Introduction
The Global Ozone Monitoring Experiment (GOME) was launched on 21 April 1995 by the European Space Agency
(ESA) on-board the second European Remote Sensing satellite (ERS-2). It was the first European UV-VIS-NIR
(ultraviolet–visible–near-infrared) spectrometer in space dedicated to observing atmospheric trace constituents
such as ozone, nitrogen dioxide, sulfur dioxide, formaldehyde, bromine, and water vapour as well as cloud and
aerosol parameters on a global scale .
The sensor operated for more than 16 years, which is a world record for this kind of instrument, until the
retirement of the ERS-2 platform in early July 2011. GOME is the predecessor of a series of similar follow-up
instruments like SCIAMACHY Scanning Imaging Absorption Spectrometer for Atmospheric Chartography,
2002–2012, on-board Envisat, OMI Ozone Monitoring Instrument, launched in 2004
on-board Aura, , GOME-2 on-board the MetOp satellite series, or the Copernicus
Sentinel missions TROPOMI on-board Sentinel-5 Precursor, Sentinel-4, and Sentinel-5, and marks
the beginning of European operational, global, long-term monitoring of climate-relevant atmospheric parameters.
The existing atmospheric data archive of GOME is of very high value and may be considered (in conjunction
with SCIAMACHY) as the basis for a future reference data set for successor sensors. The status of
scientific results of the miscellaneous GOME level 2 data products is presented in numerous publications
e.g.,.
Furthermore, GOME data form a substantial part of recently developed long-term climate data records,
for example the GOME-type Total Ozone Essential Climate Variable and the harmonised tropical
tropospheric ozone data records generated within the framework of the ESA's outstanding Climate Change
Initiative .
In 2012 at ESA's Atmospheric Science Conference, as a result of the discussion rounds, the scientific
user community formulated a set of recommendations also addressing the preservation and
further exploitation of the 16 years of GOME measurements. These recommendations have led to ESA's
GOME-Evolution project that started in April 2014. Among other topics, the objective of this activity
is to provide the Earth Observation (EO) user community with improved and consolidated GOME level 1
products, in an easily accessible common data format, based on updated GOME calibration algorithms and
improved in-flight calibration characterisation for the complete mission. Homogenisation of the current
GOME level 1 products has become necessary because so far they were generated using different processor
versions and, thus, were not fully consistent during the complete mission.
Furthermore, a detailed investigation of the long-term performance of the GOME instrument for the entire
mission period was carried out in the framework of GOME-Evolution. The results will be presented in this
paper. This part of the study is an extension of the work by , who introduced a first
overview of the long-term behaviour for the 11-year time span from 1995 to 2006. Special emphasis is put
on the analysis of the Sun mean reference (SMR) spectra in order to monitor and correct for the gradual degradation
of the instrument's optical properties. have shown that the degradation is particularly severe in the UV channels 1 (∼70 %–90 %) and 2 (∼35 %–65 %), covering the spectral range of
240–316 and 311–405 nm, respectively. Similar changes are observed for SCIAMACHY and GOME-2
, whereas they are considerably smaller for OMI .
The various in-flight calibration parameters are a good means to monitor the long-term stability
of the sensor and its measurements. Instrument stability is one of the most important prerequisites to meet
the challenge of measuring very small changes in atmospheric parameters associated with long-term climate
change from space. For example, satellite sensors are required to detect ozone trends of the order of
1 % decade-1 . Amongst other things, in our study particular attention was paid to the
analysis of the long-term performance and stability of the spectral calibration, since errors in wavelength
assignment may have a significant impact on the Earth albedo and trace gas retrievals
.
In addition to the new GOME level 1 product and the revised in-flight calibration data set,
a “Climate” total column water vapour product has been developed within the
ESA GOME-Evolution project.
It is based on homogenised GOME, SCIAMACHY, and GOME-2 observations and provides a consistent
time series that is dedicated to the study of the temporal evolution of water vapour over the past 2 decades
on a global scale. Another part of the project was the creation of a web gallery
featuring the GOME/ERS-2 mission and related scientific achievements.
The paper is organised as follows: In Sect. we provide an overview of the GOME
instrument design and brief descriptions of the level-0-to-1 processing chain and the new level 1
product. Section contains a summary of the calibration algorithms using results
of the on-ground instrument characterisation. An analysis of the Sun mean reference spectra and the
description of the degradation correction algorithm is presented in Sect. ,
followed by the investigation of the polarisation measurement device (PMD) data (Sect. ).
In Sect. we show results of the reflectance degradation analysis.
Section contains the detailed results of the long-term
analysis of the most important GOME in-flight calibration parameters needed for, for example, the spectral
calibration or the dark current correction. Summary and concluding remarks are finally given in
Sect. .
GOME/ERS-2
Instrument and platform characteristics
GOME is a nadir-viewing, across-track scanning spectrometer that covers the ultraviolet,
visible, and near-infrared wavelength range from 240 to 790 nm with moderate spectral resolution
of 0.2 to 0.4 nm. It measures the solar radiation reflected and scattered by the Earth's atmosphere
and surface as well as the solar irradiance. Its primary objective is the determination of the amounts
and distributions of atmospheric trace constituents, such as ozone, nitrogen dioxide, sulfur dioxide,
formaldehyde, or bromine oxide as well as cloud and aerosol parameters .
In normal viewing mode, there are three forward scans (footprint size of 320×40 km2 each –
across-track × along-track) followed by a backscan with 1.5 s
integration time each. The maximum swath width is 960 km, and global coverage is achieved
at the Equator within 3 days.
GOME is a double monochromator, which has as dispersing elements a pre-disperser prism, combined with a
holographic grating in each of the four optical channels. The earthshine radiance and solar irradiance
spectra are recorded with four linear Si-diode arrays with 1024 spectral elements each.
These detectors are cooled to 235 K by means of Peltier coolers to reduce dark
currents and to improve the signal-to-noise ratio. The four channels cover the wavelength regions of 240–316 nm
(channel 1), 311–405 nm (channel 2), 405–611 nm (channel 3), and 595–793 nm
(channel 4). Channels 1 and 2 are further electronically divided into two bands (“a” and “b”) covering the
short-wavelength and long-wavelength parts of the channels, respectively. In addition there are
four stray light bands: two shortwave of band 1a, one longwave of band 1b, and one shortwave
of band 2a. Part of the light is branched out at the pre-disperser prism and recorded with three fast
broadband silicon photo-diodes, the PMDs, whose spectral ranges
cover approximately the optical channels 2 (300–400 nm), 3 (400–580 nm), and 4 (580–750 nm),
respectively. They measure the amount of light polarised parallel to the instrument slit, which
is perpendicular to the plane of incidence of the scan mirror. The PMDs are non-integrating detectors
which are continuously sampled, albeit over an RC circuit which has an averaging effect over the
sampling time. The corresponding sampling time of the PMD measurements is
93.75 ms; i.e., 16 PMD measurements are available for one detector channel measurement at the default integration
time of 1.5 s.
In addition to solar and Earth nadir viewing, the various pointing geometries of the GOME scan mirror permit
polar viewing (viewing angle of 45∘) and lunar observations (viewing angle of about 80∘)
at selected times during a year. A calibration unit adjacent to the spectrometer part consists of the Sun
view port and a compartment housing a platinum–neon–chromium (Pt/Ne/Cr) hollow cathode discharge lamp.
The solar radiation is attenuated by a 20 % transmission mesh and directed via a diffuser plate
(wet-sanded aluminium plate with chromium–aluminium coating) onto the entrance slit of the spectrometer.
The calibration unit becomes optically coupled to the spectrometer by appropriate positioning of the scan
mirror.
A detailed overview of the GOME instrument, its operation, and its scientific methods can be found in
the GOME Users Manual and in . For understanding the algorithm
principles described in the following sections, a simple functional diagram of the GOME instrument is
shown in Fig. . The most important instrument components relevant to the
level-0-to-1 calibration are as follows:
the scan mirror whose position is linked to the observation mode, e.g., nadir or pole scanning,
static or moon view, and the calibration mode (the latter comprises solar measurements, dark signal
measurements, and spectral lamp measurements);
the calibration unit that hosts the spectral calibration lamp and the Sun diffuser;
the slit that limits the instantaneous field of view to 2.9∘×0.142∘
or 40×2 km2 on the ground (the slit function, i.e., the instrument spectral
response to monochromatic input, is a convolution of projected slit width, pixel response,
and optical abberations);
the quartz pre-disperser prism where part of the light is branched out and directed towards the PMD
unit (see below);
the channel separator prism in the intermediate focus that acts as a spatial filter to
separate the wavelengths for channel 1, for channel 2, and for channels 3 and 4, respectively
(this separation serves to reduce stray light on the UV detectors, i.e., channels 1 and 2);
the dichroic filter that separates the wavelengths of channel 3 from those of channel 4 (in the
spectral range from 590 to 610 nm the filter changes from reflection to transmission);
the channel optics that consists for each channel of 4 quartz lenses mounted in one barrel;
four red LEDs which illuminate the detectors directly and which are used to characterise the
pixel-to-pixel sensitivity;
the focal plane assembly (FPA) which holds the array detector and the pre-amplifier electronics;
the PMD unit that contains three broadband PMDs whose spectral bandwidths
correspond roughly to the detector array channels 2, 3, and 4.
Functional diagram of the GOME instrument (see text for more explanations).
ERS-2 orbited the Earth at an altitude of about 790 km in a Sun-synchronous near-polar orbit;
the descending-node local Equator crossing time was about 10:30 UTC, and it had a repeat cycle of 35 days.
Each orbit took ∼100 min and the spacecraft completed ∼14 orbits per day.
Operational GOME observations are available from July 1995 onwards, although global coverage was lost in
June 2003 due to a permanent failure of the ERS-2 on-board tape recorder. Since then availability of
GOME data coverage is limited to the region where ERS-2 was in direct contact with ground stations
in the European–Atlantic sector. Over the years additional ground stations were brought online
to incrementally increase the data-gathering abilities of the satellite. The ERS-2 active mission
was completed on 4 July 2011 on orbit no. 84719. The ESA mission operations overview
provides a detailed review of the most important events over the entire mission
lifetime, which may have had an impact on the GOME data quality. Anomalies such as cooler or instrument
switch-offs, spectral lamp failures, or data gaps are reported on a yearly basis.
GOME Data Processor
The GOME Data Processor (GDP) is the operational near-real-time
and offline ground segment for the GOME instrument, incorporating,
among other things, a level 0-to-1 processing chain (GDP-L1) and the complete GOME data archive .
During the level 0-to-1 processing, GOME data are converted into calibrated physical quantities by applying
a series of calibration algorithms. Some of the calibration data were obtained during the pre-flight
on-ground calibration. Other parameters which can be directly derived from measurements using on-board
calibration sources are derived during the level 0-to-1 processing; they are marked with an asterisk
(*) in the following list. The basic calibration algorithms are as follows:
signal correction, i.e., correction for dark signal*, FPA crosstalk,
pixel-to-pixel gain (PPG)* in quantum efficiency, and stray light;
wavelength calibration*, i.e., assigning to each detector pixel its associated wavelength;
radiance calibration, i.e., conversion of the corrected detector signals to radiance
units by application of the radiance response function (this step also includes the
polarisation correction);
irradiance calibration, i.e., conversion of the corrected detector signals to irradiance
units, including the correction for BSDF (bi-directional scattering distribution function)
of the diffuser plate;
geolocation, i.e., determination of the geographical position for each detector readout
using ESA's ERS-2 orbit propagator;
quality assessment, i.e., identification of dead pixels, hot pixels, saturation, and sun-glint.
The GOME on-ground calibration was performed during the pre-flight calibration phase by
TPD/TNO (Netherlands Organization for Applied Scientific Research). The output was a data
set containing the so-called “calibration key data” such as stray light correction, BSDF
coefficients, radiance response function, and polarisation correction. In the course of
switching from on-ground to the in-flight situation, various adjustments in the key data
had to be applied that were mostly due to air–vacuum wavelength shifts and outgassing of
optical coatings. Over the years further updates of the key data have been implemented
that were related to the radiance response and to the diffuser
BSDF e.g.,. An overview of these algorithms using the
on-ground calibration data is given in Sect. .
Calibration constants which can be directly deduced from measurements using on-board calibration
sources (in-flight calibration parameters) are derived during the level 0-to-1 processing. They
are fed back immediately to the processor. This comprises the dark signal measurements on the night
side of each orbit, the internal LED measurements, and at regular intervals wavelength calibration
using the spectral lamp measurements. Figure depicts the processing flow for
calculating the respective in-flight calibration parameters including the solar reference measurements.
The calibration parameters as well as the SMR spectrum are stored in the
calibration database. Monitoring these calibration parameters provides an
excellent insight into the long-term stability of the instrument. A detailed description of the
corresponding algorithms and the results of the long-term analysis is presented in
Sect. .
Processing flow for calculating the in-flight calibration parameters from the dark signal
measurements (DARK), the internal LED measurements (LED), the spectral lamp measurements (LAMP), and
the solar measurements (SUN). The calibration parameters as well as the Sun mean reference (SMR)
spectrum are stored in the calibration database.
Figure is a flowchart indicating the order of the steps for processing the level 1 science data after the calculation of the calibration data. The individual
algorithms are applied to the pre-processed solar data and moon and earthshine measurements.
“Normalise” means the normalisation of the signal to 1 s exposure time. Detailed descriptions
of the individual algorithms are presented in Sect. for the on-ground calibration
and in Sect. for the in-flight calibration. Another step in the entire
calibration procedure is the correction of degradation (see Sect. ). Due to
degradation in optical components the calibration parameters for radiance and irradiance change in
time. However, this degradation cannot be derived from on-board calibration sources and the correction
has to be obtained offline and externally from the data processor. For GOME this has been done by
scientific analysis of the solar observations.
The last major GDP-L1 processor update was developed in 2006 in order to provide
a first complete reprocessing of the data set available at that time. The main driver for this
activity has been the gaps in the solar calibration spectra over long periods caused by pointing
issues on the ERS-2 platform. Furthermore, other algorithmic developments were included and
a detailed analysis of the long-term performance of GOME in terms of numerous diagnostic in-flight
calibration parameters were performed for the first 11-year period .
In the framework of ESA's GOME-Evolution project, the GDP-L1 Version 5.1 has been developed in order to
generate a completely homogenised, fully calibrated level 1 product for the entire 16-year mission
period. Algorithm improvements comprise a new polarisation correction (Sect. )
and an updated degradation correction (Sect. ), an improved usage of dark
signal measurements (Sect. ), and revised and improved spectral
calibration (Sect. ).
Processing flow indicating the order of steps for calculating the calibrated level 1 science data,
i.e., irradiance (left column), moon radiance (middle column), and earthshine radiance (right columns).
See text for detailed explanations.
New GOME level 1 product
The previous GOME level 1 data products, from the predecessor GDP-L1 Version 4.x and
lower,
contained geolocation, uncalibrated measurements, and all necessary calibration data
(and was thus in modern terminology more like a level 1a product). In addition an external
post-processing software “extractor” tool was needed to convert these data to calibrated
radiances, or to calibrated solar irradiance. The advantages were a small
product size and the flexibility for the scientific user to perform sensitivity studies on
the impact of different calibration steps. However, in the course of time it has turned out
that both arguments are no longer valid.
In lieu thereof, the new GOME level 1 product generated with GDP-L1 Version 5.1 contains fully
calibrated (ir)radiances, corresponding geolocation information, and selected calibration
parameters in NetCDF-4 format. Running a separate extraction tool is not necessary anymore;
several former extraction software options are now integrated in GDP-L1, and others are no longer used
. The product format and structure are designed to be similar to currently
developed or planned EO products, in particular to the Sentinel-5 Precursor mission launched in
October 2017. This should enable the application of common reading software to the different
atmospheric composition sensors with little or no adaptions required for the various products.
In addition to radiance and irradiance data, cloud parameters retrieved with the OCRA (Optical
Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks)
algorithms have been integrated in the new level 1 product, which required
reprocessing of the data record in several iterations. Following the request from the users,
another addition compared to the old product is geolocation information for each single PMD
measurement. A more detailed description of the content and structure of the new level 1 product
can be found in Appendix and in the GOME/ERS-2
Product User Manual .
GOME on-ground calibration data and correction algorithms
In this section we provide an overview of the GOME on-ground calibration data and
the basic principles of the corresponding correction algorithms. For more details we
refer to .
Correction for FPA noise and band 1a residual offset
Crosstalk correlated to the voltage controlling the Peltier coolers on the focal plane
assembly leads to noise on the array detector signal that varies slowly with time. It
can be approximated by multiplying the Peltier cooler control signal by a scaling factor
that has been obtained during the commissioning phase (April 1995 to July 1996) from one
typical orbit and is stored in the calibration key data file. The noise is correlated to
the integration time and correction is only necessary for
integration times of 6 s or longer, typical for band 1a measurements. Furthermore, the correction
is only applied to earthshine measurements. The correction algorithm comprises four steps:
(i) apply a high-pass filter to all Peltier output signals belonging to channel 1
from one orbit, (ii) calculate
an average value of the filtered Peltier output, (iii) multiply the mean Peltier output by
the scaling factor specified for the actual integration time, and (iv) subtract the noise
from the signals of the entire band to be corrected.
It appears that after the removal of the Peltier noise as described above, a residual offset
remains that has to be corrected since it is too large for, for example, ozone profile retrieval.
This additional correction has been developed in the framework of the CHEOPS-GOME study
and is implemented in the L1 processor. It uses the
signal of the stray light band 1a (just before the beginning of the nominal band 1a).
Stray light correction
After the first calibration and characterisation measurements of GOME at TPD/TNO, it became
obvious that stray light, i.e., light from wavelengths other than the nominal wavelength of a
specified detector pixel, is a major issue and needs to be corrected during the L1 processing.
In order to reduce the impact of stray light, several improvements were applied before launch
such as tilt changes to the gratings, the use of anti-reflection coatings, change of the channel
separation between channel 1 and 2, and improvement of internal baffling. Despite these improvements
a correction algorithm is still required. Specifically in channels 1 and 2 the signal readouts are
spoiled by a non-negligible amount of stray light whose main sources are as follows:
a uniform or very slowly changing quantity of stray light over the detector pixels induced by
diffuse reflections within the FPA;
ghost stray light signals induced by reflections from the surfaces of the detector arrays and the
lenses of the channel telescope – symmetrical ghosts (signals mirrored at the middle of the detector)
and asymmetrical ghosts (signals mirrored at some arbitrary detector pixel) were detected;
out-of-band stray light on the PMDs induced by radiation outside the wavelength range of the
detector arrays.
Summed contributions from uniform and ghost stray light are subtracted from the measured signal.
The relative uniform stray light levels obtained during the pre-flight calibration are 0.2 % for
channels 1 and 2 and 0.1 % for channels 3 and 4. These levels are multiplied with
the averaged signal fluxes per detector array to get the uniform stray light contribution. For
GOME, there is only one significant ghost. Its efficiencies (0.05 % for channels 1, 2, and 4 and
0.1 % for channel 3) were determined during the pre-flight characterisation and are multiplied with
the mirrored (around the pixel centre of the ghost) signal flux to get the ghost stray light contribution.
However, the calibration key data for stray light are probably not more accurate than ∼10 %, i.e.,
processing errors of 10 % of true stray light.
Radiometric calibration
The objective of the radiometric calibration is to transform the 16-bit binary units (BU)
of the detector pixel readouts into calibrated radiances (photons s-1 cm-2 nm-1 sr-1)
or, for the Sun, into calibrated irradiance (photons s-1 cm-2 nm-1). In GDP-L1 the
radiometric calibration is divided into several steps (see also Fig. ).
The radiance response function, which depends on wavelength, scan angle, and temperature, is
applied to the solar, moon, and earthshine measurements. It is a compound function in which the
scan-angle-dependent part and the temperature-dependent part are given per channel, for 9 scan
angles and for 5 temperatures, respectively. These key data are then interpolated to the actual
values of the respective measurement. Then, solar and earthshine spectra are corrected for instrument
degradation (see Sect. ).
The BSDF correction is applied to the solar measurements and comprises two parts. The basic BSDF
from the on-ground calibration depends on wavelength, azimuth angle, and the elevation of the
sunlight on the diffuser. It is expressed as parameterisation using polynomials. The second step
uses an improved azimuth dependence of the diffuser BSDF .
The azimuth dependence is fitted using a third-order polynomial in wavelength for
all channels. The polynomial coefficients are stored in a look-up-table for a number
of azimuth angles that are then linearly interpolated to the actual angle.
The earthshine radiance is additionally corrected for the so-called “radiance jump” effect
that is caused by the serial readout of the detector, i.e., the last pixel of the array is read out
93.75 ms later than the first pixel. In the case of inhomogeneous ground scenes this effect may be
visible as a jump in radiance between two neighbouring detectors. The last pixels of one detector
record the same wavelengths as the first pixels of the next channel, but at an integration time shifted
by 93.75 ms. A linear correction in wavelength is applied which re-normalises all intensities to the
same integration time, thereby using information from the PMDs (which are read out every 93.75 ms
synchronised with the first detector pixel). Although the correction adjusts the continuum level,
it cannot account for any difference in spectral features that may arise from viewing a slightly
different ground pixel. For earthshine measurements the intensity calibration also includes the
application of a polarisation correction (see Sect. ).
Polarisation correction
GOME is a polarisation-sensitive instrument. The radiance response function
described in Sect. calibrates the instrument assuming
unpolarised light. Therefore a correction factor must be applied that
describes the ratio of the throughput for actual input polarisation to the
throughput for unpolarised light. The polarisation correction algorithm (PCA)
needs the polarisation sensitivity of the instrument as well as a
characterisation of the atmospheric polarisation. It is divided into two main
parts, which both use on-ground key data. The first step is to derive the
atmospheric polarisation from theory and from measurement for a few
wavelengths. Three of these polarisation points come from the comparison of
channel array signals with broadband PMD signals; the corresponding
wavelengths are approximately 360, 500, and 700 nm. A fourth point is
obtained from theoretical assumptions and comes from a Rayleigh
single-scatter model simulation of polarisation in the UV. The second step of
the PCA is to interpolate the polarisation points to wavelength and to apply
the correction to the whole spectrum. Below ∼300 nm, polarisation is
taken as a constant. The exact wavelength and the polarisation value are
calculated based on a Rayleigh single-scattering model. In the UV region
300–315 nm the generalised distribution function (GDF) is used, which is
parameterised as function of albedo and ozone content
GDF, is used. From the wavelength where the GDF
starts to flatten out, at ∼315 nm, the GDF is smoothly connected to the
polarisation points from the PMDs, using Akima interpolation for the better
part of the spectrum longward of 315 nm . The impact of
the polarisation correction on the spectra is of the order of -1±5 %.
The largest change (-5±12 %) arises in band 1b (283–316 nm) and the
minimum impact is found in channel 4 (-0.5±0.5 %).
Within GOME-Evolution one important improvement for the GDF parameterisation has been implemented: that is to use
GOME's own retrieved total ozone columns instead of climatological ones. To this end the
level 2 ozone values are inserted into the level 1 calibration database. This was hardly
possible during the operational phase of the instrument, but for reprocessing there was
no limitation, especially because the ozone retrieval is not critically dependent on the
polarisation curve itself, i.e., within the accuracy needed for this parameterisation. Thus,
in principle no iterations between level 1 processing and level 2 processing are necessary.
Nevertheless, in practice these iterations were made in the course of several intermediate
re-processings so that ozone columns used for the final version are fully compatible with
the level 1 polarisation.
Solar irradiance, PMD measurements, and reflectance
In this section we present the long-term evolution of the solar irradiance, PMD, and
reflectance measurements. Monitoring the irradiance (Sect. ) was
used to develop a first-order degradation correction algorithm that is routinely applied
in GDP-L1 to irradiance and radiance data. Differences between the irradiance and radiance
degradation due to different light paths and a strong scan angle dependence are analysed
later in Sect. .
Sun mean reference spectrum and degradation correction algorithm
Once per day GOME recorded a short series of Sun spectra via the solar port and a diffuser plate. Thereby,
the incidence angle on the diffuser is (i) constant in azimuth (which varies only with season) and
(ii) changes in elevation as the Sun moves through the field of view. The incidence angle of the scan mirror
is 41∘ (compared to 49∘±15∘ for the nadir measurements). All measurements within
an elevation angle of ±1.5∘ with respect to the centre are averaged and corrected for the azimuth
dependence of the diffuser BSDF (see Sect. ). This yields the so-called daily SMR spectrum, which is stored in the calibration database and used for the calculation of
the earthshine reflectivity spectra. The latter serve themselves as input for almost all retrieval algorithms
for atmospheric constituents as well as cloud and aerosol properties.
The relative intensity of the GOME SMR spectra with respect to a reference spectrum from 3 July 1995
is depicted in Fig. (January 1996 to January 2011,
one spectrum per year) to demonstrate the severe impact of degradation of the optical properties.
This comparison shows that the pre-flight radiance parameters were no longer applicable to the
in-flight situation . The main degradation as a consequence of extensive
exposure to the space environment can be attributed to deposits on the scan mirror (which is
coated with a MgF2 layer), thereby changing its reflective properties. Degradation due to changes
related to the diffuser were declared to be negligible . The loss in throughput is
especially severe in channel 1. Below 300 nm intensity decreased by 80 %–95 %, which implies a
significant deterioration of the signal-to-noise ratio. Table indicates the approximate
signal-to-noise ratios for typical radiance values for channel 1 at 290 and 305 nm, channel 2 above 325 nm,
and channels 3 and 4 at the beginning, in the middle, and at the end of the GOME mission, respectively.
For channels 2, 3, and 4, the signal-to-noise ratio is well above 1000. Towards shorter
wavelengths the ratio significantly decreases due to strong ozone absorption and a weaker solar
irradiance. These values are comparable to those obtained for SCIAMACHY
their Fig. 4. In channel 1 (and also in the other channels) the
signal-to-noise ratio for GOME is expected to decrease linearly with the degradation of the
signal (for signal levels below ∼15000 BU) since the detector noise is exceeding the
shot noise. Above signal levels of ∼15000 BU shot noise becomes dominant.
Thus, the strong degradation observed in channel 1 may have a severe impact on the retrieval
of atmospheric parameters using this specific spectral region, e.g., ozone profiles .
The decrease in channel 2 is 40 %–80 %. In channel 3 the decline (10 %–40 %) started in 2001.
Throughput changes in channel 4 are relatively small. Values above 1 might be due deposits on
the coatings which can lead to changes in interference patterns and an increase in intensity
. Since mid-2001 the measurements were
additionally affected by an ERS-2 pointing problem as a consequence of the loss of the gyroscopes'
(which govern the platform steering) functionality. SCIAMACHY as well as GOME-2 suffer from degradation
in pretty much the same way , whereas OMI irradiances degraded
by only 3 %–8 % from 2005 to 2015 .
Relative intensity of GOME Sun mean reference spectra (January 1996 to 2011, one spectrum per year)
with respect to a reference spectrum from 3 July 1995. Corresponding smooth solid lines denote results of
the polynomial fit performed during the degradation correction. Wavelength regions around 470 and 600 nm
are affected by changes (outgassing) in the dichroic filter (see text for more details).
Approximate signal-to-noise ratios at the beginning, middle, and end of the GOME mission for five spectral
regions. *Note that the values for channel 1 in 1995 are for an integration time of 6 s whereas the other
values are for an integration time of 1.5 s.
Spectral region
Channel 1
Channel 1
Channel 2
Channel 3
Channel 4
at 290 nm
at 305 nm
≥325 nm
Radiance (photons s-1 cm-2 nm-1 sr-1)
∼3.0×1010
∼4.0×1011
∼2.0×1013
∼3.0×13
∼3.0×1013
Signal-to-noise: 1995
∼140*
∼1100*
∼3500
∼4000
∼2500
2001
∼20
∼180
∼2700
∼4000
∼2500
2010
∼6
∼55
∼1600
∼3500
∼2500
In Fig. at wavelength regions around 470 and 600 nm, changes
in intensity are affected by changes (outgassing of coatings) in the dichroic filter which separates
the wavelengths of channels 3 and 4. Unpredictable polarisation-sensitive changes were observed and
the radiometric calibration in these regions might be doubtful. Furthermore the outgassing is assumed
to be responsible for the slight transmission increase in channel 3 in the early part of the
mission. The low-frequency oscillating structure appearing in all channels is the result of the etalon
effect which is caused by a changing thickness of ice deposits on the detectors and which leads to spectral
interference patterns . At present no attempts are being made to correct for this effect.
In order to remediate the observed GOME science channel degradation, a correction algorithm was
developed in the framework of the ESA project GDAQI GOME Data Quality Improvement,.
This degradation correction is applied to irradiance and radiance spectra as an additional part of
the radiometric calibration. The degradation correction approach that was chosen is the comparison
of all available solar data from the entire mission period with the corresponding solar data of
a reference day in the early GOME lifetime (3 July 1995). The Sun is a reliably stable input
source to monitor the instrument throughput despite small changes in the solar spectrum due
to changes in solar activity. This study was done for both the GOME science channels and the PMDs.
The temporal changes have been determined by building ratios of all solar spectra with the solar
spectrum of the reference day t0, which may be written as
ISun(λ,t)ISun(λ,t0)=PDeg(λ,t)⋅CSED(t)⋅Residual(λ,t),
where PDeg(λ,t) is the degradation function used and is dependent on wavelength λ
and time t. CSED(t) is the intensity correction due to the seasonal variation in Sun–Earth
distance, and Residual(λ,t) is the remaining structure. Note that the impact of the etalon
effect and the changes in the dichroic filter are not accounted for. For the determination of the
degradation correction function PDeg(λ,t), a two-step approach was developed: (i) each
irradiance ratio (per channel) is approximated by a polynomial function in wavelength and (ii) each
coefficient of this polynomial in wavelength is subsequently described by a time-dependent expression.
Thus, for the degradation function PDeg(λ,t) per channel the following expression has
been obtained:
PDeg(λ,t)=∑k=0nak(t)⋅(λ-λ0)k.
λ0 is the centre wavelength in each channel. Each coefficient ak(t) of the polynomial
in wavelength is taken from a look-up table (LUT). For channels 1 and 2, third-order polynomials
(n=3) are used, whereas in channels 3 and 4 quadratic (n=2) and linear (n=1) polynomials
are used, respectively (see smooth curves in Fig. ). The LUT is
generated by smoothing the time series of each polynomial wavelength coefficient using a Savitzky–Golay
filter with a filter width of 250 days. Figure shows,
for each channel, the first polynomial coefficient (a0) and the corresponding smoothed curve as a
function of time. In channel 1 degradation started almost immediately after launch. Until 2000 the
intensity decreased to ∼50 % of the early-mission values. In channel 2 significant decrease
in intensity is observed, especially during 2000–2002. Since mid-2001 all measurements were
additionally affected by the ERS-2 pointing problem. Furthermore, channels 3 and 4 are affected
by changes in the dichroic filter, i.e., outgassing of coatings.
In GDP-L1 the degradation correction is then applied according to the following:
ISunCorr(λ,t)=ISun(λ,t)/PDeg(λ,t).
First polynomial coefficient a0 from wavelength fit as a function of time (small
dots, blue: channel 1, red: channel 2, green: channel 3, and magenta: channel 4). The light blue,
orange, light green, and light violet curves denote the corresponding smoothed curves using a
Savitzky–Golay smoothing filter with a filter width of 250 days.
In addition to the Sun the moon provides an independent irradiance source and
in principle GOME lunar measurements can be used to characterise and monitor
instrument performance and degradation . The moon
is viewed on the eclipse side of the orbit over the scan mirror at an
incidence angle of 5∘–15∘. The amount of light is of the same
order of magnitude as for the earthshine observations, though calibration
measurements are complicated by several factors such as moon availability and
phase, non-uniformity of the moon surface, polarisation, and partial slit
filling. Orbit requirements were so strict that measurements are only
possible for a very limited number of orbits per year, with the moon phase
always being ∼0.6 between the full moon and the last quarter. After 2003
no more moon measurements are available. Thus, GDP-L1 does not attempt to
generate calibrated radiances for the moon, and a long-term analysis of moon
observations has not been performed. Furthermore, for an accurate monitoring
of instrument degradation using lunar measurements, a more precise
characterisation of the reflective and scattering properties of the moon
would be necessary. However, early investigations by using
the first 18 months of GOME's lifetime confirmed the assumption that the scan
mirror (instead of the diffuser) is primarily subject to degradation.
PMD measurements and Q_factors
The relative change of the solar PMD measurements as a function of time with respect to a reference
measurement from 3 July 1995 are shown in Fig. for all three PMDs. Note
that the measurements were normalised to 1 astronomical unit (AU) in order to eliminate seasonality.
As for the SMR spectra the degradation for the PMDs is strongest for PMD 1, which
corresponds to channel 2. The signal decreases to about 40 % of the original value. The temporal
evolution for PMDs 2 and 3 is similar to the behaviour of the signals in channels 3 and 4, respectively.
Relative change of PMD signals as a function of time from 1995 to 2011 for PMDs 1, 2, and 3 (a–c). Reference measurement is from 3 July 1995.
PMD Q_factors are self-calibration constants which ensure that the calculated fractional
polarisation p of the Sun is unpolarised with p=0.5. They are defined as the relative difference
between the measured solar signal of PMDi, with i=1,2,3, and the expected PMD signal
calculated from the key data and the corresponding channel signals, when unpolarised input is assumed to be as follows:
Q_factori=(PMDi-∑jXj×channelj)/PMDi,
where channelj is the channel signal of pixel j and Xj is the ratio of the PMD signal
to the channel signal for a monochromatic input signal as obtained from on-ground calibration
measurements. Q_factors thus involve the differential degradation between PMD signals and the
channel signals since the time of on-ground calibration.
Figure shows the Q_factors for PMD 1, 2, and 3 (from top to bottom) as a function
of time from 1995 to 2011. In principle, the behaviour of the Q_factors as detected in the previous
study continued. For the first Q_factor a decrease until 2001 is observed.
From 2002 to 2011 Q_factor 1 steadily increased. That means that in the first period the degradation
of the PMD signal was stronger than the degradation of the signal in channel 2, whereas in the
second period the channel signal decreased faster then the PMD signal. Q_factor 2 increased slowly
from the beginning of the measurements – indicating that the PMD signal degraded less than the
average signal in channel 3 – and reached nearly the same value as Q_factor 1 at the end of the mission.
For Q_factor 3 note that it is already non-zero at the beginning of the measurements. This is related
to stray light (wavelength >790 nm), which affected in particular PMD 3, whereas PMD 1 had a
negligible stray light effect. Q_factor 3 remained more or less stable until 1999 followed by a
slow increase until 2011. Outliers are due to GOME operation anomalies such as cooler switch-offs,
instrument or satellite switch-offs, on-board anomalies, or special operations
(see also ).
Q_factors 1, 2, and 3 (a–c) as a function of time from 1995 to 2011.
Reflectance degradation
In Sect. the approach used to correct for instrument degradation
was described. This “soft” correction is a first-order correction as it is applied in GDP-L1
to both irradiance and radiance spectra, thereby assuming that both spectra degrade in the
same way. The ratio
R=πIμ0E,
where I is the top-of-atmosphere (TOA) radiance reflected and scattered by
the Earth's atmosphere, E is the solar irradiance, and μ0 is the
cosine of the solar zenith angle, defines the reflectance R, which is used
by many algorithms to retrieve the amount of atmospheric constituents. From
Eq. () it is clear that the reflectance remains unchanged
in the level 0-to-1 processing since the applied degradation correction
cancels out under the assumption that the BSDF does not degrade. However, the
light paths for radiance and irradiance measurements are different and the
degradation of the scan mirror indicates a strong dependence on the incidence
angle . This leads to a substantial differential degradation
of radiance and irradiance spectra and, thus, to
degradation in the reflectance, which may affect for example ozone profile
retrievals or the determination of total ozone
columns using a direct fitting approach , because these
algorithms are sensitive to absolutely calibrated reflectances. Correction
approaches for the reflectance degradation have been developed in the past
which rely on, for example, the comparison of experimental and simulated data
, the comparison of satellite reflectance spectra
with ground-based reference spectra , or the comparison of
global average reflectance with respect to global average reflectance from
the beginning of the mission . For the latter
approach the underlying assumption is that the global average reflectance
does not change in time. For irradiance degradation correction (see
Sect. ) this assumption can be regarded fulfilled, but
the earthshine radiance and, thus, the reflectance depend strongly on highly
variable atmospheric conditions such as clouds, trace gases, aerosols, or
surface albedo and on the viewing angle. Therefore, retrievals using this
correction may be inadequate for trend studies . However,
have shown that when using the latest version of the direct
fitting approach GODFIT for ozone retrieval (GODFIT version 4), GOME (as well
as OMI) performs in an extremely stable way.
In the framework of GOME-Evolution we analysed the long-term behaviour of the GOME reflectance
using measurements over so-called pseudo-invariant calibration sites (PICSs) which have been
identified and characterised by the Committee on Earth Observation Satellites (CEOS) to be
suitable for detecting the radiometric stability of satellite sensors . The advantages of
these sites are the spatial uniformity and homogeneity, their stable spectral characteristics
over time, and generally high reflectance to enhance the signal-to-noise ratio. At the moment there
are six CEOS reference PICSs, all located in the Saharan desert: Libya-1 and Libya-4, Mauritania-1 and
Mauritania-2, and Algeria-3 and Algeria-5. They are usually made up of sand dunes with
climatologically low aerosol loading, little rainfall, and practically no vegetation or human impact.
More details on the PICSs can be found in . In the past these sites have been
widely used in post-launch calibration and validation of satellite sensors
e.g.,.
For our study we selected four reference sites: Libya-1 (24.42∘ N,
13.35∘ E) and Libya-4 (28.55∘ N, 23.39∘ E) as well
as Algeria-3 (30.32∘ N, 7.66∘ E) and Algeria-5
(31.02∘ N, 2.23∘ E). The geolocation in the parentheses
denotes the centre latitudes and longitudes. Fortunately, in this area the
impact of the ERS-2 tape recorder failure in June 2003 is quite small, so
that the time series are almost complete with only a short gap in 2003. We
limit our analysis to two single wavelengths in the UV part of the spectrum
(325 and 335 nm), which mark the lower and upper limit of the fitting window
for total ozone retrieval . All GOME ground pixels
with cloud fraction less than 0.2 that fall into a square area of ±1.5∘ in latitude and longitude around the centre geolocation of the
reference site were extracted. About 3000 ground pixels were found for each
reference site that fulfill these criteria. In general, the top-of-atmosphere
reflectance of a scene measured by the satellite sensor depends on the
viewing geometry because of the anisotropy of the surface reflectance. The
reflectance is higher for west pixels, when the Sun and the satellite are on
the same side of the scene (backward-scattering viewing geometry), than for
east pixels in forward-scattering viewing geometry
. The anisotropy depends on wavelength and on
the surface properties. In the case of the Saharan PICS scenes and for
wavelengths of 325 and 335 nm, the difference in reflectance between west
and east pixels is about 25 %.
Figure shows the reflectance normalised to the reflectance at
the beginning of the mission, segregated by pixel type (east, nadir, and west, which mark
the three forward scans) for both wavelengths (solid curves denote 325 nm and dashed curves
denote 335 nm) as a function of time for the entire mission for PICS Libya-4. The data gap
from mid-2003 to early 2004 is due to the tape recorder failure. Fluctuations in reflectance
related to the seasonal variation and to short-term variation of atmospheric conditions were
smoothed out. Until late 1999 the curves show only a small amount of degradation. Afterward they start
to increase, reach a maximum in 2001, and then decrease again to values below 1 in early 2003.
From 2004 to 2011 the curves steadily increase except for the reflectance degradation
for west pixels at 325 nm (solid cyan curve), which show a slight decrease at the very end of
the mission. These ups and downs in the degradation might be related to changes in interference
patterns, which can lead to an increase or decrease in reflectance.
Furthermore, the degradation depends strongly on the pixel type, i.e., the line of sight.
Until 2003 west pixels are much less affected than nadir and east pixels, and also after 2003
the behaviour of the reflectance from west pixels is slightly different compared to nadir and east
pixels. For the other three PICSs (Libya-1, Algeria-3, and Algeria-5) we found very similar results
(no figure) with negligible differences compared to Libya-4. In addition, we analysed the
reflectance for cloud-free pixels over the Mediterranean Sea between Greece and Egypt, which
shows the same temporal evolution as the reflectance over the Saharan desert (no figure)
although the surface albedo is much lower there.
As mentioned earlier, in principle this analysis could be used for correcting the reflectance
degradation. However, the underlying requirement that the reflectance remains stable over a long
time period might not be fulfilled in every case.
Relative reflectivity as a function of time for cloud-free GOME measurements at 325 nm
(solid) and at 335 nm (dashed) for the Libya-4 reference site. Colours denote the different GOME pixel
types: east (green), nadir (blue), and west (cyan) ground pixels. The data gap between mid-2003 and
early 2004 is due to the ERS-2 on-board tape recorder failure in June 2003.
GOME in-flight calibration parameters
In this section we present the analysis of the GOME calibration parameters obtained from measurements
using on-board calibration sources and applied during the level 0-to-1 processing as described in
Sect. . For a detailed description of the individual calibration algorithms related to the
parameters we refer to the GOME Algorithm Theoretical Basis Document .
Monitoring of the individual parameters was performed with special emphasis on the analysis of the
long-term stability.
Overview
In the framework of the GOME-Evolution project the complete set of in-flight calibration data
has been revisited and re-analysed in order to draw conclusions on the long-term stability of
the GOME sensor and to optimise the GDP-L1 usage of the in-flight calibration for the entire
mission. The database contains spectral lamp measurements for the wavelength calibration
(see Sect. ), dark current measurements for all
integration time patterns (see Sect. ), and LED measurements for the pixel-to-pixel
gain correction (see Sect. ), as well as the Sun mean reference spectra
and moon and PMD measurements. After the ERS-2 tape recorder failure in June 2003 the number of
available calibration data is significantly reduced, since only data within accessibility of an
ERS-2 receiving station were transmitted to ground. In particular, no more moon measurements are
available after 2003.
Spectral calibration
The objective of the spectral calibration is to assign a certain wavelength to each individual
GOME detector pixel. Therefore, the instrument houses a platinum–chromium–neon hollow cathode
emission lamp . This lamp provides a sufficient number of atomic emission lines
of these three elements with well-known spectral positions that allow the wavelength allocation.
At first, spectral calibration parameters are calculated by the determination of the pixel
number centre of the spectral lines and the subsequent fitting of a polynomial through these
pixel-wavelength pairs. The second step is the application of the calibration parameters from
the previous step to the measurements.
Several lamp spectra were measured (i) over the orbit approximately once per month, during the
calibration timeline that was run for five orbits, and (ii) every day just before
and after the Sun calibration. The latter measurements are available until April 1998. Since
September 2001 the calibration lamp was used only during the five orbits of the monthly calibration
due to numerous lamp failures caused by the voltage not having reached its nominal value (see also ).
For the spectral calibration a total of 68 candidate emission lines within
GOME's spectral range from 240 to 790 nm were selected from the reference
lamp atlas and are stored in the calibration key database.
The lamp measurements of the individual lines can be regarded as statistical
distributions from which the moments can be calculated. They contain
characteristic information about the spectral lines that are needed to select
those lines suitable for an accurate calibration. The aforementioned moments
are the mean value, i.e., the pixel number centre of the maximum intensity,
as well as the variance, the standard deviation σ, and the skewness.
The full-width half-maximum (FWHM) is computed from the standard deviation.
To be selected, the moments of a spectral line must meet the following
statistical criteria: (i) the signal of the centre pixel shall not be below a
certain minimum, i.e., it should be well above the noise level; (ii) the FWHM
shall not be below a certain value in order to fulfill the Nyquist criteria
for the digital recording of analogue signals; and (iii) the skewness shall
not be larger than a certain value, i.e., the line must be roughly
symmetrical. Reasonable thresholds for the criteria have been determined
during the pre-flight measurements and the commissioning phase. Current
values are 50 BU s-1 for channel 1 and 300 BU s-1 for channels
2–4 for the first criterion, σ≥0.6,
FWHM≥1.5pixel, and skewness ≤0.6 for the
second and third criterion.
As mentioned before, the calibration parameters are obtained by fitting a polynomial through the
pixel–wavelength pairs. In channels 1 and 2 third-order polynomials are used, whereas in channels 3
and 4 fourth-order polynomials are used. At least seven spectral lines per channel
are needed for the fit, which is performed using the singular value decomposition algorithm
.
The statistical parameters of each individual emission line were analysed in
terms of both long- and short-term stability. Regarding short-term
variability, few lines were found whose moments show jumps between two values
leading to jumps in the fitted polynomial coefficients. Other lines fulfill
the aforementioned criteria only in very few cases, which also results in
jumps in the fitted coefficients. This analysis has led to a revised spectral
line list (by excluding the identified unstable lines) that improved the
stability of the spectral calibration for the complete mission.
Figure shows the wavelength changes of selected lamp
lines (two per channel) as a function of time. Depicted is the difference (in
nm) with respect to the wavelength at the beginning of the mission. The
stability of the wavelengths is excellent until 2004. Toward the end of the
mission the variability increases slightly, in particular in channels 1 and
3. The standard deviation of the wavelength changes is 0.0015 nm in channel
1, 0.0025 nm in channel 3, and less than 0.001 nm in channels 2 and 4.
These values are comparable to the analysis by , who used
a different wavelength calibration approach . They found
temporal variations of the wavelength calibration from 0.0015 to 0.0034 nm
for nine narrow spectral bands.
Change in wavelength (with respect to the beginning of the mission) as a function of time
from 1995 to 2011 for two selected lamp lines per channel. From (a) to (d): channel 1,
channel 2, channel 3, and channel 4.
One of the key elements in the optical system of GOME is a quartz pre-disperser prism. The wavelength
calibration is sensitive to the dispersion of this prism, whose refractive index varies
with temperature. Thus, the calibration parameters from the lamp measurements are stored in the
database as a function of this temperature. In the operational processing the most recent calibration
parameters are then selected from the database according to the pre-disperser temperatures encountered
in the actual orbit. Each individual GOME spectrum is, thus, implicitly corrected for temperature
variations that are caused by seasonal variations, the position in the orbit, and the rate of
degradation of thermally sensitive optical elements.
Figure shows the time series of the pre-disperser temperature from 1995 to 2011
(blue dots). An increase of ∼4 K within the instrument's lifetime is found which is due to degradation
of the thermal system. Furthermore, the curve exhibits a seasonal cycle with maximum values in
December–January when the Sun–Earth distance is at a minimum. Outliers are caused by instrument and
cooler switch-offs. Magenta dots denote the increase in the pre-disperser temperature along an orbit
in Kelvin per hour (K h-1) for the years 1995 to 2003. The increase along an orbit is due to warming of
the satellite by the Sun and because light passes through the instrument. This analysis relies on the
average of 60–70 days per year; for each day the temperature measured along the first orbit, which is
always located between 120 and 160∘ E, was investigated. The error bars are a measure of the
intra-annual variability. We did not analyse the dependence of the pre-disperser temperature itself
on longitude as in his Fig. 3. They found a maximum of the pre-disperser
temperature over the Atlantic and a minimum over the Pacific. Furthermore, they stated that the
temperature increase along the orbits does not show a dependence on longitude. During the first
8 years of the mission the temperature rise along the illuminated part
of one orbit increased from about 0.7 K h-1 to about 0.9 K h-1. Unfortunately, analysis of later years is
not possible due to the ERS-2 tape recorder failure and incomplete orbits.
Temperature measured at the pre-disperser prism as a function of time from 1995 to 2011
(blue) and linear fit (red). The temperature increase is about 2.5 K decade-1. Magenta
dots denote the increase in the pre-disperser temperature along one orbit in Kelvin h-1
(right y axis) for the years 1995 to 2003. Analysis of later years is not possible due to
the tape recorder failure (incomplete orbits).
Dark signal correction
The detectors integrated in GOME are random access linear photo-diode arrays. One of the characteristics
of these devices is a certain amount of dark current due to thermal leakage. It is expected that this
current will depend on the orbital position of the satellite and also how much time has elapsed since the mission began.
Therefore it is necessary to continuously monitor the dark current and the associated noise, which is
done by means of periodically taken dark-side measurements (Sect. ). In this case the
scan mirror points toward the GOME interior. The PMD detectors are non-integrating devices and,
therefore, do not have a leakage current. Nevertheless, those detectors must be corrected for
their zero offsets and the noise must be monitored (see Sect. ).
Dark current and dark current noise
The complete dark signal comprises two parts: (i) a constant value of
∼140–150 BU (binary units), which is called the
fixed pattern readout noise (FPRN), and (ii) the time-dependent leakage current itself, which is about
∼2 BU s-1. This value is quite small because of the low temperature (-38 ∘C) of the detector
arrays. The dark signal measurements have to be taken with the same integration time patterns as those
used for scanning and other calibration measurements, since it was found that a certain amount of cross-talk is present that depends on the integration time. However, the detector temperature is not taken into
account for GOME as it is in the case for the dark signal correction of the GOME-2 instrument .
The dark signal correction is the subtraction of a mean dark signal spectrum from the measured signal
Simeas,k:
Si=Simeas,k-Sidark,k‾,
where i=1,…,1024 detector pixels. The integration time pattern k describes the number of clock pulses,
where one pulse takes 93.75 ms, e.g., a time pattern of 640 is equivalent to 60 s.
The mean dark signal for n=10 consecutive measurements is defined as
Sidark,k‾=1n∑j=1nSidark,kj.
Figure shows the dark signal as a function of time for the three most
representative integration time patterns: (i) the normal scanning orbits with 12 s integration
time for band 1a and 1.5 s for the other bands (with co-adding applied), (ii) the LED measurements
for the pixel-to-pixel gain correction (see next Section) with 30 s integration time for all bands,
and (iii) the polar view mode with 60 s integration time for band 1a and 6 s for the other bands.
Figure shows the dark signal for bands 1a, 2b, 3, and 4 (from top to bottom)
and for time patterns (i) to (iii) from left to right. All panels denote a significant increase over
time.
Dark signal in binary units (BU) as a function of time from 1995 to 2011
for three integration time patterns: normal scanning mode (12 s integration time for band
1a and 1.5 s for the other bands, co-adding applied for bands 2a, 2b, 3, and 4, a, d, g, j),
LED measurement mode (30 s integration time for each band, b, e, h, k), and polar view
mode (60 s integration time for band 1a and 6 s for the other bands, c, f, i, l). From
top to bottom: band 1a, band 2b, band 3, and band 4.
Note that the dark signal in bands 2b, 3, and 4 for the normal scanning
orbits (Fig. , panels a, d,
g and j) is much higher due to the co-adding of four
measurement sequences. At present there is no explanation for the behaviour
of the signal from 2005 to 2007. It is most obvious in channel 4 for the
normal scanning mode (panel j) and for the polar view mode (panel
l). The signal decreased significantly in 2005 (by 40 BU for the
normal scanning mode), reached a minimum in the beginning of 2006, and
increased again, during which the entire development of this anomaly is quite
smooth. The jumps in the time series (e.g., seen in channels 2 and 3 for the
normal scanning mode and in channel 2 for the polar view mode) are due to
instrument or cooler switch-offs or instrument anomalies.
The noted increase in the dark signal is an increase in the leakage current, i.e., the
time-dependent part. Figure shows the dark signal as a
function of the integration time for four different years: 1997, 2002, 2007, and 2011. Different
symbols and line styles denote the individual bands 1a, 2b, 3, and 4. The y intercept represents
the FPRN, which is about 140–150 BU and remains constant over the entire time period. The slope
denotes the time-dependent leakage current, which is quite similar for all channels (∼2 BU s-1)
and which increases over time. The increase is also almost identical for all channels and amounts
to about 4 BU s-1 decade-1 (∼6.5 BU s-1 from 1995 to 2011). This is comparable to earlier work
by and our previous study as well as
, which analysed the dark signal for the GOME-2 instrument on-board the MetOp series of satellites using
the same type of detectors. For OMI, which is a nadir-viewing UV-VIS imaging spectrograph using
two-dimensional charge-coupled device (CCD) detectors , a 7-fold
dark current increase was found from 2005 to 2015 , and for GOMOS/ENVISAT
(Global Ozone Monitoring by Occultation of Stars) using the same CCD detectors as OMI an even
higher increase was found . Although the increase in the dark current seems
to be significant, there is not necessarily a negative impact on the quality of the level 1 data
products as long as appropriate dark current measurements are available and applied during the
level 0-to-1 processing.
Dark signal in binary units (BU) as a function of integration time for January
1997 (red), 2002 (black), 2007 (green),
and 2011 (blue), respectively. Different symbols and line styles denote channels 1a (solid), 2b (dotted),
3 (dashed), and 4 (dash-dotted).
We found that it is not only the leakage current itself which changed over time, but also its
distribution, which widened
considerably. Figure shows histograms of the dark signal for spectral band
1a (240–283 nm) for an integration time of 12 s (nominal scanning mode) for every 2 years from 1997
to 2011. The data correspond to Fig. a. Coloured numbers stuck
to the individual histograms denote the median values and the FWHM of the distribution. The latter is
additionally indicated by the filled rectangles. As seen in Fig. the dark signal
significantly increased over time from ∼176 to ∼242 BU. Furthermore a noticeable,
almost 3-fold broadening of the distribution was found. FWHM increased from 2.7 to 7.4 BU.
A widening of the dark current distribution was also noticed for OMI and GOMOS
.
Probability density function (PDF) of the dark signal in spectral band 1a for an integration
time of 12 s for every 2 years from 1997 to 2011. Coloured numbers denote the median value in binary units (BU) and the FWHM (in parenthesis) of the distribution. The latter is additionally indicated by the
coloured rectangles.
The noise on the signals of the detector pixel readouts is expected to be
constant over all detector pixels. For each detector pixel the standard
deviation from all leakage measurements from one orbit with the same
integration time is computed. The noise is then the average of all standard
deviations. The annual mean noise level is shown in Fig.
(blue curves, left y axis) as a function of time for three different
integration time patterns (scanning, moon and LED). The error bars denote the
standard deviation for the annual mean. The lowest noise level (∼2 BU)
is found for LED dark signal calibration measurements that have the longest
integration time (30 s), whereas the noise level for scanning and moon
integration time pattern are quite similar and about 4 BU. The values remain
more or less constant until June 2003. Afterward the noise level for LED dark
signal calibration measurements slightly increased (dotted–dashed blue
line), whereas a decrease is found for moon dark signal calibration
measurements (dashed blue curve). Red curves (right y axis) denote the
number of available dark signal calibration measurements. The most
significant decrease in the number of available measurements is for the LED
dark signal calibration measurements (dotted–dashed red curve).
Annual mean dark current noise (blue curves, left y axis) in binary units (BU)
for three integration time
patterns: scanning (solid curve), moon (dashed curve), and LED (dotted–dashed curve). Red curves
(right y axis) denote the number of available calibration measurements for the three individual
time patterns.
PMD offset and noise
The signals of the PMD detectors as non-integrating devices must be corrected for their zero offsets and
the associated noise must be monitored. Figure shows PMD offsets for each PMD
as a function of time for the entire mission period. The offset of PMD 1 is about 1320 BU, whereas it is
about 510 BU for PMDs 2 and 3.
All offsets indicate a very small increase of 0.8 % in 16 years of the mission. The increase is nearly
linear for PMDs 2 and 3, whereas for PMD 1 the increase started in 1999; the PMD 1 offset reached a
maximum at the end of 2004, decreased in 2005 and increased again thereafter. For all PMDs the offsets
seem to have two states, and jumps between the two states are due to cooler and instrument switch-offs
as well as instrument anomalies.
PMD offset in binary units (BU) as a function of time for PMD 1, 2, and 3 (a–c).
Figure shows the annual mean PMD noise as a function of time. The PMD noise is
defined as the mean value of the standard deviations which are calculated for each PMD over all 16
individual PMD measurements. It is about 0.5–1.5 BU. The previous study their Fig. 9d
has shown the impact of the South Atlantic Anomaly (SAA) on the noise level, which increases significantly when
measurements from this area are taken into account. In the new GDP-L1 version these calibration
measurements are discarded (see following section). In general the noise level remains stable over
the entire period although – as a consequence of the tape recorder failure in June 2003 – a slight
change in the noise level was found, in particular for the moon dark signal calibration measurements.
Annual mean PMD noise (blue curves, left y axis) in binary units (BU) for three integration time
patterns: scanning (solid curve), moon (dashed curve), and LED (dotted–dashed curve). Red curves
(right y axis) denote the number of available calibration measurements for the three individual
time patterns.
Impact of the South Atlantic Anomaly
The South Atlantic Anomaly is an area of enhanced flux of energetic particles due to a
dip in the Earth's inner Van Allen radiation belt. In this region low Earth orbit spacecrafts
are exposed to higher-than-normal radiation levels and may suffer from damage
. High energy protons impact the detectors of GOME, i.e., the
background signal is higher than the normal dark signal, the noise is enhanced, and the measured
spectra are also prone to intensity spikes caused by cosmic particles.
For this reason all calibration measurements in the SAA are discarded. The
algorithm to identify the SAA uses the signal from PMD 1, since we found that
the noise level on PMD 1 is a reliable indicator of the enhanced particle
bombardment in the SAA region. Figure shows a map of
the GOME long-term mean PMD 1 noise derived from the first 5 years of the
mission. The impact of the SAA clearly appears in terms of significantly
enhanced PMD 1 noise in an oval-shaped region centred at the east coast of
Brazil. The SAA spans from 50 to 0∘ S in latitude and from
90∘ W to 30∘ E in longitude. During the level 0-to-1
processing PMD measurements are grouped and for each group a noise value with
respect to the median value is calculated. If the noise value exceeds a
certain threshold all calibration measurements from the group are discarded.
This also includes the lamp measurements for the spectral calibration and the
LED measurements for the pixel-to-pixel gain correction (see next Section).
The new algorithm defines an “inside SAA” and an “outside SAA” region for
dark signal values in the calibration database.
Map of the GOME long-term mean PMD 1 noise in A/D(-converter) units (= binary units) derived from
the first 5 years of the mission. Enhanced noise levels indicate measurements affected by
the South Atlantic Anomaly.
Pixel-to-pixel gain correction
The pixel-to-pixel gain in quantum efficiency of each diode detector array is characterised
and corrected using internal LEDs. Each channel has a monochromatic red LED located between the channel
optics and the detector window (see Fig. ), i.e., the detectors are illuminated
directly without any dispersing element
in between that may suffer from degradation effects. The monitored detector signal corresponds to a
superposition of a smoothly varying signal caused by the LED characteristics and a small-scale structure
due to the slightly different sensitivity of each pixel. The determination of the correction spectra for each
of the four channels is based on a mean value of several consecutive LED measurements and a smoothed curve
through this average using a triangle filtering window:
ci=SismoothSiLED‾,
where ci is the correction factor of detector pixel i, SiLED‾ is the mean value of
several consecutive LED measurements, and Sismooth is the smoothed curve through this averaged measurements.
The latter is calculated by means of
Sismooth=∑k=-nnn-|k|n×Si+kLED‾∑k=-nnn-|k|n
using a triangle filtering window of width n=5. The application of the PPG correction is then simply
Sicorr=Sici,
where Si is the measured signal value of detector pixel i, and Sicorr is the corrected value.
Typically, the LED spectra were obtained in monthly intervals until 2003. From 2003 onward
LED measurements are limited to two or three sequences per year. The absolute radiance correction
due to the pixel-to-pixel variability is very small (∼0.02 %). However, it may not be
negligible in wavelength regions used for the retrieval of weak absorbers such as bromine
oxide. Figure shows the relative intensity of the LED spectra as a function of
time and wavelength with respect to a reference spectrum from the beginning of
the measurements (27 June 1995). The nearly linear decrease, which was already detected in the
previous study , continued until the end of the mission in 2011 and is due
to the degradation of the LEDs' brightnesses themselves. The output decreased to ∼60 %.
It is almost homogeneous over the complete wavelength range of each channel. The steepest decrease
is found in channel 1.
Relative intensity of LED spectra as a function of time (x axis) and wavelength (y axis)
with respect to a reference spectrum from 27 June 1995.
In addition we analysed the distribution of the PPG correction factors as a
function of time. Figure shows box–whisker plots of the
distribution for each channel and as a function of time. We show one
distribution per year. In channel 1 the amplitude of the PPG correction
spectrum is slightly larger than for the other channels. Nevertheless, the
distribution of the correction spectrum remains roughly stable over the
entire period, whereas for channel 2 a significant broadening of the
distribution is found. The standard deviation increased by a factor of
∼2.5 in this channel, which indicates that the variability in
sensitivity between the individual detector pixels increased significantly.
For channels 3 and 4 a broadening of the distribution of ∼40 % was
found. For all channels we noticed that the number of outliers did not
increase over the years (not shown in this plot), which indicates that the
detector as a whole is affected and that the increase is not just due to a
few strongly battered pixels.
Box–whisker plots of the distribution of the PPG correction pattern as a function of time
(one selected distribution per year) for channels 1, 2, 3, and 4 (a–d). Note
the slightly different y axis range for channel 1. Red horizontal lines denote the median,
the blue boxes denote the lower (25 %) and the upper (75 %) quartile, and the green caps
denote the minimum and maximum values (except the outliers).
Summary and conclusions
The Global Ozone Monitoring Experiment, launched in April 1995 on-board the
second European Remote Sensing satellite, provided measurements of
atmospheric trace constituents such as O3, NO2, SO2,
HCHO, BrO, and H2O as well as aerosol and cloud
parameters on a global scale for more than 16 years, before it was
decommissioned in July 2011. The existing data archive of GOME can be
considered as the European reference for follow-up atmospheric composition
sensors like SCIAMACHY, OMI, GOME-2, and the Copernicus Sentinel missions
S5P/S4/S5. Therefore, preservation as well as further improvement and
exploitation of this unique data set are highly recommended.
Within the framework of the ESA's GOME-Evolution project a homogenised level
1 data product for the complete mission was generated for the first time,
based on the new GDP-L1 Version 5.1, which contains fully calibrated
radiances, irradiances, geolocation information, and selected calibration
parameters. In addition, cloud parameters retrieved with the well-established
OCRA and ROCINN algorithms have been integrated in the new product. The
format and structure of the GOME L1 NetCDF-4 files are similar to other
state-of-the-art EO products like S5P.
Furthermore, a detailed investigation of the long-term performance of the
GOME instrument in terms of monitoring the various in-flight calibration
parameters was carried out. This should ensure the high quality of the GOME
(ir)radiance measurements that is needed to retrieve atmospheric geophysical
products with highest accuracy.
The polarisation correction algorithm was improved in the new GDP-L1 5.1.
Instead of climatological values the ozone columns derived from the GOME
measurements themselves are used for the parameterisation of the generalised
distribution function. By means of the daily solar irradiance measurements
the degradation was monitored and corrected. Degradation can be explained in
terms of deposits on the GOME scan mirror. Below 300 nm intensity decreased
by 80 %–95 %, which implies a significant deterioration of the
signal-to-noise ratio and which may have a severe impact on the challenging
retrieval of atmospheric parameters such as ozone profiles. The decrease in
channel 2 is 40 %–80 %. In channel 3 the decrease
(10 %–40 %) started in 2001, whereas throughput changes in channel 4
are relatively small. Since 2001 the measurements were additionally affected
by an ERS-2 pointing problem. A degradation correction algorithm has been
developed and further improved which relies on the intensity measured in the
early part of the mission and which comprises a wavelength- and a
time-dependent part. In GDP-L1 this correction is routinely applied to
irradiance and radiance measurements. The degradation in reflectance, i.e.,
the differential degradation between solar irradiance and Earth radiance
measurements has been monitored for two wavelengths, 325 and 335 nm (lower
and upper limits of the total ozone fitting window), using cloud-free pixels
over the Saharan desert. Changes are of the order of -10 % to 30 %
and depend on wavelength and the viewing angle. Since changes in reflectance
may result from both changes in instrument performance or changes in
atmospheric conditions, no routine corrections are applied in GDP-L1.
For the spectral calibration special attention was paid to the identification
of lamp lines that remain stable (with respect to the statistical moments)
over the whole mission. This has resulted in an updated spectral line list
used in GDP-L1 that improved the temporal stability of the wavelength
assignment for the complete mission. For the leakage current an increase of
4 BU s-1 decade-1 and a widening of the distribution were found.
Typically, in GDP-L1 dark signal measurements from the same or a very
close-by orbit are applied so that these changes do not have a negative
impact on the measurement quality. The existing dark signal correction has
been further improved by differentiating between measurements from outside
and inside the SAA. Thereby, the enhanced background signal and noise level,
which are typical for measurements from inside the SAA, are better accounted
for. The output of the LEDs that are used to monitor the pixel-to-pixel
sensitivity decreased to about 60 % of the early-mission values. For
channel 2 a significant broadening of the PPG distribution was observed.