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Sentinel-5 precursor : TROPOMI

Sentinel-5 precursor : TROPOMI. Cloud slicing retrieval: Program development and testing with SCIAMACHY/GOME-2 WFDOAS data Kai-Uwe Eichmann, Mark Weber, IUP Bremen. Background. TOZ : Total ozone / GVC : Ghost vertical column / CF : Cloud fraction. Input/output data. Level 2 data

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Sentinel-5 precursor : TROPOMI

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  1. Sentinel-5 precursor: TROPOMI Cloud slicing retrieval: Program development and testing with SCIAMACHY/GOME-2 WFDOAS data Kai-Uwe Eichmann, Mark Weber, IUP Bremen

  2. Background TOZ: Total ozone / GVC: Ghost vertical column / CF: Cloud fraction

  3. Input/output data • Level 2 data • Total ozone TOZ [DU](WFDOAS) • Ghost vertical column GVC [DU] (WFDOAS) • Cloud top height CTH [km] (SACURA or FRESCO) • Cloud fraction CF [-] (SACURA or FRESCO) • Auxiliary data • Latitude/Longitude [deg] (Instrument) • Derived data • Cloud top pressure CTP [hPa] • Output data • Cloud slicing Ozone VMR (CSV) [ppmv] between CTH(min) and CTH(max)

  4. CSL program development • IDL program (M. Weber) using an iterativeapproach to deal with outliers in the data • Speed increase with packing all WFDOAS data into one file per month, restricted to the tropics (25 deg): SCIAMACHY factor 12 (6 Min ->30 sec, GOME-2 factor 20: 20 to 1 min). • FORTRAN 90 draft version is ready (linear least square only), unpacked data version takes about 3 Min. • IDL version used for testing different parameters: • Cloud fraction = [0.8,0.9] • CTH(min) = [6, 7] km (5.5 km GOME-2) • Grid boxes: 5° Lat / [30°, 10°] Lon • Number of data per grid box: [15, 50] • Number of days: [6, 15, 30]

  5. SCIAMACHY cloud top heights Gridcell

  6. Results from SCIAMACHY WFDOAS CTH(min)=7km Outliers 2006/10: 30d of data CF(min)=90% vmr=slope*1.27E3

  7. Problem: ACCO time-dependent • Two regimes of ozone • Negative VMR due to change of ACCOduring the month • Thus monthly means are not the best choice

  8. Dividing into 6 days periods

  9. GOME-2 results • GOME-2 has about 4 times more measurements than SCIAMACHY. • CTH is generally lower (FRESCO) than for SCIAMACHY (SACURA). Differences are about 3 km on average. • This will have an effect on the calculated VMR(cloud slicing volume mixing ratio).

  10. Comparison SCIA / GOME-2

  11. Results • Reducing the cloud fraction to 0.8: • increases the number of cloudy pixels by up to 40% depending on altitude. • The effect needs to be further analyzed. • The height to pressure conversion is error prone, as the temperature profile is not taken into account. • The differences between pressures calculated with a scale height of 8 km or using the international barometric height formula are, depending on the altitude, quite different (up to 40 hPa at 15km = 40%). • Using data from a whole month for the CSL calculation is in a lot of cases not the best choice, as the above cloud ozone may change considerably during the month.

  12. Outlook • Extent the parameter study, • e.g. SACURA vs. FRESCO • Use sonde data to decide which parameters are best for the retrieval (see E. Leventidou) • Precalculated data input to FORTRAN program • Test the code with OMI data • different input parameter, definition of ACCO

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