Description of the MPI-Mainz H2O retrieval
(Version 5.0, March 2011)
Thomas Wagner, Steffen Beirle, Kornelia Mies
Contact:
Thomas Wagner
MPI for Chemistry
Joh.-Joachim-Becher-Weg 27
D-55128 Mainz
Germany
Phone: +49 (0)6131 305267
Fax:
+49 (0)6131 305388
email: [email protected]
Content:
•
•
•
•
•
•
Description of the retrieval
Choice of the settings for the DOAS fitting
Saturation correction
Correction for effects of different profile shapes of O2 ans H2O
Selection of mostly cloud-free observations
Validation
1
Description of the retrieval
H2O columns have been retrieved from ERS-2 GOME and ENVISAT SCIAMACHY
instruments by various groups using different settings (Noël et al., 1999, 2000, 2002, 2004,
2005, 2008; Mieruch et al., 2008; Casadio et al., 2000; Maurellis et al., 2000; Lang et al.,
2003; 2004; Lang, 2003; Wagner et al., 2003, 2005, 2006).
These algorithms differ not only in the used wavelength ranges (see Fig. 1), but also in the
complexity of the retrievals. Most of the algorithms make use of simultaneously retrieved O 2
or O4 absorptions to correct for the influence of clouds. A general feature is that a modified
DOAS type retrieval has to be performed, because the strong and narrow-band H 2O (and O2)
absorption lines are not fully resolved by the instrument. The respective modifications are
either applied in the spectral retrieval (e.g. weighting function modified DOAS) or
appropriate corrections are successively applied.
The SCIAMACHY water vapor retrieval described in this document analyses the H 2O and O2
absorptions in the red spectral range (614-683nm). Our water vapor product was developed
with the aim to study time series and temporal trends of the atmospheric water vapor content
with high accuracy. Thus no a-priori information or information from external data sets is
included in our algorithm. On the other hand, this approach has also some drawbacks,
especially relatively large uncertainties of individual observations mainly related to clouds. In
principle, satellite observations in the visible and NIR spectral range are sensitive to the whole
tropospheric H2O column. However, especially due to the shielding effect of clouds, the
sensitivity for the surface-near layers can be systematically reduced. In the current version of
our retrieval, the height-dependent sensitivity can not be quantified (e.g. by averaging
kernels), because no explicit radiative transfer simulations are applied (see below). However,
the rather good agreement with independent data sets (see below) indicates that H 2O
concentrations close to the surface are well captured by our retrievals.
After the spectral retrieval the derived initial trace gas SCDs are corrected for the spectral
saturation effect and finally converted into total atmospheric VCDs.
Wavelength ranges for different H2O analyses
Lang et
al.,
2007
Lang
et al.,
2002
Wagner
et al.,
2003
Noel
et al.,
1999
Casadio
et al.,
2000
Fig. 1 Wavelength ranges for H2O analyses used by different research groups
2
Choice of the settings for the DOAS fitting
The fitting parameters used for the processing of SCIAMACHY spectra are based on those
used for the analysis of GOME-1 observations [Wagner et al., 2003; 2005; 2006]. In the
chosen spectral range, medium strong absorption lines of H2O (around 650nm) and O2
(around 630nm) exist. Compared to other spectral ranges (see Fig. 1), these absorptions are
still strong enough to ensure a high signal to noise ratio. At the same time, they are
substantially weaker than e.g. at longer wavelength ranges, thus avoiding strong nonlinearities in the spectral fitting process. Another advantage of the selected fitting range is that
it includes also an absorption band of the oxygen dimer O4 (at 630nm).
Besides the trace gas reference spectra, also several other spectra are included in the spectral
fitting process. First, a synthetic Ring spectrum is included to correct for the so called Ring
effect caused by inelastic Raman scattering on air molecules. In addition, also three reference
spectra for different vegetation types are included. Especially over surfaces with strong
vegetation, it turned out that spectral features caused by vegetation show strong interference
with the atmospheric absorbers [Wagner et al., 2007]. Since the vegetation spectra describe
the spectral reflectance, the natural logarithm of the reflectance spectra are used in the spectral
fitting. To correct for possibly non-perfect polarisation correction of the SCIAMACHY
spectra, also two polarisation sensitivity spectra from the keydata are included in the spectral
analysis.
Finally, an inverse solar spectrum is included to correct for possible intensity offsets, e.g.
caused by instrumental straylight. The parameters for the spectral analysis are summarized in
Table 1. An example of the spectral DOAS analysis is shown in Fig. 2. The absorption
structures of O2, H2O and O4 are clearly visible. The residual contains noise, but also
systematic features at 630nm and 650nm caused by imperfect match of the O 2 absorption.
However, compared to the total strength of the O 2 and H2O absorptions these features are
weak. From the spectral retrieval, also the fitting errors (standard deviation) are determined
and stored in the data files (see product specification document). Also an estimate for the total
error is given, which takes into account the individual fitting errors and the effect of clouds
(see product specification document).
Fig. 2 Example of a spectral DOAS analysis. Displayed are the fitted reference spectra (black
lines) scaled to the respective spectral structures in the measured spectrum (red).
3
Table 1. DOAS settings used for H2O spectral retrieval
Property
Fitting interval
Sun reference
Wavelength calibration
Selected parameter
614-683.2nm nm
SCIAMACHY Sun irradiance
Wavelength calibration of sun reference
optimized by NLLS adjustment on
convolved Kurucz solar spectrum1.
Remarks
Absorption cross-sections
H2O
HITRAN 2006 (Rothmann et al.)
290K, convoluted with
wavelength dependent
instrument function
O2
HITRAN 2004 (Rothmann et al.)
290K, convoluted with
wavelength dependent
instrument function
O4
Greenblatt et al., 1990
Interpolated
Additional spectra
Ring spectrum
Synthetic Ring spectrum calculated from
sun spectrum using the MFC software
(Gomer et al., 1993)
Conifers
ASTER Spectral Library through the
courtesy
of the Jet Propulsion Laboratory, California
Institute of Technology,
http://speclib.jpl.nasa.gov/
Logarithm, interpolated
Grass
ASTER Spectral Library through the
courtesy
of the Jet Propulsion Laboratory, California
Institute of Technology,
http://speclib.jpl.nasa.gov/
Logarithm, interpolated
Decidous
ASTER Spectral Library through the
courtesy
of the Jet Propulsion Laboratory, California
Institute of Technology,
http://speclib.jpl.nasa.gov/
Logarithm, interpolated
ZETA_NAD
instrument keydata
Interpolated2
OBM_s_p
instrument keydata
Interpolated2
Polynomial
3rd order (4 parameters)
Intensity offset correction
Inverse solar spectrum3
1
Kurucz, R.L., I. Furenlid, J. Brault, and L. Testerman, Solar flux atlas from 296 nm to 1300
nm, National Solar Observatory Atlas No. 1, 1984.
2
keydata version 3.1
3
from SCIAMACHY solar spectrum
4
Saturation correction
Before the retrieved H2O SCD is converted into a vertical column density, a correction of the
non-linearity caused by the limited spectral resolution of the instrument is performed. For that
purpose, the non-linearity is quantitatively described by modeling of the DOAS analysis
procedure. First, a high-resolved absorption spectrum is calculated from the high resolved
H2O cross section applying the Lambert-Beer law:
I( λ ) = I 0 ( λ ) ⋅ exp(−SCD H 2O ,true ⋅ σ O 2 ( λ )
Here, a set of realistic H2O SCDs (SCDH2O,true) has to be assumed. Then, the respective
absorption spectra (and the H2O absorption cross section) are convoluted with the
SCIAMACHY instrument function. Finally, the convoluted spectra are analysed using the
exact DOAS-settings as used for the analysis of the measured spectra. The fit yields a H 2O
SCD (H2Ofit), which is smaller than the originally assumed H 2Otrue. For small SCDs, H2Ofit
depends almost linearly on H2Otrue. However, for larger SCDs, H 2Ofit is systematically smaller
than H2Otrue. Fig. 3 shows the relationship of both SCDs. The determined relationship is used
to correct the H2Ofit determined in the DOAS fit to get the true atmospheric H 2O SCD
(H2Otrue). A similar correction is also applied to the retrieved O 2 SCD (Fig. 4). The formulae
for the saturation corrections are:
SCDH2O,true = 1.51196 ⋅ 10-24 * (SCDH2O,fit)2 + 9.62779 ⋅ 10-1 * SCDH2O,fit + 5.17495 ⋅ 10+20
SCDO2,true = 1.0063790 ⋅ 10-51
* (SCDO2,fit)3 + 2.5049862 ⋅ 10-26 * (SCDO2,fit)2 + 9.4507654 ⋅ 10-1 * SCDO2,fit +
1.0617513 ⋅ 10+23
For the conversion of O2 SCDs to O2 AMFs a O2 VCD of 4.5657 ⋅ 1024
was assumed.
3E+23
Measured H2O SCD [molec/cm²]
y = 7E-49x3 - 1E-24x2 + 0.9902x + 1E+20
2
R =1
2E+23
1E+23
0
0
1E+23
2E+23
3E+23
4E+23
True H2O SCD [molec/cm²]
Fig. 3 Dependence of the retrieved H2O SCD on the true H2O SCD.
5
5E+23
AMF from spectral analysis
5
4
3
2
1
0
0
1
2
3
4
5
6
7
8
Modelled AMF
Fig. 4 Dependence of the retrieved O2 SCD on the true O2 SCD. The SCD is expressed as air
mass factor here, because the atmospheric O2 VCD is almost constant.
Correction for effects of different profile shapes of O2 and H2O
In the last step, the corrected H2O SCD is converted into the H2O VCD. For this purpose, a
measured AMF is used based on the retrieved O2 SCD (Wagner et al., 2003, 2005, 2006).
Since the atmospheric O2 VCD is (almost) constant, the ratio of the retrieved (corrected) O 2
SCD and the O2 VCD yields the measured AMF for a given observation. The application of a
measured AMF has the important advantage that the necessary information especially on the
effects of clouds is taken from the satellite observation itself. However, this correction has
also its limitations. First, since the relative profiles of H 2O and O2 are systematically different,
the respective AMF show also systematic differences, with the O 2 AMF being higher than the
H2O AMF. This effect is accounted for by applying a correction function (depending on the
solar zenith angle, viewing geometry and the surface albedo) to the retrieved H 2O VCD (see
Figs. 5 and 6). These correction functions is derived from radiative transfer calculations based
on an average (relative) H2O profile (see Fig. 7) calculated from average profiles of lapse rate
and relative humidity (see Wagner et al., 2006) and an O 2 profile from the US standard
atmosphere.
Note that the original correction function was calculated a surface albedo of 2% and cloud
free conditions. Similar results were obtained assuming more realistic values of 3% (over
ocean) and a cloud fraction of 5% (cloud with OD of 50 between 1 and 4km altitude).
Finally, another correction function for the effect of changing surface albedo is applied to the
H2O VCDs (see Fig. 8). Note that this correction was not applied in the first version of our
retrieval (Wagner et al., 2006). The surface albedo used for the correction is taken from
monthly varying albedo maps, which are composite of albedo derived from GOME-1
observations (Koelemeijer et al., 2003) for high latitude (>50°), and SCIAMACHY
observations (Grzegorski, 2009) at mid and low latitudes (<40°). For the transition between
40° and 50°, both products are interpolated linearly. Over ocean, the albedo is set to 3%. An
example for January is shown in Fig. 9.
It should be noted that especially for observations over low layer clouds with large cloud
fractions, the effect of the different relative profile shapes can cause large errors (up to more
than 100%). However, for high clouds and especially low cloud fractions, the relative cloud
effect on the O2 and H2O absorptions is similar and the application of the measured AMFs
leads to more accurate results.
6
7
Ratio O2 AMF / H2O AMF
6
5
4
3
2
1
0
0
10
20
30
40
50
60
70
80
90
SZA [°]
Fig. 5 Ratio of the AMF for O 2 and H2O as a function of the solar zenith angle (nadir viewing
direction). The ratio is determined from radiative transfer simulations assuming a H 2O profile
as shown in Fig. 7.
3.5
SZA:
0°
Reihe1
20°
Reihe2
40°
Reihe3
50°
Reihe4
60°
Reihe5
70°
Reihe6
75°
Reihe7
80°
Reihe8
3
Ratio
2.5
2
1.5
1
-150
-130
-110
-90
LOS angle
-70
-50
-30
Fig. 6 Dependence of the correction factor on the line of sight (LOS) angle of the satellite
instrument for different solar zenith angles. Here only values for a relative azimuth angle of
zero are shown for simplicity. In the retrieval, however, the actual relative azimuth angle of
individual measurements is taken into account. Note that for SCIAMACHY (and GOME-1),
only LOS between about –120 and –60° occur. Larger deviations of the LOS angles from –
90° occur for GOME-2.
7
Fig. 7 H2O profile used for the radiative transfer calculations (see Wagner et al., 2006). For
tropical regions a H2O VCD of 2 ⋅ 1023 molec/cm² and for polar regions a H2O VCD of 3 ⋅ 1022
molec/cm² was assumed.
2.5
Correction factor
2
1.5
1
0.5
0
0
0.2
0.4
0.6
0.8
1
Surface albedo
Fig. 8 Correction function taking into account the surface albedo (the H 2O VCDs are divided
by this function).
8
Fig. 9 Surface albedo in the red spectral range for January.
Since our H2O data product is not based on explicit radiative transfer calculations for
individual measurements, a straight forward estimation of the product uncertainty is difficult.
However, in the product, a total error is given based on four potential error sources:
-the fit error of H2O
-the fit error of O2
-general uncertainties of the spectral retrieval, e.g. related to uncertainties of the spectroscopic
data. This error is estimated to 10%.
-uncertainties related to radiative transfer effects, especially due to clouds. For this
uncertainty, an empirical formula taking into account the measured O2 SCD is used:
errRTM = (SCDO2,meas - SCDO2,max(SZA)*1.16)/2 ⋅ 1025molec/cm²
This error increases with decreasing O2 SCD (indicating strong cloud shielding). Note that
this formula can only provide a rough estimate. However, from sensitivity studies (e.g.
Wagner et al., 2005, 2006) it was shown that for measurements with low O 2 SCDs the
retrieved H2O VCDs systematically underestimate the true H2O VCD (see also next section).
The total (relative) error is derived from the individual error terms by the following formula:
2
2
errrel,total = fit H 2 O + fit O 2 + 0.12 + errRTM
2
Selection of mostly cloud-free observations
Since most of the atmospheric H2O column density is located close to the surface, cloud
shielding has a strong influence on the retrieved H2O VCD (see Wagner et al., 2005). For
9
large cloud fractions, the satellite observations typically underestimate the true H2O VCDs.
Thus, for the SCIAMACHY H2O product, only (mostly) cloud-free observations are selected.
To identify strong cloud effects, we consider the simultaneously retrieved O2 SCD. Only
satellite observations are considered, for which the retrieved O2 SCD is above 80% of the
maximum O2 SCD for the respective solar zenith angle (SZA), see Figs. 10 and 11.
Optical depth O2 absorption
0.3
0.25
0.2
0.15
0.1
0.05
0
10
20
30
40
50
60
70
80
90
SZA
Fig 10 Measured optical depth of the O 2 absorption at 630 nm along a satellite orbit as a
function of the SZA. The drawn line shows the maximum O 2 absorption for (mostly) nadir
observations. (here observations of the GOME-1 instrument are shown (Wagner et al., 2005).
The LOS dependence is considered by multiplication of an additional function (see Fig. 11)
1.4
Correction factor
1.3
0°
60°
20°
70°
-110
-90
LOS [°]
40°
75°
50°
80°
1.2
1.1
1
0.9
-150
-130
-70
-50
-30
Fig. 11 LOS dependence of the threshold for mainly cloud-free observations.
In Figure 12 an example for the monthly mean H2O VCDs using only mostly cloud-free
observations is shown (for March 2003).
10
Fig. 12: Monthly mean H2O VCD derived from SCIAMACHY for mostly cloud-free
conditions (March 2003).
Validation
We compared H2O VCDs retrieved from SCIAMACHY with results from GOME-1 and
GOME-2. Results from GOME-1 and GOME-2 have been validated using observations from
SSM/I observations and radiosondes (Wagner et al., 2005; Slijkhuis et al. 2008, EUMETSAT,
2010). For mainly cloud-free conditions, (effective cloud fraction <20%), the mean relative
error (compared to radio sonde observations) is typically < 20%. Over regions with high
albedo (low albedo) our retrieval tends to underestimate (overestimate) the true water vapor
column.
In Fig. 13 the H2O VCD from GOME-1 for March 2003 is shown. It was retrieved with the
same settings as described in Table 1 (but for the GOME-1 retrieval no polarisation spectra
were included). In Figs. 14 and 15 the difference between SCIAMACHY and GOME results
for March 2003 are presented.
Fig. 16 shows time series of H2O VCDs for the different sensors for different latitude bands.
During the overlap periods good agreement between the results of the different instruments is
found. Good agreement is also obtained from correlations studies of between the different
sensors (Fig. 17).
11
Fig. 13 Monthly mean H2O VCD derived from GOME-1 for mostly cloud-free conditions
(March 2003).
Fig. 14 Difference between the monthly mean H2O VCDs from SCIAMACHY (Fig. 7) and
GOME-1 (Fig. 8) for March 2003. Most of the noise-like patterns are related to the different
spatial sampling of both instruments.
12
Fig. 15 Like Fig. 14, but only for collocated observations
Fig. 16 Time series of monthly mean H2O VCDs for different latitude bands from 1996-2009
from GOME-1, SCIAMACHY and GOME-2. Good agreement between the overlap periods is
found.
13
Fig. 17 Correlation analyses between SCIAMACHY results (x-axis) and GOME-1 and
GOME-2.
Recommendations for product validation
Our H2O product was extensively validated for different measurement conditions (e.g.
EUMETSAT, 2010). Additional validation exercises should focus on measurements over
regions with high surface albedo (e.g. deserts) or elevated terrain.
Known issues
Our water vapor retrieval is optimised to yield stable and unbiased time series of the
atmospheric H2O VCD. This was the reason to chose the rather simple retrieval scheme
without using additional data sets (e.g. on cloud properties). As a consequence, however, in
specific cases, also large deviations of the retrieved H 2O VCD from the true atmospheric
value can occur. In spite of the application of ‘measured AMFs’, the largest errors are
expected to occur over low lying clouds (e.g. at the west coasts of South America or Africa),
because of the different profile shapes of H 2O and O2. For observations above low clouds
most of the atmospheric H2O VCD is shielded, while most of the O 2 VCD is still seen by the
satellite instrument. Thus in such cases the use of a measured AMF leads to a systematic
underestimation of the retrieved H2O VCD. Over regions with high albedo (low albedo) our
retrieval tends to underestimate (overestimate) the true water vapor column [EUMETSAT,
2010].
While high fitting errors (given in the data product) indicate measurements with high errors of
the H2O VCD product, additional errors might be introduced in cases with strong differences
of the H2O profile from the assumed profile. An errors estimation including different error
14
sources are given in the result files (see product specification file). These errors are difficult to
quantify. In particular, they can occur although the fitting errors are small. These errors
Currently we are working on an improved version of the H 2O retrieval including a better
cloud correction.
Up to now, no explicit treatment of measurements over elevated surface terrain is included in
our retrieval. Thus over high mountains areas (>1000m) the H2O VCD is systematically
underestimated.
References
Casadio, S., Zehner, C., Pisacane, G., and Putz, E., Empirical retrieval of the atmospheric air
mass factor (ERA) for the measurement of water vapour vertical content using GOME data,
Geophys. Res. Lett., 27, 1483–1486, 2000.
EUMETSAT, Algorithm Theoretical Basis Document for GOME-2 Total Column Products of
Ozone, NO2, tropospheric NO2, BrO, SO2, H2O, HCHO, OClO and Cloud Properties (GDP
4.4 for O3M-SAF OTO and NTO, http://atmos.caf.dlr.de/gome2/docs/DLR_GOME2_ATBD.pdf, 2010.
Greenblatt, G. D., J. J. Orlando, J. B. Burkholder, and A. R. Ravishankara, Absorption
measurements of oxygen between 330 and 1140 nm. J. Geophys. Res., 95, 18 577–18 582,
1990.
Grzegorski, M., Cloud retrieval from UV/VIS satellite instruments (SCIAMACHY and
GOME), PhD thesis, University of Heidelberg, 2009.
Gomer, T., T. Brauers, F. Heintz, J. Stutz, and U. Platt, MFC user manual, version 1.98,
Institut für Umweltphysik, University of Heidelberg, Germany, (http://ich313.ich.kfajuelich.de/Mfc/mfc.htm), 1993.
Koelemeijer, R. B. A., J. F. de Haan, and P. Stammes, A database of spectral surface
reflectivity in the range 335--772 nm derived from 5.5 years of GOME observations, J.
Geophys. Res., 2003, Vol. 108 (D2), doi 10.1029/2002JD002429
Lang, R., Williams, J. E., van der Zande, W. J., and Maurellis, A.N., Application of the
Spectral Structure Parameterization technique: retrieval of total water vapour columns from
GOME, Atmos. Chem. Phys., 3, 145–160, 2003.
Lang, R., Water vapour measurements from space, Status report, MPI for Chemistry, Mainz,
Germany, 2003.
Lang, R., and M. Lawrence, Evaluation of the hydrological cycle of MATCH driven by
NCEP reanalysis data: comparison with GOME water vapor field measurements, Atmos.
Chem. Phys. Discuss., 4, 7917-7984, 2004.
Liu, W., W. Tang, F.J. Wentz, Precipitable water and surface humidity over global oceans
from Special Sensor Microwave Imager and European Center for Medium Range Weather
Forecasts, J. Geophys. Res., 97, 2251-2264, 1992.
Maurellis, A.N., R. Lang, W.J. van der Zande, I. Aben, W. Ubachs, Precipitable Water
Column Retrieval from GOME data, Geophys. Res. Lett., 27, 903-906, 2000.
15
Mieruch, S., Noël, S., Bovensmann, H., and Burrows, J. P.: Analysis of global water vapour
trends from satellite measurements in the visible spectral range, Atmos. Chem. Phys., 8, 491504, 2008.
Noël, S., M. Buchwitz, H. Bovensmann, R. Hoogen, J.P. Burrows, Atmospheric Water
Vapour Amounts Retrievd from GOME Satellite data, Geophys. Res. Lett., 26, 1841-1844,
1999.
Noël, S., Bovensmann, H., and Burrows, J. P., Water vapour retrieval from GOME data
including cloudy scenes, Proc. ENVISAT/ERS Symposium, Gothenburg, 2000.
Noël, S., Buchwitz, M., Bovensmann, H., and Burrows, J. P., Retrieval of Total Water Vapour
Column Amounts from GOME/ERS-2 Data , Adv. Space Res., 29, 1697–1702, 2002.
Noël, S., Buchwitz, M., and Burrows, J. P.: First retrieval of global water vapour column
amounts from SCIAMACHY measurements, Atmos. Chem. Phys., 4, 111-125, 2004.
Noël, S., Buchwitz, M., Bovensmann, H., and Burrows, J. P.: Validation of SCIAMACHY
AMC-DOAS water vapour columns, Atmos. Chem. Phys., 5, 1835-1841, 2005.
Noël, S., Mieruch, S., Bovensmann, H., and Burrows, J. P.: Preliminary results of GOME-2
water vapour retrievals and first applications in polar regions, Atmos. Chem. Phys., 8, 15191529, 2008.
Rothman, L., A. Barbe, D. Chris Benner, L.R. Brown, C. Camy-Peyret, M.R. Carleer , K.
Chance, C. Clerbaux, V. Dana, V.M. Devi, A. Fayt, J.-M. Flaud, R.R. Gamache, A. Goldman,
D. Jacquemart, K.W. Jucks, W.J. Lafferty, J.-Y. Mandin, S.T. Massie, V. Nemtchinov, D.A.
Newnham, A. Perrin, C.P. Rinsland, J. Schroeder, K.M. Smith, M.A.H. Smith, K. Tang, R.A.
Toth, J. Vander Auwera , P. Varanasi, K. Yoshino, The HITRAN molecular spectroscopic
database: edition of 2000 including updates through 2001. J. Quant. Spectrosc. Radiat.
Transfer 82, 5-44 (2003).
Slijkhuis, S., Comparison of H2O
DLR/GOME/H2O/02 Iss.1, 06.11.2008
retrievals
from
GOME
and
GOME-2,
Wagner, T., S. Beirle, M. Grzegorski, S. Sanghavi, U. Platt, El-Niño induced anomalies in
global data sets of water vapour and cloud cover derived from GOME on ERS-2, J. Geophys.
Res, 110, D15104, doi:10.1029/2005JD005972, 2005.
Wagner T., S. Beirle, M. Grzegorski, U. Platt, Global trends (1996–2003) of total column
precipitable water observed by Global Ozone Monitoring Experiment (GOME) on ERS-2 and
their relation to near-surface temperature, J. Geophys. Res., 111, D12102,
doi:10.1029/2005JD006523, 2006.
Wagner, T., Beirle, S., Deutschmann, T., Grzegorski, M., and Platt, U.: Satellite monitoring
of different vegetation types by differential optical absorption spectroscopy (DOAS) in the
red spectral range, Atmos. Chem. Phys., 7, 69-79, 2007.
16