algorithms and chemical data assimilation activities at environment canada chris mclinden air...

Algorithms and chemical data assimilation activities at Environment Canada

Chris McLindenAir Quality Research Division, Environment Canada

2nd TEMPO Science Team MeetingHampton, VA 21-22 May 2014

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

-140 -120 -100 -80 -6025

30

35

40

45

50

55

60

65

70

0

0.2

0.4

0.6

0.8

1

Fraction

Snow Cover

• Best products

IMS (Interactive multi-sensor) CMC CaLDAS (Cdn. Land Data Assimilation System)

Provider NOAA/NESDIS Environment Canada / Canadian Meteorological Centre

Availability Near-real time Near-real time

Spatial Extent Northern Hemisphere North America / Global

Spatial resolution (current) 4 x 4 km2 10 x 10 km2 / 24 x 24 km2

Spatial resolution (future) 1 x 1 km2 2.5 x 2.5 km2 / 10 x 10 km2

(~2015/2016)

Temporal resolution Current: daily; future: 12-hour Current: 12-hour; future: 6 hour or better

Field provided Snow extent (yes / no) Snow depth*

Input information satellite imagery; derived mapped products; surface observations

CMC: analysis using surface observations and (simple) surface modelCaLDAS: Data assimilation of land-surface model, satellite imagery; surface observations

* Could be used to identify fresh snow

Snow reflectivity

• Surface very heterogeneous

• Current OMI retrievals: 0.6 everywhere

Longitude

Lat

itud

e

-112.5 -112 -111.5 -111 -110.556.4

56.6

56.8

57

57.2

57.4

57.6

57.8

Longitude

-112.5 -112 -111.5 -111 -110.5

Alb

edo

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

OMI (354 nm, 0.5, from O'Byrne et al., 2010 )

Fort McMurray

Fort McKay

2005Fort McMurray

Fort McKay

2011

MODIS (477 nm, 5 km from MOD43C3 product)

Reflectivity

• Temporal changes can be important

• This change if unaccounted for amounts to a +1-1.5%/yr change in NO2

2000-2001

0.01

0.02

0.03

0.04

0.05

0.06

0.072002-2004 2005-2007 2008-2010 2011-2012

MODIS reflectivity, summer average

Reflectivity

2011

DOMINO SCD - DOMINO AMF

56.4

56.6

56.8

57

57.2

57.4

57.6

DOMINO SCD - EC AMF

Latit

ude

SP SCD - SP AMF

56.4

56.6

56.8

57

57.2

57.4

57.6

SP SCD - EC AMF

VC

D [ 1

01

5 cm

-2]

0

0.5

1

1.5

2

2.5

3

3.5

4

NASA SCD - AMF=0.36

-112.5 -112 -111.5 -111 -110.556.4

56.6

56.8

57

57.2

57.4

57.6

Longitude

NASA SCD - EC AMF

-112.5 -112 -111.5 -111 -110.5

VC

D [

DU

]

-0.1

0

0.1

0.2

0.3

0.4

135 W

120 W

105 W 90

W 75

W

60 W

45 N

60 N

75 N

Original New EC

100% increase

40% increase

Reprocessing leads to significant increases in NO2 and SO2

- profiles from GEM-MACH- monthy-mean albedo from MODIS (snow, snow-free)- snow flagging from IMS

NO

2S

O2

McLinden et al., ACP, 2014

CIMELAerosol Optical Depth at 340 nm

Pandora 104SO2 Vertical Column Density in DU(1 DU = 2.69 x 1016 mol cm-2)

Pandora 104NO2 Vertical Column Density in mol cm-2

August 23is in black

Local Time

Different colours represent different days

Remote sensingInstruments (CIMEL and Pandora) at Fort McKay

5 pm

Local Time

from Vitali Fioletov, EC

NO

2S

O2

Aer

osol

opt

ical

dept

h

• 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

220 230 240 250 260 270 280 2900

0.5

1

1.5

2x 10

16

Julian Day Number

NO

2 V

CD

[cm

-2]

PandoraOMI(EC)OMI(TEMIS)

Satellite Validation – OMI NO2

Sept 16?

OMI pixel

Wind direction

-114 -113 -112 -111 -110 -10955.5

56

56.5

57

57.5

58

58.5

NO2 : 04 Sep 2013 13:58 MDT

Longitude

Lat

itu

de

0

1

2

3

4

5

6

7

-114 -113 -112 -111 -110 -10955.5

56

56.5

57

57.5

58

58.5

NO2 : 04 Sep 2013 13:58 MDT

Longitude

Lat

itu

de

0

1

2

3

4

5

6

7

OMI GEM-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 C

olumn D

ensity (x1015 cm

-2)

Removal of the stratospheric NO2 signal

-150 -100 -5020

30

40

50

60

70

0

0.1

0.2

0.3

0.4

0.5

Annual mean, from OMI (2009)

• 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

Fraction

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 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

Two year running means –DOMINO and SP NO2 using Env. Canada AMFs

Operational objective analysisOperational objective analysis

experimental since 2003, operational Feb 2013

ozonefine particles

Curently 10 km (2.5 km in 2 years) – O3, PM2.5, each hour (NO2, AQHI, AOD, SO2)

soon available on Weather Office http://weather.gc.ca/mainmenu/airquality_menu_e.html

Objective analysis of NO2

Real-time, hourly

zoom in OA near Toronto

OA average summer 2012 OA

averaged analysis increments

CDAT-Option 1CDAT-Option 1 Real-time maps of surface pollutants based on Airnow and TEMPO observationsCDAT-Option 2CDAT-Option 2 Stratospheric assimilation of NO2

CDAT-Option 3CDAT-Option 3 Integrated surface-tropospheric-stratospheric assimilation of NO2 (Airnow+TEMPO) and

other species and dataCDAT-OSSECDAT-OSSE OSSEs (pre-launch) and OSEs (post-launch)

Possible contribution to TEMPOPossible contribution to TEMPO

Thanks for your attention!