VALIDATION OF CARBON MONOXIDE VERTICAL COLUMN DENSITIESRETRIEVED FROM SCIAMACHY INFRARED NADIR OBSERVATIONS

P. Hochstaffl1,2, S. Gimeno Garcia2, F. Schreier2, M. Hamidouche2, and G. Lichtenberg2

1Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Austria2DLR — Remote Sensing Technology Institute, Oberpfaffenhofen, Germany

ABSTRACT

This validation study examines the accuracy of car-bon monoxide (CO) total columns derived from nadirmeasurements of the SCanning Imaging AbsorptionspectroMeter for Atmospheric CHartographY (SCIA-MACHY). Therefore, an intercomparison of the COcolumns estimated from SCIAMACHY measurementswith coincidented and colocated retrievals provided byseveral ground-based (g-b) stations affiliated to the Net-work for the Detection of Atmospheric CompositionChange (NDACC) and the Total Carbon Column Observ-ing Network (TCCON) had been performed. The studydemonstrated that the SCIAMACHY CO total columnvalidation results depend on many aspects. The resultsindicate particularly the importance of appropriate post-processing of the BIRRA retrievals (esp. filtering). Itshows that the CO product is sensitive to settings in re-trieval algorithm. Furthermore, the analysis gives evi-dence of a degrading channel 8 detector in later years. Inconclusion, for most cases monthly mean SCIAMACHYCO total columns agree within the standard deviationwhen compared to g-b measurements.

Key words: Validation; CO; BIRRA; SCIAMACHY;NDACC; TCCON.

1. INTRODUCTION

The underlying study presents an intercomparison of COcolumns estimated from SCIAMACHY using the BeerInfraRed Retrieval Algorithm (BIRRA) with coincidentand colocated retrievals provided by g-b Fourier Trans-form InfraRed (FTIR) spectroscopy observations fromvarious sites affiliated to NDACC and TCCON.

Verification and validation are critical elements of anycode development in order to ensure that geophysicalquantities derived from in-orbit radiometric measure-ments meet quality requirements for the intended scien-tific studies and applications (Gottwald & Bovensmann2011).

SCIAMACHY was a passive remote sensing spectrome-ter aboard ENVISAT. The spectral information of the in-coming solar radiation was recorded from the Ultraviolet(UV, 10 – 380 nm) to ShortWave InfraRed (SWIR, 1.4 –2.3 µm) region of the spectrum. Molecules of greenhousegases such as CO2, CH4 or H2O and gases with indirectradiative forcing such as CO absorb radiation at specificwavelengths/frequencies that are characteristic of theirstructure (see Zdunkowski et al. 2007). The SWIR regionreveals spectral information on those gases by means ofchanges in intensity. Channels 6, 7 and 8 covered theSWIR region where channel 8 was mainly used for COretrievals and channel 6 was exploited by most CH4 andCO2 retrievals. In down-looking (nadir) geometry radia-tion observed from Top Of Atmosphere (TOA) originatesfrom the Sun that is backscattered and reflected from theEarth’s surface and the Earth’s atmosphere. It is referredas back-scattered (solar) radiation and is sensitive to thetroposphere where CO concentration is highest. Further-more, the SWIR region is especially suitable to sourcesand sinks analysis.

The goal of this validation study is to examine the accu-racy of the SCIAMACHY derived CO total columns.

2. BIRRA — BEER INFRARED RETRIEVAL AL-GORITHM

BIRRA handles the retrieval of vertical column densitiesfrom SWIR infrared nadir observations. Basically thereare two versions of the algorithm — a so called prototypeversion and a version that is part of the operational pro-cessor (F. Schreier 2015, personal communication). Inthis study the BIRRA prototype version was used for COvertical column retrievals from channel 8.

A retrieval algorithm consists of a forward model, whichcomputes an atmospheric transmittance spectrum for aprescribed set of conditions, and an inverse method,which compares each measured spectrum with the cal-culation, and scales an a priori profile to produce a syn-thetic spectrum that achieves the best fit to the measuredspectrum (Gimeno Garcıa et al. 2011).

BIRRA is using several input files to define parameters

such as temperature, pressure and molecular concentra-tions (e.g. AFGL, CIRA or NCEP), topographical in-formation (GTOPO30) and specifications for the leastsquares solver (PORT library). The spectroscopic inputis coming from databases such as HITRAN or GEISAcontaining information on the spectroscopic behavior ofabsorbing molecules (Rothman et al. 2013; Jacquinet-Husson et al. 2011).

2.1. The forward model

The BIRRA forward model for SWIR radiative transferis based on Generic Atmospheric Radiation Line-by-LineInfrared Code (GARLIC) (Schreier et al. 2014) describ-ing the intensity (radiance) I at wavenumber ν accordingto Beer’s law for a double path through the atmosphere,

I(ν) =r(ν)

cos θIsun(ν)T↓(ν)T↑(ν)

=r(ν)

cos θIsun(ν)

× exp

− sun∫earth

∑m

κmν (s′) ds′

× exp

− sat∫earth

∑m

κmν (s′′) ds′′

, (1)

where θ is the solar zenith angle (SZA), r the surfacealbedo and T↑ and T↓ (with T = T↑ T↓) describe thetransmission between reflection point at earth surface ataltitude zsrf and observer and between sun and reflec-tion point, respectively. In the SWIR range the crosssections of absorbers strongly depend on pressure, tem-perature and wavenumber which are all taken into ac-count by the algorithm to produce a geophysical product.κmν (s) = nm(s) kma

(ν, p(s), T (s)

)designates the ab-

sorption coefficient of molecule m and nm is the molec-ular number density. The absorption cross section kma iscalculated by the sum of the contributions of all lines

kma =∑l

S(m)l

(T (s)

)× g(ν; ν

(m)l , γ

(m)l

(p(s), T (s)

)), (2)

with line position ν(m)l , strength S(m)

l and width γ(m)l .

The Voigt line shape is denoted by g.

Accordingly, the total path (sun-earth-sat) transmission inEq. (1) can be written in terms of the molecular scale fac-tor αm (to be estimated) and the nrefm (z) a-priori numberdensity of molecule m according to

T ≡ T↑ T↓

= exp

(−∑m

τm

)

= exp

(−∑m

αm τrefm

), (3)

where τ refm is the a-priori total optical depth computedwith nrefm .

Finally, the measured spectrum is modeled by convolu-tion of the monochromatic intensity spectrum with a nor-malized instrument spectral response function S (SRF)

I(ν) ≡ (I ⊗ S)(ν)

=

∞∫−∞

I(ν′) × S(ν − ν′)dν′ , (4)

yielding the forward model F according to

F(x) ≡ I(ν)

=r(ν)

cos θIsun(ν)

× exp

[−∑m

αm τrefm (ν)

]⊗ S(ν, γ) + b(ν) . (5)

In this equation the state vector x ≡ (α, γ, r,b) includesmolecular scale parameters α, and optionally ”auxiliary”parameters γ (SRF half-width), r (surface reflectivity)and b (baseline correction).

2.2. Separable least squares

The nonlinear least problem is solved by least squaresfitting of the unknown quantities x. Therefore, BIRRA istypically using a separable least squares fit, i.e. splittingthe vector x of parameters into two vectors x →

(η,β

).

This allows formulating the problem as follows

F(η,β) = Φ(η) · β , (6)

yielding a linear problem for β and a nonlinear problemfor η

minη

{minβ

{∥∥y −Φ(η)β∥∥2}} . (7)

3. CO FROM SCIAMACHY

For CO fitting BIRRA utilizes the window from 4280 to4305 cm−1. This is much smaller than the total range ofSCIAMACHY’s channel 8 reaching from 4191 to 4426cm−1. The designated VCD output provided by BIRRAis a proxy-normalized CO total column according to

xCO =αCO

αCH4

∞∫zsrf

nrefCO(z) dz (8)

VCDrefCO ≡

∞∫zsrf

nrefCO(z) dz , (9)

Figure 1. World map created by Feist (2015) showingstations affiliated to g-b observing networks routinelymeasuring trace gases such as CO in the mid-infrared(NDACC) and shortwave-infrared (TCCON).

where VCDrefCO is the a-priori vertical column density of

CO. The proxy therefore is CH4 and is used to compen-sate for errors induced by high clouds.

4. GROUND-BASED FOURIER TRANSFORMSPECTROMETERS

In this study the g-b observing networks, namely NDACCand TCCON, are used to provide CO measurements forthe validation of SCIAMACHY’s CO level-2 product re-trieved from BIRRA.

4.1. NDACC

Within NDACC the Infrared Working Group (IRWG)represents a network of infrared Fourier transform spec-trometers and currently consists of over twenty siteswith measurements dating back up to two decades.The retrievals are obtained from solar absorption spec-tra recorded in the mid-infrared (unlike SCIAMACHY)spectral range (Ostler et al. 2014).

4.2. TCCON

TCCON was established in 2004 and observes solar ab-sorption of direct sunlight by atmospheric gases in theSWIR (like SCIAMACHY) spectral region. Today thereare over 20 operational TCCON sites around the globe.Though established in 2004, continuous measurementsare not available before 2007 for most stations and gases.

5. COMPARISON METHODOLOGY

Comparing CO measurements from SCIAMACHY withobservations from NDACC and TCCON g-b instruments

require some important adjustments described conse-quently.

NDACC sites are retrieving vertical profiles of trace gasnumber densities nrtvCO(z). The conversion of verticalprofiles of the number density into total columns, i.e.VCDCO is done by vertical integration of nrtvCO(z)

VCDCO ≡∞∫

zgbs

nrtvCO(z) dz . (10)

Finally, an altitude correction of the BIRRA derived xCOcolumns (see Eq. (8)) was applied by adapting the lowerbound of integration to zsrf → zgbs:

xCO =αCO

αCH4

∞∫zgbs

nrefCO(z) dz (11)

TCCON is providing its results on CO measurements indry-air mean volume mixing ratios xCOdry (details seeWunch et al. 2015).

xCOdry =VCDCO

Nair=

VCDCO

NO2

· 0.2095 . (12)

For the retrieval of VCDCO the network scales a-prioriVMR profiles similar to BIRRA to produce a syntheticspectrum that achieves the best fit to the measured spec-trum (see Eqs. (5) and (7)).

6. RESULTS OF THE CO VALIDATION

The results section has been divided into subsectionsaccording to areas of investigations, namely the effectof post-processing (esp. filtering), the impact of variousBIRRA settings on the product and least squares fittingof s-b and g-b observations.

6.1. Post-Processing

A major issue revealed within this study is the post-processing, including filtering of SCIAMACHY data inorder to get the most satisfying results. ’Filter A’ wasdefined as a conservative filter while ’Filter B’ was cho-sen as an alternative, more tolerant filter. It should retainlow clouds, therefore improving the results over ’dark’surfaces (e.g. the ocean). ’Filter B’ delivers about 15%to 20% more pixels for comparison to the Izana g-b sitewithin the sampling area. However, Fig. 2 indicates that’Filter A’ shows a slightly better match with smaller stan-dard deviations compared to ’Filter B’. Both filters do notshow systematic deviations to either greater or smallerVCD values.

Figure 2. Time series comparing monthly mean CO totalcolumns for ’Filter A’ and ’Filter B’ for Izana (NDACC).

Figure 3. Comparison of monthly mean xCO totalcolumns derived from SCIAMACHY measurements usingBIRRA’s ’rrrws’ and ’rws’ options.

6.2. BIRRA Settings

Former studies by Gimeno Garcıa et al. (2011) indicatedthat modeling of the surface albedo has large effects onthe final product hence it was examined in this validation.More specifically two retrievals were performed compris-ing two different polynomials for modeling the surfacealbedo r(ν). The first retrieval was calculating xCO us-ing a 0th order polynomial (denoted ’rws’) while a sec-ond one was initiated applying a 2nd order polynomial(denoted ’rrrws’). The ’ws’ indicates that the width of theSRF and the wavenumber shift were fitted as auxiliaryparameters. The results in Fig. 3 shows that the smallerxCO values derived in the ’rrrws’ run give a much bettermatch to g-b observed CO total columns compared to the’rws’ run.

6.3. Least Squares Fitting

The representation of CO’s annual cycle in the s-b datawas investigated. In a first step a 3rd order polynomial fitwas applied for to the annual s-b data in order to examinethe annual cycle

y(t) = a0 + a1 t + a2 t2 + a3 t

3 . (13)

2003-01-01

2003-03-01

2003-05-01

2003-07-01

2003-09-01

2003-11-01

2004-01-01

Date/Time

0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

CO

VC

D [

1x1

0 m

ole

c cm

]

1e18

Zugspitze (NDACC)

SCIAMACHY

Figure 4. The 3rd order polynomial of the SCIAMACHYdata compared to Zugspitze (NDACC) for the year 2003.

a1, a2, a3 were calculated by minimizing the squareof the residuals R. Fig. 4 shows that for sites suchas Zugspitze the SCIAMACHY data exhibit a sine-likefunction of the CO annual cycle similar to g-b data. How-ever, the comparison to some other sites revealed lessagreement.

In a further step, a fitting function y(t) for the indepen-dent variable t that estimates y for a given t was applied.Assuming that CO is in general comprising a sinusoidalcycle throughout the year at a specific location, a leastsquare fit minimization and therefore curve fitting wasapplied on the SCIAMACHY data. The fitting curve wasdefined as

y(t) = A sin(ω t + φ) + b · t+ c

= A cos(φ)︸ ︷︷ ︸a1

sin(ω t) +A sin(φ)︸ ︷︷ ︸a2

cos(ω t)

+ b︸︷︷︸a3

· t + c︸︷︷︸a4

(14)

with t comprising the time in days, ω defined as2π

365,

φ the phase shift, b the slope of the function and c theoffset. a1, a2, a3 and a4 are the unknown parametersthat were calculated according to Eq. (14). The resultsfor Jungfraujoch are presented in Fig. 5. It was foundthat especially later years in the mission (from ≈ 2005onward) induce significant deviations.

Finally, a comparison of least squares fitted data for theyear 2007 to Parkfalls (TCCON) is presented in Fig. 6. Itshows that the cycles fit quite well although no g-b mea-surements were available from October to December.

7. CONCLUSION

It is demonstrated that the results of validating SCIA-MACHY CO total columns are depending on many as-pects, among which the most important are the defini-tion of the filter criteria applied in postprocessing and set-tings such as the model of the surface albedo defined in

Figure 5. The fitted sine curve for the SCIAMACHYdata compared to Jungfraujoch (NDACC) comprising theyears 2003 – 2005.

2007-01-01

2007-03-01

2007-05-01

2007-07-01

2007-09-01

2007-11-01

2008-01-01

Date/Time

0

50

100

150

200

250

xC

O [

ppbv]

SCIAMACHY xCO month-mean l1bv7.04

Parkfalls (TCCON) xCO month-mean

Figure 6. Trigonometric least squares fit compared toParkfalls (TCCON) for the year 2007.

the retrieval. Foremost, the difficulties in the validationof SCIAMACHY CO measurements by g-b FTIR obser-vations due to the sensitivity of s-b data to a wide vari-ety of constellations are revealed. In most configurationsthe SCIAMACHY CO total columns show a tendency toslightly underestimate CO for most g-b sites. Apart fromthat, the s-b retrievals agree with the g-b estimates, par-ticularly in the early years of the mission. Nonetheless, italso reveals that there is potential to improve the products(particularly for the later years in the mission). Further-more, the increased deviation of the space-borne and g-bcolumns in the later years of the mission clearly indicatesthe impact of the degrading channel 8 detector.

8. ACKNOWLEDGEMENT

The data used in this study were provided from NDACCand TCCON sites. SCIAMACHY data were provided bythe DLR-department Atmospheric Processors (ATP). Wethank all these institutions for either providing us accessto their data or for making them available.

REFERENCES

Feist, D. 2015, Global FTS-Network, Website,[https://oc.bgc-jena.mpg.de]

Gimeno Garcıa, S., Schreier, F., Lichtenberg, G., & Sli-jkhuis, S. 2011, Atmos. Meas. Tech., 4, 2633

Gottwald, M. & Bovensmann, H., eds. 2011, SCIA-MACHY — Exploring the Changing Earth’s Atmo-sphere (Dordrecht, NL: Springer)

Jacquinet-Husson, N., Crepeau, L., Armante, R., et al.2011, J. Quant. Spectrosc. & Radiat. Transfer, 112,2395

Ostler, A., Sussmann, R., Rettinger, M., et al. 2014, At-mos. Meas. Tech., 7, 4081

Rothman, L., Gordon, I., Babikov, Y., et al. 2013, J.Quant. Spectrosc. & Radiat. Transfer, 130, 4

Schreier, F., Gimeno Garcıa, S., Hedelt, P., et al. 2014, J.Quant. Spectrosc. & Radiat. Transfer, 137, 29

Wunch, D., Toon, G. C., Sherlock, V., et al. 2015, TheTotal Carbon Column Observing Network’s GGG2014Data Version, Tech. rep.

Zdunkowski, W., Trautmann, T., & Bott, A. 2007, Radi-ation in the atmosphere: a course in theoretical meteo-rology (Cambridge Univ Pr)