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TEMPORAL SIGNATURES OF AIR QUALITY OBSERVATIONS AND MODEL OUTPUTS: DO TIME SERIES DECOMPOSITION METHODS CAPTURE RELEVANT TIME SCALES?
Citation:
Porter, P. S., J L. Swall, R Gilliam, E. L. Gego, C. Hogrefe, J. S. Irwin, AND S T. Rao. TEMPORAL SIGNATURES OF AIR QUALITY OBSERVATIONS AND MODEL OUTPUTS: DO TIME SERIES DECOMPOSITION METHODS CAPTURE RELEVANT TIME SCALES? Presented at 27th NATO/CCMS International Technical Meeting on Air Pollution Modeling and its Application, Banff, BC, CANADA, October 25 - 29, 2004.
Impact/Purpose:
The goal of this task is to thoroughly characterize the performance of the emissions, meteorological and chemical/transport modeling components of the Models-3 system, with an emphasis on the chemical/transport model, CMAQ. Emissions-based models are composed of highly complex scientific hypotheses concerning natural processes that can be evaluated through comparison with observations, but not validated. Both performance and diagnostic evaluation together with sensitivity analyses are needed to establish credibility and build confidence within the client and scientific community in the simulations results for policy and scientific applications. The characterization of the performance of Models-3/CMAQ is also a tool for the model developers to identify aspects of the modeling system that require further improvement.
Description:
Time series decomposition methods were applied to meteorological and air quality data and their numerical model estimates. Decomposition techniques express a time series as the sum of a small number of independent modes which hypothetically represent identifiable forcings, thereby helping to untangle complex processes. Mode-to-mode comparison of observed and modeled data provides a mechanism for model evaluation.
The decomposition methods included empirical orthogonal functions (EOF), empirical mode decomposition (EMD), and wavelet filters (WF). EOF, a linear method designed for stationary time series, is principal component analysis (PCA) applied to time-lagged copies of a given time series. EMD is a relatively new nonlinear method that operates locally in time and is suitable for nonstationary and nonlinear processes; it is not, in theory, bandwidth limited, and the number of modes is automatically determined. Wavelet filters are linear and band-width guided with the number of modes set by the analyst.
The purpose of this paper is to compare the performance of decomposition techniques in characterizing time scales in meteorological and air quality variables. Aiding this comparison is an analysis of simulated time series that have features in common with observations. These features include a smooth wave with a period that slowly changes from 40 to 60 days, a cosine wave with a period of one week, and additive red noise. Use of a 40 to 60 day simulated wave was motivated by the Julian-Madden effect observed in some chosen because they represent relatively easy and difficult tests, respectively, for decomposition methods. Modeled estimates of temperature are forced to closely track observations from a dense observation network; temporal modes of observations and model temperature time series, while the existence of a one-week wave has been the object of many air quality studies as it is considered an indication of anthropogenic forcing, estimates should therefore be in close agreement. Comparison of modeled and observed PM2.5, on the other hand, is a more difficult test for decomposition techniques.
The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce's National Oceanic and Atmospheric Administration (NOAA) and under agreement number DW13921548. Although this manuscript has been peer reviewed by EPA and NOAA and approved for publication, it does not necessarily represent their views or policies.