Articles | Volume 16, issue 24
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, Ulas Im, Efisio Solazzo, Roberto Bianconi, Alba Badia, Alessandra Balzarini, Rocío Baró, Roberto Bellasio, Dominik Brunner, Charles Chemel, Gabriele Curci, Hugo Denier van der Gon, Johannes Flemming, Renate Forkel, Lea Giordano, Pedro Jiménez-Guerrero, Marcus Hirtl, Oriol Jorba, Astrid Manders-Groot, Lucy Neal, Juan L. Pérez, Guidio Pirovano, Roberto San Jose, Nicholas Savage, Wolfram Schroder, Ranjeet S. Sokhi, Dimiter Syrakov, Paolo Tuccella, Johannes Werhahn, Ralf Wolke, Christian Hogrefe, and Stefano Galmarini


Total article views: 3,327 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,059 1,050 218 3,327 81 83
  • HTML: 2,059
  • PDF: 1,050
  • XML: 218
  • Total: 3,327
  • BibTeX: 81
  • EndNote: 83
Views and downloads (calculated since 30 Jun 2016)
Cumulative views and downloads (calculated since 30 Jun 2016)


Latest update: 23 Jun 2024
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