This map shows the geographic impact of Dong‐Jun Seo'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 Dong‐Jun Seo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dong‐Jun Seo more than expected).
This network shows the impact of papers produced by Dong‐Jun Seo. 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 Dong‐Jun Seo. The network helps show where Dong‐Jun Seo may publish in the future.
Co-authorship network of co-authors of Dong‐Jun Seo
This figure shows the co-authorship network connecting the top 25 collaborators of Dong‐Jun Seo.
A scholar is included among the top collaborators of Dong‐Jun Seo 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 Dong‐Jun Seo. Dong‐Jun Seo is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Seo, Dong‐Jun, et al.. (2019). Improving Medium-range Probabilistic Quantitative Precipitation Forecast for Heavy-to-extreme Events through the Conditional Bias-penalized Regression. AGU Fall Meeting Abstracts. 2019.1 indexed citations
5.
Seo, Dong‐Jun, et al.. (2019). Streamflow data assimilation for hydrologic river routing: advances and challenges. AGU Fall Meeting Abstracts. 2019.1 indexed citations
6.
Seo, Dong‐Jun. (2017). Integrated Sensing and Prediction of Flash Floods for the Dallas-Fort Worth Metroplex (DFW).3 indexed citations
7.
Alizadeh, Babak, et al.. (2016). Integrating Ensemble Forecasts of Precipitation and Streamflow into Decision Support for Reservoir Operations in North Central Texas. AGU Fall Meeting Abstracts. 2016.1 indexed citations
8.
Prat, O. P., et al.. (2015). Merging Radar Quantitative Precipitation Estimates (QPEs) from the High-resolution NEXRAD Reanalysis over CONUS with Rain-gauge Observations. AGU Fall Meeting Abstracts. 2015.1 indexed citations
9.
Seo, Dong‐Jun, et al.. (2015). On improving ensemble forecasting of extreme precipitation using the NWS Meteorological Ensemble Forecast Processor (MEFP). AGU Fall Meeting Abstracts. 2015.3 indexed citations
10.
Prat, O. P., et al.. (2014). Long-Term Large-Scale Bias-Adjusted Precipitation Estimates at High Spatial and Temporal Resolution Derived from the National Mosaic and Multi-Sensor QPE (NMQ/Q2) Precipitation Reanalysis over CONUS. AGU Fall Meeting Abstracts. 2014.1 indexed citations
11.
Seo, Dong‐Jun, et al.. (2014). High-resolution flash flood forecasting for large urban areas - Sensitivity to scale of precipitation input and model resolution. CUNY Academic Works (City University of New York). 2013.2 indexed citations
12.
Chandrasekar, V., Haonan Chen, Brenda Philips, et al.. (2013). The CASA Dallas Fort Worth Remote Sensing Network ICT for Urban Disaster Mitigation. EGUGA.13 indexed citations
Liu, Yuqiong, James Dean Brown, Julie Demargne, & Dong‐Jun Seo. (2010). Using Wavelet Analysis to Assess Timing Errors in Streamflow Predictions. EGU General Assembly Conference Abstracts. 5456.1 indexed citations
16.
Koren, Victor, et al.. (2009). Reducing uncertainties in model initial conditions via variational assimilation of hydrologic and hydrometeorological data into distributed hydrologic models. AGU Fall Meeting Abstracts. 2009.1 indexed citations
17.
Schaake, John C., et al.. (2008). 6.2 A STATISTICAL-DISTRIBUTED MODELING APPROACH FOR FLASH FLOOD PREDICTION.
18.
Smith, Michael, V. Koren, Ziya Zhang, et al.. (2004). NOAA NWS distributed hydrologic modeling research and development.5 indexed citations
19.
Seo, Dong‐Jun, et al.. (2003). Real-time variational assimilation of streamflow and radar-based precipitation data into operational hydrologic forecasting. EGS - AGU - EUG Joint Assembly. 14671.3 indexed citations
20.
Smith, Michael, et al.. (2002). Evaluating the Results of DMIP: How the NWS will Move Forward with Distributed Modeling. AGUSM. 2002.1 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.