Fenghua Ling

453 total citations
21 papers, 144 citations indexed

About

Fenghua Ling is a scholar working on Global and Planetary Change, Atmospheric Science and Environmental Engineering. According to data from OpenAlex, Fenghua Ling has authored 21 papers receiving a total of 144 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Global and Planetary Change, 11 papers in Atmospheric Science and 7 papers in Environmental Engineering. Recurrent topics in Fenghua Ling's work include Climate variability and models (11 papers), Meteorological Phenomena and Simulations (10 papers) and Hydrological Forecasting Using AI (6 papers). Fenghua Ling is often cited by papers focused on Climate variability and models (11 papers), Meteorological Phenomena and Simulations (10 papers) and Hydrological Forecasting Using AI (6 papers). Fenghua Ling collaborates with scholars based in China, Japan and Australia. Fenghua Ling's co-authors include Jing‐Jia Luo, Yue Li, Lei Bai, Toshio Yamagata, Wanli Ouyang, Xiaohui Zhong, Lin Ouyang, Zhibin Wang, Swadhin K. Behera and Dachao Jin and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Geophysical Research Letters.

In The Last Decade

Fenghua Ling

15 papers receiving 139 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fenghua Ling China 7 84 69 53 37 17 21 144
Peter Kalverla Netherlands 6 144 1.7× 105 1.5× 64 1.2× 37 1.0× 28 1.6× 10 210
Carla Bromberg Brazil 2 141 1.7× 116 1.7× 70 1.3× 19 0.5× 20 1.2× 4 227
Jonathan Heek United States 1 86 1.0× 74 1.1× 45 0.8× 11 0.3× 15 0.9× 2 145
G. Rutledge United States 4 54 0.6× 66 1.0× 13 0.2× 19 0.5× 10 0.6× 7 131
Tai‐Long He China 9 152 1.8× 116 1.7× 65 1.2× 17 0.5× 5 0.3× 17 221
Rudolf Hankers Germany 5 104 1.2× 43 0.6× 142 2.7× 42 1.1× 18 1.1× 8 242
Wang‐chun Woo China 3 154 1.8× 102 1.5× 28 0.5× 11 0.3× 5 0.3× 5 178
V. Rakesh India 13 292 3.5× 283 4.1× 53 1.0× 29 0.8× 9 0.5× 32 346
Laura Mansfield United States 5 75 0.9× 68 1.0× 29 0.5× 6 0.2× 11 0.6× 7 124
Martina Lagasio Italy 9 202 2.4× 165 2.4× 59 1.1× 32 0.9× 6 0.4× 30 267

Countries citing papers authored by Fenghua Ling

Since Specialization
Citations

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

Fields of papers citing papers by Fenghua Ling

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fenghua Ling

This figure shows the co-authorship network connecting the top 25 collaborators of Fenghua Ling. A scholar is included among the top collaborators of Fenghua Ling 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 Fenghua Ling. Fenghua Ling 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.
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3.
Chang, Chih-Pei, Bin Mu, Wei Han, et al.. (2025). AI Weather and Climate Prediction and Applications. Bulletin of the American Meteorological Society. 106(12). E2571–E2578.
4.
Ling, Fenghua, Lei Bai, Jing‐Jia Luo, et al.. (2025). Data-driven global ocean modeling for seasonal to decadal prediction. Science Advances. 11(33). eadu2488–eadu2488.
5.
Chen, Kang, Tao Han, Fenghua Ling, et al.. (2025). The operational medium-range deterministic weather forecasting can be extended beyond a 10-day lead time. Communications Earth & Environment. 6(1). 6 indexed citations
6.
Zhu, Zhiwei, et al.. (2025). Refine Extreme Hot Day Predictions With the Sea Surface Temperature Tendency. Geophysical Research Letters. 52(18).
7.
Sun, Qiming, et al.. (2024). Current progress in subseasonal-to-decadal prediction based on machine learning. SHILAP Revista de lepidopterología. 24. 100201–100201. 1 indexed citations
8.
Ling, Fenghua, Jing‐Jia Luo, Lei Bai, et al.. (2024). Diffusion model-based probabilistic downscaling for 180-year East Asian climate reconstruction. npj Climate and Atmospheric Science. 7(1). 14 indexed citations
9.
Wang, Shixin, Tiexi Chen, Jing‐Jia Luo, et al.. (2024). Warming climate is helping human beings run faster, jump higher and throw farther through less dense air. npj Climate and Atmospheric Science. 7(1). 2 indexed citations
10.
Ling, Fenghua, et al.. (2024). ResoNet: Robust and Explainable ENSO Forecasts with Hybrid Convolution and Transformer Networks. Advances in Atmospheric Sciences. 41(7). 1289–1298. 6 indexed citations
11.
Qi, Li, Tomoki Tozuka, Jing‐Jia Luo, et al.. (2024). Emergent constraint on the projected central equatorial Pacific warming and northwestern Pacific monsoon trough change. Environmental Research Letters. 19(5). 54003–54003. 1 indexed citations
12.
Ling, Fenghua, et al.. (2024). Improving the Seasonal Forecast of Summer Precipitation in Southeastern China Using a CycleGAN-based Deep Learning Bias Correction Method. Advances in Atmospheric Sciences. 42(1). 26–35. 1 indexed citations
13.
Bai, Lei, Hao Chen, Tao Han, et al.. (2024). Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling. 23325–23351.
14.
Ling, Fenghua, Lin Ouyang, Jing‐Jia Luo, et al.. (2024). Improving global weather and ocean wave forecast with large artificial intelligence models. Science China Earth Sciences. 67(12). 3641–3654. 3 indexed citations
15.
Ling, Fenghua, et al.. (2023). Research on Provincial-Level Soil Moisture Prediction Based on Extreme Gradient Boosting Model. Agriculture. 13(5). 927–927. 13 indexed citations
16.
Ouyang, Lin, Fenghua Ling, Yue Li, Lei Bai, & Jing‐Jia Luo. (2023). Wave forecast in the Atlantic Ocean using a double-stage ConvLSTM network. Atmospheric and Oceanic Science Letters. 16(4). 100347–100347. 16 indexed citations
17.
Ling, Fenghua, Jing‐Jia Luo, Yue Li, et al.. (2022). Multi-task machine learning improves multi-seasonal prediction of the Indian Ocean Dipole. Nature Communications. 13(1). 7681–7681. 48 indexed citations
18.
Ling, Fenghua, Yue Li, Jing‐Jia Luo, Xiaohui Zhong, & Zhibin Wang. (2022). Two deep learning-based bias-correction pathways improve summer precipitation prediction over China. Environmental Research Letters. 17(12). 124025–124025. 17 indexed citations
19.
Feng, Ming, Fabio Boschetti, Fenghua Ling, et al.. (2022). Predictability of sea surface temperature anomalies at the eastern pole of the Indian Ocean Dipole—using a convolutional neural network model. Frontiers in Climate. 4. 11 indexed citations
20.
Luo, Jing‐Jia, Fenghua Ling, Yoo‐Geun Ham, & Jeong-Hwan Kim. (2020). Seasonal-to-multiyear prediction of ENSO using machine deep learning. 1 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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