M. Das Gupta

542 total citations
32 papers, 379 citations indexed

About

M. Das Gupta is a scholar working on Atmospheric Science, Global and Planetary Change and Oceanography. According to data from OpenAlex, M. Das Gupta has authored 32 papers receiving a total of 379 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Atmospheric Science, 23 papers in Global and Planetary Change and 13 papers in Oceanography. Recurrent topics in M. Das Gupta's work include Meteorological Phenomena and Simulations (27 papers), Climate variability and models (22 papers) and Tropical and Extratropical Cyclones Research (11 papers). M. Das Gupta is often cited by papers focused on Meteorological Phenomena and Simulations (27 papers), Climate variability and models (22 papers) and Tropical and Extratropical Cyclones Research (11 papers). M. Das Gupta collaborates with scholars based in India, United States and Switzerland. M. Das Gupta's co-authors include V. S. Prasad, S. Indira Rani, Upal Saha, S. V. Singh, Swati Basu, Ashis K. Mitra, E. N. Rajagopal, T. N. Krishnamurti, Gopal Iyengar and Raghavendra Ashrit and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, International Journal of Remote Sensing and Quarterly Journal of the Royal Meteorological Society.

In The Last Decade

M. Das Gupta

31 papers receiving 363 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M. Das Gupta India 11 323 291 85 32 11 32 379
J. E. Jack Reeves Eyre United States 8 253 0.8× 226 0.8× 47 0.6× 47 1.5× 6 0.5× 13 285
A. K. Mitra India 8 351 1.1× 307 1.1× 58 0.7× 51 1.6× 19 1.7× 38 408
Sophie Cloché France 9 238 0.7× 211 0.7× 78 0.9× 29 0.9× 18 1.6× 12 290
Nazario Tartaglione Italy 9 320 1.0× 284 1.0× 52 0.6× 29 0.9× 24 2.2× 30 361
Peter C. Banacos United States 7 309 1.0× 309 1.1× 31 0.4× 34 1.1× 10 0.9× 9 361
Duk-Jin Won South Korea 4 317 1.0× 260 0.9× 54 0.6× 42 1.3× 6 0.5× 9 340
Frank P. Colby United States 8 318 1.0× 325 1.1× 31 0.4× 30 0.9× 18 1.6× 18 373
Alexander Sterin Switzerland 9 372 1.2× 354 1.2× 62 0.7× 11 0.3× 11 1.0× 22 416
Bryce E. Harrop United States 12 388 1.2× 422 1.5× 76 0.9× 12 0.4× 15 1.4× 33 459
Hai Bui Norway 7 255 0.8× 197 0.7× 68 0.8× 15 0.5× 7 0.6× 14 267

Countries citing papers authored by M. Das Gupta

Since Specialization
Citations

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

Fields of papers citing papers by M. Das Gupta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Das Gupta

This figure shows the co-authorship network connecting the top 25 collaborators of M. Das Gupta. A scholar is included among the top collaborators of M. Das Gupta 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 M. Das Gupta. M. Das Gupta 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.
Rani, S. Indira, Gibies George, Sumit Kumar, et al.. (2023). Assimilation of aircraft observations over the Indian monsoon region: Investigation of the effects of COVID‐19 on a reanalysis. Quarterly Journal of the Royal Meteorological Society. 149(752). 894–910. 2 indexed citations
2.
Rani, S. Indira, et al.. (2022). Assessing the quality of novel Aeolus winds for NWP applications at NCMRWF. Quarterly Journal of the Royal Meteorological Society. 148(744). 1344–1367. 11 indexed citations
3.
4.
Rani, S. Indira, et al.. (2021). Evaluation of the benefits of assimilation of Meteosat-8 observations in an NWP system over the Indian Ocean region. Meteorology and Atmospheric Physics. 133(5). 1555–1576. 4 indexed citations
5.
Rani, S. Indira, et al.. (2021). Validation and assimilation of INSAT atmospheric motion vectors: Case studies for tropical cyclones. Journal of Earth System Science. 130(4). 1 indexed citations
6.
Saha, Upal, et al.. (2021). Assessment of newly-developed high resolution reanalyses (IMDAA, NGFS and ERA5) against rainfall observations for Indian region. Atmospheric Research. 259. 105679–105679. 52 indexed citations
7.
Pradhan, Maheswar, Ankur Srivastava, Suryachandra A. Rao, et al.. (2021). Are ocean-moored buoys redundant for prediction of Indian monsoon?. Meteorology and Atmospheric Physics. 133(4). 1075–1088. 1 indexed citations
8.
Rani, S. Indira, et al.. (2016). Validation of INSAT-3D atmospheric motion vectors for monsoon 2015. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9881. 988125–988125. 2 indexed citations
9.
Gupta, M. Das, et al.. (2015). Verification of visibility forecasts from NWP model with satellite and surface observations. MAUSAM. 66(3). 603–616. 3 indexed citations
10.
Iyengar, Gopal, Raghavendra Ashrit, Kuldeep Sharma, et al.. (2014). Improved Prediction of Cyclone Phailin (9-12 October 2013) with 4DVAR Assimilation. Current Science. 107(6). 952–954. 2 indexed citations
11.
Rani, S. Indira, et al.. (2014). Intercomparison of Oceansat-2 and ASCAT Winds with In Situ Buoy Observations and Short-Term Numerical Forecasts. ATMOSPHERE-OCEAN. 52(1). 92–102. 18 indexed citations
12.
Gupta, M. Das, et al.. (2013). An inter‐comparison of Kalpana‐1 and Meteosat‐7 atmospheric motion vectors against radiosonde winds and NWP forecasts during monsoon 2011. Meteorological Applications. 21(4). 820–830. 7 indexed citations
13.
Abhilash, S., Someshwar Das, S. R. KALSI, et al.. (2007). Assimilation of Doppler weather radar observations in a mesoscale model for the prediction of rainfall associated with mesoscale convective systems. Journal of Earth System Science. 116(4). 275–304. 8 indexed citations
14.
Bohra, A. K., Swati Basu, E. N. Rajagopal, et al.. (2006). Heavy rainfall episode over Mumbai on 26 July 2005: Assessment of NWP guidance. Current Science. 90(9). 1188–1194. 56 indexed citations
15.
Gupta, M. Das, et al.. (2005). Validation of upper-air observations taken during the ARMEX-I and its impact on the global analysis-forecast system. MAUSAM. 56(1). 139–146. 5 indexed citations
16.
Gupta, M. Das, et al.. (2003). Assimilation of special observations taken during the INDOEX and its impact on the global analysis-forecast system. Atmósfera. 16(2). 103–118. 7 indexed citations
17.
Mitra, Ashis K., et al.. (2003). Observed daily large-scale rainfall patterns during BOBMEX-1999. Journal of Earth System Science. 112(2). 223–232. 2 indexed citations
18.
Mitra, Ashis K., M. Das Gupta, S. V. Singh, & T. N. Krishnamurti. (2003). Daily Rainfall for the Indian Monsoon Region from Merged Satellite and Rain Gauge Values: Large-Scale Analysis from Real-Time Data. Journal of Hydrometeorology. 4(5). 769–781. 53 indexed citations
19.
Kar, Sarat C., et al.. (2003). Analyses of Orissa Super Cyclone using TRMM (TMI), DMSP (SSM/I) and OceanSat-I (MSMR) Derived Data. 9(1-2). 1–18. 11 indexed citations
20.
Basu, Swati & M. Das Gupta. (2001). Impact of INDOEX data in the NCMRWF analysis-forecast system and evolution of boundary layer structure during IFP-99.. Current Science. 80. 7–11. 4 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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