David M. Straus

3.5k total citations
81 papers, 2.6k citations indexed

About

David M. Straus is a scholar working on Atmospheric Science, Global and Planetary Change and Oceanography. According to data from OpenAlex, David M. Straus has authored 81 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 69 papers in Atmospheric Science, 69 papers in Global and Planetary Change and 36 papers in Oceanography. Recurrent topics in David M. Straus's work include Climate variability and models (68 papers), Meteorological Phenomena and Simulations (55 papers) and Oceanographic and Atmospheric Processes (31 papers). David M. Straus is often cited by papers focused on Climate variability and models (68 papers), Meteorological Phenomena and Simulations (55 papers) and Oceanographic and Atmospheric Processes (31 papers). David M. Straus collaborates with scholars based in United States, Australia and South Korea. David M. Straus's co-authors include J. Shukla, Cristiana Stan, J. G. Charney, Franco Molteni, Susanna Corti, Hai Lin, Jorgen S. Frederiksen, Eric D. Maloney, Courtney Schumacher and James L. Kinter and has published in prestigious journals such as Physical Review Letters, Journal of Geophysical Research Atmospheres and Journal of Climate.

In The Last Decade

David M. Straus

80 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David M. Straus United States 27 2.4k 2.2k 802 86 57 81 2.6k
Jiangyu Mao China 28 2.3k 1.0× 2.2k 1.0× 961 1.2× 71 0.8× 35 0.6× 102 2.9k
Stefano Tibaldi United Kingdom 24 2.5k 1.0× 2.5k 1.1× 643 0.8× 142 1.7× 9 0.2× 55 2.8k
D. R. Jackett Australia 23 1.1k 0.5× 1.0k 0.5× 1.6k 1.9× 31 0.4× 21 0.4× 37 2.2k
Maarten H. P. Ambaum United Kingdom 22 1.6k 0.7× 1.6k 0.7× 644 0.8× 64 0.7× 26 0.5× 64 2.1k
K. C. Mo United States 15 1.3k 0.5× 1.0k 0.5× 204 0.3× 90 1.0× 11 0.2× 34 1.7k
B. G. Hunt Australia 23 1.0k 0.4× 1.2k 0.6× 192 0.2× 85 1.0× 31 0.5× 88 1.6k
Takmeng Wong United States 21 2.5k 1.0× 2.2k 1.0× 296 0.4× 81 0.9× 7 0.1× 50 2.7k
Erland Källén Sweden 16 1.6k 0.6× 1.6k 0.7× 444 0.6× 104 1.2× 9 0.2× 40 2.0k
Shigeo Yoden Japan 27 1.6k 0.7× 1.7k 0.8× 327 0.4× 40 0.5× 17 0.3× 107 2.1k
Emily Shuckburgh United Kingdom 26 1.8k 0.8× 1.7k 0.7× 1.1k 1.3× 52 0.6× 7 0.1× 59 2.5k

Countries citing papers authored by David M. Straus

Since Specialization
Citations

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

Fields of papers citing papers by David M. Straus

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David M. Straus. 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 David M. Straus. The network helps show where David M. Straus may publish in the future.

Co-authorship network of co-authors of David M. Straus

This figure shows the co-authorship network connecting the top 25 collaborators of David M. Straus. A scholar is included among the top collaborators of David M. Straus 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 David M. Straus. David M. Straus 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
1.
Straus, David M., et al.. (2023). Intrinsic predictability limits arising from Indian Ocean Madden–Julian oscillation (MJO) heating: effects on tropical and extratropical teleconnections. Weather and Climate Dynamics. 4(4). 1001–1018. 4 indexed citations
2.
Burls, Natalie, Ross C. Blamey, Benjamin A. Cash, et al.. (2019). The Cape Town “Day Zero” drought and Hadley cell expansion. npj Climate and Atmospheric Science. 2(1). 83 indexed citations
3.
Huang, Bohua, et al.. (2018). Seasonal prediction skill and predictability of the Northern Hemisphere storm track variability in Project Minerva. Climate Dynamics. 52(11). 6427–6440. 7 indexed citations
4.
Straus, David M., et al.. (2018). Control of Storminess over the Pacific and North America by Circulation Regimes. Climate Dynamics. 52(7-8). 4749–4770. 25 indexed citations
5.
Stan, Cristiana, David M. Straus, Jorgen S. Frederiksen, et al.. (2017). Review of Tropical‐Extratropical Teleconnections on Intraseasonal Time Scales. Reviews of Geophysics. 55(4). 902–937. 249 indexed citations
6.
Straus, David M., et al.. (2017). Circulation Response to Fast and Slow MJO Episodes. Monthly Weather Review. 145(5). 1577–1596. 55 indexed citations
7.
Kucharski, Fred, In‐Sik Kang, David M. Straus, & Martin P. King. (2009). Teleconnections in the Atmosphere and Oceans. Bulletin of the American Meteorological Society. 91(3). 381–383. 13 indexed citations
8.
Straus, David M. & V. Krishnamurthy. (2007). The Preferred Structure of the Interannual Indian Monsoon Variability. Pure and Applied Geophysics. 164(8-9). 1717–1732. 12 indexed citations
9.
Wu, Qigang & David M. Straus. (2003). Multiple planetary flow regimes and the eddy forcing in Northern Hemisphere wintertime variability. Geophysical Research Letters. 30(16). 3 indexed citations
10.
Shukla, J., L. Marx, D. A. Paolino, et al.. (2000). Dynamical Seasonal Prediction. Bulletin of the American Meteorological Society. 81(11). 2593–2606. 254 indexed citations
11.
Shukla, J., D. A. Paolino, David M. Straus, et al.. (2000). Dynamical seasonal predictions with the COLA atmospheric model. Quarterly Journal of the Royal Meteorological Society. 126(567). 2265–2291. 27 indexed citations
12.
Straus, David M. & Peter Ditlevsen. (1999). Two-dimensional turbulence properties of the ECMWF reanalyses. Tellus A Dynamic Meteorology and Oceanography. 51(5). 749–749. 34 indexed citations
13.
Straus, David M., et al.. (1997). Vertical Structure and Dominant Horizontal Scales of Baroclinic Waves in the NASA DAO and NCEP Reanalyses. Monthly Weather Review. 125(12). 3266–3278. 1 indexed citations
14.
Park, Chung-Kyu, David M. Straus, & Ka-Ming Lau. (1990). An Evaluation of the Structure of Tropical Intraseasonal Oscillations in Three General Circulation Models. Journal of the Meteorological Society of Japan Ser II. 68(4). 403–417. 31 indexed citations
15.
Straus, David M. & J. Shukla. (1988). The seasonal cycle of energetics from the GLAS/UMD climate GCM. 2 indexed citations
16.
Straus, David M. & J. Shukla. (1988). A comparison of a GCM simulation of the seasonal cycle of the atmosphere with observations part I: Mean fields and the annual harmonic. ATMOSPHERE-OCEAN. 26(4). 541–574. 4 indexed citations
17.
Lindzen, Richard S., et al.. (1984). An Observational Study of Large-Scale Atmospheric Rossby Waves during FGGE. Journal of the Atmospheric Sciences. 41(8). 1320–1335. 36 indexed citations
18.
Straus, David M. & Milton Halem. (1981). A Stochastic-Dynamical Approach to the Study of the Natural Variability of the Climate. Monthly Weather Review. 109(3). 407–421. 7 indexed citations
19.
Straus, David M., N. W. Ashcroft, & H. P. Beck. (1977). Phase separation of metallic hydrogen-helium alloys. Physical review. B, Solid state. 15(4). 1914–1928. 8 indexed citations
20.
Straus, David M. & N. W. Ashcroft. (1976). Thermal diffuse x-ray scattering in simple metals. Physical review. B, Solid state. 14(2). 448–458. 5 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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