Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenHYMN retrieval strategy zugbreharkir iza reu PSSPSS PSSPSS PSSPSS PSSSPSSS PSSPSS 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

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenHYMN retrieval strategy zugbreharkir iza reu PSSSPSSS PSSSPSSS PSSSPSSS PSSSPSSS PSSSPSSS CH4 micro windows, interfering species

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenHYMN retrieval strategy zugbreharkir Iza reu PPPSPS PPSPS HDO CH4 micro windows, interfering species

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenHYMN retrieval strategy zugbreharkir Iza reu PSPS PSPS PPSPS PSSPSS HDO H2O ? ? CH4 micro windows, interfering species

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenHYMN retrieval strategy zugbreharkir Iza reu PSSSPSSS PSSSPSSS PSSPSS PSSSPSSSS PSSPSS ? ? CH4 micro windows, interfering species

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenHYMN retrieval strategy H2O dofs=3, HDO dofs=1 H2O dofs=1, HDO dofs=3 H2O dofs=1, HDO dofs=1 At ZUG we don´t find a significant impact of joint profile retrieval of H2O, HDO versus scaling (others?) AV i (  i ) = AV i (  i ) = AV i (  i ) = 0.504

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenHYMN retrieval strategy At ZUG we don´t find a significant impact of ECMW versus Munich radio sonde (others?) Munich radio sondeECMWF Sigma i Sigma i/sqrt(n i ) day-to-day ECMWF Munich radio sonde

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenHYMN retrieval strategy At ZUG we find a very small reduction of the diurnal variation using Frankenberg versus HITRAN 04 line data stdv of diurnal variation AV i (  i )

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenHYMN retrieval strategy At ZUG we don´t se obvious impact on profiles using Frankenberg versus HITRAN 04 line data (others?) HITRAN 04 dofs = 3 dofs = 2 Frankenberg fit line data dofs = 2 dofs = 3

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenHYMN retrieval strategy

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenHYMN retrieval strategy ?

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenHYMN retrieval strategy Bremen and Reunion (dofs  2, diagonal S a ) are significantly unter-estimating true variability

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenHYMN retrieval strategy

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenSCIA precision val. paper status/input There can be a significant a priori impact on your columns precision AV i (  i ) (  ) note strong a priori impact for profile scaling (dofs = 1)

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenSCIA precision val. paper status/input  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)

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenSCIA precision val. paper status/input AV i (  i )  (daily means) detected day-to-day variability diurnal variation dofs=2 dofs=2.5 dofs=3 It is easy to under- / overestimate XCH4 day-to-day variability because of special regularization settings (e.g., diagonal S a with dofs  2: Bremen, Reunion) (Thikonov-L 1 -tuning) Zugspitze 2003ISSJ 2003Reunion 04/07

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenSCIA precision val. paper status/input  Input (II): provide kmat.dat (K x, S e ) 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 (K x, S e ) 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

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenSCIA precision val. paper status/input 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)

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenSCIA precision val. paper status/input 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)  i of day i (18 Sep) = 0.13 % n i = 9 columns, 10 min integration per column XCH4 AV i (  i )AV i (  i /sqrt(n i ))  & in per cent

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenSCIA precision val. paper status/input Zugspitze FTIR daily means If there is a significant annual cycle: normalize first by dividing by 3rd order polynomial fit!  (daily means) 0.8 % Input (VI): calculate sigma of day-to-day-variability for 2003 & 2004 separately; (we offer to do that for you, if you like)

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenSCIA precision val. paper status/input Input (IV): provide statistical numbers for SCIA, 2003 & 2004 separately (we offer to do that for you, if you like) 2003 SCIA AV i (n i ) * Ai(i)Ai(i)A i (  i /sqrt(n i )) day-to-day** SCIA 200 km SCIA 500 km SCIA 1000 km *pixels per day all sigmas in % **first divide data by 3rd order polynomial fit to correct for annual cycle

Research Center KarlsruheRalf Sussmann IMK-IFU Garmisch-PartenkirchenSCIA precision val. paper status/input SCIA IMPA-DOAS v49 now reflects our a priori understanding of the impact of pixel selection radius on columns variability  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) selection radius tropopause altitude surface level Case a): (planetary-)wave length > selection radius Case b): (planetary-)wave length < selection radius north south altitude z