1. Title Inorganic PM at selected sites during intensive period 2008:
EMEP model vs. measurements
Presented by Svetlana Tsyro
TFMM 11th meeting, Larnaca, Cyprus May 2010

2. SEVIRI/Meteosat-9 ash product (Courtesy Mike Pavolonis NOAA)
Eyjafjallajoküll ash modelling
SEVIRI/Meteosat-9 ash product
(Courtesy Mike Pavolonis NOAA)
AOD 550nm: model vs. MODIS
EMEP: PM10 total column = 0.8–1 g/m2 (core)
Ash loading > 20g/m2
Meteorologisk Institutt met.no

3. OUTLINE
* Bar-diagrams of PM composition will not be presented
* Model comparison with hourly and weekly data will not be presented
* Model results will be compared with daily measurements of inorganic PM components
What is the effect of meteorological input on model results and performance?
Meteorologisk Institutt met.no

4. Effect of meteorology - 1
PARLAM-PS met. model was used to generate meteorological input to the EMEP model from 1998 to 2006 (consistency for trends, but not kept updated, 50x50km)
Last year, the model calculations were made using meteorological input from HIRLAM (0.2ºx0.2º), also ECMWF was tested
The effect was greater than due to meteorological variability - considerably (up to 20-30%) lower surface concentrations of all aerosols
We looked closer at the differences btw meteorological data, which could cause this
Meteorologisk Institutt met.no

5. Vertical profiles
SO4
PMco
PM2.5
HIRLAM
PARLAM - old
PM2.5:
PAR-HIR
Using HIRLAM – lower concentrations at surface/ below km, but higher above that
Meteorologisk Institutt met.no

6. PARLAM – HIRLAM: turbulent mixingKz
HIRLAM
Meteorologisk Institutt met.no

7. Precipitation PARLAM HIRLAM PAR - HIR
Annual bias around 1% for both models
PARLAM: within 5% all seasons
HIRLAM: +25 in winter
-25 in summer
Meteorologisk Institutt met.no

8. Effect of meteorology - 2
Runs for 2008 have been made with meteorological input generated with the HIRLAM and ECMWF (IFS) models
The results have been compared with daily measurements of inorganic PM components during the intensive period 17 Sept – 16 Oct 2008
Meteorologisk Institutt met.no

9. HIRLAM vs. ECMWF meteorology
Kz based on ECMWF and HIRLAM
Wind velocity 10m: verification with SYNOP
Meteorologisk Institutt met.no

10. Precipitation: HIRLAM - ECMWF
Sept. 2008
Oct. 2008
Precipitation SYNOP
Lower surface concentrations could be expected using HIRLAM compared to ECMWF
Meteorologisk Institutt met.no

11. Melpitz DE44: PM10 ECMWF – red HIRLAM - blue
0.7/0.57
0.71/0.49
0.68/0.52
0.51/0.34
0.42/0.36
0.71/0.71
Model underestimation (exc. NO3, Na), more so using HIRLAM
ECMWF – better correlations, slightly higher concentrations
Most of episodes are reproduce with both met, models
Meteorologisk Institutt met.no

12. FLEXTRA trajectories
Meteorologisk Institutt met.no

13. Melpitz DE44: PM2.5 Model underestimation, except NO3
0.68/0.46
0.62/0.50
0.69/0.57
0.61/0.42
0.68/0.64
0.44/0.62
Model underestimation, except NO3
ECMWF – better correlations, slightly higher conc (exc. EC, Na),
Meteorologisk Institutt met.no

14. PM2.5 Ispra IT04
0.53/0.56
0.57/0.62
0.29/0.45
0.46/0.73
0.30/0.44
Larger difference btw model results using HIRLAM and ECMWF
Model overestimates all components, exc. EC, with both met.data
PM2.5 is overestimated with ECMWF, underestimated with HIRLAM
HIRLAM – better correlations, esp. for EC, NO3, NH4
Meteorologisk Institutt met.no

15. IT04
Meteorologisk Institutt met.no

16. K-puszta HU02
0.56/0.42
0.72/0.77
obs SO4 peaks 13 and 16 Oct,
but only 13 Oct for NO3 and NH4
0.50/0.64
0.64/0.73
Model underestimates PM10 and SO4, overestimates NO3 with ECMWF, underestimates with HIRLAM
HIRLAM – better correlations
Meteorologisk Institutt met.no

17. HU02
Meteorologisk Institutt met.no

18. Kosetice CZ03 Model underestimates using both ECMWF and HIRLAM
ECMWF – better correlations (appear to better reproduce the episodes)
Meteorologisk Institutt met.no

19. Summary
Using rather limited data set we looked at how the model manages to reproduce various PM components during EMEP 2008 intensive period
Using different meteorological input data is shown to have a considerable effect on model results
For Sept-Oct 2008 intensive period, the model tends to underestimate PM components in most of the cases; the correlation is quite good
The results using ECMWF meteorology appear to agree better with observations in terms of correlation and also bias
Studying time-series of concentrations together with trajectory maps helps to explain the success or failure of the model to reproduce observations …
Meteorologisk Institutt met.no

20. For more about model vs. intensive measurements comparison, please, read EMEP PM retort 4/2010
Thank you!
Meteorologisk Institutt met.no

21. PARLAM – HIRLAM 20: dry depositionU*
Vg fine
Meteorologisk Institutt met.no