Popat Salunke

689 total citations
22 papers, 495 citations indexed

About

Popat Salunke is a scholar working on Atmospheric Science, Global and Planetary Change and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Popat Salunke has authored 22 papers receiving a total of 495 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Atmospheric Science, 20 papers in Global and Planetary Change and 5 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Popat Salunke's work include Climate variability and models (19 papers), Meteorological Phenomena and Simulations (16 papers) and Cryospheric studies and observations (6 papers). Popat Salunke is often cited by papers focused on Climate variability and models (19 papers), Meteorological Phenomena and Simulations (16 papers) and Cryospheric studies and observations (6 papers). Popat Salunke collaborates with scholars based in India, United States and Saudi Arabia. Popat Salunke's co-authors include Saroj K. Mishra, Sandeep Sahany, Shipra Jain, Naveen Choudhary, In‐Sik Kang, Anupam Dewan, John Fasullo, S. Dash, Ben Kravitz and Sourangsu Chowdhury and has published in prestigious journals such as Scientific Reports, Nature Climate Change and Climatic Change.

In The Last Decade

Popat Salunke

22 papers receiving 486 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Popat Salunke India 15 417 307 85 55 50 22 495
Ramzah Dambul Malaysia 11 444 1.1× 359 1.2× 43 0.5× 53 1.0× 60 1.2× 32 599
Ajay Raghavendra United States 13 445 1.1× 307 1.0× 34 0.4× 70 1.3× 48 1.0× 24 552
Muhammad Latif Pakistan 13 392 0.9× 228 0.7× 102 1.2× 25 0.5× 96 1.9× 30 512
Pankaj Bhardwaj India 13 258 0.6× 174 0.6× 48 0.6× 84 1.5× 29 0.6× 25 360
Omar V. Müller Argentina 10 400 1.0× 204 0.7× 73 0.9× 26 0.5× 61 1.2× 17 462
XU Chong-hai China 4 296 0.7× 192 0.6× 76 0.9× 20 0.4× 66 1.3× 4 362
Rupak Rajbhandari Nepal 9 394 0.9× 361 1.2× 151 1.8× 13 0.2× 72 1.4× 10 559
María de los Milagros Skansi Argentina 7 327 0.8× 226 0.7× 69 0.8× 14 0.3× 45 0.9× 10 414
Changyong Park South Korea 12 369 0.9× 292 1.0× 65 0.8× 31 0.6× 56 1.1× 27 442
Masilin Gudoshava United Kingdom 9 318 0.8× 229 0.7× 37 0.4× 31 0.6× 94 1.9× 15 424

Countries citing papers authored by Popat Salunke

Since Specialization
Citations

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

Fields of papers citing papers by Popat Salunke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Popat Salunke

This figure shows the co-authorship network connecting the top 25 collaborators of Popat Salunke. A scholar is included among the top collaborators of Popat Salunke 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 Popat Salunke. Popat Salunke 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.
Salunke, Popat, et al.. (2024). Artificial intelligence predicts normal summer monsoon rainfall for India in 2023. Scientific Reports. 14(1). 1495–1495. 6 indexed citations
2.
Salunke, Popat, et al.. (2023). Climate projections for Himalaya–Tibetan Highland. Theoretical and Applied Climatology. 155(2). 1055–1065. 4 indexed citations
3.
Mishra, Saroj K., et al.. (2023). A need for actionable climate projections across the Global South. Nature Climate Change. 13(9). 883–886. 17 indexed citations
4.
Salunke, Popat, et al.. (2023). Future projections of seasonal temperature and precipitation for India. Frontiers in Climate. 5. 20 indexed citations
5.
Mishra, Saroj K., et al.. (2022). Potential effects of the projected Antarctic sea-ice loss on the climate system. Climate Dynamics. 60(1-2). 589–601. 2 indexed citations
6.
Pokam, Wilfried M., et al.. (2022). An investigation into the role of synoptic conditions on Central African precipitation variability. Acta Geophysica. 70(2). 943–962. 3 indexed citations
7.
Mishra, Saroj K., et al.. (2021). Future projections of temperature and precipitation for Antarctica. Environmental Research Letters. 17(1). 14029–14029. 25 indexed citations
8.
Mishra, Saroj K., et al.. (2021). Response of the Indian summer monsoon to global warming, solar geoengineering and its termination. Scientific Reports. 11(1). 9791–9791. 14 indexed citations
9.
Dasari, Hari Prasad, et al.. (2021). Variations of Energy Fluxes with ENSO, IOD and ISV of Indian Summer Monsoon Rainfall over the Indian Monsoon Region. Atmospheric Research. 258. 105645–105645. 4 indexed citations
10.
Jain, Shipra, et al.. (2020). Historical and projected low-frequency variability in the Somali Jet and Indian Summer Monsoon. Climate Dynamics. 56(3-4). 749–765. 23 indexed citations
11.
Mishra, Saroj K., et al.. (2019). Numerical Modeling of the Dynamics of Malaria Transmission in a Highly Endemic Region of India. Scientific Reports. 9(1). 11903–11903. 9 indexed citations
12.
Jain, Shipra, et al.. (2019). Assessment of CMIP5 multimodel mean for the historical climate of Africa. Atmospheric Science Letters. 20(8). 34 indexed citations
13.
Jain, Shipra, Popat Salunke, Saroj K. Mishra, Sandeep Sahany, & Naveen Choudhary. (2019). Advantage of NEX-GDDP over CMIP5 and CORDEX Data: Indian Summer Monsoon. Atmospheric Research. 228. 152–160. 58 indexed citations
14.
Mishra, Saroj K., Shipra Jain, Popat Salunke, & Sandeep Sahany. (2019). Past and future climate change over the Himalaya–Tibetan Highland: inferences from APHRODITE and NEX-GDDP data. Climatic Change. 156(3). 315–322. 15 indexed citations
15.
Jain, Shipra, Popat Salunke, Saroj K. Mishra, & Sandeep Sahany. (2018). Performance of CMIP5 models in the simulation of Indian summer monsoon. Theoretical and Applied Climatology. 137(1-2). 1429–1447. 65 indexed citations
16.
Sahany, Sandeep, Saroj K. Mishra, & Popat Salunke. (2018). Historical simulations and climate change projections over India by NCAR CCSM4: CMIP5 vs. NEX-GDDP. Theoretical and Applied Climatology. 135(3-4). 1423–1433. 35 indexed citations
17.
Jain, Shipra, Saroj K. Mishra, Popat Salunke, & Sandeep Sahany. (2018). Importance of the resolution of surface topography vis-à-vis atmospheric and surface processes in the simulation of the climate of Himalaya-Tibet highland. Climate Dynamics. 52(7-8). 4735–4748. 19 indexed citations
18.
Salunke, Popat, Shipra Jain, & Saroj K. Mishra. (2018). Performance of the CMIP5 models in the simulation of the Himalaya-Tibetan Plateau monsoon. Theoretical and Applied Climatology. 137(1-2). 909–928. 32 indexed citations
19.
Mishra, Saroj K., Sandeep Sahany, Popat Salunke, In‐Sik Kang, & Shipra Jain. (2018). Fidelity of CMIP5 multi-model mean in assessing Indian monsoon simulations. npj Climate and Atmospheric Science. 1(1). 46 indexed citations
20.
Dey, Sagnik, et al.. (2017). Comparative Study of Heat Indices in India Based on Observed and Model Simulated Data. Current World Environment. 12(3). 530–546. 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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