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Model selection for local and regional meteorological normalisation of background concentrations of tropospheric ozone
Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
2003 (English)In: Atmospheric Environment, ISSN 1352-2310, Vol. 37, no 28, 3923-3931 p.Article in journal (Refereed) Published
Abstract [en]

Meteorological normalisation of time series of air quality data aims to extract anthropogenic signals by removing natural fluctuations in the collected data. We showed that the currently used procedures to select normalisation models can cause over-fitting to observed data and undesirable smoothing of anthropogenic signals. A simulation study revealed that the risk of such effects is particularly large when: (i) the observed data are serially correlated, (ii) the normalisation model is selected by leave-one-out cross-validation, and (iii) complex models, such as artificial neural networks, are fitted to data. When the size of the test sets used in the cross-validation was increased, and only moderately complex linear models were fitted to data, the over-fitting was less pronounced. An empirical study of the predictive ability of different normalisation models for tropospheric ozone in Finland confirmed the importance of using appropriate model selection strategies. Moderately complex regional models involving contemporaneous meteorological data from a network of stations were found to be superior to single-site models as well as more complex regional models involving both contemporaneous and time-lagged meteorological data from a network of stations.

Place, publisher, year, edition, pages
2003. Vol. 37, no 28, 3923-3931 p.
National Category
Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-22635DOI: 10.1016/S1352-2310(03)00502-8Local ID: 1919OAI: oai:DiVA.org:liu-22635DiVA: diva2:242948
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2012-12-14
In thesis
1. Considering meteorological variation in assessments of environmental quality trends
Open this publication in new window or tab >>Considering meteorological variation in assessments of environmental quality trends
2003 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Time series of environmental data are collected to monitor the effectiveness of new emission reduction policies or to determine the general state of the environment. However, small gradual changes in such variables can easily be concealed by large fluctuations caused by prevailing weather conditions. Hence, there is a real need for procedures that facilitate separation of such natural variation from anthropogenic effects.

Taking meteorological or hydrological variables into consideration in a trend analysis can be done in several ways. The technique chosen to accomplish this objective depends on characteristics of the data set, for example the length of the time series and sampling frequencies, and the kind of relationships that exist between the response variable and the covariates. Two different approaches were examined in the studies underlying this thesis: multivariate non-parametric tests and parametric normalisation procedures. The non-parametric trend test proposed here was newly desinged, thus it was also necessary to conduct simulation studies to examine the performance of this method. By comparison, normalisation techniques have been used over the past few decades mainly to adjust for the impact of meteorological effects on air quality data. The choice of explanatory variables for such procedures was studied: first by examining variable selection procedures based on cross-validation, paying special attention to serially correlated response data; and secondly by considering variables derived from complex physics-based models as alternatives to measured variables. A number of other aspects that might influence the ability to detect trends were also explored, including level shifts due to instrument malfunctions.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2003. 41 p.
Series
Linköping Studies in Statistics, ISSN 1651-1700 ; 3
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-22755 (URN)2073 (Local ID)91-7373-615-5 (ISBN)2073 (Archive number)2073 (OAI)
Public defence
2003-04-25, Sal Key 1, Keyhuset, Linköpings universitet, Linköping, 10:15 (Swedish)
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2012-12-14

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Libiseller, ClaudiaGrimvall, Anders

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