Taereem Kim

620 total citations
20 papers, 471 citations indexed

About

Taereem Kim is a scholar working on Global and Planetary Change, Water Science and Technology and Atmospheric Science. According to data from OpenAlex, Taereem Kim has authored 20 papers receiving a total of 471 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Global and Planetary Change, 10 papers in Water Science and Technology and 9 papers in Atmospheric Science. Recurrent topics in Taereem Kim's work include Climate variability and models (12 papers), Hydrology and Watershed Management Studies (10 papers) and Meteorological Phenomena and Simulations (7 papers). Taereem Kim is often cited by papers focused on Climate variability and models (12 papers), Hydrology and Watershed Management Studies (10 papers) and Meteorological Phenomena and Simulations (7 papers). Taereem Kim collaborates with scholars based in United States, South Korea and China. Taereem Kim's co-authors include Jun‐Haeng Heo, Ju‐Young Shin, Tiantian Yang, Yang Hong, Lujun Zhang, Sunghun Kim, Hanbeen Kim, Shang Gao, Jonathan J. Gourley and Ziyu Ding and has published in prestigious journals such as Water Resources Research, Geophysical Research Letters and Journal of Hydrology.

In The Last Decade

Taereem Kim

18 papers receiving 465 citations

Peers

Taereem Kim

GPC

WST

Meral Büyükyıldız Türkiye

Fatih Tosunoğlu Türkiye

Faisal Baig United Arab Emirates

Yingchun Huang China

Boosik Kang South Korea

Morteza Lotfirad Iran

Guy Shalev United States

Baoxu Zhao China

Fuad Yassin Canada

Khada Nanda Dulal Japan

Meral Büyükyıldız Türkiye

Taereem Kim

230 ×0.7

GPC

240 ×0.9

WST

283 ×1.3

72 ×0.5

42 ×0.8

Citations per year, relative to Taereem Kim Taereem Kim (= 1×) peers Meral Büyükyıldız

Countries citing papers authored by Taereem Kim

Since Specialization

Citations

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

Fields of papers citing papers by Taereem Kim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Taereem Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Taereem Kim. A scholar is included among the top collaborators of Taereem Kim 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 Taereem Kim. Taereem Kim 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

Kim, Taereem, Gabriele Villarini, Andreas F. Prein, et al.. (2025). Climate change reduces the wind chill hazard across Alaska. Communications Earth & Environment. 6(1).

Kim, Taereem, Gabriele Villarini, J. Done, et al.. (2025). Ensemble downscaled climate dataset for Alaska and Hawaii under historical and future conditions. Scientific Data. 12(1). 1089–1089.

Kim, Taereem, Gabriele Villarini, J. Done, et al.. (2024). Dominant Sources of Uncertainty for Downscaled Climate: A Military Installation Perspective. Journal of Geophysical Research Atmospheres. 129(12). 1 indexed citations

Villarini, Gabriele, et al.. (2024). Understanding the Impact of Precipitation Bias‐Correction and Statistical Downscaling Methods on Projected Changes in Flood Extremes. Earth s Future. 12(3). 9 indexed citations

Kim, Taereem & Gabriele Villarini. (2024). Projected changes in daily precipitation, temperature and wet‐bulb temperature across Arizona using statistically downscaled CMIP6 climate models. International Journal of Climatology. 44(6). 1994–2010. 5 indexed citations

Villarini, Gabriele, et al.. (2023). Disentangling the Sources of Uncertainties in the Projection of Flood Risk Across the Central United States (Iowa). Geophysical Research Letters. 50(22). 3 indexed citations

Villarini, Gabriele, et al.. (2023). Evaluation of CMIP6 HighResMIP for Hydrologic Modeling of Annual Maximum Discharge in Iowa. Water Resources Research. 59(8). 9 indexed citations

Kim, Taereem, Tiantian Yang, Lujun Zhang, & Yang Hong. (2022). Near real-time hurricane rainfall forecasting using convolutional neural network models with Integrated Multi-satellitE Retrievals for GPM (IMERG) product. Atmospheric Research. 270. 106037–106037. 28 indexed citations

Kim, Hanbeen, Taereem Kim, Ju‐Young Shin, & Jun‐Haeng Heo. (2022). Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty. Water. 14(3). 478–478. 8 indexed citations

10.

Zhang, Lujun, Taereem Kim, Tiantian Yang, Yang Hong, & Qian Zhu. (2021). Evaluation of Subseasonal-to-Seasonal (S2S) precipitation forecast from the North American Multi-Model ensemble phase II (NMME-2) over the contiguous U.S.. Journal of Hydrology. 603. 127058–127058. 37 indexed citations

11.

Kim, Taereem, Tiantian Yang, Shang Gao, et al.. (2021). Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS. Journal of Hydrology. 598. 126423–126423. 111 indexed citations

12.

Yang, Tiantian, et al.. (2021). A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region. Journal of Hydrology. 602. 126723–126723. 47 indexed citations

13.

Kim, Hanbeen, Ju‐Young Shin, Taereem Kim, Sunghun Kim, & Jun‐Haeng Heo. (2020). Regional frequency analysis of extreme precipitation based on a nonstationary population index flood method. Advances in Water Resources. 146. 103757–103757. 16 indexed citations

14.

Kim, Taereem, Ju‐Young Shin, Hanbeen Kim, & Jun‐Haeng Heo. (2020). Ensemble‐Based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection. Water Resources Research. 56(6). 29 indexed citations

15.

Shin, Ju‐Young, et al.. (2019). Spatial and temporal variations in rainfall erosivity and erosivity density in South Korea. CATENA. 176. 125–144. 50 indexed citations

16.

Kim, Taereem, Ju‐Young Shin, Hanbeen Kim, Sunghun Kim, & Jun‐Haeng Heo. (2019). The Use of Large-Scale Climate Indices in Monthly Reservoir Inflow Forecasting and Its Application on Time Series and Artificial Intelligence Models. Water. 11(2). 374–374. 35 indexed citations

17.

Kim, Taereem, Ju‐Young Shin, Sunghun Kim, & Jun‐Haeng Heo. (2017). Identification of relationships between climate indices and long-term precipitation in South Korea using ensemble empirical mode decomposition. Journal of Hydrology. 557. 726–739. 47 indexed citations

18.

Cho, Woncheol, et al.. (2014). Flood Frequency Analysis for the Annual Peak Flows Simulated by an Event-Based Rainfall-Runoff Model in an Urban Drainage Basin. Water. 6(12). 3841–3863. 32 indexed citations

19.

Park, Jiyeon, et al.. (2014). Future Inflow Simulation Considering the Uncertainties of TFN Model and GCMs on Chungju Dam Basin. Journal of Korea Water Resources Association. 47(2). 135–143. 3 indexed citations

20.

Kim, Taereem, Hongjoon Shin, & Jun‐Haeng Heo. (2013). A Study on Empirical Distribution Function with Unknown Shape Parameter and Extreme Value Weight for Three Parameter Weibull Distribution. Journal of Korea Water Resources Association. 46(6). 643–653. 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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