W. A. Perkins

2.2k total citations
54 papers, 1.1k citations indexed

About

W. A. Perkins is a scholar working on Atmospheric Science, Global and Planetary Change and Computer Vision and Pattern Recognition. According to data from OpenAlex, W. A. Perkins has authored 54 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Atmospheric Science, 18 papers in Global and Planetary Change and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in W. A. Perkins's work include Climate variability and models (15 papers), Meteorological Phenomena and Simulations (12 papers) and Image and Object Detection Techniques (9 papers). W. A. Perkins is often cited by papers focused on Climate variability and models (15 papers), Meteorological Phenomena and Simulations (12 papers) and Image and Object Detection Techniques (9 papers). W. A. Perkins collaborates with scholars based in United States and Canada. W. A. Perkins's co-authors include Gregory J. Hakim, Eric J. Steig, Robert Tardif, David M. Anderson, David Noone, Julien Emile‐Geay, Thomas J. Laffey, Tin A. Nguyen, Nathan Steiger and Michael P. Erb and has published in prestigious journals such as Journal of Applied Physics, IEEE Transactions on Pattern Analysis and Machine Intelligence and Geophysical Research Letters.

In The Last Decade

W. A. Perkins

49 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
W. A. Perkins United States	18	639	540	178	125	73	54	1.1k
Brian Argrow United States	24	355 0.6×	271 0.5×	80 0.4×	74 0.6×	21 0.3×	124	1.9k
Mark Petersen United States	21	625 1.0×	469 0.9×	68 0.4×	159 1.3×	68 0.9×	76	1.7k
Lanning Wang China	17	586 0.9×	497 0.9×	81 0.5×	44 0.4×	14 0.2×	78	1.4k
John B. Drake United States	14	764 1.2×	471 0.9×	24 0.1×	36 0.3×	16 0.2×	48	1.4k
Edward C. Waymire United States	17	286 0.4×	523 1.0×	64 0.4×	31 0.2×	18 0.2×	43	1.6k
Dean G. Duffy United States	16	261 0.4×	200 0.4×	34 0.2×	22 0.2×	21 0.3×	38	1.2k
S. Aigrain United Kingdom	41	489 0.8×	85 0.2×	221 1.2×	25 0.2×	91 1.2×	134	5.3k
C. Basdevant France	18	456 0.7×	312 0.6×	21 0.1×	35 0.3×	19 0.3×	37	1.5k
Umberto Amato Italy	22	337 0.5×	336 0.6×	250 1.4×	213 1.7×	11 0.2×	102	1.5k
Clive Temperton United Kingdom	18	580 0.9×	406 0.8×	28 0.2×	57 0.5×	7 0.1×	36	1.2k

Countries citing papers authored by W. A. Perkins

Since Specialization

Citations

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

Fields of papers citing papers by W. A. Perkins

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by W. A. Perkins. 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 W. A. Perkins. The network helps show where W. A. Perkins may publish in the future.

Co-authorship network of co-authors of W. A. Perkins

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

All Works

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20 of 20 papers shown

Clark, Spencer K., Oliver Watt‐Meyer, Anna Kwa, et al.. (2025). ACE2‐SOM: Coupling an ML Atmospheric Emulator to a Slab Ocean and Learning the Sensitivity of Climate to Changed CO ₂. 2(4).

McGibbon, Jeremy, Spencer K. Clark, Brian Henn, et al.. (2024). Global Precipitation Correction Across a Range of Climates Using CycleGAN. Geophysical Research Letters. 51(4). 5 indexed citations

Watt‐Meyer, Oliver, Noah Brenowitz, Spencer K. Clark, et al.. (2024). Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation. Journal of Advances in Modeling Earth Systems. 16(2). 10 indexed citations

Perkins, W. A., et al.. (2024). Emulation of Cloud Microphysics in a Climate Model. Journal of Advances in Modeling Earth Systems. 16(4). 2 indexed citations

Zhu, Feng, Julien Emile‐Geay, Gregory J. Hakim, et al.. (2024). cfr (v2024.1.26): a Python package for climate field reconstruction. Geoscientific model development. 17(8). 3409–3431. 3 indexed citations

Kwa, Anna, Spencer K. Clark, Brian Henn, et al.. (2023). Machine‐Learned Climate Model Corrections From a Global Storm‐Resolving Model: Performance Across the Annual Cycle. Journal of Advances in Modeling Earth Systems. 15(5). 12 indexed citations

Bretherton, Christopher S., Brian Henn, Anna Kwa, et al.. (2022). Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations. Journal of Advances in Modeling Earth Systems. 14(2). 44 indexed citations

Clark, Spencer K., Noah Brenowitz, Brian Henn, et al.. (2022). Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations. Journal of Advances in Modeling Earth Systems. 14(9). 19 indexed citations

Watt‐Meyer, Oliver, Noah Brenowitz, Spencer K. Clark, et al.. (2021). Correcting Weather and Climate Models by Machine Learning Nudged Historical Simulations. Geophysical Research Letters. 48(15). 60 indexed citations

10.

McGibbon, Jeremy, Noah Brenowitz, Spencer K. Clark, et al.. (2021). fv3gfs-wrapper: a Python wrapper of the FV3GFS atmospheric model. Geoscientific model development. 14(7). 4401–4409. 12 indexed citations

11.

Tardif, Robert, Gregory J. Hakim, W. A. Perkins, et al.. (2019). Last Millennium Reanalysis with an expanded proxy database and seasonal proxy modeling. Climate of the past. 15(4). 1251–1273. 157 indexed citations

12.

Perkins, W. A. & Gregory J. Hakim. (2017). Reconstructing paleoclimate fields using online data assimilation with a linear inverse model. Climate of the past. 13(5). 421–436. 19 indexed citations

13.

Bousserez, Nicolas, Daven K. Henze, W. A. Perkins, et al.. (2016). Constraints on methane emissions in North America from future geostationary remote-sensing measurements. Atmospheric chemistry and physics. 16(10). 6175–6190. 18 indexed citations

14.

Perkins, W. A. & Gregory J. Hakim. (2016). Reconstructing past climate by using proxy data and a linear climate model. NOAA Institutional Repository. 1 indexed citations

15.

Nguyen, Tin A., et al.. (1985). Checking an expert systems knowledge base for consistency and completeness. International Joint Conference on Artificial Intelligence. 375–378. 128 indexed citations

16.

Perkins, W. A.. (1983). INSPECTOR: A Computer Vision System that Learns to Inspect Parts. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-5(6). 584–592. 17 indexed citations

17.

Perkins, W. A.. (1980). Area Segmentation of Images Using Edge Points. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-2(1). 8–15. 62 indexed citations

18.

Perkins, W. A.. (1979). Segmentation of images by expansion and contraction. International Joint Conference on Artificial Intelligence. 699–701. 1 indexed citations

19.

Perkins, W. A.. (1979). Region Segmentation of Images by Expansion and Contraction of Edge Points.. International Joint Conference on Artificial Intelligence. 699–701. 1 indexed citations

20.

Perkins, W. A.. (1977). Model-based vision system for scenes containing multiple parts. International Joint Conference on Artificial Intelligence. 20(4). 678–684. 13 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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