[707] - Bias Correction Techniques to Improve Air Quality Ensemble Predictions: Focus on O3 and PM Over Portugal

All available metadata of the dataset is listed below. Some features are available, e.g. download of dataset or additional description file.

By downloading files from this dataset you accept the license terms of TR32DB Data policy agreement and TR32DBData Protection Statement.
Adequate reference when this dataset will be discussed or used in any publication or presentation is mandatory. In this case please contact the dataset creator.
Due to the speed of the filesystem and depending on the size of the archive and the file to be extracted, it may take up to thirty (!) minutes until a download is ready! Beware of that when confirming since you may not close the tab because otherwise, you will not get your file!
Monteiro, A., Elbern, H., Ribeiro, I., Tchepel, O., Sa, E., Ferreira, J., Carvalho, A., Martins, V., Strunk, A., Galmarini, S., Schaap, M., Builtjes, P., Miranda, A. I., Borrego, C., 2013. Bias Correction Techniques to Improve Air Quality Ensemble Predictions: Focus on O3 and PM Over Portugal. Environmental Modeling and Assessment, 18 (5), 533 - 546. DOI: 10.1007/s10666-013-9358-2.
Citation Options
Export as: Select the file format for your download.Citation style: Select the displayed citation style.
Title(s):Main Title: Bias Correction Techniques to Improve Air Quality Ensemble Predictions: Focus on O3 and PM Over Portugal
Description(s):Abstract: Five air quality models were applied over Portugal for July 2006 and used as ensemble members. Each model was used, with its original set up in terms of meteorology, parameterizations, boundary conditions and chemical mechanisms, but with the same emission data. The validation of the individual models and the ensemble of ozone (O3) and particulate matter (PM) is performed using monitoring data from 22 background sites. The ensemble approach, based on the mean and median of the five models, did not improve significantly the skill scores due to large deviations in each ensemble member. Different bias correction techniques, including a subtraction of the mean bias and a multiplicative ratio adjustment, were implemented and analysed. The obtained datasets were compared against the individual modelled outputs using the bias, the root mean square error (RMSE) and the correlation coefficient. The applied bias correction techniques also improved the skill of the individual models and work equally well over the entire range of observed O3 and PM values. The obtained results revealed that the best bias correction technique was the ratio adjustment with a 4-day training period, demonstrating significant improvements for both analysed pollutants. The increase in the ensemble skill found comprehends a bias reduction of 88 % for O3, and 92 % for PM10, and also a decrease in 23 % for O3 and 43 % for PM10 in what concerns the RMSE. In addition, a spatial bias correction approach was also examined with successful skills comparing to the uncorrected ensemble for both pollutants.
Identifier(s):DOI: 10.1007/s10666-013-9358-2
Responsible Party
Creator(s):Author: Alexandra Monteiro
Author: Hendrik Elbern
Author: Isabel Ribeiro
Author: Oxana Tchepel
Author: Elisa Sa
Author: Joana Ferreira
Author: Anabela Carvalho
Author: Vera Martins
Author: Achim Strunk
Author: Stefano Galmarini
Author: Martijn Schaap
Author: Peter Builtjes
Author: Ana Isabel Miranda
Author: Carlos Borrego
Publisher:Springer Netherlands
TR32 Topic:Atmosphere
Subject(s):CRC/TR32 Keywords: Air Quality Modelling, Additive Bias Correction, Multiplicative Bias Correction, Spatial Bias Correction
File Details
File Name:2012_Monteiro_EnvironModAssess.pdf
Data Type:Text
Size(s):14 Pages
File Size:1228 kB (1.199 MB)
Date(s):Date Accepted: 2013-02-04
Issued: 2013-02-15
Mime Type:application/pdf
Data Format:PDF
Download Permission:OnlyTR32
General Access and Use Conditions:For internal use only
Access Limitations:For internal use only
Licence:TR32DB Data policy agreement
North:-no map data
Measurement Region:Other
Measurement Location:--Other--
Specific Informations - Publication
Article Type:Journal
Source:Environmental Modeling and Assessment
Number Of Pages:14
Page Range:533 - 546
Metadata Details
Metadata Creator:Hendrik Elbern
Metadata Created:2013-12-03
Metadata Last Updated:2013-12-12
Funding Phase:2
Metadata Language:English
Metadata Version:V40
Dataset Metrics
Page Visits:366
Metadata Downloads:0
Dataset Downloads:2
Dataset Activity