Dong-Hyun Cha
Ulsan National Institute of Science and Technology
About the Lab
Professor Dong-Hyun Cha's research lab specializes in numerical weather prediction and atmospheric remote sensing, focusing on improving the accuracy of weather forecasts—particularly for extreme events like heavy precipitation—through innovative bias correction techniques. The lab develops phase-aware methods that leverage cloud-top temperature (CTT) to distinguish and correct systematic errors in all-sky infrared radiances within NWP systems such as WRF/WRFDA. Their work emphasizes the physical interpretation of cloud-related biases, enhancing model performance by addressing errors in cloud formation and positioning. The lab integrates machine learning and physical modeling to advance the understanding and correction of radiative biases in operational weather prediction.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
2Abstract This repository provides the Python implementation and sample datasets for the Phase-Aware Bias Correction method, as described in the manuscript: "Improving Heavy Precipitation Forecasts via Phase-Aware Bias Correction of All-Sky Infrared Radiances Using Cloud-Top Temperature" (submitted to Journal of Advances in Modeling Earth Systems (JAMES)). Key Methodology The provided code addresses systematic biases in all-sky infrared (IR) radiances within numerical weather prediction (NWP) sys
Abstract This repository provides the Python implementation and sample datasets for the Phase-Aware Bias Correction method, as described in the manuscript: "Improving Heavy Precipitation Forecasts via Phase-Aware Bias Correction of All-Sky Infrared Radiances Using Cloud-Top Temperature" (submitted to Journal of Advances in Modeling Earth Systems (JAMES)). Key Methodology The provided code addresses systematic biases in all-sky infrared (IR) radiances within numerical weather prediction (NWP) sys
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