Full paper - Envisat

GOME-MERIS water vapour total column inter-comparison on
global scale: January-June 2003
Stefano Casadio(1) and Claus Zehner(2)
(1) SERCO SpA, C/o ESA/ESRIN, via Galileo Galiei, Frascati (RM), Italy
(2) ESA/ESRIN, via Galileo Galiei, Frascati (RM), Italy
Introduction
'MEdium Resolution Imaging Spectrometer' and 'Global Ozone Monitoring Experiment' instruments
are flying onboard different platforms (ENVISAT and ERS-2 respectively). They are characterized by
different spatial resolution, and are based upon different measurement principles. Nevertheless, it is
demonstrated here that atmospheric parameters estimated from GOME spectral measurements can be
used to approximate MERIS cloud and water vapour products with good precision. The advantage of
using GOME cloud and water vapour data resides in the consolidated quality of these products, which
have been validated extensively during the last decade, and on the possibility to compare GOME and
MERIS products on global scale. The region of interest for this analysis is limited to ±60° latitude, to
avoid possible problems in discriminating Earth’s surface ice from clouds. The time period analysed is
January-June 2003: in fact this is period in which GOME products were still acquired on global scale
before the failure of the ERS-2 on-board recording system.
Products description
The MERIS level 2 products used in this work are relative to the first reprocessing (2004) that took
place in ESRIN. The second reprocessing will be completed in 2005.
The official GOME Data Processor, GDP, has been implemented and run at DLR. The latest version
(GDPv4) is the result of roughly ten years of scientific research. GDPv4 products relevant for this work
consist of ECMWF surface pressure analysis, SP, and cloud fraction, CF.
The GOME IGAM algorithm has been developed by the Institute for Geophysics, Astrophysics and
Meteorology (IGAM, KF University of Graz, Austria) in collaboration with the Max Plank Institute for
Chemistry (Mainz, Germany).
MERIS data pre-processing
The space resolution of MERIS is much higher than for GOME, so an averaging procedure has been
developed in order to resample the MERIS data onto the GOME grid. The MERIS pre-processing
algorithm consists of:
• Selection of MERIS pixels contained in the GOME pixel.
• Selection of valid MERIS products contained in the selected measurement set.
• Computation of statistical quantities of selected products (i.e. mean, std, % valid products).
The selection for MERIS pixels within the GOME ground pixel is achieved by using a fast and
efficient algorithm (developed in house). The rationale for developing this procedure resides in the
different across-track viewing mechanisms for MERIS and GOME. In fact, while MERIS acquires
simultaneously the complete across-track field of view (roughly 1000 km) using five different
acquisition units (cameras), GOME scans from left to right (forward scans) and back (backward scan)
in cycles of 6.5 seconds (nominal mode). Hence, the GOME field of view is slightly tilted with respect
to MERIS pixel locations, and a simple re-sampling of MERIS products would not account for this
effect. The method described above is applied to all available MERIS and GOME measurements of a
given orbit, and the resulting data sets are passed to the inter-comparison routines. In the case of
MERIS cloud top pressure, the percentage of available products within the GOME pixel is taken as an
estimate of its ‘cloud fraction’, and will be compared to GDPv4 CF for validation. The percentage of
available MERIS water vapour products will be used to discriminate partially and fully covered GOME
pixels, in order to minimize the impact of MERIS missing data in the statistical analysis.
To process the 30Gb of GOME and 1.2Tb MERIS data needed for this study the ESRIN's Grid onDemand Infrastructure and Services handling, processing power and connectivity, provided a swift
delivery of results. The Grid on-Demand, which is run from ESRIN, offers access to, and support for,
science-oriented Earth Observation GRID services and applications, including access to a number of
global geophysical Envisat and ERS-2 products and ESA's toolkits like BEAT and BEAM. The GRID
integrates high-speed connectivity, distributed processing resources and large volumes of data to
provide science and industrial partners with improved access to end products.
GDPv4 and MERIS cloud fractions
The GDPv4 cloud fraction is the fraction of the GOME pixel expected to be cloudy. To retrieve cloud
fraction information from MERIS products it was decided to use the percentage of MERIS cloudy
pixels (i.e. the cloud mask) within the GOME pixel.
In Figure 1 the GOME CFs are compared to the MERIS for the six months separately (January 2003 on
top left, June 2003 on bottom right). In each plot the correlation coefficient and the gain and intercept
of the linear fit are reported, while the different colours indicate the density of data (increasing from
blue to red in a logarithmic scale).
Figure 1 GOME vs. MERIS cloud fractions for the Jan-Jun 2003 time period.
Results are surprisingly good, demonstrating that, despite the time delay between the probing time (30
minutes), the two instruments investigate essentially the same atmosphere. Nevertheless, it should be
noted that the MERIS cloud fractions tend to be slightly lower than the GDP counterpart, indicating a
possible difficulty of MERIS algorithm to fully discriminate cloudy and cloud free areas.
IGAM and MERIS water vapour columns
The MERIS water vapour columns (WVC) are in [g cm-2] units, while IGAM WVCs are in [g kg-1].
The unit conversion for IGAM WVC has been preformed using the following formula:
WVC = WVC ⋅ SP / g / 100 , where SP is the ECMWF surface pressure extracted from the GDPv4
level 2 products, ‘g’ is the acceleration of gravity, and the factor 100 comes from unit conversion.
As for Figure 1, the GOME water vapour columns are compared to the MERIS data on a monthly
basis. In Figure 2 the cloud free scenes over sea surface are inter-compared, while in Figure 3 the cloud
free land surface products are selected. The sea-land discrimination is necessary to investigate the
possible algorithm deficiencies in handling the Earth’s reflectance.
Figure 2 GOME vs. MERIS cloud free water vapour columns over sea surface for the Jan-Jun
2003 time period.
Figure 3 GOME vs. MERIS cloud free water vapour columns over land surface for the Jan-Jun
2003 time period.
The different degree of correlation is evident between land and sea products. While over land GOME
and MERIS data are essentially identical (extremely limited scatter, very high correlation coefficients),
for sea scenes the situation is slightly more complicated. Although the majority of data shows very
good correlation, MERIS ‘mid values’ water columns (i.e. 1÷3 g cm-2) are often lower than the GOME
co-located products. This tendency does not show any seasonal behaviour. The lower signal-to-noise
ratio for sea scenes with respect to land (the higher reflectance of land surface increases the radiative
fluxes at TOA by large factors) could be one of the reasons for this peculiar behaviour of MERIS water
vapour products. A further confirmation that the ‘sea’ MERIS products could be affected by retrieval
problems is given in Figure 4 and Figure 5, where GOME and MERIS water vapour columns are
compared to SSM/I monthly mean data. Obviously, when comparing level 2 (instantaneous
measurements) to level 3 products (smoothed 2D fields) the data scatter is expected to increase, and
this effects is clearly visible in the monthly plots. Nevertheless, while for GOME water products the
correlation with SSM/I is still relatively high (except for a scaling factor, see Figure 4), the MERIS
data show the same behaviour as in Figure 2, i.e. MERIS tend to underestimate the water columns in
the 1÷3 g cm-2 range.
Figure 4 GOME vs. SSM/I cloud free water vapour columns over sea surface for the Jan-Jun
2003 time period.
Figure 5 MERIS vs. SSM/I cloud free water vapour columns over sea surface for the Jan-Jun
2003 time period.
In Figure 6 and Figure 7 the differences between SSM/I and MERIS ÷ GOME cloud free water
vapour column are shown for the six months of interest. The larger differences are found in the Indian
and Pacific oceans for both GOME and MERIS, but the MERIS seems to underestimate SSM/I by a
much larger amount than GOME.
Figure 6 SSM/I – MERIS cloud free water vapour column differences for Jan-Jul 2003.
Figure 7 SSM/I – IGAM cloud free water vapour column differences for Jan-Jul 2003.
Conclusions
In general, cloud and water vapour fields are characterized by high variability in space and time and
two instruments looking at the same place with a time delay of 30 minutes are expected to investigate
different atmospheres. Nevertheless, the GOME nominal swath measurement mode is characterized by
a very large West-East size (320 km), and the atmosphere is expected to move essentially in this
direction: this should minimize the effect of wind advection because most of the atmosphere probed by
MERIS will still be contained in the GOME field of view even after 30 minutes (at least, in most of the
cases). In this context the official GOME Data Processor cloud fractions are compared to MERIS cloud
products showing very good correlation, thus indicating the correctness of the assumption on MERISGOME co-location.
For water vapour column estimates the situation is more complicated. The MERIS-IGAM correlation
for land scenes is excellent, with a very limited scatter, while for sea scenes MERIS seems to slightly
underestimate the water content, particularly in case of concentrations in the 1-3 g cm-2 range. This
behaviour is confirmed by results of inter-comparison with SSM/I water vapour monthly mean
products. Results indicate that MERIS water vapour estimates for dark (sea) scenes are probably
affected by retrieval deficiencies.
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