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Development of TEMPO NO 2 Development of TEMPO NO 2

Development of TEMPO NO 2 - PowerPoint Presentation

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Development of TEMPO NO 2 - PPT Presentation

Algorithm to Infer Vertical Columns from Total Slant Columns Randall Martin Dalhousie SAO with contributions from Nick Krotkov NASA Goddard Lok Lamsal NASA Goddard ID: 777195

surface snow cloud tempo snow surface tempo cloud algorithm trace reflectance gas amp amf lamsal lok account stratospheric aerosols

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Slide1

Development of TEMPO NO2 Algorithm to Infer Vertical Columns from Total Slant Columns

Randall Martin (Dalhousie, SAO)with contributions fromNick Krotkov (NASA Goddard), Lok Lamsal (NASA Goddard), Jintai Lin (Peking U), Chris McLinden (Environment Canada), Caroline Nowlan (SAO)

TEMPO Science Team MeetingHuntsville, Alabama27 May 2015

Slide2

Attention Needed to Removal of Stratospheric NO2Adaptation to OMI algorithms to TEMPO will require consideration of diurnal variation and boundaries (more from Lok Lamsal)

Annual mean, from OMI (2009) Fraction of total NO2 column in the troposphere can be smallUrban/Industrial areas: 30-80%Rural/background areas: 10-30%FractionFigure from Chris McLindenTropospheric Fraction

Stratospheric Assimilation May Offer Advantages

Slide3

Need to Account for Atmospheric Scattering and Vertical Profile (Air Mass Factor; AMF) in UV-Vis RetrievalsOften Largest Source of Uncertainty in LEO Retrievals

dt()Io

I

B

EARTH SURFACE

Radiative Transfer Model

Scattering weight

Atmospheric Chemistry Model

“a-priori” Shape factor

Calculate

w

(

) as function of:

solar and viewing zenith angle

surface

reflectance,

pressure

cloud pressure,

aerosol

INDIVIDUAL

TEMPO SCENES

Mixing

ratio

C(

)

() is temperature dependent cross-section

sigma (

)

Slide4

Challenges in Development of AMF Calculation for TEMPO

Representing diurnal variation in surface reflectance at launch (use GOES-R spatially & TROPOMI spectrally)Diurnal variation in trace gas profile (changes rapidly in morning; need to evaluate; more from Lok Lamsal)Resolve geophysical fields at fine resolution (e.g. BEHR)Accounting for aerosols (still unresolved for LEO). Existing cloud products partially account for aerosolSurface reflectance (BRDF & snow)

Concentration

A

ltitude

Slide5

Current Operational LEO AMF Algorithms Treat Aerosol as Cloud: Prone to Bias when Together Including Aerosols in Retrievals of Both Clouds and Trace Gases Can Consistently Account for Their Effects

Clouds Decrease Sensitivity to Trace Gas Below

Aerosols Increase Sensitivity to

Trace Gas

Within Haze Layer

“Average” Tends to Decrease

Sensitivity to Trace Gas

Below

Accounting for Effects of Aerosol Haze on Cloud and NO

2

Increases R

2

(0.72

0.96)

of OMI NO

2

vs Ground-based DOAS Observations in China

(Lin et al., ACP, 2014)

Slide6

Should Account for Bidirectional Reflectance Distribution Function (BRDF)

Sun Behind ObserverSun Opposite Observer

Can introduce changes in retrieved NO

2

of +/- 50% (Lin et al., ACPD, 2015)

Information available from MODIS and forthcoming from GEOS-R

Will also be important over snow

AMF Sensitive to Surface

Figure from

Lok

Lamsal

Slide7

Address Potential Retrieval Bias for Snow-Covered ScenesCreate Separate Reflectance Databases for Snow-Free and Snowy ScenesSnow Identification: IMS (NOAA/NESDIS) or CaLDAS (Env. Canada)

O’Byrne et al., JGR, 2010

With Cloud

Fraction

Threshold (f < 0.3)

-0.5

0

1.0

All

Cloud

Fractions

0.5

Slide8

Plan for TEMPO Algorithm ImplementationBasic algorithm (default standard algorithm)empirical stratospheric removalAMF without aerosolsLambertian reflectanceCombined snow-free & snow-covered reflectance…Research algorithm (components become standard when mature)stratospheric assimilationAMF with aerosols

BRDFSeparate snow-free & snow-covered reflectance…

Slide9

Slide10

Snow CoverBest productsIMSCMC  CaLDASProvider

NOAA/NESDISEnvironment Canada / Canadian Meteorological CentreAvailabilityNear-real timeNear-real timeSpatial ExtentNorthern HemisphereNorth America / GlobalSpatial resolution (current)4 x 4 km210 x 10 km2 / 24 x 24 km2Spatial resolution (future)1 x 1 km22.5 x 2.5 km2 / 10 x 10 km2 (~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 observationsCMC: analysis using surface observationsCaLDAS: Data assimilation of land-surface model, satellite imagery; surface observations

* Could be used to identify fresh snow

Figure from Chris

McLinden