Dong Jun Seo

What is he best known for?

The fields of study he is best known for:

• Statistics
• Meteorology
• Hydrology

His primary areas of investigation include Meteorology, Rain gauge, Streamflow, Precipitation and Distributed element model. His Meteorology research incorporates themes from Runoff model, Climatology, NEXRAD, Weather radar and Range. Dong Jun Seo combines subjects such as Hydrometeorology and Service with his study of Climatology.

His research integrates issues of Radar rainfall, Remote sensing and Conditional expectation in his study of Rain gauge. His Streamflow study combines topics from a wide range of disciplines, such as Forcing, National weather service, Mode and Water supply. Precipitation and Routing are frequently intertwined in his study.

His most cited work include:

• The WSR-88D Rainfall Algorithm (780 citations)
• Overall distributed model intercomparison project results (399 citations)
• The distributed model intercomparison project (DMIP): Motivation and experiment design (344 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Meteorology, Precipitation, Streamflow, Data assimilation and Climatology. His work carried out in the field of Meteorology brings together such families of science as Remote sensing and Flood forecasting. His Precipitation research is multidisciplinary, incorporating elements of Algorithm, Flood myth, Flash flood and Multi sensor.

Dong Jun Seo has included themes like Routing, Hydrograph, Potential evaporation and Hydrological modelling in his Streamflow study. His Data assimilation research is multidisciplinary, incorporating perspectives in Hydrology, Water quality and Water resources. While the research belongs to areas of Climatology, Dong Jun Seo spends his time largely on the problem of Ensemble forecasting, intersecting his research to questions surrounding Probability distribution.

He most often published in these fields:

• Meteorology (57.71%)
• Precipitation (30.29%)
• Streamflow (31.43%)

What were the highlights of his more recent work (between 2017-2021)?

• Streamflow (31.43%)
• Data assimilation (24.57%)
• Meteorology (57.71%)

In recent papers he was focusing on the following fields of study:

Dong Jun Seo spends much of his time researching Streamflow, Data assimilation, Meteorology, Remote sensing and Precipitation. His Streamflow research focuses on subjects like Operational forecasting, which are linked to National weather service, Water resource management and Drainage basin. His Data assimilation study integrates concerns from other disciplines, such as Snow, SNOTEL and Flood forecasting.

He has researched Meteorology in several fields, including Mean squared error, Routing, Communication channel and Hydrological modelling. In the subject of general Precipitation, his work in Rain gauge is often linked to Terrain, thereby combining diverse domains of study. His biological study deals with issues like Hydrometeorology, which deal with fields such as Conditional bias.

Between 2017 and 2021, his most popular works were:

• Hyper-resolution 1D-2D urban flood modelling using LiDAR data and hybrid parallelization (20 citations)
• Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods (10 citations)
• Multiscale Postprocessor for Ensemble Streamflow Prediction for Short to Long Ranges (7 citations)

In his most recent research, the most cited papers focused on:

• Statistics
• Meteorology
• Hydrology

Dong Jun Seo mostly deals with Meteorology, Precipitation, Streamflow, Flood myth and Operational forecasting. His Meteorology study combines topics in areas such as Optimal estimation, Estimator and Cross-validation. His research in Streamflow intersects with topics in Routing and Distributed element model.

His Flood myth research includes themes of Lidar, Digital elevation model and Parallel computing. In his research on the topic of Operational forecasting, Stochastic modelling is strongly related with National weather service. His study looks at the relationship between Kalman filter and topics such as Mean squared error, which overlap with Rain gauge.