research center karlsruheralf sussmann imk-ifu garmisch-partenkirchenhymn retrieval strategy...
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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
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen HYMN retrieval strategy
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CH4 Total HDO CO2 CO22 Solar
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CH4 micro windows, interfering species
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen HYMN retrieval strategy
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CH4 Total Solar N2O O3
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CH4 micro windows, interfering species
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen HYMN retrieval strategy
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Total CH4 NO2 OCS Solar
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HDO
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CH4 micro windows, interfering species
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen HYMN retrieval strategy
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Total CH4 HDO NO2 H2O Solar
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CH4 micro windows, interfering species
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen HYMN 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?)
AVi(i) = 0.501 AVi(i) = 0.521
AVi(i) = 0.504
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen HYMN retrieval strategy
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
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen HYMN retrieval strategy
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)
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen HYMN 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 Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen HYMN retrieval strategy
HYMN-CH4-a prioris
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0.00E+00 2.00E-07 4.00E-07 6.00E-07 8.00E-07 1.00E-06 1.20E-06 1.40E-06 1.60E-06 1.80E-06 2.00E-06
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)
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen HYMN retrieval strategy
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?
?
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen HYMN retrieval strategy
CH4 regularization. Issue: for direct quantitative intecomparison the layering would have to be the same!
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Bremen
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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
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen HYMN retrieval strategy
N2O regularization. Issue: for direct quantitative intercomparison the layering would have to be the same!
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Bremen
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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
Research Center Karlsruhe Ralf Sussmann
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
2004
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dofs
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gunson
stremmeAVi (i)
( )
note strong a priori impact for profile scaling (dofs = 1)
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen SCIA 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 Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen SCIA precision val. paper status/input
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
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen SCIA precision val. paper status/input
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
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen SCIA precision val. paper status/input
Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-031400
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SCIA 200 km SCIA 500 km SCIA 1000 km Zugspitze FTIR
XC
H4
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Date
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 Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen SCIA 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)
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
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen SCIA precision val. paper status/input
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)
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen SCIA precision val. paper status/input
Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-031400
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SCIA 200 km SCIA 500 km SCIA 1000 km Polynomial Fit of Data1_200km
XC
H4
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Date
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
Research Center Karlsruhe Ralf Sussmann
IMK-IFU Garmisch-Partenkirchen SCIA 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 radiusCase b): (planetary-)wave length < selection radius
north
south
altitude z