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Articles by Jungho Im
Deep learning-based forecasting provides a pathway to closing wildfire information gaps in underserved regions
Abstract The 2025 Los Angeles wildfires highlighted growing risks from climate-driven extremes and the need for reliable wildfire forecasting beyond short lead times. Although the European Centre for Medium-Range Weather Forecasts provides global fire weather index forecasts, these products are primarily designed for long-range climate outlooks, and their practical effectiveness remains uncertain in regions with limited local forecasting infrastructure.
Global patterns of urban heat shaped by climate and morphology
Abstract Urban morphology and background climate are evolving drivers of the urban heat island (UHI) effect, yet the thermal influence of surrounding urban structure and its coupling with climate remain elusive. Here, we show that climate and morphology jointly shape global urban heat. Using a six-class urban typology, long-term climatology, and machine learning, we quantify the thermal influence of surrounding urban structure and project future urban heat across 2,213 cities.
Correction to “Quantifying Multi-pollutant Co-exposure via Deep Learning-Based Simultaneous Prediction Using Geostationary Satellite Data” Click to copy article link Article link copied!
The legend colors for NO2 and O3 were interchanged in Figure 3. This has been corrected in the revised figure. The legends in the related time series plots have also been corrected in Figures S16 and S17 in the Supporting Information, and the Supporting Information has been updated accordingly. The hourly O3 time-series and corresponding statistics in Figure 3 (BTH) and Supporting Information Figures S16 (YRD) and S17 (SMA) have been corrected. The underlying model predictions remain unchanged.
Beyond localized methane plume detection: a dual-path deep learning framework for sensor-agnostic global hyperspectral methane plume monitoring
Abstract Methane (CH4) is a dominant driver of near-term warming, yet global emission monitoring remains constrained by slow processing and large uncertainties. Hyperspectral spectrometers enable sensitive detection of CH4 plumes, but the relative advantages of enhancement-based (ENH) and radiance-based (RAD) approaches have not been systematically evaluated.
Quantifying Multi-pollutant Co-exposure via Deep Learning-Based Simultaneous Prediction Using Geostationary Satellite Data Click to copy article link Article link copied!
1. Introduction Click to copy section linkSection link copied! Air pollution exposure is inherently multi-pollutant, as the air we breathe is a complex mixture of pollutants─particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and sulfur dioxide (SO2).
Unveiling teleconnection drivers for heatwave prediction in South Korea using explainable artificial intelligence - npj Climate and Atmospheric Science
Abstract Increasing heatwave intensity and mortality demand timely and accurate heatwave prediction. The present study focused on teleconnection, the influence of distant land and ocean variability on local weather events, to drive long-term heatwave predictions. The complexity of teleconnection poses challenges for physical-based prediction models.
Improving Short-Term Prediction of Ocean Fog Using Numerical Weather Forecasts and Geostationary Satellite-Derived Ocean Fog Data Based on AutoML
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Insights into Canopy Escape Ratio from Canopy Structures: Correlations Uncovered through Sentinel-2 and Field Observation
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
A new statistical downscaling approach for short‐term forecasting of summer air temperatures through a fusion of deep learning and spatial interpolation - Cho - Quarterly Journal of the Royal Meteorological Society - Wiley Online Library
REFERENCES , , , , & (2019) Machine learning for precipitation nowcasting from radar images. arXiv preprint arXiv:1912.12132. , , , & (2022, October) The relationship between air temperature and land surface temperature in a desert climate city. In: Remote sensing technologies and applications in urban environments VII, Vol. 12269. Bellingham, WA: SPIE, pp. 64–70. , , , & (2015) Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania.
Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests
Open AccessArticle by 1, 2, 3, 1,† and 1,*,† 1 Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, China 2 Department of Sustainable Resources Management, State University of New York College of Environmental Science and Forestry, Syracuse, NY 13210, USA 3 Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44610, Republic of Korea * Author to...
Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2.5 predictability | npj Climate and Atmospheric Science
Abstract Concentrations of ambient particulate matter (such as PM2.5 and PM10) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM2.5 prediction systems.
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