Sudhakar Dipu

1.2k total citations
17 papers, 742 citations indexed

About

Sudhakar Dipu is a scholar working on Global and Planetary Change, Atmospheric Science and Earth-Surface Processes. According to data from OpenAlex, Sudhakar Dipu has authored 17 papers receiving a total of 742 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Global and Planetary Change, 16 papers in Atmospheric Science and 4 papers in Earth-Surface Processes. Recurrent topics in Sudhakar Dipu's work include Atmospheric aerosols and clouds (14 papers), Atmospheric chemistry and aerosols (12 papers) and Atmospheric Ozone and Climate (7 papers). Sudhakar Dipu is often cited by papers focused on Atmospheric aerosols and clouds (14 papers), Atmospheric chemistry and aerosols (12 papers) and Atmospheric Ozone and Climate (7 papers). Sudhakar Dipu collaborates with scholars based in Germany, United States and India. Sudhakar Dipu's co-authors include G. Pandithurai, P. C. S. Devara, K. K. Dani, S. Tiwari, R. T. Pinker, D. S. Bisht, A. S. Panicker, Thara V. Prabha, B. N. Goswami and Johannes Quaas and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, Geophysical Research Letters and Atmospheric Environment.

In The Last Decade

Sudhakar Dipu

16 papers receiving 724 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sudhakar Dipu Germany 9 702 686 92 91 39 17 742
Cyrielle Denjean France 19 663 0.9× 607 0.9× 72 0.8× 136 1.5× 82 2.1× 31 733
Alexandra Tsekeri Greece 14 600 0.9× 624 0.9× 106 1.2× 60 0.7× 39 1.0× 38 668
Andreas Veira Germany 10 503 0.7× 499 0.7× 75 0.8× 86 0.9× 22 0.6× 15 563
S. Pereira Portugal 14 491 0.7× 501 0.7× 46 0.5× 122 1.3× 43 1.1× 27 584
P. A. Durkee United States 14 705 1.0× 705 1.0× 79 0.9× 99 1.1× 47 1.2× 28 776
Timothy Logan United States 13 459 0.7× 473 0.7× 43 0.5× 69 0.8× 35 0.9× 30 510
Alexander Ukhov Saudi Arabia 10 334 0.5× 309 0.5× 54 0.6× 92 1.0× 55 1.4× 16 398
Reuven H. Heiblum Israel 12 500 0.7× 510 0.7× 83 0.9× 38 0.4× 23 0.6× 18 569
Minjun Deng China 6 453 0.6× 446 0.7× 32 0.3× 80 0.9× 56 1.4× 8 518
Isabel L. McCoy United States 13 534 0.8× 554 0.8× 92 1.0× 33 0.4× 13 0.3× 32 608

Countries citing papers authored by Sudhakar Dipu

Since Specialization
Citations

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

Fields of papers citing papers by Sudhakar Dipu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sudhakar Dipu

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

All Works

17 of 17 papers shown
1.
Dipu, Sudhakar, et al.. (2024). Emulation of Forward Modeled Top-of-Atmosphere MODIS-Based Spectral Channels Using Machine Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18. 1896–1911. 1 indexed citations
2.
Handorf, Dörthe, et al.. (2024). Arctic climate response to European radiative forcing: a deep learning study on circulation pattern changes. Weather and Climate Dynamics. 5(4). 1223–1268.
3.
Mülmenstädt, Johannes, Andrew S. Ackerman, Ann M. Fridlind, et al.. (2024). Can general circulation models (GCMs) represent cloud liquid water path adjustments to aerosol–cloud interactions?. Atmospheric chemistry and physics. 24(23). 13633–13652. 5 indexed citations
4.
Dipu, Sudhakar, Matthias Schwarz, Annica M. L. Ekman, et al.. (2022). Exploring Satellite-Derived Relationships between Cloud Droplet Number Concentration and Liquid Water Path Using a Large-Domain Large-Eddy Simulation. Tellus B. 74(1). 176–176. 5 indexed citations
5.
Dipu, Sudhakar, Johannes Quaas, Martin F. Quaas, et al.. (2021). Substantial Climate Response outside the Target Area in an Idealized Experiment of Regional Radiation Management. Climate. 9(4). 66–66. 3 indexed citations
6.
Mülmenstädt, Johannes, Edward Gryspeerdt, Marc Salzmann, et al.. (2019). Separating radiative forcing by aerosol–cloud interactions and rapid cloud adjustments in the ECHAM–HAMMOZ aerosol–climate model using the method of partial radiative perturbations. Atmospheric chemistry and physics. 19(24). 15415–15429. 19 indexed citations
7.
Gryspeerdt, Edward, Tom Goren, Odran Sourdeval, et al.. (2019). Constraining the aerosol influence on cloud liquid water path. Atmospheric chemistry and physics. 19(8). 5331–5347. 131 indexed citations
8.
Hutchison, Keith D., et al.. (2019). A Methodology for Verifying Cloud Forecasts with VIIRS Imagery and Derived Cloud Products—A WRF Case Study. Atmosphere. 10(9). 521–521. 3 indexed citations
9.
Dipu, Sudhakar, Johannes Quaas, Ralf Wolke, et al.. (2017). Implementation of aerosol–cloud interactions in the regional atmosphere–aerosol model COSMO-MUSCAT(5.0) and evaluation using satellite data. Geoscientific model development. 10(6). 2231–2246. 8 indexed citations
10.
Panicker, A. S., G. Pandithurai, P.D. Safai, et al.. (2014). Observations of black carbon induced semi direct effect over Northeast India. Atmospheric Environment. 98. 685–692. 13 indexed citations
11.
Dipu, Sudhakar, Thara V. Prabha, G. Pandithurai, et al.. (2013). Impact of elevated aerosol layer on the cloud macrophysical properties prior to monsoon onset. Atmospheric Environment. 70. 454–467. 80 indexed citations
12.
Devara, P. C. S., Sumit Kumar, G. Pandithurai, P.D. Safai, & Sudhakar Dipu. (2013). Comparison between urban aerosol products retrieved from collocated Cimel and Prede Sun/sky radiometers at Pune, India. Meteorology and Atmospheric Physics. 120(3-4). 189–200. 4 indexed citations
13.
Pandithurai, G., Sudhakar Dipu, Thara V. Prabha, et al.. (2012). Aerosol effect on droplet spectral dispersion in warm continental cumuli. Journal of Geophysical Research Atmospheres. 117(D16). 70 indexed citations
14.
Panicker, A. S., G. Pandithurai, & Sudhakar Dipu. (2010). Aerosol indirect effect during successive contrasting monsoon seasons over Indian subcontinent using MODIS data. Atmospheric Environment. 44(15). 1937–1943. 47 indexed citations
15.
Panicker, A. S., G. Pandithurai, P.D. Safai, Sudhakar Dipu, & Dong‐In Lee. (2010). On the contribution of black carbon to the composite aerosol radiative forcing over an urban environment. Atmospheric Environment. 44(25). 3066–3070. 69 indexed citations
16.
Pandithurai, G., Toshinari Takamura, Toshiaki Takano, et al.. (2009). Aerosol effect on cloud droplet size as monitored from surface‐based remote sensing over East China Sea region. Geophysical Research Letters. 36(13). 39 indexed citations
17.
Pandithurai, G., Sudhakar Dipu, K. K. Dani, et al.. (2008). Aerosol radiative forcing during dust events over New Delhi, India. Journal of Geophysical Research Atmospheres. 113(D13). 245 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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