Alexei Belochitski
- Atmospheric Science top 10%
- Global and Planetary Change top 10%
- Environmental Engineering
- Artificial Intelligence
- Statistical and Nonlinear Physics
- Co-authors
- Vladimir M. KrasnopolskyMichael S. Fox‐RabinovitzYefim L. KoganStephen J. LordY. T. HouHendrik L. TolmanMichel DéquéB. Dugas
- Topics
- Meteorological Phenomena and Simulations (12 papers)Climate variability and models (9 papers)Hydrological Forecasting Using AI (5 papers)
- Partner nations
- United StatesFranceAustralia
In The Last Decade
Alexei Belochitski
15 papers receiving 296 citations
Peers
Comparison fields: 5 of 32
- Atmospheric Science 255
- Global and Planetary Change 218
- Environmental Engineering 52
- Artificial Intelligence 43
- Statistical and Nonlinear Physics 25
Countries citing papers authored by Alexei Belochitski
This map shows the geographic impact of Alexei Belochitski'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 Alexei Belochitski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexei Belochitski more than expected).
Fields of papers citing papers by Alexei Belochitski
This network shows the impact of papers produced by Alexei Belochitski. 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 Alexei Belochitski. The network helps show where Alexei Belochitski may publish in the future.
Co-authorship network of co-authors of Alexei Belochitski
This figure shows the co-authorship network connecting the top 25 collaborators of Alexei Belochitski. A scholar is included among the top collaborators of Alexei Belochitski 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 Alexei Belochitski. Alexei Belochitski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 16 | |
| 2 | 6 | |
| 3 | 85 | |
| 4 | 22 | |
| 5 | Accurate and fast neural network emulations of long and short wave radiation for the NCEP Global Forecast System model | 2 |
| 6 | 21 | |
| 7 | Development of neural network convection parameterizations for climate and NWP models using Cloud Resolving Model simulations | 3 |
| 8 | 6 | |
| 9 | 52 | |
| 10 | 30 | |
| 11 | 2 | |
| 12 | 29 | |
| 13 | 27 | |
| 14 | 1 | |
| 15 | 3 |
About Alexei Belochitski
Alexei Belochitski is a scholar working on Atmospheric Science, Global and Planetary Change and Environmental Engineering, having authored 15 papers that have together received 305 indexed citations. Recurring topics across this work include Meteorological Phenomena and Simulations (12 papers), Climate variability and models (9 papers) and Hydrological Forecasting Using AI (5 papers). The work is most often cited by research in Atmospheric Science (255 citations), Global and Planetary Change (218 citations) and Environmental Engineering (52 citations). Alexei Belochitski has collaborated with scholars based in United States, France and Australia. Frequent co-authors include Vladimir M. Krasnopolsky, Michael S. Fox‐Rabinovitz, Yefim L. Kogan, Stephen J. Lord, Y. T. Hou, Hendrik L. Tolman, Michel Déqué, B. Dugas, Jean‐François Côté and John L. McGregor. Their work appears in journals such as Journal of the Atmospheric Sciences, Monthly Weather Review and Neural Networks.
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.