investigating the long- and short-term variations in meteorology in atlanta
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Investigating the long- and short-term variations in meteorology in Atlanta. Lucas Henneman 25 April, 2013. Motivation. Negative health outcomes due to high levels of air pollution have been addressed by enacting regulations on polluters - PowerPoint PPT PresentationTRANSCRIPT
Investigating the long- and short-term variations in meteorology in Atlanta
Lucas Henneman25 April, 2013
MotivationNegative health outcomes due to high levels of
air pollution have been addressed by enacting regulations on polluters
Industry, researchers, and policy-makers are interested in the effectiveness of these regulations
Statistical techniques can be used to separate changes in air quality due to emissions from changes due to meteorological fluctuations
ObjectivesCompare methods for isolating variations at
specific frequencies in a daily time series of maximum temperature
Use these results to remove fluctuations in measured gas-phase pollutant concentrations caused by meteorology
ApproachA daily gas-phase species concentration signal can be decomposed into 5 elements as follows:
Similarly, a daily meteorological signal can be decomposed into 4 elements:
LT: long-term (>365 days)S: seasonal and long meso-scale (30–365 days)W: weekly–present only in pollutant signal (7 days)STM: short-term meteorological (<30 days)WN: white-noise (~1 day)
Data
Figure 1. 28-County metro-Atlanta (20-county area shaded) with major roadways (red lines), coal-fired power plants (green points), and central monitor (yellow star) indicated.
20 km
Wansley
McDonough
Yates
Bowen
Figure 1. 28-County metro-Atlanta (20-county area shaded) with major roadways (red lines), coal-fired power plants (green points), and central monitor (yellow star) indicated.
20 km
Wansley
McDonough
Yates
Bowen
• Temperature from the Jefferson Street monitoring station in downtown Atlanta
• Time period: January 2000 – December 2011
• Max Temperature used for a daily metric
• Less than 10% blank values in the Jefferson Street data, estimated with linear interpolation
Mean Year Method Adapted from Kuebler et al. (2002).
Long-term (LT) component is captured with the Kolmogorov-Zurbenko (KZ) filter, (365-day averaging window passed three times) (Rao & Zurbenko, 1994)
Seasonal (S) element is captured by averaging each day over the N years of data (here, 12 years) to create a Mean Year (MY), which is smoothed with a local regression loess smoother
s[MY(t)] = S(t)
Subtracting LT and S from the original signal yields the daily deviations, or ∆1’s.
Signal – LT – MY = STM + WN = ∆1
Fast Fourier Transform Filter Method
Adapted to isolate specific frequencies of variations of meteorological and air pollution signals
LT and S are both removed using the low-pass FFT filter with a cutoff of 30 days (the upper limit of meso-scale meteorology–e.g. hurricanes and fronts)
LT + S = FFT>30(Signal)
Signal – FFT>30(Signal) = STM + WN = ∆2
The ∆2’s are regressed against daily deviations in pollution species concentrations to investigate the relationships between meteorology and air pollution.
Examples of FFT filtering
www.cd4car.com
http://paulbourke.net/miscellaneous/imagefilter/
Spectral AnalysisMean Year Method
FFT Method
Spectral AnalysisMean Year Method
FFT Method
Conclusions and Future Work
Both methods are effective at removing the annual component of a meteorological signal
Both methods produce ∆’s that are stationary (validated by KPSS hypothesis test)
The Mean Year method imperfectly preserves frequencies below a period of 30 days
The FFT Filter method allows for precise removal of variations at known frequencies
Future WorkInvestigate the need for detrending before
application of FFT filter (likely not necessary for temperature)
Apply the FFT method to other meteorological and pollution variables to quantify meteorological affect on gas-phase species concentrations
Use the analysis to link higher daily deviations with specific met variables (e.g. ozone with higher incoming solar radiation)
ReferencesDuchon, C. and Hale, R. (2012) Fourier Analysis, in Time Series Analysis in
Meteorology and Climatology: An Introduction, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781119953104.ch1
Kuebler, J., Van den Bergh, H., & Russell, A. G. (2001). Long-term trends of primary and secondary pollutant concentrations in Switzerland and their response to emission controls and economic changes. Atmospheric Environment, 35(8), 1351–1363. doi:10.1016/S1352-2310(00)00401-5
Kwiatkowski, D., P. C. B. Phillips, P. Schmidt and Y. Shin. "Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root." Journal of Econometrics. Vol. 54, 1992, pp. 159–178.
Rao, S., & Zurbenko, I. (1994). Detecting and tracking changes in ozone air quality. Air & waste, 44, 1089–1092. Retrieved from http://www.tandfonline.com/doi/abs/10.1080/10473289.1994.10467303
Stull, R. B. (1988). Ch. 8: Some Mathematical & Conceptual Tools: Part 2. Time Series. Introduction to Boundary Layer Meteorology.
Questions?