Title Inorganic PM at selected sites during intensive period 2008: EMEP model vs. measurements Presented by Svetlana Tsyro TFMM 11th meeting, Larnaca, Cyprus 12-14 May 2010

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

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

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

Vertical profiles SO4 PMco PM2.5 HIRLAM PARLAM - old PM2.5: PAR-HIR Using HIRLAM – lower concentrations at surface/ below 1-1.5 km, but higher above that Meteorologisk Institutt met.no

PARLAM – HIRLAM: turbulent mixing Kz HIRLAM Meteorologisk Institutt met.no

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

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

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

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

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

FLEXTRA trajectories Meteorologisk Institutt met.no

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

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

IT04 Meteorologisk Institutt met.no

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

HU02 Meteorologisk Institutt met.no

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

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

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

PARLAM – HIRLAM 20: dry deposition U* Vg fine Meteorologisk Institutt met.no