Machine learning significantly improves the simulation of hourly-to-yearly scale cloud nuclei concentration and radiative forcing in polluted atmosphere

Name: Machine learning significantly improves the simulation of hourly-to-yearly scale cloud nuclei concentration and radiative forcing in polluted atmosphere
Creator: State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing; State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences; Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing; School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen; Institute of Atmospheric Physics
Published: 2025-06-25T00:00:00+08:00

Zenodo2025-05-27 更新2026-04-07 收录

云凝结核预测

辐射强迫模拟

数据链接：

https://zenodo.org/doi/10.5281/zenodo.15737652 数据链接链接失效反馈

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资源简介：

This study used an efficient machine learning method (Random Forest) to construct a new framework of CCN prediction by combining multi-source dataset and the CCN concentration simulated by the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model, which predicts well both regional and hourly-to-yearly scale CCN concentration at typical supersaturations in the North China Plain. The dataset is provided in csv format.

提供机构：

State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing; State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences; Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing; School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen; Institute of Atmospheric Physics

创建时间：

2025-06-25