YL
Member Since 2020
Yoonjin Lee
Research Scientist I, Cooperative Institute for Research in the Atmosphere
AGU Research
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GREMLIN: GOES Radar Estimation via Machine Learning to Inform NWP
WEATHER, WATER, AND CLIMATE APPLICATIONS FROM GEOSTATIONARY SATELLITES OF THE PRESENT AND FUTURE I POSTER
atmospheric sciences | 12 december 2023
Kyle Hilburn, Yoonjin Lee
Imagery from the Geostationary Operational Environmental Satellite (GOES) has been a key element of U.S. operational weather forecasting since 1975. T...
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Retrieval of boundary layer precipitable water from GOES ABI using machine learning techniques
WEATHER, WATER, AND CLIMATE APPLICATIONS FROM GEOSTATIONARY SATELLITES OF THE PRESENT AND FUTURE II ORAL
atmospheric sciences | 12 december 2023
Yoonjin Lee, Kyle Hilburn
Boundary layer precipitable water is an important quantity for accurate prediction of convective initiation. Observing low-level moisture is especiall...
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GREMLIN: GOES Radar Estimation via Machine Learning to Inform NWP
EARTH OBSERVATIONS FROM GEOSTATIONARY SATELLITES: APPLIED RESEARCH AND APPLICATIONS IV POSTER
atmospheric sciences | 15 december 2021
Kyle Hilburn, Yoonjin Lee, Imme Ebert-Uphoff
Earth observations from the GOES-R Series provide high-resolution rapidly refreshing information to support situational awareness in weather forecasti...
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Using neural network methods to estimate cloud properties from satellite imagery – challenges and recent approaches
PRECIPITATION THROUGH THE EYES OF MACHINE LEARNING AND ADVANCED STATISTICS: REMOTE SENSING, UNCERTAINTIES, AND VARIABILITY I ORAL
hydrology | 14 december 2021
Imme Ebert-Uphoff, Kyle Hilburn, Ryan Lagerquist, ...
Machine learning methods, especially neural networks, have demonstrated remarkable abilities to detect and utilize spatial patterns in satellite image...
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Critical Research Pathways in Satellite-Based Dense Optical Flow Techniques for Atmospheric Science
EARTH OBSERVATIONS FROM GEOSTATIONARY SATELLITES: APPLIED RESEARCH AND APPLICATIONS I ORAL
atmospheric sciences | 14 december 2021
Jason Apke, Steven D. Miller, Matthew A. Rogers, K...
Over the last three years, the computation of Advanced Baseline Imager-enabled Dense Optical Flow (DOF), or the apparent brightness feature motions in...
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Applying machine learning methods to detect convection using GOES-16 ABI data
PRECIPITATION THROUGH THE EYES OF MACHINE LEARNING AND ADVANCED STATISTICS: REMOTE SENSING, UNCERTAINTIES, AND VARIABILITY I ORAL
hydrology | 14 december 2021
Yoonjin Lee, Christian D. Kummerow, Imme Ebert-Uph...
Initiating deep atmospheric convection in high-resolution regional models, such as RAP/HRRR, is achieved by applying latent heating in convective regi...
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Assimilating GOES-R Latent Heating in FV3 using Machine Learning
EARTH OBSERVATIONS FROM GEOSTATIONARY SATELLITES: APPLIED RESEARCH AND APPLICATIONS II
atmospheric sciences | 07 december 2020
Yoonjin Lee, Kyle Hilburn, Milija Zupanski, Ting-C...
Imagery from the GOES series has been a key element of U.S. operational weather forecasting for four decades. While GOES observations are used extensi...
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Impacts of Assimilating Vertical Velocity, Latent Heating, or Hydrometeor Water Contents Retrieved From a Single Reflectivity Data Set
JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES
03 february 2018
Yoonjin Lee, Christian Kummerow, Milija Zupanski
Assimilation of observation data in cloudy regions has been challenging due to the unknown properties of clouds such as cloud depth or cloud drop s...