Articles | Volume 17, issue 23
https://doi.org/10.5194/gmd-17-8553-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-8553-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
02 Dec 2024
Development and technical paper |
| 02 Dec 2024
| 02 Dec 2024
A revised ocean mixed layer model for better simulating the diurnal variation in ocean skin temperature
Eui-Jong Kang
School of Earth and Environmental Sciences, Seoul National University (SNU), Seoul, South Korea
Byung-Ju Sohn
CORRESPONDING AUTHOR
School of Earth and Environmental Sciences, Seoul National University (SNU), Seoul, South Korea
School of Atmospheric Physics, Nanjing University of Information Science and Technology (NUIST), Nanjing, China
School of Earth and Environmental Sciences, Seoul National University (SNU), Seoul, South Korea
Wonho Kim
Korea Institute of Atmospheric Prediction Systems (KIAPS), Seoul, South Korea
Young-Cheol Kwon
Numerical Modeling Center, Korea Meteorological Administration (KMA), Daejeon, South Korea
Seung-Bum Kim
Numerical Modeling Center, Korea Meteorological Administration (KMA), Daejeon, South Korea
Hyoung-Wook Chun
Numerical Modeling Center, Korea Meteorological Administration (KMA), Daejeon, South Korea
Chao Liu
School of Atmospheric Physics, Nanjing University of Information Science and Technology (NUIST), Nanjing, China
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A random forest (RF) model framework for Fengyun-4A (FY-4A) daytime and nighttime quantitative precipitation estimation (QPE) is established using FY-4A multi-band spectral information, cloud parameters, high-density precipitation observations and physical quantities from reanalysis data. The RF model of FY-4A QPE has a high accuracy in estimating precipitation at the heavy-rain level or below, which has advantages for quantitative estimation of summer precipitation over East Asia in future.
Lian Zong, Yuanjian Yang, Meng Gao, Hong Wang, Peng Wang, Hongliang Zhang, Linlin Wang, Guicai Ning, Chao Liu, Yubin Li, and Zhiqiu Gao
Atmos. Chem. Phys., 21, 9105–9124, https://doi.org/10.5194/acp-21-9105-2021, https://doi.org/10.5194/acp-21-9105-2021, 2021
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In recent years, summer O3 pollution over eastern China has become more serious, and it is even the case that surface O3 and PM2.5 pollution can co-occur. However, the synoptic weather pattern (SWP) related to this compound pollution remains unclear. Regional PM2.5 and O3 compound pollution is characterized by various SWPs with different dominant factors. Our findings provide insights into the regional co-occurring high PM2.5 and O3 levels via the effects of certain meteorological factors.
Saehee Lim, Meehye Lee, Paolo Laj, Sang-Woo Kim, Kang-Ho Ahn, Junsoo Gil, Xiaona Shang, Marco Zanatta, and Kyeong-Sik Kang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1247, https://doi.org/10.5194/acp-2020-1247, 2021
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This study identifies the main drivers of the formation and transformation processes of submicron particles and highlights that the thick coating of rBC was a result of active conversion of hygroscopic inorganic salts leading to fine aerosol pollution. Consequently, we suggest BC particles as a key contributor to PM2.5 mass increase, which implies that BC reduction is an effective mitigation against haze pollution as well as climate change in Northeast Asia.
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Short summary
Sea surface temperature (SST) is vital in climate, weather, and ocean sciences because it influences air–sea interactions. Errors in the ECMWF model's scheme for predicting ocean skin temperature prompted a revision of the ocean mixed layer model. Validation against infrared measurements and buoys showed a good correlation with minimal deviations. The revised model accurately simulates SST variations and aligns with solar radiation distributions, showing promise for weather and climate models.
Sea surface temperature (SST) is vital in climate, weather, and ocean sciences because it...
