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Development and application of a multi-scale modelling framework for urban high-resolution NO2 pollution mapping 期刊论文
Atmospheric Chemistry and Physics, 2022
作者:  Zhaofeng Lv, Zhenyu Luo, Fanyuan Deng, Xiaotong Wang, Junchao Zhao, Lucheng Xu, Tingkun He, Huan Liu, and Kebin He
收藏  |  浏览/下载：13/0  |  提交时间：2022/06/24
Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning 期刊论文
Atmospheric Chemistry and Physics, 2021
作者:  Emma Lumiaro, Milica Todorović, Theo Kurten, Hanna Vehkamäki, and Patrick Rinke
收藏  |  浏览/下载：5/0  |  提交时间：2021/09/14
The behavior of high-CAPE (convective available potential energy) summer convection in large-domain large-eddy simulations with ICON 期刊论文
Atmospheric Chemistry and Physics, 2021
作者:  Harald Rybka, Ulrike Burkhardt, Martin Köhler, Ioanna Arka, Luca Bugliaro, Ulrich Görsdorf, Ákos Horváth, Catrin I. Meyer, Jens Reichardt, Axel Seifert, and Johan Strandgren
收藏  |  浏览/下载：8/0  |  提交时间：2021/04/06
Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning 期刊论文
Atmospheric Chemistry and Physics, 2020
作者:  Roland Stirnberg, Jan Cermak, Simone Kotthaus, Martial Haeffelin, Hendrik Andersen, Julia Fuchs, Miae Kim, Jean-Eudes Petit, and Olivier Favez
收藏  |  浏览/下载：46/0  |  提交时间：2020/08/09
Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns 期刊论文
Atmospheric Chemistry and Physics, 2020
作者:  Marc Guevara, Oriol Jorba, Albert Soret, Hervé Petetin, Dene Bowdalo, Kim Serradell, Carles Tena, Hugo Denier van der Gon, Jeroen Kuenen, Vincent-Henri Peuch, and Carlos Pérez García-Pando
收藏  |  浏览/下载：51/0  |  提交时间：2020/08/09
The behavior of high-CAPE summer convection in large-domain large-eddy simulations with ICON 期刊论文
Atmospheric Chemistry and Physics, 2020
作者:  Harald Rybka, Ulrike Burkhardt, Martin Köhler, Ioanna Arka, Luca Bugliaro, Ulrich Görsdorf, Ákos Horváth, Catrin I. Meyer, Jens Reichardt, Axel Seifert, and Johan Strandgren
收藏  |  浏览/下载：7/0  |  提交时间：2020/07/21
Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements 期刊论文
Atmospheric Chemistry and Physics, 2020
作者:  Arshad Arjunan Nair and Fangqun Yu
收藏  |  浏览/下载：6/0  |  提交时间：2020/06/16