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Tropospheric Formaldehyde (CH 2 O) from Satellite Observations. Isabelle De Smedt 1 , M. Van Roozendael 1 , R. Van Der A 2 , H. Eskes 2 . 1: BIRA-IASB, 2: KNMI. Formaldehyde in the troposphere.

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Tropospheric Formaldehyde (CH 2 O) from Satellite Observations.

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Tropospheric formaldehyde ch 2 o from satellite observations

Tropospheric Formaldehyde (CH2O) from Satellite Observations.

  • Isabelle De Smedt1, M. Van Roozendael1,

  • R. Van Der A2, H. Eskes2.

  • 1:BIRA-IASB, 2: KNMI

Formaldehyde in the troposphere

Formaldehyde in the troposphere

  • NOx/VOCs ratio determines the production of ozone in the troposphere. Satellite observation of NO2 and CH2O support air quality control.

  • CH2O is one of the most abundant carbonyl compounds and a central component of VOCs oxidation. Its observation can help to constrain VOC emissions.

  • Sources: - Methane oxidation (background)

    - Biogenic VOCs oxidation (isoprene)

    - Anthropogenic hydrocarbon oxidation

    - Biomass burning (as first and secondary product)

  • Sinks : Oxidation by OH radical and Photolysis

    - Major source of CO

    - Production of HO2

  • CH2O has a spectral signature of absorption in the near UV and can therefore been retrieved from satellite observations with the DOAS technique.

The temis project

The TEMIS Project

  • Objective within the TEMIS project: improve the quality of CH2O retrieval from satellite and provide a consistent long term series of CH2O observation combining different instruments.

  • Two satellite instruments:

    • GOME on ERS2:

      • launched in 1995. Full coverage until June 2003.

      • 320 x 40 km2 ground pixel

      • sun-synchronous orbit, 10:30

      • global coverage in 3 days


      • launched in 2002.

      • 60 x 30 km2 ground pixel

      • sun-synchronous orbit, 10:00

      • global coverage in 6 days

The temis project1

The TEMIS Project

  • Tropospheric CH2O is a joined product between BIRA-IASB and KNMI.

  • DOAS technique in two independent steps:

    • Fit of slant columns (absorption along the satellite viewing path). SCD are retrieved with the WINDOAS software.

    • Determination of air mass factors to obtain vertical columns. AMF are computed with radiative transfer calculations to model scattering in the troposphere and CH2O profile shape from 3D-CTM.

1 ch 2 o slant columns

1: CH2O Slant Columns

  • In UV, main absorbers are Ozone and Ring effect.

  • CH2O optical depth smaller.

Optical densities: SC.σ(λ)

SC O3 = 2x1019 mol/cm²

SC NO2 = 5x1016

SC CH2O = 1x1016

SC BrO = 1x1014

  • Fit very sensitive to:

    • S/N ratio

    • Other molecules absorption

    • Fitting window

    • DOAS corrections

1 ch 2 o slant columns1

1: CH2O Slant Columns

  • DOAS settings have been optimized in order to obtain a consistent time series combining the two instruments.

  • Particularly, the fitting windows has been shifted more in the UV to avoid a spectral artefact in SCIAMACHY spectra.

  • I0: radiance selected daily in the Pacific Ocean.

  • Reference sector correction based on the background of CH2O in the Pacific only due to CH4 oxidation.

Tropospheric formaldehyde ch 2 o from satellite observations

1: CH2O Slant Columns

GOME CH2O SCD [x1015 mol/cm²]


  • Compared to first version of the TEMIS GOME CH2O product, SCD have been analysed in a new fitting window (328.5-346 nm).

  • For GOME: Reduction of the background noise and several artefacts above desert regions.

  • For SCIA: Allows to retrieve CH2O consistent with GOME.

2 amf determination

2: AMF Determination

  • Scattering by clouds and air particles makes the AMF dependant on the vertical distribution of the molecule. 

  • Scattering properties of the atmosphere modelled with a RTM (Disort). WF depend on observation angles, cloud properties, albedo and ground Altitude.

  • Cloud Correction based on the independent pixel approximation and on the FRESCO product.

  • Vertical distribution of CH2O is taken from the tropospheric 3D-CTM IMAGES. The profile shape S(z) is the normalized profile: S(z) = P(z)/∫P(z).

Profile as seen by GOME




  • Intex-A campaign

  • Jul.2004

Ch 2 o vertical columns

CH2O Vertical Columns

Apr.1996 – Dec.2001

Jan.2003 – Jun.2007

  • GOME CH2O VC averaged over 7 years (from 1996 to 2002) and the SCIAMACHY CH2O VC over the next 4 years and half (from 2003 to mid 2007).

  • The general agreement between both instruments allows the generation of a combined long-term time series of CH2O vertical columns covering a full decade from 1997 until 2006.

Tropospheric formaldehyde ch 2 o from satellite observations

CH2O Vertical Columns

GOME – SCIAMACHY CH2O VCD [x1016 mol/cm²]

Jan. – Jun. 2003

Over the 6 first months of 2003:

  • General agreement within 7.5x1015 mol/cm².

  • SCIA is higher than GOME from 40° in latitudes N and S.

  • South Atlantic Anomaly effect is different.

Tropospheric formaldehyde ch 2 o from satellite observations

CH2O Vertical Columns: America

  • Good agreement over the six first months

  • Stronger seasonal variability with SCIA.

  • Very good agreement in South America

  • Stronger SAA effect with SCIA

Tropospheric formaldehyde ch 2 o from satellite observations

CH2O Vertical Columns: Asia

  • Good agreement over the six first months, SCIA a bit lower.

  • Much stronger seasonal variability with SCIA.

  • Very good agreement in biomass burning regions.

Tropospheric formaldehyde ch 2 o from satellite observations

CH2O Vertical Columns: Africa

  • Very good overall agreement in Africa.

Special event greek fires this summer

Special Event: Greek Fires this Summer

CH2O as measured by SCIA on 26 August 2007 superimposed over an image made by MODIS. Due to the strong north-easterly wind the smoke from the forest fires is blown all the way to the coast of Lybia.

Total vcd error evaluation

Total VCD Error Evaluation

  • Vertical columns calculated from the slant columns (SC), air mass factors (AMF) and a zonal correction above Pacific Ocean (SCO and VCO):

  • As the determination of the SC, AMF and VCO are independent, the total error on the tropospheric vertical column can be expressed as:

  • σSC: error on the SC. Can be separated into its random and systematic part.

  • σAMF : error on the AMF evaluation.

  • σVC0 : error on the background correction above Pacific Ocean.

Global error budget

Global Error Budget

  • Total Error around 25%.

  • At low and mid latitudes, AMF error dominates with the main contribution from clouds and profile shape uncertainties.

  • At higher latitudes, SC error dominates because of higher Ozone concentrations.

  • Monthly average allows to reduce SC random error.



  • Modellers community:

    • IMAGES (J-F Muller and J. Stavrakou, BIRA-IASB, Brussels): current user, paper in preparation.

    • CHIMERE (G. Dufour, LISA, Paris): future user, data provided.

    • GEOS-CHEM (P. Palmer, Tropospheric Chemistry Earth Observation Modelling and Measurement Group, University of Edinburgh): other possible user.

  • National and regional environmental protection agencies:

    • Europe: UBA-Austria, EMPA Switzerland and LANUV Northrhine-Westfalia: users within Promote.

    • China: National Satellite Meteorological Centre NSMC (Peng Zhang), Institute of Atmospheric Physics, CAS IAP-CAS (Pucai Wang): Contacts in China through the AMFIC project, can help to find users there.

Conclusions and outlook

Conclusions and Outlook

  • On the TEMIS website, you will find:

    • Daily, monthly and yearly maps for GOME and SCIAMACHY.

    • Data files with averaging kernels and error estimation for each satellite pixel.

  • The dataset will be regularly extended with fresh SCIA data.

  • The analysis of GOME-2 data will start within the next months. The global coverage in 1,5 day should allow to reduce the noise in the results.

  • Consistency between the platforms needs to be evaluated regularly (changes in time) and validated with ground-based measurements that become more and more available for CH2O.

Conclusions and outlook1

Conclusions and Outlook

  • The quality and the consistency of the data is very important to be able to detect possible trends in emissions.

  • A derived product based on inverse modelling could be developed within TEMIS to provide constraints on VOC emissions. Possibilities of more users working on emission inventories (GFED, MEGAN).

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