Chengsi Liu

439 total citations
18 papers, 297 citations indexed

About

Chengsi Liu is a scholar working on Atmospheric Science, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Chengsi Liu has authored 18 papers receiving a total of 297 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Atmospheric Science, 18 papers in Global and Planetary Change and 8 papers in Environmental Engineering. Recurrent topics in Chengsi Liu's work include Meteorological Phenomena and Simulations (18 papers), Climate variability and models (18 papers) and Precipitation Measurement and Analysis (9 papers). Chengsi Liu is often cited by papers focused on Meteorological Phenomena and Simulations (18 papers), Climate variability and models (18 papers) and Precipitation Measurement and Analysis (9 papers). Chengsi Liu collaborates with scholars based in United States, China and Austria. Chengsi Liu's co-authors include Ming Xue, Rong Kong, Youngsun Jung, Bin Wang, Juanjuan Liu, Wei Cheng, Qingnong Xiao, Ying‐Hwa Kuo, Alexandre O. Fierro and Edward R. Mansell and has published in prestigious journals such as Geophysical Research Letters, Monthly Weather Review and Weather and Forecasting.

In The Last Decade

Chengsi Liu

16 papers receiving 289 citations

Peers

Chengsi Liu

GPC

OCEAN

Fengjiao Chen China

Yangruixue Chen China

Fabrizio Baordo United Kingdom

Vineeth Krishnan Valappil United Arab Emirates

Sana Mahmood United Kingdom

Daniel Watters United Kingdom

Noureddine Semane Morocco

E. L. McGrath‐Spangler United States

Xuwu Xiang United States

M. Das Gupta India

Fengjiao Chen China

Chengsi Liu

342 ×1.2

244 ×0.9

GPC

41 ×0.7

21 ×0.8

OCEAN

14 ×2.3

Citations per year, relative to Chengsi Liu Chengsi Liu (= 1×) peers Fengjiao Chen

Countries citing papers authored by Chengsi Liu

Since Specialization

Citations

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

Fields of papers citing papers by Chengsi Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chengsi Liu

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

All Works

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18 of 18 papers shown

Xue, Ming, et al.. (2024). Development of Multiscale EnKF within GSI and Its Applications to Multiple Convective Storm Cases with Radar Reflectivity Data Assimilation Using the FV3 Limited-Area Model. Monthly Weather Review. 152(8). 1839–1857.

Xue, Ming, et al.. (2023). Implementation and Testing of Radar Data Assimilation Capabilities Within the Joint Effort for Data Assimilation Integration Framework With Ensemble Transformation Kalman Filter Coupled With FV3‐LAM Model. Geophysical Research Letters. 50(11). 2 indexed citations

Kong, Rong, Ming Xue, Edward R. Mansell, Chengsi Liu, & Alexandre O. Fierro. (2023). Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case. Advances in Atmospheric Sciences. 41(2). 263–277. 1 indexed citations

Gao, Jidong, et al.. (2023). Test of Power Transformation Function to Hydrometeor and Water Vapor Mixing Ratios for Direct Variational Assimilation of Radar Reflectivity Data. Weather and Forecasting. 38(10). 1995–2010. 1 indexed citations

Li, Huiqi, et al.. (2022). Use of Power Transform Total Number Concentration as Control Variable for Direct Assimilation of Radar Reflectivity in GSI En3DVar and Tests with Six Convective Storms Cases. Monthly Weather Review. 150(4). 821–842. 3 indexed citations

Liu, Chengsi, et al.. (2022). Use of a Reflectivity Operator Based on Double-Moment Thompson Microphysics for Direct Assimilation of Radar Reflectivity in GSI-Based Hybrid En3DVar. Monthly Weather Review. 150(4). 907–926. 8 indexed citations

Liu, Chengsi, et al.. (2022). Object-Based Verification of GSI EnKF and Hybrid En3DVar Radar Data Assimilation and Convection-Allowing Forecasts within a Warn-on-Forecast Framework. Weather and Forecasting. 37(5). 639–658. 7 indexed citations

Kong, Rong, Ming Xue, Chengsi Liu, Alexandre O. Fierro, & Edward R. Mansell. (2022). Development of New Observation Operators for Assimilating GOES-R Geostationary Lightning Mapper Flash Extent Density Data Using GSI EnKF: Tests with Two Convective Events over the United States. Monthly Weather Review. 150(8). 2091–2110. 5 indexed citations

Kong, Rong, Ming Xue, Chengsi Liu, & Youngsun Jung. (2020). Comparisons of Hybrid En3DVar with 3DVar and EnKF for Radar Data Assimilation: Tests with the 10 May 2010 Oklahoma Tornado Outbreak. Monthly Weather Review. 149(1). 21–40. 8 indexed citations

10.

Liu, Chengsi, et al.. (2020). Use of Power Transform Mixing Ratios as Hydrometeor Control Variables for Direct Assimilation of Radar Reflectivity in GSI En3DVar and Tests with Five Convective Storm Cases. Monthly Weather Review. 149(3). 645–659. 12 indexed citations

11.

Kong, Rong, Ming Xue, Alexandre O. Fierro, et al.. (2020). Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI EnKF for the Analysis and Short-Term Forecast of a Mesoscale Convective System. Monthly Weather Review. 148(5). 2111–2133. 25 indexed citations

12.

Jung, Youngsun, et al.. (2020). Direct Assimilation of Radar Data With Ensemble Kalman Filter and Hybrid Ensemble‐Variational Method in the National Weather Service Operational Data Assimilation System GSI for the Stand‐Alone Regional FV3 Model at a Convection‐Allowing Resolution. Geophysical Research Letters. 47(19). 24 indexed citations

13.

Jung, Youngsun, et al.. (2020). Evaluating Forecast Performance and Sensitivity to the GSI EnKF Data Assimilation Configuration for the 28–29 May 2017 Mesoscale Convective System Case. Weather and Forecasting. 36(1). 127–146. 11 indexed citations

14.

Liu, Chengsi, Ming Xue, & Rong Kong. (2019). Direct Variational Assimilation of Radar Reflectivity and Radial Velocity Data: Issues with Nonlinear Reflectivity Operator and Solutions. Monthly Weather Review. 148(4). 1483–1502. 19 indexed citations

15.

Liu, Chengsi, Ming Xue, & Rong Kong. (2018). Direct Assimilation of Radar Reflectivity Data Using 3DVAR: Treatment of Hydrometeor Background Errors and OSSE Tests. Monthly Weather Review. 147(1). 17–29. 35 indexed citations

16.

Kong, Rong, Ming Xue, & Chengsi Liu. (2017). Development of a Hybrid En3DVar Data Assimilation System and Comparisons with 3DVar and EnKF for Radar Data Assimilation with Observing System Simulation Experiments. Monthly Weather Review. 146(1). 175–198. 41 indexed citations

17.

Liu, Chengsi & Ming Xue. (2015). Relationships among Four-Dimensional Hybrid Ensemble–Variational Data Assimilation Algorithms with Full and Approximate Ensemble Covariance Localization. Monthly Weather Review. 144(2). 591–606. 18 indexed citations

18.

Wang, Bin, Juanjuan Liu, Wei Cheng, et al.. (2010). An economical approach to four-dimensional variational data assimilation. Advances in Atmospheric Sciences. 27(4). 715–727. 77 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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