Retrieval, validation and assimilation of SCIAMACHY ozone columns

Henk Eskes, ERS-ENVISAT symposium 2004

Retrieval, validation and assimilation of Retrieval, validation and assimilation of SCIAMACHY ozone columnsSCIAMACHY ozone columns

Henk Eskes, Ronald van der A, Ellen Brinksma,Pepijn Veefkind, Johan de Haan, Pieter Valks Royal Netherlands Meteorological Institute (KNMI)

1) Ozone column retrieval2) Validation of SCIAMACHY ozone columns3) Data assimilation and validation


Ozone column retrieval: new DOAS algorithm

Heritage:• Based on the OMI operational ozone column algorithm

OMI-DOAS - Pepijn Veefkind, J. De Haan• Implementation for GOME

TOGOMI - P. Valks, R. Van Oss• Implementation for SCIAMACHY

TOSOMI - H. Eskes and R. van der A

ESA ITT - GOME total-ozone algorithm• BIRA• IFE - University Bremen• KNMI


Innovations compared to e.g. GOME Fast Delivery, GDP vs 3

• New treatment of rotational Raman scattering (J. de Haan)• Empirical air-mass factor approach• TOMS v8 ozone profile data base• FRESCO cloud cover and cloud top height• Radiative transfer improvements• T-dep O3 cross section: ECMWF temperature profiles


Normalized IncidentSunlight

Ozone Absorption

Single Rayleigh ScatteringCabannes

not scrambled96.2 %

Ramanscrambled

3.8 %

OzoneAbsorption

OzoneAbsorption

Received by sensor

Rotational Raman: impact on retrieval


New approach to rotational Raman scattering

Difference between old and new treatment of Raman


Empirical air-mass factor


DOAS fit example

rms 0.5 %


SCIAMACHY TOSOMI Example

QuickTime™ en eenTIFF (ongecomprimeerd)-decompressor

zijn vereist om deze afbeelding weer te geven.


Validation: midlatitude example

BrewerDe Bilt (52.1 N, 5.18E) bias: -1.8% rms: 4.3%

No obviousseasonalbias


Validation: summary

Av. Bias [%]

RMS Bias number

AllAll -1.3-1.3 5.05.0 9696 Dobson -1.1 4.5 54 Brewer -1.9 5.0 28 SAOZ -3.1 7.6 9 DOAS 4.9 8.1 3 other -1.1 5.4 2Polar Lat >60 -1.7 5.7 11 Lat<-60 1.1 6.5 7Midlat Lat 30 to 60 -1.4 4.9 51 Lat –30 to -60 -4.0 6.3 6(Sub)tropical Lat –30 to 30 -1.0 4.3 21


Validation: summary vs. latitude

Main conclusions:

• Tosomi 1.5% lower than ground based

• RMS 4.9% increasing with ozone variability (representativity)

• No clear geographical location dependence

• No clear seasonal dependence


Ozone data assimilation at KNMI

TM3DAM assimilation code:• Driven by 6h meteo from ECMWF (wind, temperature, pres):

analyses and 10-day forecasts• Same vertical levels as ECMWF (subset in lower troposphere)• Second moments advection (Prather)• Sub-optimal Kalman filter, detailed error covariance modelling• Ozone chemistry parametrizations:

• Cariolle gas phase• "Cold tracer" scheme for heterogeneous chemistry


Assimilation: example


Validation: RMS Tosomi retrieval - assimilation

rms about 3%average

total rms =retrieval +forecast +represent.

DU


Validation: RMS Tosomi retrieval - GOME-Togomi assimilation

rms about 3%average

total rms =retrieval +forecast +represent.


Tropospheric Emission Monitoring Internet Service

ESA-Data User Programme project

Sciamachy ozone related products:

• Near-real time total ozone from Sciamachy, delivery to ECMWF for assimilation

• 9-day medium-range forecasts of ozone • UV forecasts

http://www.temis.nl/


Summary / conclusions

Tosomi: SCIAMACHY ozone column retrieval algorithm based on OMI-DOAS

Validation with ground-based observations: No clear latitude dependence (or seasonal dependence) bias -1.5 %

Validation with data assimilation: Important for the development of the retrieval implementation OmF: shows neutral lon-lat behaviour, rms 3%

Data available on http://www.temis.nl/ Near-real time ozone columns, analysis records and 9-day forecasts of ozone and UV index 2004 ozone hole


Validation of Tosomi retrieval with assimilation



Theoreticalerror due toinaccurate lookup-table discretisation



Before …

DU


Ozone hole 2004


Ozone hole 2004

EP TOMS

SCIAMACHY


Ozone hole 2004

Retrieval, validation and assimilation of SCIAMACHY ozone columns

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