Algorithms and chemical data assimilation activities at Environment Canada

1. Algorithms and chemical data assimilation activities at Environment Canada Chris McLinden Air Quality Research Division, Environment Canada 2nd TEMPO Science Team Meeting Hampton, VA 21-22 May 2014

2. Retrievals over snow • Fraction of OMI observations over snow (during ‘snow’ months November-March) • Currently snow and cloud are difficult to distinguish and measurements over snow are less accurate; often these data are not used  poor sampling in winter • Improving retrievals would greatly improve monitoring capabilities Fraction

3. Snow Cover • Best products * Could be used to identify fresh snow

4. Snow reflectivity • Surface very heterogeneous • Current OMI retrievals: 0.6 everywhere OMI (354 nm, 0.5, from O'Byrne et al., 2010 ) MODIS (477 nm, 5 km from MOD43C3 product)

5. Reflectivity • Temporal changes can be important • This change if unaccounted for amounts to a +1-1.5%/yr change in NO2 2000-2001 2002-2004 2011-2012 2005-2007 2008-2010 2011 Reflectivity MODIS reflectivity, summer average

6. New EC Original Reprocessing leads to significant increases in NO2 and SO2  100% increase  NO2 • profiles from GEM-MACH • monthy-mean albedo from MODIS (snow, snow-free) • snow flagging from IMS SO2  40% increase  McLinden et al., ACP, 2014

7. Remote sensing Instruments (CIMEL and Pandora) at Fort McKay CIMEL Aerosol Optical Depth at 340 nm Aerosol optical depth Local Time Pandora 104 SO2 Vertical Column Density in DU (1 DU = 2.69 x 1016 mol cm-2) SO2 August 23 is in black Local Time 5 pm Pandora 104 NO2 Vertical Column Density in mol cm-2 NO2 Different colours represent different days from Vitali Fioletov, EC

8. Satellite Validation – OMI NO2 • Comparisons of NO2 total vertical column density • OMI NO2 using recalculated AMFs consistently in better agreement • One exception is Sept 16 where VCDOMI,trop < 0 OMI pixel Sept 16? Wind direction

9. Comparison of OMI NO2 with GEM-MACH2.5 forecast; where GEM-MACH values have been averaged over the individual OMI pixels OMI GEM-MACH 2.5 km forecast Vertical Column Density (x1015 cm-2)

10. Removal of the stratospheric NO2 signal • Fraction of total NO2 column in the troposhere • Urban/Industrial areas: 30-80%; Rural/background areas: 10-30% • With most of Canada <25%, it is crucial to have an unbiased method for removing stratospheric NO2 • With 20% in trop: a 10% high bias in strat-NO2 a 40% low bias in trop-NO2 Annual mean, from OMI (2009) Fraction

11. Two year running means – DOMINO and SP NO2 using Env. Canada AMFs OMI VCD, relative to 2005; (DOMINO+SP)/2 (DOMINO-SP)/2 Surface vmr, relative to 2005/06 DOMINO – SP difference  up to 0.5 ppb (10%) at surface

12. Two year running means – DOMINO and SP NO2 using Env. Canada AMFs OMI VCD, relative to 2005; (DOMINO+SP)/2 (DOMINO-SP)/2 Surface vmr, relative to 2005/06 DOMINO – SP difference  up to 1 ppb (30%) at surface

13. fine particles ozone Operational objective analysis Curently 10 km (2.5 km in 2 years) – O3, PM2.5, each hour (NO2, AQHI, AOD, SO2) experimental since 2003, operational Feb 2013 soon available on Weather Office http://weather.gc.ca/mainmenu/airquality_menu_e.html

14. OA average summer 2012 OA Real-time, hourly averaged analysis increments zoom in OA near Toronto Objective analysis of NO2

15. Possible contribution to TEMPO CDAT-Option 1 Real-time maps of surface pollutants based on Airnow and TEMPO observations CDAT-Option 2Stratospheric assimilation of NO2 CDAT-Option 3 Integrated surface-tropospheric-stratospheric assimilation of NO2 (Airnow+TEMPO) and other species and data CDAT-OSSE OSSEs (pre-launch) and OSEs (post-launch)

16. Thanks for your attention!