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

Research Center Karlsruhe Ralf Sussmann

IMK-IFU Garmisch-Partenkirchen HYMN retrieval strategy

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Total CH4 CO2 Solar HDO H2O

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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



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CH4 Total HDO CO2 CO22 Solar

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2835.50 2835.55 2835.60 2835.65 2835.70 2835.75 2835.800.80

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CH4 Total Solar N2O O3

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Total CH4 NO2 OCS Solar

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HDO

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? ?




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Total CH4 HDO NO2 H2O Solar

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H2O dofs=3, HDO dofs=1



At ZUG we don´t find a significant impact of joint profile retrieval of H2O, HDO versus scaling (others?)

AVi(i) = 0.501 AVi(i) = 0.521

AVi(i) = 0.504



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

ECMWFMunich radio sonde



At ZUG we find a very small reduction of the diurnal variation using Frankenberg versus HITRAN 04 line data

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dofs

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ma Hitran 2004

Hase fitted

Frankenbergfitted

stdv of diurnal variation

AVi(i)



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



HYMN-CH4-a prioris

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Kiruna

Izana

Zugspitze

Reunion

ISSJ

Harestua

Bremen

Spitsbergen

Paramaribo

Profile from G.C. Toon, (Balloon-FTIR data) *1.0465

HALOE (5yr mean 2000-2005) for [-10,-30] lat. & [40,70] lon.

Satellite data (15698 - 53985m) + CH4 HALOE 60.0N January

One profile based on HALOE occultation measurements near 46ºN (1995 annual mean)



HYMN-N2O a prioris

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Kiruna

Izana

Zugspitze

Reunion

ISSJ-2003

ISSJ-2006

Harestua

Bremen

Spitsbergen

Paramaribo

ballon, MIPAS, ACE profiles fittet with fermi-dirac-function

US Standard 1976 skaled by yearly trend of 0.25% up to 2004

*1.0129 shape estimated from ATMOS Refmod95 & Reftoon

where do the ISSJ surface values come from?

?



CH4 regularization. Issue: for direct quantitative intecomparison the layering would have to be the same!

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Bremen

ISSJ

Reunion

Harestua

Izana/Kiruna: The log-retrieval (log of absolute VMR or per cent profile?) is constrained mainly by a first derivative constraint, forcing the slope of the solution towards the slope of the a-priori, layer-steps increasing with altitude, dofs = 3; tuned versus what?dofs = 3. 4 km off diag, constant layering, dofs 2.1, tuned versus what?

Zugspitze: Tikhonov first derivative, altitude constant for relative VMR variations, exponential 66-layering, tuned to minimize diurnal variation and profile oszillations -> dofs 2 - 2.5

no off diag, based on HALOE occultation 46ºN (1995), layer-steps increasing with altitude, dofs 2.97, no Sa tuning?

4 km off diag, Sa from HALOE climatology, layer-steps increasing with altitude, dofs 2.2; tuned versus what?3 km off diag, layer-steps increasing with altitude, dofs?

Issue: the impact of layering on regularization is easily overlooked; e.g., Harestua is NOT an altitude constant regularization (like Bremen) since the layer-steps are increasing with altitude

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



N2O regularization. Issue: for direct quantitative intercomparison the layering would have to be the same!

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Bremen

ISSJ

Reunion

Harestua

Izana/Kiruna: Tikhonov L0+L1; layer-steps increasing with altitude, percentage variability altitude independent; dofs 3.5

4 km off diag, constant layering, dofs?

Zugspitze: Tikhonov first derivative, percentage variability altitude independent, exponential 66-layering, optimized diurnal var. and profile oszillations, dofs 3

no off diag, based on works performed by Arndt Meier, layer-steps increasing with altitude, dofs 3.655 km off diag, layer-steps increasing with altitude, dofs 3.2

4 km off diag, layer-steps increasing with altitude, dofs?

Issue: the impact of layering on regularization is easily overlooked; e.g., Reunion and Harestua are NOT altitude constant regularizations (like Bremen) since the layer-steps are increasing with altitude


IMK-IFU Garmisch-Partenkirchen SCIA precision val. paper status/input

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vmr (ppb)

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There can be a significant a priori impact on your columns precision

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stremmeAVi (i)

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note strong a priori impact for profile scaling (dofs = 1)



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)



AVi (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 Sa with dofs 2: Bremen, Reunion)

(Thikonov-L1-tuning)

Zugspitze 2003ISSJ 2003 Reunion 04/07



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



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SCIA 200 km SCIA 500 km SCIA 1000 km Zugspitze FTIR

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H4

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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)



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)

18 Sep 2003

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7:00:00 AM 9:24:00 AM 11:48:00 AM 2:12:00 PM 4:36:00 PM

Time of Day

i of day i (18 Sep) = 0.13 % ni = 9 columns, 10 min integration per column

XCH4

AVi (i) AVi (i/sqrt(ni)) & in per cent



Zugspitze FTIRdaily means

2003 dofs 2

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Datum

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XC

H4

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)



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SCIA 200 km SCIA 500 km SCIA 1000 km Polynomial Fit of Data1_200km

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Input (IV): provide statistical numbers for SCIA, 2003 & 2004 separately (we offer to do that for you, if you like)

2003 SCIA

AVi(ni)* Ai(i) Ai(i/sqrt(ni)) day-to-day**

SCIA 200 km

14.4 1.601 0.487 1.253

SCIA 500 km

70.2 1.664 0.310 0.795

SCIA 1000 km

169.1 1.780 0.178 0.624

*pixels per day

all sigmas in %

**first divide data by 3rd order polynomial fit to correct for annual cycle



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 radiusCase b): (planetary-)wave length < selection radius

north

south

altitude z

Documents

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