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The Capability of Deep Learning Model to Predict Ozone Across Continents in China, the United States and Europe

التفاصيل البيبلوغرافية
العنوان: The Capability of Deep Learning Model to Predict Ozone Across Continents in China, the United States and Europe
المؤلفون: Weichao Han, Tai‐Long He, Zhe Jiang, Rui Zhu, Dylan Jones, Kazuyuki Miyazaki, Yanan Shen
المصدر: Geophysical Research Letters, Vol 50, Iss 24, Pp n/a-n/a (2023)
بيانات النشر: Wiley, 2023.
سنة النشر: 2023
المجموعة: LCC:Geophysics. Cosmic physics
مصطلحات موضوعية: Geophysics. Cosmic physics, QC801-809
الوصف: Abstract Data‐driven methods have been extensively applied to predict atmospheric compositions. Here, we explore the capability of a deep learning (DL) model to make ozone (O3) predictions across continents in China, the United States (US) and Europe. The DL model was trained and validated with surface O3 observations in China and the US in 2015–2018. The DL model was applied to predict hourly surface O3 over three continents in 2015–2022. Compared to baseline simulations using GEOS‐Chem (GC) model, our analysis exhibits mean biases of 2.6 and 4.8 μg/m3 with correlation coefficients of 0.94 and 0.93 (DL); and mean biases of 3.7 and 5.4 μg/m3 with correlation coefficients of 0.95 and 0.92 (GC) in Europe in 2015–2018 and 2019–2022, respectively. The comparable performances between DL and GC indicate the potential of DL to make reliable predictions over spatial and temporal domains where a wealth of local observations for training is not available.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1944-8007
0094-8276
Relation: https://doaj.org/toc/0094-8276; https://doaj.org/toc/1944-8007
DOI: 10.1029/2023GL104928
URL الوصول: https://doaj.org/article/9f67ea73c031441db8fe893a15c308e5
رقم الأكسشن: edsdoj.9f67ea73c031441db8fe893a15c308e5
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:19448007
00948276
DOI:10.1029/2023GL104928