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URN: urn:nbn:de:swb:90-656665

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

Kioutsioukis, I.; Im, U.; Solazzo, E.; Bianconi, R.; Badia, A.; Balzarini, A.; Baró, R.; Bellasio, R.; Brunner, D.; Chemel, C.; Curci, G.; Van Der Gon, H. D.; Flemming, J.; Forkel, R.; Giordano, L.; Jiménez-Guerrero, P.; Hirtl, M.; Jorba, O.; Manders-Groot, A.; Neal, L.; ... mehr

Abstract:
Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O$_{3}$), nitrogen dioxide (NO$_{2}$) and particulate matter (PM$_{10}$). Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme) and AirBase databases. The goal of the study is to quantify to what extent we can extr ... mehr


Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU)
Publikationstyp Zeitschriftenaufsatz
Jahr 2016
Sprache Englisch
Identifikator ISSN: 1680-7316, 1680-7324
KITopen ID: 1000065666
HGF-Programm 12.02.04; LK 01
Erschienen in Atmospheric chemistry and physics
Band 16
Heft 24
Seiten 15629-15652
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