Myong-In Lee
Ulsan National Institute of Science and Technology
研究室紹介
Professor Myong-In Lee's research lab specializes in atmospheric science and numerical weather prediction, focusing on improving the accuracy of heavy precipitation forecasts through innovative bias correction techniques. The lab develops phase-aware methodologies that leverage cloud-top temperature (CTT) to distinguish between different types of systematic errors in all-sky infrared radiances within NWP models. Their work emphasizes correcting cloud formation errors by integrating cloud microphysical characteristics into radiance bias correction frameworks, enhancing the reliability of weather and climate models. The lab also contributes to advancing data assimilation systems, particularly within the WRF/WRFDA framework.
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