Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Chul Han Song"'
Autor:
Arman Pouyaei, Bavand Sadeghi, Yunsoo Choi, Jia Jung, Amir H. Souri, Chun Zhao, Chul Han Song
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 13, Iss 6, Pp n/a-n/a (2021)
Abstract To improve the representation of convective mixing of atmospheric pollutants in the presence of clouds, we developed a convection module based on Kain and Fritsch (KF) method and implemented it in the Community Multiscale Air Quality model.
Externí odkaz:
https://doaj.org/article/532aa45bdab34a639a31971aa0512d2a
Publikováno v:
Sensors, Vol 21, Iss 3, p 941 (2021)
Weather is affected by a complex interplay of factors, including topography, location, and time. For the prediction of temperature in Korea, it is necessary to use data from multiple regions. To this end, we investigate the use of deep neural-network
Externí odkaz:
https://doaj.org/article/3e8014bdadf74ff6bb62354570dbe528
Publikováno v:
Atmosphere, Vol 10, Iss 11, p 718 (2019)
In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitati
Externí odkaz:
https://doaj.org/article/779ce91c653647d89ccf51985d89217a
Autor:
Chul Han Song, Chung Man Kim
Publikováno v:
Asian Journal of Atmospheric Environment, Vol 5, Iss 2, Pp 97-104 (2011)
Recent studies have indicated that the observed nitric acid (HNO3) uptake rates (RHNO3) onto dust particles are much slower than RHNO3 used in the previous modeling studies. Three factors that possibly affect RHNO3 onto dust particles are discussed i
Externí odkaz:
https://doaj.org/article/9a136101c1b64950a68e8722e3ae57de
Autor:
Bok H. Baek, Rizzieri Pedruzzi, Minwoo Park, Chi-Tsan Wang, Younha Kim, Chul-Han Song, Jung-Hun Woo
Publikováno v:
Geoscientific Model Development. 15:4757-4781
The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile emissions inventory model designed to efficiently estimate high-quality emissions from motor vehicle emission sources. It can estimate air pollutant, greenh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35b08584e5b9afa9296beeb8132fab8f
https://doi.org/10.5194/gmd-15-4757-2022
https://doi.org/10.5194/gmd-15-4757-2022
The Korean Air Chemistry Modeling System (K_ACheMS) has been developed to enhance the predictability of PM2.5 in South Korea. In the current version (v2.0) of K_ACheMS, two meteorological models are used to produce meteorological fields. The first mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5e2bdd20ffe8db0e5aa54cb42ae8f91b
https://doi.org/10.5194/egusphere-egu23-10089
https://doi.org/10.5194/egusphere-egu23-10089
Autor:
Jongjae Lee, Chang-Keun Song, Rokjin Park, Chul-Han Song, Soontae Kim, Myong-In Lee, Jung Hun Woo, Hyeonmin Kim, Jinhyeok Yu, Minah Bae, Seung-Hee Lee, Jinseok Kim
The GEMS MAP of Air Pollution (GMAP) 2021 field campaign for South Korea was conducted in October-November 2021 to understand the changes in air quality after the KORUS-AQ field study and to support efficient pollution management for ozone and aeroso
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6cecf079164e564acd5382cb3ea4be3
https://doi.org/10.5194/egusphere-egu23-4777
https://doi.org/10.5194/egusphere-egu23-4777
Publikováno v:
Geoscientific Model Development. 15:2773-2790
In this study, we developed a data assimilation (DA) system for chemical transport model (CTM) simulations using an ensemble Kalman filter (EnKF) technique. This DA technique is easy to implement in an existing system without seriously modifying the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bda8a41953442762a60f798256de675a
https://doi.org/10.5194/gmd-15-2773-2022
https://doi.org/10.5194/gmd-15-2773-2022
Publikováno v:
Environmental Pollution. 322:121099
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3497acba61a60e261456d3c0b94833c
https://doi.org/10.1016/j.envpol.2023.121099
https://doi.org/10.1016/j.envpol.2023.121099