Henk Eskes, NO2 workshop KNMI, 10 Sep 2007 A combined retrieval, modelling and assimilation approach...

Henk Eskes, NO2 workshop KNMI, 10 Sep 2007

A combined retrieval, modelling and assimilation approach to estimate tropospheric NO2 from OMI measurements

Ruud Dirksen, Folkert Boersma1, Henk Eskes, Ronald van der A, Pepijn Veefkind, Pieternel Levelt,Ellen Brinksma, Eric Bucsela2, Ed Celarier2, Jim Gleason2

KNMI, the Netherlands1 Harvard, USA2 NASA, USA


Eleven years of NO2 data

GOME, 1996 - 2003SCIAMACHY, 2003 - presentKNMI - IASB retrieval

OMI, 2004 - presentKNMI - NASA retrieval

Images and detailed data products, including averaging kernels and error estimates,available on the TEMIS and PROMOTE web siteswww.temis.nlwww.gse-promote.org


OMI instrument overview

• Nadir viewing imaging spectrometer• Wide swath (2600km) & high spatial resolution

(13x24/48 km2) => Daily global coverage• 2D-CCD detectors: instantaneous recording of FOV and

spectrum• Spectral range 270-500nm (0.4 – 0.6nm resolution)• Data products:

O3 column & profile, NO2, aerosols, clouds, SO2, BrO, OClO, HCHO

• EOS-AURA platform, launched 15 July 2004• Sun-synchronous orbit, equator overpass time 13:40


There are two OMI products: 1. Operational product (NASA-KNMI)

2. Near-real time product (KNMI-NASA)

• To provide users with near-real time air pollution monitoring environmental agencies, air quality forecasters EU GEMS project:

- NRT comparison OMI with regional AQ models over Europe

• To generate a long-term consistent dataset fromGOME, SCIAMACHY, OMI, GOME-2

Near real time OMI data publicly available since October 2006 via www.temis.nlBoersma et al. Atmos.Chem.Phys. 7, 2103, 2007

OMI near-real time product: Purpose


NO2 retrieval developed at KNMI

Combinedretrieval-modelling-assimilation-approach

Boersma et al. JGR, D04311, 2004


NO2 retrieval: error contributions

Typical 30-50% error for individual pixelsBoersma et al. JGR 2004


Ingredient 1: Daily chemical forecast with OMI NO2 assimilation

Large uncertainty on slant columns with estimated high tropospheric column


Ingredient 2: Near-real time OMI tropospheric NO2 retrieval

NASA/KNMI DOAS algorithm

Processing time < 2 mins


Reprocessing status

L1b reprocessing of entire mission completed L2 reprocessing will commence within a few weeks DOMINO reprocessing follows immediately after

completion of L2 reprocessing (~2 months)

Changes No de-striping (optional - after the retrieval) HDF-EOS5 output format to be consistent with other OMI

products


Reprocessing changes

Better handling of flagging in L2 algorithm (filtering of e.g. saturated pixels) less spurious pixels

Annual mean solar irradiance spectrum => reduction of stripes, improved S/N

Better dark current correction => reduction of stripes. Stripe at least factor 3 smaller



v3 (2007 reprocessing)v2



v3 with stripe filteringv3 (2007 reprocessing)


GOME NO2 intercomparison

IPCC contextVan Noije et al., ACP 2006

NO2 products:• Univ. Bremen (A. Richter)• Harvard, Dalhousie (R. Martin)• IASB / KNMI


GOME NO2 intercomparison

IUP Bremen Dalhousie KNMI/BIRA

Ns425 – 455 nm 425-450 nm 426.3 – 451.3 nm

Ns,stRef. sector scaled to SLIMCAT strat.

Ref. Sector Data-assimilation in TM4

Cloud fraction (albedo)

FRESCO 0.2 cloud fraction; only cloud selection, no further correction

GOMECAT (Kuruso) FRESCO (0.8)

Cloud pressure Not used GOMECAT FRESCO

Albedo GOME (Koelemeijer) GOME(Koelemeijer) TOMS/GOME (1x1)

Profile shape MOZART-2 run for 1997, monthly averages on 2.8 x 2.8 °

GEOS-CHEM TM4 (3x2)

Temperature correction

No ? Yes, ECMWF T-profiles


Validation with surface measurements

Schaub et al., ACP 6, 2006

Cloud free

Cloudy

Daniel Schaub, EMPA


SCIAMACHY vs. Chimère: yearly mean 2003

Blond et al., JGR 112, 2007


KNMI OMI near-real time NO2, 13-16 Oct 2005

SundaySaturday


Chimere model @ OMI overpass time, 13-16 Oct 2005


Tropospheric NO2 by OMI, May 2006


Recent results: Greek forest fires August 2007


MediterraneanApr 2006 May 2006

Sep 2006 Oct 2006


OMI - SCIAMACHY, May & July 2006

Differences due to diurnal variation in emission

Boersma et al. submitted to JGR

Boersma et al., submitted to JGR


Dandelions, OMI L2 and OMI NRT


GOME / SCIA / OMI experience• retrieval improvements should focus on cloud/aerosol

treatment, surface albedo, stratospheric background, profile shape

• Comparison algorithms: KNMI-NASA-IUP-BIRA-Dal-UHei

2007 reprocessing major improvement

SCIA + OMI : morning vs afternoon

Comparison with regional-scale models to exploit resolution

Future: Use of NRT-OMI (GOME-2) for air-quality applications

ConclusionsConclusions


1. Find window with smallest variation in initial columns

2. Compute mean column vs. across track viewing angle

3. FFT analysis to smooth

Stripe correction


SCIAMACHY - OMI, October 2004


SCIAMACHY, October 2004


OMI, October 2004


SCIAMACHY - OMI, October 2004


Trend over China

GOME, 1997 SCIA, 2004

R.J. van der A et al., JGR 111, 2006


Van der A et al., 2006

Trend in NO2 column over China


Van der A et al., 2006

Trend NO2 concentration worldwide


Seasonal dependence, GOME en SCIAMACHY


Seasonal dependence, TM4 model


Source attribution


OMI - SCIAMACHY, average

Henk Eskes, NO2 workshop KNMI, 10 Sep 2007 A combined retrieval, modelling and assimilation approach...

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