sentinel-5 precursor : tropomi
Post on 24-Feb-2016
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DESCRIPTIONSentinel-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 - PowerPoint PPT Presentation
Sentinel-5 precursor: TROPOMICloud slicing retrieval: Program development and testing with SCIAMACHY/GOME-2 WFDOAS data
Kai-Uwe Eichmann, Mark Weber, IUP BremenBackground
TOZ: Total ozone / GVC: Ghost vertical column / CF: Cloud fractionInput/output dataLevel 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 dataLatitude/Longitude [deg] (Instrument)Derived dataCloud top pressure CTP [hPa]Output dataCloud slicing Ozone VMR (CSV) [ppmv] between CTH(min) and CTH(max)CSL program developmentIDL program (M. Weber) using an iterative approach to deal with outliers in the dataSpeed 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] LonNumber of data per grid box: [15, 50]Number of days: [6, 15, 30]SCIAMACHY cloud top heights
GridcellResults from SCIAMACHY WFDOAS
CTH(min)=7km2006/10: 30d of dataCF(min)=90%Outliersvmr=slope*1.27E3
Two regimes of ozoneNegative VMR due to change of ACCO during the monthThus monthly means are not the best choiceProblem: ACCO time-dependent
Dividing into 6 days periods
GOME-2 resultsGOME-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).
Comparison SCIA / GOME-2
ResultsReducing 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.
OutlookExtent the parameter study, e.g. SACURA vs. FRESCOUse sonde data to decide which parameters are best for the retrieval (see E. Leventidou)Precalculated data input to FORTRAN programTest the code with OMI data different input parameter, definition of ACCO