1 / 22

har: pre-profile fit of HDO via MW´s 1 & 2

CH4 micro windows, interfering species. har: pre-profile fit of HDO via MW´s 1 & 2 kir/iza: pre-profile fit of H2O, O3, N2O, NO2, HCl (MW?), OCS. CH4 micro windows, interfering species. CH4 micro windows, interfering species. HDO. CH4 micro windows, interfering species. ?. ?. HDO. H2O.

raja
Télécharger la présentation

har: pre-profile fit of HDO via MW´s 1 & 2

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CH4 micro windows, interfering species har: pre-profile fit of HDO via MW´s 1 & 2 kir/iza: pre-profile fit of H2O, O3, N2O, NO2, HCl (MW?), OCS

  2. CH4 micro windows, interfering species

  3. CH4 micro windows, interfering species HDO

  4. CH4 micro windows, interfering species ? ? HDO H2O

  5. CH4 micro windows, interfering species ? ?

  6. At ZUG we don´t find a significant impact of joint profile retrieval of H2O, HDO versus scaling (others?) H2O dofs=3, HDO dofs=1 H2O dofs=1, HDO dofs=1 AVi(i) = 0.501 AVi(i) = 0.521 H2O dofs=1, HDO dofs=3 AVi(i) = 0.504

  7. At ZUG we don´t find a significant impact of ECMW versus Munich radio sonde (others?) Munich radio sonde ECMWF Sigma i 0.516868287 0.518528544 Sigma i/sqrt(ni) 0.24454497 0.244846405 day-to-day 0.786555915 0.766957709 Munich radio sonde ECMWF

  8. At ZUG we find a very small reduction of the diurnal variation using Frankenberg versus HITRAN 04 line data stdv of diurnal variation AVi(i)

  9. At ZUG we don´t se obvious impact on profiles using Frankenberg versus HITRAN 04 line data (others?) HITRAN 04 Frankenberg fit line data dofs = 2 dofs = 2 dofs = 3 dofs = 3

  10. ?

  11. Bremen and Reunion (dofs 2, diagonal Sa) are significantly unter-estimating true variability

  12. There can be a significant a priori impact on your columns precision AVi (i) note strong a priori impact for profile scaling (dofs = 1) ( )

  13.  Input (I): provide mean tropopause altitude for your site Therefore we construct a set of consistent a priori´s which we provide to each station: We use the CH4 profile from reftoon corrected for tropopause altitude (via the linear transformation described in Arndt Meier´s thesis)  Provide us the mean tropopause altitude for your station(s)

  14. It is easy to under- / overestimate XCH4 day-to-day variability because of special regularization settings (e.g., diagonal Sa with dofs  2: Bremen, Reunion) ISSJ 2003 Reunion 04/07 Zugspitze 2003 dofs=2 detected day-to-day variability AVi (i) dofs=2.5  (daily means) diurnal variation dofs=3 (Thikonov-L1-tuning)

  15.  Input (II): provide kmat.dat (Kx, Se) from 15 different retrievals Therefore we construct a set of consistent R matrices for each station: We provide you a ready to use R matrix based upon the Tikhonov L1 operator wich is set in a way to yield dofs = 2 (or 2.5, to be decided)  provide kmat.dat (Kx, Se) from 15 different retrievals with the Toon a priori adapted to your site. The ensemble should cover the full span of SZA´s and columns for your site

  16. Input(III): prepare for years 2003 and 2004 four indiv. columns data sets: FTIR, SCIA 200 km, SCIA 500 km, SCIA 1000 km calculate XCH4 for FTIR by dividing CH4 column by daily air column (sum up 3rd block in fasmas file)

  17. Input (V): calculate i of day i, average over all days i, separate numbers for 2003 & 2004; (we offer to do that for you, if you like) in per cent AVi (i) & AVi (i/sqrt(ni))  i of day i (18 Sep) = 0.13 % ni = 9 columns, 10 min integration per column XCH4

  18. Input (VI): calculate sigma of day-to-day-variability for 2003 & 2004 separately; (we offer to do that for you, if you like) Zugspitze FTIR daily means  (daily means) 0.8 % If there is a significant annual cycle: normalize first by dividing by 3rd order polynomial fit!

  19. Input(IV): provide statistical numbers for SCIA, 2003 & 2004 separately (we offer to do that for you, if you like) 2003 SCIA all sigmas in % *pixels per day **first divide data by 3rd order polynomial fit to correct for annual cycle

  20. SCIA IMPA-DOAS v49 now reflects our a priori understanding of the impact of pixel selection radius on columns variability Case a): (planetary-)wave length > selection radius Case b): (planetary-)wave length < selection radius tropopause altitude altitude z surface level selection radius north south  an average of (SCIA) pixels witin a certain selection radius tends to see the same (case a) or slightly smaller (case b) day-to-day columns variability compared to a point-type measurement (Zugspitze FTIR)

More Related