1. Algorithms and chemical data assimilation activities at Environment Canada Chris McLinden Air Quality Research Division, Environment Canada 2 nd 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 IMS (Interactive multi-sensor) CMC  CaLDAS (Cdn. Land Data Assimilation System) ProviderNOAA/NESDISEnvironment Canada / Canadian Meteorological Centre AvailabilityNear-real time Spatial ExtentNorthern HemisphereNorth America / Global Spatial resolution (current)4 x 4 km 2 10 x 10 km 2 / 24 x 24 km 2 Spatial resolution (future)1 x 1 km 2 2.5 x 2.5 km 2 / 10 x 10 km 2 (~2015/2016) Temporal resolutionCurrent: daily; future: 12-hourCurrent: 12-hour; future: 6 hour or better Field providedSnow extent (yes / no)Snow depth* Input informationsatellite imagery; derived mapped products; surface observations CMC: analysis using surface observations and (simple) surface model CaLDAS: Data assimilation of land- surface model, satellite imagery; surface observations * 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 NO 2 2000-20012002-2004 2005-20072008-2010 2011-2012 MODIS reflectivity, summer average Reflectivity 2011

6. Original New EC  100% increase   40% increase  Reprocessing leads to significant increases in NO 2 and SO 2 - profiles from GEM-MACH - monthy-mean albedo from MODIS (snow, snow-free) - snow flagging from IMS NO 2 SO 2 McLinden et al., ACP, 2014

7. CIMEL Aerosol Optical Depth at 340 nm Pandora 104 SO 2 Vertical Column Density in DU (1 DU = 2.69 x 10 16 mol cm -2 ) Pandora 104 NO 2 Vertical Column Density in mol cm -2 August 23 is in black Local Time Different colours represent different days Remote sensing Instruments (CIMEL and Pandora) at Fort McKay 5 pm Local Time from Vitali Fioletov, EC NO 2 SO 2 Aerosol optical depth

8. Comparisons of NO 2 total vertical column density OMI NO 2 using recalculated AMFs consistently in better agreement One exception is Sept 16 where VCD OMI,trop < 0 Satellite Validation – OMI NO 2 Sept 16? OMI pixel Wind direction

9. OMIGEM-MACH 2.5 km forecast Comparison of OMI NO2 with GEM-MACH2.5 forecast; where GEM-MACH values have been averaged over the individual OMI pixels Vertical Column Density (x10 15 cm -2 )

10. Removal of the stratospheric NO 2 signal Annual mean, from OMI (2009) Fraction of total NO 2 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 NO 2 –With 20% in trop: a 10% high bias in strat-NO 2  a 40% low bias in trop-NO 2 Fraction

11. 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 Two year running means – DOMINO and SP NO 2 using Env. Canada AMFs

12. 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 Two year running means – DOMINO and SP NO 2 using Env. Canada AMFs

13. Operational objective analysis experimental since 2003, operational Feb 2013 ozone fine particles Curently 10 km (2.5 km in 2 years) – O 3, PM2.5, each hour (NO 2, AQHI, AOD, SO 2 ) soon available on Weather Office http://weather.gc.ca/mainmenu/airquality_menu_e.html

14. Objective analysis of NO 2 Real-time, hourly zoom in OA near Toronto OA average summer 2012 OA averaged analysis increments

15. CDAT-Option 1 CDAT-Option 1 Real-time maps of surface pollutants based on Airnow and TEMPO observations CDAT-Option 2 CDAT-Option 2 Stratospheric assimilation of NO 2 CDAT-Option 3 CDAT-Option 3 Integrated surface-tropospheric-stratospheric assimilation of NO2 (Airnow+TEMPO) and other species and data CDAT-OSSE CDAT-OSSE OSSEs (pre-launch) and OSEs (post-launch) Possible contribution to TEMPO

16. Thanks for your attention!