STSWG Subgroup Response to NRSWG Question #1:

Meteorological Variability

STSWG Subgroup Members:

David Chock

Cyril Durrenberger

S.T. Rao

Jay Rosenthal

Steve Ziman

U.S. EPA Support:

Bill Cox, David Sanders, John Silvasi

The STSWG Subgroup on Meteorological Variability recommends that the following response be offered to Sarah Peirce-Sandner's group working on the Reasonable Further Progress (RFP) Issue Paper, with regard to Meteorological Variability.

Statement:	The question on Meteorological Variability should be rephrased as follows:

"Do we have tools that would enable us to discern changes in ozone air quality due to control policies in the presence of meteorological variability?"

Response:

The question as originally provided can be broken down into three main issues: (1) the time interval required for detecting a trend; (2) the treatment of statistical outliers; and, (3) do tools currently exist for conducting such analyses.

(1) The STSWG Meteorological Variability subgroup unanimously recommends that a minimum of 5-6 years of continuous ambient monitoring data be considered when establishing meteorological trends for the purpose of assessing Meteorological Variability.

(2)	The STSWG Meteorological Variability Subgroup does not recommend treating any observed data to be statistical outliers so long as they are properly validated. An air quality space-time series can be decomposed into many different space-time scales. The large space-time scale behavior, like the regional-seasonal behavior, are smoother than the smaller space-time scale behavior. What is considered as noise (or outliers) in one space-time scale, may be considered as signal (useful and meaningful data) in another space-time scale.

(3)	The STSWG Meteorological Variability Subgroup unanimously agrees that adequate analytical tools exist for separating the signal and noise components of air quality and meteorological time series, and for investigating air quality behavior under different meteorological regimes. The tools for smoothed (large-time scale) series can be used to address the mean air quality behavior whereas those for smaller- time scale series can be used to address air quality under more extreme (higher concentration) conditions. Bear in mind, however, that the meteorological conditions found and subsequently specified for the more extreme- (higher) concentrations tend to occur less frequently and therefore the determined air quality trend has more fluctuations and requires a longer time interval to establish. Ultimately, the selection of the "right" tool should be based on the methodology which would adequately address the physics, chemistry and geography of the individual situation and location.