Zobrazeno 1 - 10 of 56 pro vyhledávání: '"Joo-Wan Cha"'
1
Akademický článek
Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018
Autor: Ju-Young Shin, Yonghun Ro, Joo-Wan Cha, Kyu-Rang Kim, Jong-Chul Ha
Publikováno v: Advances in Meteorology, Vol 2019 (2019)
Machine learning algorithms should be tested for use in quantitative precipitation estimation models of rain radar data in South Korea because such an application can provide a more accurate estimate of rainfall than the conventional ZR relationship-
Externí odkaz: https://doaj.org/article/f5ffa8e85f8748a5877323f7b27887cf
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2
Akademický článek
Estimation of reference evapotranspiration in South Korea using GK-2A AMI channel data and a tree-based machine learning method
Autor: Bu-Yo Kim, Joo Wan Cha
Publikováno v: Science of Remote Sensing, Vol 10, Iss , Pp 100171- (2024)
Changes in evapotranspiration can affect water availability and climate, leading to extreme weather and severe impact on ecosystems. In particular, increased water stress in farmland, forests, and mountainous areas with limited water resources can re
Externí odkaz: https://doaj.org/article/233c4b537d6b45e79d221eb05cd7ca14
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3
Akademický článek
Progressive and Prospective Technology for Cloud Seeding Experiment by Unmanned Aerial Vehicle and Atmospheric Research Aircraft in Korea
Autor: Woonseon Jung, Joo Wan Cha, A.-Reum Ko, Sanghee Chae, Yonghun Ro, Hyun Jun Hwang, Bu-Yo Kim, Jung Mo Ku, Ki-Ho Chang, Chulkyu Lee
Publikováno v: Advances in Meteorology, Vol 2022 (2022)
This study applies a novel cloud seeding method using an unmanned aerial vehicle (UAV) and a research aircraft in Korea. For this experiment, the UAV sprayed a cloud seeding material (calcium chloride), and the aircraft monitored the clouds in the so
Externí odkaz: https://doaj.org/article/732310407a3a481098f5868adaae490d
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4
Akademický článek
Theoretical Calculations of Directional Scattering Intensities of Small Nonspherical Ice Crystals: Implications for Forward Scattering Probes
Autor: Seonghyeon Jang, Jeonggyu Kim, Greg M. McFarquhar, Sungmin Park, Seoung Soo Lee, Chang Hoon Jung, Sang Seo Park, Joo Wan Cha, Kyoungmi Lee, Junshik Um
Publikováno v: Remote Sensing, Vol 14, Iss 12, p 2795 (2022)
In situ aircraft measurements of the sizes and concentrations of liquid cloud droplets and ice crystals with maximum dimensions (Dmax) less than ~50 μm have been measured mainly using forward scattering probes over the past half century. The operati
Externí odkaz: https://doaj.org/article/fb70cd5cf42e496b9ae68d709735622a
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5
Analysis of Rain Drop Size Distribution to Elucidate the Precipitation Process using a Cloud Microphysics Conceptual Model and In Situ Measurement
Autor: Joo Wan Cha, Hae Jung Koo, Bu-Yo Kim, Belorid Miloslav, Hyun Jun Hwang, Min Hoo Kim, Ki-Ho Chang, Yong Hee Lee
Publikováno v: Asia-Pacific Journal of Atmospheric Sciences. 59:257-269
Raindrop size distribution (DSD) is an important parameter in rainfall research and can be used for quantitative precipitation estimation (QPE) in meteorology and hydrology. DSD also improves the understanding of the uncertainty of cloud microphysica
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::82b3aa35536f5162f700f5b20b19491a
https://doi.org/10.1007/s13143-022-00299-w
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6
Short-Term Visibility Prediction Using Tree-Based Machine Learning Algorithms and Numerical Weather Prediction Data
Autor: Bu-Yo Kim, Miloslav Belorid, Joo Wan Cha
Publikováno v: Weather and Forecasting. 37:2263-2274
Accurate visibility prediction is imperative in the interests of human and environmental health. However, the existing numerical models for visibility prediction are characterized by low prediction accuracy and high computational cost. Thus, in this
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fe47ffdb6a63e2e5737302652f06f41a
https://doi.org/10.1175/waf-d-22-0053.1
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9
Akademický článek
Visibility Prediction over South Korea Based on Random Forest
Autor: Bu-Yo Kim, Joo Wan Cha, Ki-Ho Chang, Chulkyu Lee
Publikováno v: Atmosphere, Vol 12, Iss 5, p 552 (2021)
In this study, the visibility of South Korea was predicted (VISRF) using a random forest (RF) model based on ground observation data from the Automated Synoptic Observing System (ASOS) and air pollutant data from the European Centre for Medium-Range
Externí odkaz: https://doaj.org/article/951796abe6094b539fd6e8e67b056c3b
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