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Articles 1 - 6 of 6
Full-Text Articles in Other Oceanography and Atmospheric Sciences and Meteorology
Automated Machine Learning For High-Resolution Daily And Hourly Methane Emission Mapping For Rice Paddies Over South Korea: Integrating Modis, Era5-Land, And Soil Data, Jiah Jang, Seung Hee Kim, Menas Kafatos, Jaeil Cho, Gayoung Yoo, Sujong Jeong, Yangwon Lee
Automated Machine Learning For High-Resolution Daily And Hourly Methane Emission Mapping For Rice Paddies Over South Korea: Integrating Modis, Era5-Land, And Soil Data, Jiah Jang, Seung Hee Kim, Menas Kafatos, Jaeil Cho, Gayoung Yoo, Sujong Jeong, Yangwon Lee
Institute for ECHO Articles and Research
Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine …
Synthesis Study Of Evapotranspiration Evolution In The Mekong Delta Induced By Land Use And Land Cover Changes In The Decades Of 1990–2020, Emiliana Valentini, Serena Sapio, Son V. Nghiem, Seung Hee Kim, Sara Liburdi, Andrea Taramelli
Synthesis Study Of Evapotranspiration Evolution In The Mekong Delta Induced By Land Use And Land Cover Changes In The Decades Of 1990–2020, Emiliana Valentini, Serena Sapio, Son V. Nghiem, Seung Hee Kim, Sara Liburdi, Andrea Taramelli
Institute for ECHO Articles and Research
Evapotranspiration (ET) plays a key role in the water cycle and balance, and its estimation is of paramount importance in hydrological studies. This variable is also strongly influenced by the land use and land cover (LULC). This study use a synthesis approach to analyse the relationship between ET variation and LULC transformation in the period 1990–2020. The study area represents a major agricultural region in the Mekong Delta of Vietnam between the Mekong and Bassac rivers. The multivariate dataset was ingested into the hydrological model Soil and Water Assessment Tool to examine the multidecadal ET evolution across the study region. …
Multi-Satellite Image Matching And Deep Learning Segmentation For Detection Of Daytime Sea Fog Using Gk2a Ami And Gk2b Goci-Ii, Jonggu Kang, Hiroyuki Miyazaki, Seung Hee Kim, Menas Kafatos, Daesun Kim, Jinsoo Kim, Yangwon Lee
Multi-Satellite Image Matching And Deep Learning Segmentation For Detection Of Daytime Sea Fog Using Gk2a Ami And Gk2b Goci-Ii, Jonggu Kang, Hiroyuki Miyazaki, Seung Hee Kim, Menas Kafatos, Daesun Kim, Jinsoo Kim, Yangwon Lee
Institute for ECHO Articles and Research
Traditionally, sea fog detection technologies have relied primarily on in situ observations. However, point-based observations suffer from limitations in extensive monitoring in marine environments due to the scarcity of observation stations and the limited nature of measurement data. Satellites effectively address these issues by covering vast areas and operating across multiple spectral channels, enabling precise detection and monitoring of sea fog. Despite the increasing adoption of deep learning in this field, achieving further improvements in accuracy and reliability necessitates the simultaneous use of multiple satellite datasets rather than relying on a single source. Therefore, this study aims to achieve higher …
Multimodel And Multidiagnostic Ensemble-Based Deep Convective Area Forecast For Aviation Operations Using The Global Unified Model And Korean Integrated Model, Yi-June Park, Jung-Hoon Kim, Dan-Bi Lee, Hyun-Joo Choi, Soo-Hyun Kim, Matthias Steiner, Seung Hee Kim
Multimodel And Multidiagnostic Ensemble-Based Deep Convective Area Forecast For Aviation Operations Using The Global Unified Model And Korean Integrated Model, Yi-June Park, Jung-Hoon Kim, Dan-Bi Lee, Hyun-Joo Choi, Soo-Hyun Kim, Matthias Steiner, Seung Hee Kim
Institute for ECHO Articles and Research
To provide safe and efficient guidance for aircraft operation to avoid deep convective areas (DCAs) in Korea and broader East Asia, we developed a multimodel and multidiagnostic ensemble (MMDE)-based forecast system. This system utilizes two global numerical weather prediction (NWP) models based on the Unified Model (UM) and the Korean Integrated Model (KIM) operated by the Korea Meteorological Administration. To predict hazardous weather conditions, we defined DCAs using ground-based radar mosaic data where the 15-dBZ echo-top height exceeds flight level (FL) 250 (about z = 6.5 km) and FL350 (about z = 9 km). For predictors, we employed a …
Murphy Scale: A Locational Equivalent Intensity Scale For Hazard Events, Yi Victor Wang, Antonia Sebastian
Murphy Scale: A Locational Equivalent Intensity Scale For Hazard Events, Yi Victor Wang, Antonia Sebastian
Institute for ECHO Articles and Research
Empirical cross-hazard analysis and prediction of disaster vulnerability, resilience, and risk requires a common metric of hazard strengths across hazard types. In this paper, the authors propose an equivalent intensity scale for cross-hazard evaluation of hazard strengths of events for entire durations at locations. The proposed scale is called the Murphy Scale, after Professor Colleen Murphy. A systematic review and typology of hazard strength metrics is presented to facilitate the delineation of the defining dimensions of the proposed scale. An empirical methodology is introduced to derive equivalent intensities of hazard events on a Murphy Scale. Using historical data on …
Assessment Of Aerosol Optical Depth Under Background And Polluted Conditions Using Aeronet And Viirs Datasets, Mijin Kim, Seung Hee Kim, Woogyung Vincent Kim, Yun Gon Lee, Jhoon Kim, Menas C. Kafatos
Assessment Of Aerosol Optical Depth Under Background And Polluted Conditions Using Aeronet And Viirs Datasets, Mijin Kim, Seung Hee Kim, Woogyung Vincent Kim, Yun Gon Lee, Jhoon Kim, Menas C. Kafatos
Institute for ECHO Articles and Research
We investigated aerosol optical depth (AOD) under background and polluted conditions using Aerosol Robotic Network (AERONET) and Visible Infrared Imaging Radiometer Suite (VIIRS) observations. The AOD data were separated into background, high, and median AOD (BAOD, HAOD, and MAOD, respectively) based on the cumulative AOD distribution at each point and then their spatiotemporal variations were analyzed. Persistent pollutant emissions from industrial activity in South Asia (SUA) and Northeast Asia (NEA) produced the highest BAOD values. Gridded-BAODs obtained from VIIRS Deep Blue AOD products showed widespread high-level BAOD over the oceans associated with transport from dust and biomass burning events. The …