in New Zealand, Sweden and Australia. Peter Kuma's co-authors include Laura E. Revell, Sally Gaw, Eric C. Le Ru, Walter R. C. Somerville, Adrian McDonald, Frida A.‐M. Bender, Simon P. Alexander, Mike Harvey, Olaf Morgenstern and Ján Mašek and has published in prestigious journals such as Nature, Atmospheric chemistry and physics and Quarterly Journal of the Royal Meteorological Society.

In The Last Decade

Peter Kuma

17 papers receiving 421 citations

Hit Papers

Direct radiative effects of airborne microplastics 2021 2026 2022 2024 2021 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peter Kuma New Zealand 9 219 159 154 152 50 18 437
Alex Schuddeboom New Zealand 9 235 1.1× 188 1.2× 182 1.2× 160 1.1× 43 0.9× 17 466
Chengde Yang China 7 161 0.7× 70 0.4× 198 1.3× 134 0.9× 45 0.9× 13 405
Zhu Mei China 4 350 1.6× 60 0.4× 36 0.2× 168 1.1× 86 1.7× 5 408
Weimin Wang China 8 443 2.0× 210 1.3× 145 0.9× 339 2.2× 129 2.6× 21 714
Xianglun Kong China 7 373 1.7× 48 0.3× 25 0.2× 292 1.9× 93 1.9× 11 464
Karin Kvale Germany 13 383 1.7× 66 0.4× 56 0.4× 216 1.4× 97 1.9× 33 623
Paula Masiá Spain 11 332 1.5× 24 0.2× 44 0.3× 227 1.5× 72 1.4× 21 418
Tianning Wu United States 7 512 2.3× 38 0.2× 59 0.4× 416 2.7× 104 2.1× 13 655
Xuehai Liu China 8 255 1.2× 41 0.3× 32 0.2× 211 1.4× 85 1.7× 20 389
T.D. Rathod India 8 371 1.7× 30 0.2× 50 0.3× 306 2.0× 90 1.8× 17 461

Countries citing papers authored by Peter Kuma

Since Specialization
Citations

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

Fields of papers citing papers by Peter Kuma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peter Kuma

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Kuma. A scholar is included among the top collaborators of Peter Kuma 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 Peter Kuma. Peter Kuma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
2.
Kuma, Peter, Frida A.‐M. Bender, Alex Schuddeboom, Adrian McDonald, & Øyvind Seland. (2023). Machine learning of cloud types in satellite observations and climate models. Atmospheric chemistry and physics. 23(1). 523–549. 12 indexed citations
3.
Fiddes, Sonya, et al.. (2023). Assessing the cloud radiative bias at Macquarie Island in the ACCESS-AM2 model. Atmospheric chemistry and physics. 23(23). 14691–14714. 4 indexed citations
4.
Kuma, Peter, et al.. (2023). Climate Model Code Genealogy and Its Relation to Climate Feedbacks and Sensitivity. Journal of Advances in Modeling Earth Systems. 15(7). 30 indexed citations
5.
Guyot, Adrien, Alain Protat, Simon P. Alexander, et al.. (2022). Detection of supercooled liquid water containing clouds with ceilometers: development and evaluation of deterministic and data-driven retrievals. Atmospheric measurement techniques. 15(12). 3663–3681. 8 indexed citations
6.
Kuma, Peter, Frida A.‐M. Bender, Alex Schuddeboom, Adrian McDonald, & Øyvind Seland. (2022). Machine learning of cloud types in satellite observations and climate models. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
7.
Kuma, Peter, et al.. (2022). Climate Model Code Genealogy and Its Relation to Climate Feedbacks and Sensitivity. Zenodo (CERN European Organization for Nuclear Research). 3 indexed citations
8.
Kuma, Peter, Frida A.‐M. Bender, Alex Schuddeboom, Adrian McDonald, & Øyvind Seland. (2022). Machine learning of cloud types in satellite observations and climate models. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
9.
Kremser, Stefanie, Jordis S. Tradowsky, G. E. Bodeker, et al.. (2021). The winter 2019 air pollution (PM 2.5 ) measurement campaign in Christchurch, New Zealand. Earth system science data. 13(5). 2053–2075. 2 indexed citations
10.
Kuma, Peter, Adrian McDonald, Olaf Morgenstern, et al.. (2021). Ground-based lidar processing and simulator framework for comparing models and observations (ALCF 1.0). Geoscientific model development. 14(1). 43–72. 15 indexed citations
11.
Revell, Laura E., Peter Kuma, Eric C. Le Ru, Walter R. C. Somerville, & Sally Gaw. (2021). Direct radiative effects of airborne microplastics. Nature. 598(7881). 462–467. 262 indexed citations breakdown →
12.
Toohey, D. W., Laura E. Revell, Karine Sellegri, et al.. (2020). Constraining the Surface Flux of Sea Spray Particles From the Southern Ocean. Journal of Geophysical Research Atmospheres. 125(4). 19 indexed citations
13.
Kuma, Peter, Adrian McDonald, Olaf Morgenstern, et al.. (2020). Evaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observations. Atmospheric chemistry and physics. 20(11). 6607–6630. 33 indexed citations
14.
Kuma, Peter, Adrian McDonald, Olaf Morgenstern, et al.. (2020). Ground-based lidar processing and simulator framework for comparing models and observations. 1 indexed citations
15.
Klekociuk, Andrew, John French, Simon P. Alexander, Peter Kuma, & Adrian McDonald. (2019). The state of the atmosphere in the 2016 southern Kerguelen Axis campaign region. Deep Sea Research Part II Topical Studies in Oceanography. 174. 7 indexed citations
16.
Kuma, Peter, et al.. (2018). An analysis of the cloud environment over the Ross Sea and Ross Ice Shelf using CloudSat/CALIPSO satellite observations: the importance of synoptic forcing. Atmospheric chemistry and physics. 18(13). 9723–9739. 13 indexed citations
17.
Geleyn, J.‐F., Ján Mašek, Radmila Brožková, et al.. (2017). Single interval longwave radiation scheme based on the net exchanged rate decomposition with bracketing. Quarterly Journal of the Royal Meteorological Society. 143(704). 1313–1335. 11 indexed citations
18.
Mašek, Ján, et al.. (2015). Single interval shortwave radiation scheme with parameterized optical saturation and spectral overlaps. Quarterly Journal of the Royal Meteorological Society. 142(694). 304–326. 15 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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