Michael Langguth
Impact in
- Atmospheric Science top 10%
- Meteorological Phenomena and Simulations
- Precipitation Measurement and Analysis
- Environmental Engineering top 10%
- Hydrological Forecasting Using AI
- Air Quality Monitoring and Forecasting
Papers in
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- Meteorological Phenomena and Simulations 5
- Precipitation Measurement and Analysis 3
- Tropical and Extratropical Cyclones Research 1
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- Climate variability and models 4
- Co-authors
- Amirpasha Mozaffari (4 shared papers)Bing Gong (4 shared papers)Martin G. Schultz (3 shared papers)Scarlet Stadtler (2 shared papers)Felix Kleinert (1 shared paper)Lukas Hubert Leufen (1 shared paper)Clara Betancourt (1 shared paper)Yan Ji (2 shared papers)
In The Last Decade
Michael Langguth
6 papers receiving 345 citations
Michael Langguth's Hit Papers
Peers
Comparison fields: 5 of 69
- Atmospheric Science 193
- Environmental Engineering 142
- Global and Planetary Change 141
- Artificial Intelligence 60
- Oceanography 21
Countries citing papers authored by Michael Langguth
This map shows the geographic impact of Michael Langguth'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 Michael Langguth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Langguth more than expected).
Fields of papers citing papers by Michael Langguth
This network shows the impact of papers produced by Michael Langguth. 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 Michael Langguth. The network helps show where Michael Langguth may publish in the future.
Co-authors
The 12 scholars most cited alongside Michael Langguth, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Can deep learning beat numerical weather prediction? Hit paper breakdown → | 2021 | 294 |
| 2 | 2022 | 30 | |
| 3 | 2023 | 15 | |
| 4 | 2023 | 11 | |
| 5 | 2022 | 4 | |
| 6 | 2020 | 2 |
About Michael Langguth
Michael Langguth is a scholar working on Atmospheric Science, Global and Planetary Change, Computer Networks and Communications, Information Systems and Information Systems and Management, having authored 6 papers that have together received 356 indexed citations. Recurring topics across this work include Meteorological Phenomena and Simulations (5 papers), Climate variability and models (4 papers), Precipitation Measurement and Analysis (3 papers), Hydrological Forecasting Using AI (1 paper), Scientific Computing and Data Management (1 paper), Tropical and Extratropical Cyclones Research (1 paper), Research Data Management Practices (1 paper) and Distributed and Parallel Computing Systems (1 paper). The work is most often cited by research in Atmospheric Science (193 citations), Environmental Engineering (142 citations), Global and Planetary Change (141 citations), Artificial Intelligence (60 citations) and Oceanography (21 citations). Michael Langguth has collaborated with scholars based in Germany and China. Frequent co-authors include Amirpasha Mozaffari, Bing Gong, Martin G. Schultz, Scarlet Stadtler, Felix Kleinert, Lukas Hubert Leufen, Clara Betancourt, Yan Ji, Xiefei Zhi and Gordon Pipa. Their work appears in journals such as Geoscientific model development, Data Intelligence, Quarterly Journal of the Royal Meteorological Society and Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences.
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.