Mu Mu

1.4k total citations
68 papers, 1.1k citations indexed

About

Mu Mu is a scholar working on Atmospheric Science, Global and Planetary Change and Oceanography. According to data from OpenAlex, Mu Mu has authored 68 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Atmospheric Science, 45 papers in Global and Planetary Change and 29 papers in Oceanography. Recurrent topics in Mu Mu's work include Climate variability and models (42 papers), Meteorological Phenomena and Simulations (42 papers) and Oceanographic and Atmospheric Processes (25 papers). Mu Mu is often cited by papers focused on Climate variability and models (42 papers), Meteorological Phenomena and Simulations (42 papers) and Oceanographic and Atmospheric Processes (25 papers). Mu Mu collaborates with scholars based in China, Netherlands and Czechia. Mu Mu's co-authors include Feifan Zhou, Zhina Jiang, Qiang Wang, Henk A. Dijkstra, Hongli Wang, Guokun Dai, Wansuo Duan, Kun Zhang, Qiang Wang and Dahe Qin and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, Journal of Fluid Mechanics and Journal of Climate.

In The Last Decade

Mu Mu

64 papers receiving 1.0k citations

Peers

Mu Mu

GPC

OCEAN

Valentin Dymnikov Russia

Patrick Laloyaux United Kingdom

Jan Barkmeijer Netherlands

M. A. Pedder United Kingdom

Martin Ehrendorfer Austria

Jan‐Huey Chen United States

Ronald Gelaro United States

Yannick Trémolet United Kingdom

Joseph G. Sela United States

Rolf H. Langland United States

Valentin Dymnikov Russia

Mu Mu

1.2k ×1.4

1.2k ×1.6

GPC

566 ×1.3

OCEAN

49 ×0.6

24 ×0.8

Citations per year, relative to Mu Mu Mu Mu (= 1×) peers Valentin Dymnikov

Countries citing papers authored by Mu Mu

Since Specialization

Citations

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

Fields of papers citing papers by Mu Mu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mu Mu

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Mu, Mu, Bo Qin, Tao Lian, et al.. (2025). Toward skillful forecasting of super El Niño events using a diffusion-based westerly wind burst parameterization. npj Climate and Atmospheric Science. 8(1).

Mu, Mu, et al.. (2025). A fast physics-based perturbation generator of machine learning weather model for efficient ensemble forecasts of tropical cyclone track. npj Climate and Atmospheric Science. 8(1). 2 indexed citations

Wang, Qiang, et al.. (2025). The predictability study of oceanic deep learning models: Taking Kuroshio intrusion into South China Sea as an example. Ocean Modelling. 198. 102622–102622.

Qin, Bo, Mu Mu, Yuntao Wei, et al.. (2024). The first kind of predictability problem of El Niño predictions in a multivariate coupled data‐driven model. Quarterly Journal of the Royal Meteorological Society. 150(765). 5452–5471. 9 indexed citations

Mu, Mu, et al.. (2022). Influence of Arctic Sea Ice Concentration on Extended‐Range Prediction of Strong and Long‐Lasting Ural Blocking Events in Winter. Journal of Geophysical Research Atmospheres. 127(5). 27 indexed citations

Wang, Qiang, et al.. (2021). Optimal Precursors Triggering Sudden Shifts in the Antarctic Circumpolar Current Transport Through Drake Passage. Journal of Geophysical Research Oceans. 126(12). 6 indexed citations

Dai, Guokun, Mu Mu, Chunxiang Li, Zhe Han, & Lei Wang. (2020). Evaluation of the Forecast Performance for Extreme Cold Events in East Asia With Subseasonal‐to‐Seasonal Data Sets From ECMWF. Journal of Geophysical Research Atmospheres. 126(1). 12 indexed citations

Dai, Guokun & Mu Mu. (2020). Arctic Influence on the Eastern Asian Cold Surge Forecast: A Case Study of January 2016. Journal of Geophysical Research Atmospheres. 125(23). 20 indexed citations

Zhang, Kun, Mu Mu, Qiang Wang, Baoshu Yin, & Shixuan Liu. (2019). CNOP‐Based Adaptive Observation Network Designed for Improving Upstream Kuroshio Transport Prediction. Journal of Geophysical Research Oceans. 124(6). 4350–4364. 19 indexed citations

10.

Mu, Mu, et al.. (2019). Optimal Precursors Triggering the Kuroshio Intrusion Into the South China Sea Obtained by the Conditional Nonlinear Optimal Perturbation Approach. Journal of Geophysical Research Oceans. 124(6). 3941–3962. 14 indexed citations

11.

Wang, Qiang, et al.. (2018). Effect of the Decadal Kuroshio Extension Variability on the Seasonal Changes of the Mixed‐Layer Salinity Anomalies in the Kuroshio‐Oyashio Confluence Region. Journal of Geophysical Research Oceans. 123(12). 8849–8861. 7 indexed citations

12.

Duan, Wansuo, et al.. (2016). Estimating observing locations for advancing beyond the winter predictability barrier of Indian Ocean dipiole event predictions. EGUGA. 2935. 2 indexed citations

13.

Chen, Boyu, et al.. (2013). Impact of Assimilating Dropwindsonde Data Deployed at Different Sites on Typhoon Track Forecasts. EGU General Assembly Conference Abstracts. 1 indexed citations

14.

Wang, Qiang, Mu Mu, & Henk A. Dijkstra. (2013). The similarity between optimal precursor and optimally growing initial error in prediction of Kuroshio large meander and its application to targeted observation. Journal of Geophysical Research Oceans. 118(2). 869–884. 46 indexed citations

15.

Zhou, Feifan & Mu Mu. (2011). The impact of horizontal resolution on the CNOP and on its identified sensitive areas for tropical cyclone predictions. Advances in Atmospheric Sciences. 29(1). 36–46. 19 indexed citations

16.

Mu, Mu. (2008). A REM adjoint system and its data assimilation experiments of 4D-Var. Acta Meteorologica Sinica. 1 indexed citations

17.

Mu, Mu & Zhina Jiang. (2008). A new approach to the generation of initial perturbations for ensemble prediction: Conditional nonlinear optimal perturbation. Science Bulletin. 53(13). 2062–2068. 56 indexed citations

18.

Sun, Liang, Mu Mu, De‐Jun Sun, & Xie‐Yuan Yin. (2005). Passive mechanism of decadal variation of thermohaline circulation. Journal of Geophysical Research Atmospheres. 110(C7). 40 indexed citations

19.

Mu, Mu. (2002). Adjoint approach to VDA of "on-off" processes based on nonlinear perturbation equation. 2 indexed citations

20.

Mu, Mu. (1991). NONLINEAR STABILITY CRITERIA FOR MOTIONS OF MULTILAYER QUASI-GEOSTROPHIC FLOW. 2 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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