Articles | Volume 16, issue 24
Atmos. Chem. Phys., 16, 15629–15652, 2016

Special issue: Global and regional assessment of intercontinental transport...

Atmos. Chem. Phys., 16, 15629–15652, 2016

Research article 20 Dec 2016

Research article | 20 Dec 2016

Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

Ioannis Kioutsioukis et al.


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Lorena Grabowski on behalf of the Authors (04 Nov 2016)  Author's response
ED: Publish as is (01 Dec 2016) by Gregory Carmichael
Short summary
Four ensemble methods are applied to two annual AQMEII datasets and their performance is compared for O3, NO2 and PM10. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill at each station over the single models and the ensemble mean. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31 % compared to using the full ensemble in an unconditional way.
Final-revised paper