1. Topic 3: Meteorology and data filtering

2. Issues
How to treat meteorology: Should we do meteorological trend adjustment? If seasonality is changing, how to reflect it in trend analysis?
Data filtering for trend analysis (selection of extremes): Statistical filtering, Meteorological filtering, Filtering based on particular episodes, Filtering based on regulatory thresholds

3. Meteorology: issues
Impact of meteorology on different variables is not the same (different mechanisms.
Potential impact is through changing:
Temperature (impact on reaction rates and intensity of convection)
Humidity and precipitation (dry and wet deposition rates)
Cloudiness (photolysis rates and reaction constants)
Wind (transport patterns from different sources)

4. How to take meteorology into consideration
Adjust a data set based on meteorological observations (multi-parametric regression)
Analyze trend in different meteorological bins (examples of such analysis in USA -> used for calculations of “climate penalty”)
Analyze trends for different transport sectors
In model studies special runs with varying/fixed meteorological conditions (question remains how to compare such trends with observations)

5. TFMM Meeting, Bologna, April, 9th, 2014
ozone
monthly trends
O3 [ppb]
(S. Gilge)
TFMM Meeting, Bologna, April, 9th, 2014

6. ozone mean annual variation (5 year steps)
trend reversal causes change in seasonal cycle (e.g. Parrish et al., GRL, 40, , doi: /grl.50303, 2013) .
(S. Gilge)
TFMM Meeting, Bologna, April, 9th, 2014

7. TFMM Meeting, Bologna, April, 9th, 2014
trends depending on WD
NO2 HPB
overall no significant trend, slight increase
wind directions: WSW „rural background“ and NE “slightly polluted”
slight decreasing trend at both main wind directions (?)
-8 ppt/a (-0.2%)
EEA: -1-2%/a
-11 ppt/a (-0.5%)
(S. Gilge)
TFMM Meeting, Bologna, April, 9th, 2014

8. TFMM Meeting, Bologna, April, 9th, 2014
trends depending on WD
NO2 HPB
decrease of WSW (low m.r.) and increase of NE (high m.r.) occurrence
net: increase of NO2 gas mixing ratios
(S. Gilge)
TFMM Meeting, Bologna, April, 9th, 2014

9. Predictor variables (A,B) Meteorological variables Day of week Time
Method
Generalized additive model (GAM) - GAM response functions of predictor variables are smooth curves
Target variables (y)
Ozone (hourly values)
Daily mean
Daily max
MTDM (mean of ten highest daily max)
Predictor variables (A,B)
Meteorological variables
Day of week
Time
Seasonal analysis
Summer, JJA
Winter, DFF
15 sites in Switzerland
Rural, suburban, urban
(by Christoph Hueglin)

10. Measured and meteo. adjusted mean of daily max. O3
(by Christoph Hueglin)

11. Summary trend estimation (1991-2009) of daily max. O3, JJA
(by Christoph Hueglin)

12. Trend of daily mean O3 (1996-2006) during summer (May – Sep.)
Stephan Henne (Empa) within GEOmon
0.31 ± 0.02 ppbv/yr
0.00 ± 0.01 ppbv/yr
413 sites (data from EEA/Airbase)
rural to urban background
visual inspection of time series
Meteorological variables derived from
ECMWF analysis
(by Christoph Hueglin)

13. Data filtering Data set “cleaning” (removal of extremes):
Statistical filtering (remove outliers, e.g. at 2σ)
Meteorological filtering (remove specific meteorological conditions)
Filtering based on particular episodes (remove pollution episodes based on species correlations or wind sector)
Filtering based on regulatory thresholds (or any other level, e.g. one can use particular percentile)
Selection of seasonal values
Selections of time of the day

14. Contribution to topic 3 Data filtering
With the aim of specifically investigating possible long-term changes of O3 as a function of the different diurnal phases of this vertical mixing (mountain thermal winds), we performed trend analysis for different sub-sets: “all data”, “night-time” data (from 23:00 to 4:00 UTC+1), “day-time” data (from 11:00 to 16:00 UTC+1) and “evening” data (from 17:00 to 22:00 UTC+1)
We perform a selection (on a daily basis) of heath-waves: by excluding these events (115 days on ), we did not find significantly different trends.
Basing on a comparison with other remote stations in Europe (Jungfraujoch, Mt. Krvacev, Sonnblick), O3 data are more and more consistent during winter

15. Meteorology: discussion
What variables absolutely need meteorological adjustment:
O3, PM, ……..
Do we do meteorological adjustment (do we understand how it works)?
If we do meteorological adjustment, how do we do it?

16. Filtering: discussion
What variables absolutely need prior filtering?
Do we do filtering?
If we do filtering, how do we do it?
- Seasonal
Day vs night
Meteorological (T, WS)
«Pollution episodes» based on multi species
Metrics (max, mean, percentile)