ArticleEnvironmental science and ecotechnology2021
Multi-stage ensemble-learning-based model fusion for surface ozone simulations: A focus on CMIP6 models.
Article in Environmental science and ecotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A derivatization-assisted SERS platform using Au TOHs/hydrogel for point-of-use detection of trace ozone in air.Mikrochimica acta · 2026Article
- A global land daily 10-km-resolution surface ozone dataset from 2013-2022.Scientific data · 2025Article
- Establishing a Multifaceted Comprehensive Maternity Cohort Facilitates Understanding of How Environmental Exposures Impact Perinatal Health.Environment & health (Washington, D.C.) · 2024Review
- Knowledge-guided machine learning reveals pivotal drivers for gas-to-particle conversion of atmospheric nitrate.Environmental science and ecotechnology · 2024Article
- Antagonism between ambient ozone increase and urbanization-oriented population migration on Chinese cardiopulmonary mortality.Innovation (Cambridge (Mass.)) · 2023Article
- Excess mortality associated with high ozone exposure: A national cohort study in China.Environmental science and ecotechnology · 2023Article
- Cohort-based long-term ozone exposure-associated mortality risks with adjusted metrics: A systematic review and meta-analysis.Innovation (Cambridge (Mass.)) · 2022Review
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Authors and funding
2 authors.
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Abstract
Accurately simulating the geographical distribution and temporal variability of global surface ozone has long been one of the principal components of chemistry-climate modelling. However, the simulation outcomes have been reported to vary significantly as a result of the complex mixture of uncertain factors that control the tropospheric ozone budget. Settling the cross-model discrepancies to achieve higher accuracy predictions of surface ozone is thus a task of priority, and methods that overcome structural biases in models going beyond naïve averaging of model simulations are urgently required. Building on the Coupled Model Intercomparison Project Phase 6 (CMIP6), we have transplanted a conventional ensemble learning approach, and also constructed an innovative 2-stage enhanced space-time Bayesian neural network to fuse an ensemble of 57 simulations together with a prescribed ozone dataset, both of which have realised outstanding performances (R
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