Manil Maskey

1.0k citations
72 papers · 470 · h-index 9

Impact in

    • Tropical and Extratropical Cyclones Research
    • Meteorological Phenomena and Simulations
    • Precipitation Measurement and Analysis
  • Oceanography top 10%
    • Ocean Waves and Remote Sensing

Papers in

Manil Maskey

62 papers receiving 453 citations

Peers

Manil Maskey
Comparison fields: 5 of 76
  • Atmospheric Science 247
  • Oceanography 101
  • Global and Planetary Change 174
  • Information Systems and Management 45
  • Computational Mathematics 3
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Daniel Q. Duffy United States
Kaifeng Bi China
Daniel Mandl United States
Sara Graves United States
Gilberto Ribeiro de Queiroz Brazil
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Lianchong Zhang China
Redouane Lguensat France
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Citations per field
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Citations per year

Countries citing papers authored by Manil Maskey

Since Specialization
Citations

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

Fields of papers citing papers by Manil Maskey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Manil Maskey, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Manil Maskey Line = papers co-authored together Manil Maskey links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 72 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017190
2 202052
3 200926
4 201821
5 202014
6 201613
7 202111
8 201911
9 20179
10 20177
11
Using Deep Learning for Tropical Cyclone Intensity Estimation
20177
12 20187
13 20196
14 20226
15 20185
16 20195
17 20175
18 20204
19 20084
20 20124

About Manil Maskey

Manil Maskey is a scholar working on Artificial Intelligence, Information Systems and Management, Global and Planetary Change, Atmospheric Science and Computer Vision and Pattern Recognition, having authored 72 papers that have together received 470 indexed citations. Recurring topics across this work include Scientific Computing and Data Management (15 papers), Distributed and Parallel Computing Systems (6 papers), Meteorological Phenomena and Simulations (6 papers), Tropical and Extratropical Cyclones Research (6 papers), Flood Risk Assessment and Management (5 papers), Atmospheric aerosols and clouds (5 papers), Computational Physics and Python Applications (5 papers) and Geochemistry and Geologic Mapping (5 papers). The work is most often cited by research in Atmospheric Science (247 citations), Oceanography (101 citations), Global and Planetary Change (174 citations), Information Systems and Management (45 citations) and Computational Mathematics (3 citations). Manil Maskey has collaborated with scholars based in United States, Japan and Romania. Frequent co-authors include Rahul Ramachandran, Daniel J. Cecil, Ramazan Aygün, Jeffrey J. Miller, Aaron Kaulfus, U. S. Nair, Kevin Murphy, Hamed Alemohammad, Timothy S. Newman and Mike Botts. Their work appears in journals such as IEEE Geoscience and Remote Sensing Magazine, Computers & Geosciences, Pattern Analysis and Applications, IEEE Transactions on Image Processing and IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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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