The OMI (Ozone Monitoring Instrument on board NASA's Earth Observing System (EOS) Aura satellite)
OMCLDO2 cloud product supports trace gas retrievals of for example ozone and
nitrogen dioxide. The OMCLDO2 algorithm derives the effective cloud fraction
and effective cloud pressure using a DOAS (differential
optical absorption spectroscopy) fit of the
O
The Ozone Monitoring Instrument (OMI)
is an imaging spectrometer developed by
the Netherlands and Finland that was launched in 2004 on board the
NASA Earth Observing System (EOS) Aura satellite (Levelt et al., 2006). OMI has a continuous spectral
coverage from 270 to 500 nm, with a resolution of approximately 0.5 nm. The
primary data products from OMI are concentrations of trace gases, including
ozone, nitrogen dioxide and formaldehyde. The trace gas retrieval algorithms
rely on information on cloud properties for each ground pixel. Such
information is important, because clouds have a significant impact on the
photon path. The photon path strongly affects the information on trace gases
contained in the satellite observations. Clouds and aerosols play a double
role: they shield the atmosphere below them, thus reducing the sensitivity
to the trace gases in these layers, while increasing the sensitivity to
layers above the clouds. In tropospheric trace gas retrievals of e.g.
NO
The O
The OMCLDO2 retrieval is similar to the FRESCO (Fast REtrieval Scheme for Clouds from the Oxygen A-band)
algorithm (Wang et al., 2008),
with the difference that it is based on the O
This paper describes version 2.0 of the OMCLDO2 product. Compared to the current operational version 1.2.3, version 2.0 contains the following improvements and extensions:
A temperature correction is implemented which is needed because of the
density-squared dependence of the O Besides the independent pixel approximation, a second cloud model is
implemented, which represents the scene as a Lambertian surface at a
certain pressure level. The retrieved parameters are the scene albedo and scene
pressure. The look-up tables that are used to derive the cloud fraction
and pressure have a higher number of nodes, especially for the surface albedo
and the surface altitude. A method has been implemented to remove outliers from the spectral fitting. The resolution of the surface altitude look-up table is brought in
line with the average OMI spatial resolution. The gas absorption cross sections are made consistent with the OMI
NO
This paper is organized as follows: in Sect. 2 we describe the OMCLDO2
algorithm, focusing on the improvements that have been introduced in this
version. In Sect. 3 we discuss the differences in the retrieval results of
the new versus the previous algorithm. In Sect. 4 we present comparisons of
the OMI-derived cloud pressures to ground-based radar–lidar observations.
The OMCLDO2 retrieval consists of two main steps: first a DOAS (differential
optical absorption spectroscopy) fit is performed in the spectral region
between 460 and 490 nm to derive the O
The DOAS fit is performed on the Earth's reflectance. OMI measures the
Earth's radiance and once per day the solar irradiance. The wavelength grids
of the Earth radiance and solar irradiance differ, because of the Doppler
shift of the solar irradiance, because of the temperature variations of the OMI optical
bench over an orbit and because of the non-homogeneous filling of the
instrument slit for partly cloudy scenes (Voors et al., 2006). For each
ground pixel, the irradiance (
We solve Eq. (1) using a modified Levenberg–Marquardt method, using the
combined errors for the radiance and irradiance as weights. The fit
parameters are the slant columns
For the conversion of the DOAS fit parameters into cloud fraction and pressure, and scene albedo and scene pressure, we use radiative transfer modelling. In the new version of the OMCLDO2 algorithm we use two cloud models in the radiative transfer modelling: the independent pixel approximation (IPA) (see e.g. Zuidema and Evans, 1998) and the Lambertian equivalent reflectance (LER) model. The IPA reflectance at the top of the atmosphere is the weighted average of the clear and cloudy part. In our implementation of IPA, we calculate the cloudy part by treating the cloud as an opaque Lambertian reflector. For the LER method, we model the scene by assuming a Lambertian surface that covers the entire pixel. It is noted that the clouds and the ground surface in the IPA model are both treated as opaque Lambertian reflectors. Therefore, the name LER may be somewhat confusing, but it is used for consistency with the existing literature. For each ground pixel, both the IPA and LER method are applied. The original version of the OMCLDO2 algorithm applied only the IPA method (Acarreta et al., 2004).
For the IPA method, the effective cloud fraction
To retrieve cloud parameters, such as the effective cloud fraction (Eq. 2), knowledge is required on the surface reflectance and surface pressure. We represent the clouds as Lambertian reflectors with an albedo of 0.8. Different studies have found that this is an optimal choice for the purpose of cloud corrections in trace retrieval schemes (see Stammes et al., 2008, and references therein). By using a large value for the cloud albedo, optically thin clouds that cover the entire ground pixel will be represented as a Lambertian reflector that covers only a small part of the pixel. Thus, the cloud-free part will implicitly model the transmission of light through the cloud, which is otherwise absent in the Lambertian cloud model.
The independent pixel approximation versus the Lambertian equivalent
reflectance model. In the IPA a ground pixel is modelled as the weighted sum
of a cloudy part, (a Lambertian surface with an albedo of
Nodes for the radiative transfer calculations used for the OMCLDO2 algorithm v2.0 and v1.2.3. Where the nodes are the same between versions, the table cells are merged. Note that cloud fractions smaller than 0 and larger than 1 are included to enlarge the parameters space.
For very small cloud fractions the information in the measured spectrum is not enough to accurately determine the cloud pressure. For a cloud fraction of 0, the cloud pressure is undetermined. In case of surface albedos close to 0.8, e.g. over snow and ice, the IPA retrieval for both cloud fraction and pressure will become unstable, because the algorithm cannot distinguish between the cloud and the surface. An evaluation of such cases over Greenland shows rapid variations of the cloud fraction between 0 and 1, and variations of the cloud pressure between the surface pressure and 150 hPa. For such cases, the LER method may be a good fallback, because the LER method does not need to distinguish between the cloud and the surface, as it only fits the scene albedo and the scene pressure.
For both the IPA and LER model, we use the same set of forward model
simulations of the reflectance between 460 and 490 nm; see Table 1. These
simulations are performed for a mid-latitude summer standard atmosphere. The
correction for different temperature profiles is discussed later on in this
section. On the simulated reflectance the same DOAS fit is performed as for
the measured OMI spectra (Eq. 1). For all the nodes listed in Table 1, we
obtain the slant column O
The radiative transfer modelling described above provides the
O
Instead of having the slant column O
Therefore, we invert the tables to give look-up tables with slant column
O
The final results of the inversion procedure are LUTs for the cloud fraction, cloud pressure, scene albedo and scene pressure on the nodes listed in Table 2. In the retrieval algorithm linear interpolation is applied in all dimensions, except for the solar zenith angle, for which spline interpolation is applied. This is implemented because of the non-linear behaviour at large solar zenith angles.
The previous version of the OMCLDO2 algorithms also made use of inverted
LUTs. However, they were not calculated using radial basis functions but
computed on ad hoc fits of the continuum reflectance and slant column
O
Example of a slice of the effective cloud fraction LUT (top panel)
and effective cloud pressure LUT (bottom panel), showing the LUT value as a
function of the continuum reflectance
Nodes for the continuum reflectance and the slant column
O
As will be described in this section, the slant column amount of
O
To understand the temperature effect of the O
In hydrostatic equilibrium, the integral over the altitude can be replaced by
an integral over the pressure, using d
In order to investigate the magnitude of the bias that is introduced if the temperature dependence is ignored, simulations of the retrieval were performed. In the retrieval the mid-latitude summer profile is used, while for the simulations either a mid-latitude winter profile or a subarctic winter profile is used. The bias was calculated for different true pressure levels of the cloud and for different cloud fractions. Figure 3 shows that the maximum bias in the retrieved cloud pressure ranges from less than 50 hPa at large cloud fractions to 200 hPa at very small cloud fractions. As discussed in the Introduction, clouds can have a shielding or an enhancing effect on sensitivity of satellite measurements of trace gases. Tropospheric trace gas retrievals are commonly limited to ground pixels with effective cloud fraction below approximately 0.2–0.3, for which the cloud-free reflectance dominates the scene. Figure 3 shows that for these cases the bias in the cloud pressure due to the temperature effect is very large (20–200 hPa). Such biases could change the effect of the clouds as assumed in the trace gas retrieval, from shielding to enhancing, or vice versa, and have a significant effect on the retrieved trace gas column.
The OMCLDO2 retrieval is based on a LUT approach, and generating LUTs for many
different temperature profiles is not feasible. Therefore, we introduce a
correction factor
Bias in the retrieved pressure (
Results from the OMCLDO2 version 2 algorithm for 14 May 2005.
To implement the temperature correction factor, new look-up tables for the
O
Impact of the improvements of the effective cloud fraction and effective cloud pressure retrievals.
The OMCLDO2 version 2 uses the following input. For the absorption
cross sections for O
In this section we first compare the OMCLDO2 version 2 with version 1.2.3 for 1 day of data. Next, the impacts of each of the improvements are discussed separately. The impacts of the improvements are summarized in Table 3.
Figure 4 shows the OMCLDO2 retrieval results for 14 May 2005. This day has been selected arbitrarily from the OMI data record. Note that we also have analysed other days, which show consistent results. Figure 4a and b show the effective cloud fraction and the effective cloud pressure. Figure 4c and d show the difference between versions 2 and 1.2.3. For areas with low effective cloud fractions, the effective cloud fraction is approximately 0.01 larger in version 2. Over the high latitudes in the Northern Hemisphere considerably large positive and negative differences occur. These occur over snow and ice, where the retrieval algorithm has problems distinguishing the clouds from the highly reflective surface. Under such conditions, the accuracy of the retrieved effective cloud fraction will be very low. Due to the assumed cloud albedo of 0.8, the cloud fraction will become undetermined when the surface albedo is close to this value.
The differences in effective cloud pressure are shown in Fig. 4d. Version 2
shows higher cloud pressure in the tropics and sub-tropics, and lower cloud
pressures at mid- and high latitudes. As discussed below, this zonally
dependent effect is caused by the temperature correction introduced in
version 2. Especially in the tropics, the differences in the cloud pressures
are largest in regions with low cloud fractions. Overall, the uncertainty in
the cloud pressure retrievals is a strong function of the effective cloud
fraction. For small cloud fractions, the effect of the cloud on the
top-of-atmosphere reflectance is very small, resulting in large uncertainties on the
retrieved cloud pressure. In the limit of cloud-free conditions, the cloud
pressure becomes undetermined. For large cloud fractions, the clouds dominate
the reflectance, and the cloud pressure can be determined with high precision.
This is illustrated in Fig. 5, which shows the precision of the effective
cloud pressure retrievals as a function of the effective cloud pressure. The
precision is calculated by the propagation of the DOAS fit errors of the
O
Box-and-whisker plot of the precision of the effective cloud pressure as a function of the effective cloud fraction for 14 May 2005.
The correction for the temperature dependence is described above. Based on a
temperature climatology, a correction factor is computed and applied to the
O
To test the impact of the temperature correction factor on the effective
cloud fraction and pressure, we produced datasets with and without the
temperature correction applied for 2 days of OMI data in different seasons
(14 May and 15 November 2005). While the impact on the cloud fraction is
negligible, the impact on the cloud pressure can be significant. Figure 6
shows the difference between the retrievals without and with the correction
applied, as a function of the effective cloud fraction. The impact of the
correction on the cloud pressure increases towards smaller cloud fractions.
Depending on whether the correction factor is smaller or larger than 1, the
impact on the cloud pressure can be both positive or negative. For cloud
fractions below 0.2, the impact of the temperature correction can be as large
as
Difference in the effective cloud pressure due to the temperature
correction (without correction minus with correction) plotted as a function of
the effective cloud fraction. The colours of the symbols indicate the
temperature correction factor
Figure 6 can be compared to Fig. 3, which is based on retrieval simulations. Although Fig. 6 shows the difference with and without the temperature corrections, and Fig. 3 shows the difference with the simulated truth, the behaviour and magnitude of the bias are very similar. It is noted that for Fig. 3 only temperature profiles have been used which are colder in the troposphere than the reference mid-latitude summer atmosphere. Therefore, Fig. 3 shows only positive biases, whereas in the tropics and sub-tropics Fig. 6 also shows negative values.
To test the impact of the LUTs that are used to derive the effective cloud fraction and effective cloud pressure, we produced datasets using version 2 algorithm with the new and the old LUTs. The cloud fraction with the new LUTs is about 0.01 larger than with the old version, except over snow and ice regions, where the cloud fraction with the new LUT is in most cases significantly smaller. Because over snow- and ice-covered regions the cloud fraction is highly uncertain as the algorithm is not able to distinguish clouds from highly reflective surfaces, this impact is not unexpected.
Box-and-whisker plots of the effective cloud pressure as a function of the effective cloud fraction. The top plot is for the old LUTs, the middle for the new LUTs and the bottom plot for the difference of old minus new.
Difference in the effective cloud pressure (old DEM minus new DEM) for effective cloud fractions exceeding 0.1 over Europe for 14 May 2005. Left panel: map of the differences over Europe; right panel: histogram of the differences over Europe on a logarithmic scale.
The effect of the new LUTs on the effective cloud pressure is shown in Fig. 7c.
This figure shows the difference in the cloud pressure (old minus new) as
a function of the effective cloud pressure. The differences become
significant at cloud fractions smaller than 0.25, where the difference shows
an oscillating behaviour. At a
Figure 7a and b also show that the effective cloud pressure for the largest
The outlier removal procedure that was introduced in version 2 of the algorithm removes spectral pixels from the DOAS fit after evaluation of the fitting residuals. Outliers can have different behaviour: they can be transient, e.g. occurring only for spectral pixels for a few pixels, or they can occur systematically for certain spectral pixels. When outliers are detected, they are removed from the data, which will decrease the number of wavelengths used in the DOAS fit. Figure 4h shows the number of wavelengths used in the fit for 14 May 2005. The most prominent feature is the reduced values over South America caused by the South Atlantic Anomaly (SAA). In this region the number of high energetic particles hitting the OMI detectors is significantly increased (Dobber et al., 2006), resulting in spikes in the data. It is noted that also the Level 0–1B processor flags transient pixels, so Fig. 4h is the result of the Level 1B flags in combination with the outlier removal procedure. In addition to the SAA, Fig. 4h also shows stripes in the along-track direction, as well as features related to geophysical conditions (for example higher values for Australia and India).
The impact of the outlier removal procedure was tested by running the
algorithm with and without the procedure switched on for 14 May 2005. The
differences in the retrieved effective cloud fraction are negligible, whereas
the impact on the effective cloud pressure depends on the cloud fraction. The
mean difference is not significant, but the standard deviation of the
difference varies from 16 hPa for
We also inspected the root-mean-square error (RMSE) of the DOAS fit as a fit quality indicator. Although the difference in RMSE with and without the outlier removal did not differ significantly from 0, the distribution is skewed towards larger RMSE values when the outlier removal is switched off. This indicates that the outlier removal procedure improves the fit for cases with a high RMSE.
Version 2 of the algorithm uses a DEM with a resolution of approximately 20 km, which is closer to the spatial resolution of OMI compared to the 3 km resolution DEM used in previous versions. The 20 km resolution DEM is constructed from the Global Multi-resolution Terrain Elevation Data 2010 (Danielson and Gesch, 2011).
The impact of the new DEM will be largest in mountainous terrain. Figure 8
illustrates the effect on the retrieved effective cloud pressures over Europe
for 14 May 2005. This is the same day as shown in Fig. 4. Figure 8a shows
that significant impacts of the new DEM are restricted to the main mountain
ranges. The difference between using the old and new DEM can be both positive
and negative. The impact increases towards the lower cloud fractions, when
more signal comes from the surface and an accurate knowledge of the surface
altitude becomes more important. Figure 8b shows that for most pixels the
impact is smaller than
In the new version of the algorithm, absorption cross sections and the Raman
radiance spectrum have been updated. The impact of this change was tested by
running the algorithm with the old and the new cross sections. The impact on
the cloud fraction was negligible. Using the new cross sections increased the
effective cloud pressures by 23
As described in the algorithm section, for each ground pixel the scene albedo
and scene pressure are derived. The most important application of these
parameters is over bright surfaces such as snow and ice, where the surface
albedo becomes close to the assumed cloud albedo of 0.8 and no meaningful
cloud fraction and pressure can be derived. Figure 9 shows a comparison of
the retrieved scene pressure with the surface pressure derived from the DEM,
assuming a sea level pressure of 1013 hPa. The figure shows a very good
agreement between the retrieved scene pressure and the DEM over Greenland.
This figure presents the comparison for the OMI cross-track pixel 20, but
other cross-track pixels show similar results. It demonstrates the capabilities of
the scene pressure for bright surfaces. Also, it is an indirect validation of
the retrieved O
Top panel: map of the position of the ground pixel centres. Bottom panel: comparison of the retrieved scene pressure and the surface pressure derived from the DEM, plotted as a function of the longitude.
Over dark scenes, such as over oceans under conditions with low cloud fractions, the scene pressure is less well understood. For some areas over the ocean the retrieved scene pressure is significantly larger than the sea level pressure. For scene albedos of less than 5 %, about 3 % of the scene pressures exceed 1050 hPa, and 50 % exceed 1013 hPa. We note that scene pressures larger than 1013 hPa are the results of extrapolation and therefore should be used with great caution. For dark scenes we recommend using the cloud fraction and cloud pressure, taking into account that there will be a large uncertainty in the cloud pressure in these cases (see Fig. 5).
The changes made in version 2 of the OMCLDO2 algorithm have a stronger impact on the cloud pressure retrieval than on the cloud fraction retrieval. Therefore, we focus in this section on comparisons of the cloud pressure retrievals with correlative data. Because of the use of the IPA cloud model (Fig. 1), it is not straightforward to compare the retrieved cloud pressure to profile information on cloud parameters. We compare the OMI retrievals with ground-based radar data, for which the sensitivity to cloud droplet size is very different; the OMI retrievals are sensitive to the optical extinction which scales with droplet size to the 2nd power, whereas the Radar reflectivity scales with droplet size to the 6th power. Thus, when using these Radar data, it is not possible to compare the same quantity, which is required in a validation study. Rather than conducting a validation study, we focus on explaining the differences between the OMI retrievals and the radar–lidar data, given their different sensitivities. This comparison uses a similar approach to that used for comparing Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) cloud products with radar–lidar data (Wang and Stammes, 2014).
We present comparisons for three sites: Cabauw, the Netherlands; Lindenberg, German; and the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), USA, for the period January to June 2005. These datasets were selected because of the continuous data availability for these sites in the Cloudnet (Illingworth et al., 2007) database. Cloudnet is a network of stations for the continuous evaluation of cloud and aerosol profiles.
We use the Cloudnet Level 2 classification product (Illingworth et
al., 2007), which is based on the combination of Radar and Lidar observations and
is available approximately every 30 s. This product classifies each
vertical layer as 1 of 11 classes, which distinguish ice and water clouds,
precipitation, aerosols, insects, clear sky and combinations thereof. We
attribute a value of 1 to layers that are classified as cloudy (classes 1–7)
and 0 to layers identified as non-cloudy. For profiles containing at least
one cloudy layer, we compute the cloud mid-height as the average of the
altitude of the cloudy layers. Next we average all the profiles in the time
window of
It is noted that this procedure for computing the cloud mid-height does not
take the optical thickness of the layers into account; an optically thick
cloud and optically thin cloud are weighted the same in the cloud mid-height.
Weighting with the optical thickness – or even better, with the sensitivity
of the O
Further filtering of the Cloudnet data was done using the following
criteria:
the standard deviation of the cloud mid-height should not exceed 1.5 km,
to avoid cases with large temporal variability during the OMI overpass; at least one layer in the profile should be cloudy during at least
50 % of the time averaging window.
The effective cloud altitude retrieved from OMI (red), compared to
radar–lidar cloud information for Cabauw (blue), the Netherlands. The grey
background is the vertically resolved cloud occurrence derived from the
radar–lidar data for the period
For the OMI cloud data, we average all the ground pixels of which the centre
is within 30 km distance of the ground station. For these pixels we
determine the mean and standard deviation for the cloud fraction and
pressure. We convert the cloud pressure to altitude using a scaling height
of 8 km. We filter the OMI data using the following criteria:
the effective cloud fraction should exceed 0.2, because the cloud
pressure for low cloud fraction has a large uncertainty; the standard deviation of effective cloud pressures should not
exceed 1.5 km, to exclude cases with large horizontal variability.
Figure 10 shows a comparison between the Cloudnet data and the OMI effective
cloud pressure for the collocations over Cabauw for the period January to
June 2005. The cases presented in this figure are ordered by increasing
mid-height of ground-based data. The following regimes can be distinguished
in this dataset:
Case 1–50: these are low level clouds with limited vertical extent.
The OMI effective cloud height and the ground station mid-height are in good agreement. Case 51–129: according to Cloudnet the majority of these cases
consist of vertically extended, and often multi-layered, cases. For these
cases the OMI effective cloud height is generally lower than the ground station mid-height. Case 130–135: these cases have high clouds with limited vertical
extent. The OMI effective cloud height compares well, except for the
outlier for case 131. However, the number of collocations in this regime is small.
It is noted that the boundaries of these three regimes are not hard.
Figure 10 shows that for single layer clouds with a limited vertical extent
the O
The retrieved effective cloud altitude from OMI, plotted as a function of the radar–lidar-derived cloud altitude. Closed symbols are for single-layer clouds; open symbols are for multi-layer clouds.
When we include not only Cabauw but also Lindenberg and the ARM-SGP site, we
get a similar picture. Figure 11 shows a comparison for all these sites for
the period January–June 2005, where the single- and multi-layer cloud cases
are distinguished. Good correlation is observed for the cloud range of
0–2.5 km, where the single cloud layers dominate. In the region between 2.5 and
approximately 8 km the multi-layer clouds dominate and the O
The comparison between the Cloudnet data was repeated for the old version of the OMCLDO2 algorithm. The results were very similar to those presented in Figs. 10 and 11. This is expected because for effective cloud fractions larger than 20 % the difference between the old and the new algorithm is not very large. Moreover, the difference between the two algorithm versions is smaller than with the ground-based data, because of the different sensitivity of ground-based versus satellite observations and because of representation errors in both space and time.
We present a new version of the OMI OMCLDO2 Level 2 cloud product. This
product is an important input for several of the operational OMI Level 2
algorithms. The new version contains six major improvements:
the correction for the temperature sensitivity of the DOAS fit; improved look-up tables for computing the effective cloud fraction and effective cloud pressure; retrieval of the scene pressure and scene albedo for every ground pixel,
using the Lambertian equivalent reflectance model; outlier removal procedure in the DOAS fit. updated gas absorption cross sections; introduction of a DEM with a similar resolution as the OMI ground pixels.
We show that the impact of these changes on the retrieved effective cloud
fraction is for most ground pixels less than 0.01. The impact on the
effective cloud pressure is larger: especially for cloud fractions less than
approximately 0.3 the differences compared to the previous operational
version can be as large as 200 hPa. These differences are mainly caused by
the temperature correction and the introduction of the new look-up tables.
Due to the temperature the differences have a latitudinally and seasonally
dependent behaviour, where the updated algorithm gives higher cloud pressures
at higher latitudes and lower pressures in the tropics and sub-tropics. Also
it was found that the new look-up tables give better results at low cloud
fractions.
Cloud pressure retrievals have been compared to ground-based radar–lidar observations in Cabauw, Lindenberg and the ARM-SGP site. It was found that for low clouds, up to approximately 2.5 km, the satellite retrievals and ground-based results compare favourably. For clouds in the range between 2.5 and approximately 8 km the ground-based observations indicate many multi-layer and vertically extensive clouds. For these clouds the satellite-retrieved cloud heights are generally lower, probably because the algorithm is more sensitive to the optically thick low-level clouds. For high clouds (> 8 km) mixed results are found. The differences with the radar–lidar can be explained by the different sensitivity of the radar–lidar observations versus the satellite observations.
We conclude that the new version of the OMCLDO2 product is a significant improvement of the previous versions, especially for the cloud pressure at cloud fractions smaller than approximately 0.3. This is very important for cloud corrections in retrievals of gases like nitrogen dioxide, sulphur dioxide and formaldehyde, which are very sensitive to the cloud pressure.
After reprocessing of the entire OMI data record, the stability of the product should be investigated, and the scene pressure and scene albedo should be validated.
The OMCLDO2 dataset is available from the NASA archives:
This work was funded by the Netherlands Space Office under the OMI Science Contract.
We acknowledge the EU Cloudnet and the ACTRIS (European Research Infrastructure for the observation of Aerosol, Clouds, and Trace gases) projects for providing the cloud classification datasets for the Cabauw, Lindenberg and the ARM-SGP sites. We thank Phil Durbin and his team for the processing of the various versions of the OMCLDO2 algorithm. We also thank two anonymous reviewers for their important and constructive comments. Edited by: N. Kramarova Reviewed by: two anonymous referees