Fei Ji

2.4k total citations
59 papers, 1.6k citations indexed

About

Fei Ji is a scholar working on Global and Planetary Change, Atmospheric Science and Water Science and Technology. According to data from OpenAlex, Fei Ji has authored 59 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 53 papers in Global and Planetary Change, 41 papers in Atmospheric Science and 9 papers in Water Science and Technology. Recurrent topics in Fei Ji's work include Climate variability and models (50 papers), Meteorological Phenomena and Simulations (32 papers) and Cryospheric studies and observations (13 papers). Fei Ji is often cited by papers focused on Climate variability and models (50 papers), Meteorological Phenomena and Simulations (32 papers) and Cryospheric studies and observations (13 papers). Fei Ji collaborates with scholars based in Australia, China and Italy. Fei Ji's co-authors include Jason P. Evans, Marie Ekström, De Li Liu, Daniel Argüeso, Qiang Yu, Lluís Fita, Alejandro Di Luca, Pete Smith, Bin Wang and Xihua Yang and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, The Science of The Total Environment and Geophysical Research Letters.

In The Last Decade

Fei Ji

54 papers receiving 1.6k citations

Peers

Fei Ji

GPC

WST

EEBS

Vittal Hari India

Sajjad Saeed Belgium

Annette L. Hirsch Australia

Nathalie Schaller United Kingdom

J. V. Revadekar India

Ana M. B. Nunes Brazil

Xiaohua Pan United States

Pankaj Kumar India

Diana Rechid Germany

Faye Cruz Philippines

Vittal Hari India

Fei Ji

1.3k ×1.0

GPC

722 ×0.8

300 ×1.1

WST

166 ×0.8

178 ×0.9

EEBS

Citations per year, relative to Fei Ji Fei Ji (= 1×) peers Vittal Hari

Countries citing papers authored by Fei Ji

Since Specialization

Citations

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

Fields of papers citing papers by Fei Ji

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fei Ji

This figure shows the co-authorship network connecting the top 25 collaborators of Fei Ji. A scholar is included among the top collaborators of Fei Ji 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 Fei Ji. Fei Ji is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Virgilio, Giovanni Di, Fei Ji, Jason P. Evans, et al.. (2025). Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2. Geoscientific model development. 18(3). 703–724. 4 indexed citations

Virgilio, Giovanni Di, Jason P. Evans, Fei Ji, et al.. (2025). Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble. Geoscientific model development. 18(3). 671–702. 4 indexed citations

Wang, Bin, De Li Liu, Chao Chen, et al.. (2025). Future climate change increases the risk of wheat yield loss due to agricultural drought in southeastern Australia. European Journal of Agronomy. 173. 127909–127909.

Cheung, Kevin K. W., Fei Ji, Nidhi Nishant, et al.. (2025). Comparison of BARRA and ERA5 in replicating mean and extreme precipitation over Australia. Hydrology and earth system sciences. 29(15). 3527–3543.

Duc, Hiep Nguyen, John Leys, Matthew Riley, et al.. (2024). Effects of Dust Storm and Wildfire Events on Phytoplankton Growth and Carbon Sequestration in the Tasman Sea, Southeast Australia. Atmosphere. 15(3). 337–337. 2 indexed citations

Wang, Bin, De Li Liu, Chao Chen, et al.. (2024). Soil with high plant available water capacity can mitigate the risk of wheat growth under drought conditions in southeastern Australia. European Journal of Agronomy. 164. 127460–127460. 3 indexed citations

Chiew, Francis H. S., Hongxing Zheng, Nicholas J. Potter, et al.. (2022). Different Hydroclimate Modelling Approaches Can Lead to a Large Range of Streamflow Projections under Climate Change: Implications for Water Resources Management. Water. 14(17). 2730–2730. 16 indexed citations

Nishant, Nidhi, et al.. (2022). Evaluation of Present-Day CMIP6 Model Simulations of Extreme Precipitation and Temperature over the Australian Continent. Atmosphere. 13(9). 1478–1478. 14 indexed citations

Nishant, Nidhi, Fei Ji, Yuming Guo, et al.. (2022). Future population exposure to Australian heatwaves. Environmental Research Letters. 17(6). 64030–64030. 20 indexed citations

10.

Ji, Fei, Nidhi Nishant, Jason P. Evans, et al.. (2022). Rapid Warming in the Australian Alps from Observation and NARCliM Simulations. Atmosphere. 13(10). 1686–1686. 3 indexed citations

11.

Virgilio, Giovanni Di, Fei Ji, Nidhi Nishant, et al.. (2022). Selecting CMIP6 GCMs for CORDEX Dynamical Downscaling: Model Performance, Independence, and Climate Change Signals. Earth s Future. 10(4). 80 indexed citations

12.

Nishant, Nidhi, Jason P. Evans, Giovanni Di Virgilio, et al.. (2021). Introducing NARCliM1.5: Evaluating the Performance of Regional Climate Projections for Southeast Australia for 1950–2100. Earth s Future. 9(7). 34 indexed citations

13.

Feng, Puyu, Bin Wang, De Li Liu, et al.. (2020). Machine learning-based integration of large-scale climate drivers can improve the forecast of seasonal rainfall probability in Australia. Environmental Research Letters. 15(8). 84051–84051. 28 indexed citations

14.

Wang, Bin, Lihong Zheng, De Li Liu, et al.. (2018). Using multi‐model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia. International Journal of Climatology. 38(13). 4891–4902. 126 indexed citations

15.

Evans, Jason P., Acacia Pepler, Alejandro Di Luca, et al.. (2015). Future changes in extreme East coast lows. UNSWorks (University of New South Wales, Sydney, Australia). 1131. 1 indexed citations

16.

Evans, Jason P., et al.. (2014). Design of a regional climate modelling projection ensemble experiment – NARCliM. Geoscientific model development. 7(2). 621–629. 184 indexed citations

17.

Ji, Fei, et al.. (2014). The distribution of large-scale drought/flood of summer in China under different configurations of monsoon and polar vortex. Acta Physica Sinica. 63(5). 59201–59201. 5 indexed citations

18.

Ji, Fei, Matthew Riley, Hamish Clarke, et al.. (2013). High resolution rainfall projections for the Greater Sydney Region. Piantadosi, J., Anderssen, R.S. and Boland J. (eds) MODSIM2013, 20th International Congress on Modelling and Simulation. 3 indexed citations

19.

Evans, Jason P., et al.. (2013). A regional climate modelling projection ensemble experiment – NARCliM. 7 indexed citations

20.

Su, Wenying, et al.. (2000). Outgoing longwave radiation and cloud radiative forcing of the Tibetan Plateau. Journal of Geophysical Research Atmospheres. 105(D11). 14863–14872. 7 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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