JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 104, NO. D15, PAGES 18,801-18,814, AUGUST 20, 1999

Validation of Global Ozone Monitoring Experiment cloud fractions relevant for accurate ozone column retrieval

R. B. A. Koelemeijer and P. Stammes Royal Netherlands Meteorological Institute, De Bilt, The Netherlands

Abstract. The Global Ozone Monitoring Experiment (GOME), launched on board the ERS-2 satellite of the European Space Agency, is a spectrometer measuring the Earth's reflectivity between 240 and 790 nm. The main geophysical product of GOME is the ozone vertical column density, also called ozone column. For accurate ozone column retrievals the presence of clouds should be taken into account. Therefore, as part of the operational ozone column retrieval algorithm, cloud fraction is derived by the initial cloud fitting algorithm (ICFA) from the spectral reflectivity between 758 and 778 nm, which encloses the 02 A band. In ICFA, cloud top pressure is assumed a priori, and is taken from the International Satellite Cloud Climatology Project (ISCCP) database. We validated the ICFA (version 2.3) cloud fraction product in two ways. First, a statistical approach was performed comparing monthly average ICFA cloud fractions with monthly average cloud fractions from ISCCP. Global cloud patterns in monthly average ICFA cloud fraction maps compare reasonably well with those from ISCCP. Second, a detailed comparison for individual pixels was performed between ICFA cloud fractions and cloud fractions derived from collocated Along Track Scanning Radiometer-2 (ATSR-2) data. We found that large differences exist between the (effective) cloud fractions from ATSR-2 and ICFA. The mean difference between the cloud fractions

of ATSR-2 and ICFA is 0.18; the standard deviation of the difference is 0.23. It is argued that the errors in the ICFA cloud fractions are probably due to errors in the assumed cloud top pressure. A modified version of ICFA, which is less sensitive to the assumed cloud top pressure, is presented. This yields a much improved agreement with the ATSR-2 cloud fractions. Effects of errors in cloud fraction and cloud top height on the vertical ozone column density retrieved from GOME are discussed.

1. Introduction

The measurement of geophysical quantities by means of satellite remote sensing of backscattered shortwave solar radiation is often complicated due to the presence of clouds. For example, surface albedo or aerosol optical thickness retrieval is already nearly impossible when the observed scene is only slightly cloudy. Retrieval of the ozone column is less sensitive to the presence of clouds, but to achieve an accuracy of the ozone column within a few percent, cloud detection and correction for cloud effects is required.

With ozone column retrieval from the Total Ozone

Mapping Spectrometer (TOMS) measurements, knowl- edge has been gained on the correction of effects of clouds in ozone data. TOMS measures the terrestrial

radiance in six channels and uses radiance ratios of

a triplet of wavelengths to derive the ozone vertical

Copyright 1999 by the American Geophysical Union.

Paper number 19999JD900279. 0148-0227/99/1999JD900279509.00

column density [McPeters et al., 1996, and references therein]. As part of the TOMS ozone algorithm, an effective cloud fraction is derived from the reflectivity measurements at 380 nm, assuming a cloud albedo of 0.8. In TOMS version 6, a gimple empirical relation was used to prescribe cloud top pressure as a function of latitude only. However, Thompson et al. [1993] and Hudson et al. [1995] found that TOMS ozone columns were systematically too high over areas with persis- tent marine stratus clouds in the subtropics, because of underestimation of the cloud top pressure. There- fore with the release of TOMS version 7, cloud top heights are taken from the International Satellite Cloud Climatology Project (ISCCP) database [Schiffer and Rossow, 1983; Rossow and Gatder, 1993], and this error is largely reduced [Hsu et al., 1997].

Difficulties with clouds may be expected with ozone data from the Global Ozone Monitoring Experiment (GOME) as well, although the GOME and TOMS in- struments and their ozone retrieval approaches are com- pletely different. GOME is a spectrometer measuring from 240 to 790 nm, which resolves the ozone absorp-

18,801

18,802 KOELEMEIJER AND STAMMES: VALIDATION OF GOME CLOUD FRACTIONS

tion structures of the Huggins bands between 325 and 335 nm. In the operational GOME data processor [Deutsches Zentrum fiir Luft- und Raumfahrt (DLR), 1994] the ozone column is derived using differential op- tical absorption spectroscopy, which is a novel approach to derive the ozone column from satellite ultraviolet ra-

diance measurements. This technique has been used in the past to interpret ground-based direct-Sun and zenith-sky measurements [e.g., Noxon, 1975; Platt et al., 1979; Platt, 1994]. Using differential optical absorp- tion spectroscopy, the ozone slant column density can be derived, which is the number of ozone molecules which sunlight has passed on its path through the atmosphere before it reaches the satellite. The slant column density, as well as the vertical column density, are expressed in molecules/cm 2, or Dobson units (DU). To obtain the ozone vertical column density from the slant column density, the latter must be divided by the appropriate air mass factor. The air mass factor is the ratio of the

average optical path length of photons scattered in the atmosphere and detected by the satellite instrument, to the vertical path through the atmosphere. Thus the air mass factor increases when the average path length of light propagating through the atmosphere increases and is influenced by clouds.

Because of the large pixel size of GOME (320 x 80 km 2, default value), the chance is large that the ground scene observed by GOME is partly covered by clouds. Therefore, as part of the GOME ozone column retrieval, an effective cloud fraction is derived for each GOME

ground pixel using the Initial Cloud Fitting Algorithm (ICFA). Like the TOMS algorithm, cloud top height is assumed to be known a priori and is taken from the ISCCP database.

Recent GOME ozone validation results were pre- sented by Lambert et al. [1997, 1999], who validated the GOME ozone columns by comparison with ground- based ozone measurements from a global network of zenith sky ultraviolet-visible spectrometers, as well as Dobson and Brewer spectrophotometers. They found that for solar zenith angles smaller than 70 ø , the mean difference between the ground-based measurements and GOME ozone values is within 4- 4%; for solar zenith an- gles larger than 70 ø, GOME underestimates the ozone columns by 8% on average. The difference between GOME and ground-based measurements depends on the ozone column, GOME values being smaller than the ground-based for large ozone columns, but being larger than the ground-based measurements during ozone hole conditions. There is evidence that the scatter between

the ground-based measurements and GOME measure- ments increases with increasing cloudiness. Compari- son of GOME and TOMS ozone values show that the

GOME ozone columns are about 5% lower than TOMS

on average (J. Gleason, private communication, 1998), with a small seasonal variation. The reason for this

difference is still under investigation.

The validation of the ICFA version 2.3 presented in this .paper is necessary to contribute to an error analysis of the GOME ozone retrievals and may be useful for retrievals of other trace gases, such as NO2 [Burrows et al., 1997], and other products which rely on ICFA cloud fraction as well.

In section 2 the GOME instrument and the initial

cloud fitting algorithm are treated. Section 3 describes the global monthly average performance of ICFA as compared to ISCCP. Section 4 contains a more detailed, but regional, comparison for individual pixels between ICFA results and cloud fractions derived from Along Track Scanning Radiometer-2 (ATSR-2) data. In sec- tion 5, differences between the monthly average and in- dividual pixel results are discussed. Consequences of er- rors in cloud parameters on the retrieved GOME ozone vertical column density are discussed in section 6. Sum- mary and conclusions are given in section 7.

2. Description of GOME and ICFA 2.1. GOME Instrument

In this subsection a brief description of the ERS- 2/GOME instrument is given. The ERS-2 has been put into a polar, sun-synchronous orbit at an altitude of 780 km, with a local crossing time at the equator of 1030 local solar time for the descending node [European Space Agency, 1995]. The velocity is about 6.7 km/s with respect to the ground, corresponding to an orbital period of about 100 min.

Radiation enters GOME via a scanning mirror, which scans across track with a maximal scanning angle of 31 ø with respect to the nadir position. The corresponding swath width is 960 km. During each forward scan of 4.5 s, GOME scans from east to west and integrates 3 times 1.5 s, leading to three ground pixels, called "East," "Nadir," and "West." After the forward scan, the motion of the scan mirror is reversed for 1.5 s, and the "Backscan" ground pixel is measured. The reverse scan is 3 times faster than the forward scan, and there- fore the Backscan ground pixel is 3 times larger than the East, Nadir, and West ground pixels. The default ground pixel size of GOME is 40 x 320 km 2 (along x across track) for the East, Nadir, and West pixels, and 40 x 960 km 2 for the Backscan pixel.

The terrestrial radiance and solar irradiance are mea-

sured contiguously between 240 and 790 nm with a spec- tral resolution of about 0.2 nm in the ultraviolet, and about 0.4 nm in the near infrared. The spectral sam- pling is about twice as high as the spectral resolution. The spectral resolution of GOME is not sufficient to re- solve all individual spectral lines, such as the Fraunhofer absorption lines in the solar spectrum, and individual absorption lines of H20 and O2 in the Earth's spec- trum. The Earth's radiance I at a certain wavelength A• is convoluted with the GOME slitfunction f to yield the measured radiance

KOELEMEIJER AND STAMMES' VALIDATION OF GOME CLOUD FRACTIONS 18,803

i(x) - f(x,_ x) dx',

and a similar equation holds for the measured solar irra- diance perpendicular to the beam, 7rP0. In simulations of I(A) the integration extends from with •A sufficiently large to yield convergence. The integral over the slitfunction is normalized according to

- - (2) The Earth's reflectivity at the top of the atmosphere Rme• is obtained from

_

•meas(X) -- Po() ' (3) in which •o is the cosine of the solar zenith angle 0o.

2.2. Description of ICFA

2.2.1. Introduction. In this subsection we will

briefly describe the initial cloud fitting algorithm. More information is given by DLR [1994]. Cloud information is obtained from spectral fitting of a simulated spectrum of a (partly) cloudy scene to the spectrum measured by GO ME in the wavelength region 758-778 nm, which encloses the O2 A band. The reflectivity outside the 02 A band depends mainly on cloud optical thickness, cloud fraction, and surface albedo, whereas the depth and shape of the band are sensitive to cloud top height (or pressure) as well [Fischer and Grassl, 1991; Kuze and Chance, 1994]. As an example to demonstrate the influence of clouds on the depth of the 02 A band, a clear and a cloudy spectrum of the 02 A band as mea- sured by GOME are shown in Figure 1, normalized to unity at A=758.5 nm. The refiectivities at 758.5 nm

1.0

0.8

0.6

0.4

O.g

0.0

oudy pixel t !t fi;i[ i' ...... clear pixel

!\ /! TM, r' t/ /! '.. •.,

•,\ .I '1

?55 760 765 770 775 780

wavelength (rim)

Figure 1. Spectra of the 02 A band, normalized to unity at A=758.5 nm. Data acquired on July 23, 1995, at 1156:29 UT (cloudy pixel) and 1158:23 UT (clear pixel). Location: Atlantic Ocean, off the coast of Mo- rocco.

(in the continuum) are 0.043 and 0.56 for the clear and cloudy spectrum, respectively. Clearly, the 02 A band is less deep for the cloudy case, due to the screening of 02 below the cloud.

A prototype of ICFA has been described by Kuze and Chance [1994], in which they performed a sensitiv- ity study demonstrating that, in principle, simultaneous retrieval of cloud top pressure and cloud fraction is fea- sible from the GOME reflectivity measurements in and around the 02 A band. However, they also showed that there is a trade-off between the determination of cloud

top pressure and cloud fraction, since for low clouds with high cloud fraction the depth of the 02 A band is similar to that for high clouds with low cloud frac- tion. Therefore in the implementation of ICFA in the operational GOME data processor [DLR, 1994] an (ef- fective) cloud fraction is derived, but cloud top pressure is assumed to be known a priori and is taken from the ISCCP database.

Also, Kuze and Chance [1994] showed that ambigu- ity remains between cloud fraction and cloud top re- flectivity; only their product is determined, which can be converted to an effective cloud fraction, assuming a fixed cloud top reflectivity a priori. In ICFA, cloud top reflectivity is put equal to that of a cloud with an optical thickness of 20, corresponding to a moderately thick cloud. Consequently, the cloud fractions derived by ICFA should be interpreted as effective cloud, frac- tions for clouds with an optical thickness of 20. The a priori chosen cloud optical thickness, or cloud top reflec- tivity, should not be too small, to avoid conflicts with another assumption made in the operational GOME data processor, namely, that ozone below the cloud can- not be detected, and the ozone below the cloud should be added to the derived ozone vertical column density to yield the total ozone vertical column density of the cloudy atmosphere.

We note further that although cloud fraction is a property of the cloud itself and independent of wave- length, the effective cloud fraction is slightly wavelength dependent, since the cloud optical thickness is wave- length dependent. The cloud optical thickness varies by a few percent over the large spectral domain covered by GO ME. This is not taken into account in the GO ME data processor at present.

2.2.2. Retrieval method. The operational re- trieval method to derive cloud fraction is based on

least squares minimization of the difference between the spectral reflectivity measured by GOME and the simu- lated spectral reflectivity in the wavelength region 758- 778 nm [DLt•, 1994]. To simulate the reflectivity of a GOME pixel, some assumptions are made. Each pixel is assumed to consist of a clear part and a cloudy part, their sizes being determined by the (effective) cloud fraction. The reflectivity of a partly cloudy scene is ob- tained by summation of the contributions of the clear and cloudy parts. The atmosphere consists of multiple

18,804 KOELEMEIJER AND STAMMES: VALIDATION OF GOME CLOUD FRACTIONS

layers above a Lambertian ground surface. To calculate the atmospheric transmittance due to oxygen absorp- tion, the atmosphere is assumed to behave as a purely absorbing medium. Thus Rayleigh and aerosol scatter- ing is neglected, and the light is scattered only once at the surface or at the cloud top. Reflection by clouds is approximated by reflection by an elevated bright sur- face; thus scattering inside the cloud is neglected. To correct for the neglection of scattering by molecules and aerosols, a closure term is added to the simulated reflec- tivity. With these approximations, the reflectivity of a partly cloudy scene at the top of the atmosphere, Rsim, can be written as

l•sim(} ) : C l•cloud(} ) -•- (1 - c) Rsurf(X) + Rclosure(/•), (4)

in which c is the cloud fraction, Rsurf is the reflectivity of the clear part of the pixel, Rcioud is the reflectivity of the cloudy part, and Rclosure is the closure term.

Let us define the convoluted atmospheric transmit- tance T(X, p) along the slant path s from the Sun to a certain level in the atmosphere at pressure p and back to the satellite by

T(•,p) - f(•- •) exp [-sr(•,p)] d• •. (5)

Here r(A •, p) is the vertical absorption optical thickness of oxygen above that level at pressure p. The expo- nent describes the attenuation of light along the slant atmospheric path s. If the plane-parallel assumption applies, the slant path is calculated as s = 1/•0 + 1/• (• is the cosine of the viewing zenith angle). For large solar zenith angles, a spherical correction is applied to calculate the slant path. With this definition of T(A, p) and the assumptions mentioned above, we can write Rcloud and Rsurf as

RciouO(A) - Ac T(A, pc), (6)

•surf(•)---- As T(A, ps), (7)

where ps is the surface pressure, and pc is the cloud top pressure. In (6) and (7), Ac and As are the cloud top reflectivity and surface albedo, respectively, and are assumed to be independent of wavelength within the spectral interval 758-778 nm. Furthermore, in (6) and (7), it is assumed that the solar irradiance does not vary within each wavelength interval over which the convo- lution integral is performed.

The closure term in (4) is a small correction term and is assumed to be linear in wavelength, which seems to be appropriate for such a narrow wavelength interval of 20 nm. Then, Rclosure can be written as

l•closure(• ) : Q' (1 - A/A0). (8)

Here ? is a free parameter which is determined dur- ing the fitting procedure; thus any solar zenith angle dependence of 7 is implicitly taken into account. The

parameter A0 indicates some reference wavelength. Us- ing (4)-(8), Rsim can be written as

P•im(X) = O• T(A, pc) + • T(A, ps) + ? (1 - A/A0), (9)

which is linear in the fitting parameters a, •, and ?. In ICFA, a linear least squares fitting is performed by variation of a, •, and ?, and minimizing

X 2- 1 k [•meas(•i)--•sim(•i)] 2 •i=l •meas (•i) . (10) The summation is over all measurement points in the wavelength interval between 758 and 778 nm, which comprises N=120 wavelengths. The cloud fraction is then obtained from the fitting parameter a:

C-Ac , (11) assuming Ac to be equal to the reflectivity of a cloud with an optical thickness of 20 at 760 nm, while the surface albedo can be determined from •:

A• = 1-c' (12) The cloud fraction is used as input for the ozone column retrieval algorithm and is supplied in the GOME level 2 data product. The fitting results for/• and ? are not used.

3. Monthly Average ICFA Validation Using ISCCP

3.1. Comparison Method

To investigate the global performance of ICFA, a sta- tistical approach is followed, by analyzing monthly av- erage cloud fractions of July 1995 and January 1998. At the time of writing, these months were the only July and January months which were processed with the most re- cent version of the GOME data processor, version 2.3. All ICFA data of each month have been binned into grid cells of 2.5 ø x 2.5 ø. For each grid cell, the monthly mean value of the cloud fraction is calculated. The monthly average ICFA cloud fractions have been compared to ISCCP monthly average cloud fractions, averaged over the period 1984-1990 (7 years).

When comparing ICFA and ISCCP monthly average cloud fractions, caution should be exercised, because the ICFA effective cloud fractions are different from the

ISCCP cloud fractions. As is pointed out in subsec- tion 2.2, the ICFA cloud fraction corresponds to an ef- fective cloud fraction, assuming a cloud optical thick- ness of 20, corresponding to a moderately thick cloud. In ISCCP, cloud fraction is derived from radiation mea- surements by visible and thermal infrared imaging in- struments on board meteorological geostationary and polar orbiting satellites [Rossow and Gatder, 1993]. A thresholding algorithm is applied to these radiation

KOELEMEIJER AND STAMMES: VALIDATION OF GOME CLOUD FRACTIONS 18,805

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Figure 2. (a) Global map of ICFA monthly average effective cloud Ëactions for July 1995. (b) Global map of ISCCP monthly average cloud fractions for July (average 1984-1990).

measurements, separating all measured pixels in clear and cloudy pixels. The cloud detection thresholds for ISCCP are typically such that clouds with an optical thickness <•1 can be detected already. The monthly av- erage cloud fraction (for each grid cell) is then defined as the number of cloudy pixels per month divided by the total number of pixels per month. Therefore, in general, ISCCP cloud fractions are expected to be higher than the ICFA cloud fractions. However, this comparison is important to investigate whether the main global cloud structures are detected by ICFA.

ISCCP data of July 1995 and January 1998 are not yet available; therefore we compare with monthly aver- age ISCCP/C2 data for January and July, averaged over the period 1984-1990. Although the ICFA and ISCCP data are from different years, the interannual variabil- ity in monthly average cloud fraction is generally small enough to allow a meaningful comparison. Also, all ICFA data are obtained around 1030 local solar time, whereas the ISCCP data are obtained at many different local solar times, and represent 24 hour average values. The diurnal cycle in monthly average cloud fraction on

18,806 KOELEMEIJER AND STAMMES: VALIDATION OF GOME CLOUD FRACTIONS

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•::•..-'jiii?:.-.:j...-j.f'"'"'"'"' '•"•"'"'""••'""•:•••!•.""'""'" '"'"•:'• "•-- .-:.•::'m•-•'"":i•'<"'""-'-'"'•• ß "'""'"•'-':•-. ----"'•••'•:.:":".:.'•::•: ........ ':'" ....... ' ..... '" ..................... ".";•::::•i"..--; •:..-'•i•ff"" "'""---'-'- .' '•-.....'•':57: ............... ' ........ :.• ......... '"'•' '•'",-- -"-- --'::•....;•..:jii!"•.':•' - •:.•:..•i•: -...

%3"::':':. ß

: ..'i::-' "::'.:":':i::.i.. :-:.':i: ....... 0.0 .....................

Figure 3. (a) Global map of ICFA monthly average effective cloud fractions for January 1998. (b) Global map of ISCCP monthly average cloud fractions for January (average 1984-1990).

these scales is small however [Rossow et al., 1993], and does not prohibit a meaningfid comparison either.

From a histogram analysis of the ICFA data, we found that ICFA cloud fractions which were obtained when

00 > 75 ø, that is, near the polar regions, are often unre- alistically high (equal to unity). This may indicate that the model assumptions in ICFA are too coarse, such as the decoupling between scattering and absorption, and the representation of molecular and aerosol scattering by a closure term. The retrievals with 00 > 75 ø were

considered to be unreliable and were excluded from the

calculation of monthly average values. The ICFA data shown here are averages of all ground pixel types (East, Nadir, West, and Backscan). No significant differences in ICFA results for the different pixel types were found.

3.2. Global Monthly Average Comparison

A grey scale map of ICFA monthly average cloud frac- tions for July 1995 is shown in Figure 2a. For some parts of the Earth, ICFA data are not available due

KOELEMEIJER AND STAMMES: VALIDATION OF GOME CLOUD FRACTIONS 18,807

to polar night conditions, or due to ERS-2 data trans- mission which takes place in a part of the orbit over the Himalayas. For comparison, ISCCP average cloud fractions for July over the period 1984-1990 are shown in Figure 2b. From comparison of Figures 2a and 2b it can be observed that the main global cloud struc- tures, which can easily be recognized in ISCCP data, can be distinguished in the ICFA data as well. For ex- ample, ICFA gives high cloud fractions in the intertrop- ical convergence zone around 10øN, at the Southern Hemisphere south of 30 ø S, and in the Northern At- lantic and Northern Pacific Ocean. Low cloud fractions

are observed over subsidence areas between 0 ø - 30 ø S

and 10 ø- 40 ø N. Figures 3a and 3b show a similar com- parison, but now for January. Although for the midlat- itudes the correlation between Figures 3a and 3b is rea- sonable, a striking dissimilarity forms the area around the equatorial Pacific Ocean. In January 1998, how- ever, the global circulation in this area was far from the mean climatological situation, due to an exceptionally strong E1 Nifio. Under E1 Nifio conditions, the sea sur- face temperature is rather uniform over this area, and both the Intertropical Convergence Zone and South Pa- cific Convergence Zone move toward the equator and merge. Also, the location of the convergence zone shifts eastward from its normal position in the western part of the Pacific [Philander, 1990]. The observed ICFA cloud patterns in the equatorial Pacific correlate well with monthly average maps of cloud liquid water con- tent in January 1998 derived from SSM/I data [Weng and Grody, 1994], and reported by the Climate Pre- diction Center [1998]. Also, cloud fractions over ocean derived from Earth Probe TOMS reflectivity data in January 1998 showed reasonable correlation with ICFA cloud patterns. Especially those features not present in the ISCCP data set, and which were specific for January 1998, were captured by the TOMS data, and confirmed the ICFA results.

1.0 '

0.4

0.2

-80 -60 -40 -20 0 20 40 60 80

latitude (degrees)

Figure 4. Zonal average cloud fraction in July from ISCCP (period 1984-1990) and zonal average effective cloud fraction from ICFA (July 1995).

1.0 i • i i i i i i i

ISCCP

• 0.8 • 0.6 • -

z 0.4

0.2

-80 -60 -40 -20 0 20 40 60 80

latitude (degrees)

Figure 5. Zonal average cloud fraction in January from ISCCP (period 1984-1990) and zonal average effective cloud fraction from ICFA (January 1998).

3.3. Zonal Averages

Figures 4 and 5 show the zonal average cloud fraction from ISCCP and ICFA for July and January, respec- tively. The curves correlate well, except for latitudes north of 60øN in July. In Figure 5 it can be observed that between 15øS and 10øN two peaks are present in the ISCCP data, whereas in the ICFA data only one peak is present, which is located south of the equator due to E1 Nifio. It is clear that the ICFA cloud fractions

are in general much lower than the ISCCP cloud frac- tions. The global average cloud fraction of ISCCP is 0.62 in July and 0.63 in January, whereas for ICFA it is 0.23 in July and 0.25 in January for ICFA, as is shown in Table 1. This difference in absolute value is not sur-

prising, since ICFA should be interpreted as an effective cloud fraction for clouds with an optical thickness of 20 (at 760 nm), whereas the ISCCP cloud fraction is irre- spective of cloud optical thickness.

Table I also shows ICFA and ISCCP cloud frac-

tions averaged over different surface types, as well as the ratio ICFA/ISCCP. Both in the ISCCP and ICFA data, the highest cloud amounts are over the ocean, fol- lowed by vegetated land, snow, and desert. The ratio ICFA/ISCCP increases with increasing surface albedo generally. This indicates that ICFA cloud fractions are relatively higher over areas with high surface albedo.

4. Validation of ICFA for Individual

Pixels Using ATSR-2 4.1. Retrieval of Effective Cloud Fraction From ATSR-2 Data

The Along Track Scanning Radiometer-2 (ATSR-2) is an imaging radiometer which is, like GOME, on board the ERS-2 platform. The ATSR-2 has seven channels with effective wavelengths of 0.56, 0.66, 0.87, 1.6, 3.7, 11, and 12 •m [Bailey, 1995; Koelemeijer et al., 1998].

18,808 KOELEMEIJER AND STAMMES: VALIDATION OF GOME CLOUD FRACTIONS

Table 1. Monthly Average Cloud Fractions From ISCCP and ICFA: Global Average and Averages Over Various Surface Types

July Cloud Fractions January Cloud Fractions

Surface Type Albedo (755 nm) ISCCP ICFA ICFA/ISCCP ISCCP ICFA ICFA/ISCCP

global 0.62 0.23 0.38 0.63 0.25 0.40 ocean 0.02 0.68 0.25 0.36 0.70 0.28 0.40

vegetation 0.25 0.51 0.20 0.40 0.49 0.19 0.38 desert 0.50 0.26 0.14 0.54 0.25 0.12 0.49 snow 0.80 0.29 0.18 0.62 0.33 0.17 0.50

The ISCCP data are averaged over the period 1984-1990. The ICFA version 2.3 data are from July 1995 and January 1998. The ICFA cloud fractions are effective cloud fractions (see text). Column 2 shows (approximate) surface albedo values at 755 nm.

The pixel size is about I x I km 2 subsatellite. Because GONE and ATSR-2 are on the same platform, their data can be collocated accurately both in space and in time. These features of ATSR-2 make the instrument

well suitable for a detailed validation of GONE cloud

products. The operational ATSR-2 cloud flagging product is de-

veloped for sea surface temperature retrieval and can- not be used over land. Noreover, the cloud fraction obtained from counting the number of cloudy pixels would give a cloud fraction irrespective of cloud optical thickness. Therefore we have developed an alternative algorithm to derive cloud fraction from ATSR-2 data, which can be used both over land and sea, and which supplies an effective optical thickness for clouds with an optical thickness of 20, comparable to ICFA. This algorithm consists of two steps. First, cloud detection is applied to the ATSR-2 data. The cloud detection algorithm is based on the APOLLO algorithm, which was developed for cloud detection in advanced very high resolution radiometer images [Olesen and Grassl, 1985; Saunders and Kriebel, 1988]. A sequence of cloud de- tection tests is applied to all pixels in the ATSR-2 im- age. Pixels are flagged cloudy if the reflectivity at 0.66 •um, color ratio (0.87•0.66 •um), brightness temperature at 11 •m, or brightness temperature difference (11-12 •m) exceed a specified threshold. The thresholds are determined from the data itself by histogram analysis. Different thresholds are used over land and sea. The

cloud detection step yields a classification of all pixels into clear and (partly) cloudy pixels. The second step is to calculate cloud fraction c for each cloudy ATSR-2 pixel. Here c is determined from the reflectivity mea- surements at 0.66 •m using

Rmeas - Rclear

C -- Rcloud -- Rclear ' (13) where Rmeas is the measured reflectivity, and Rclear and Rcloud are refiectivities for a clear and fully cloudy at- mosphere, calculated with a doubling-adding radiative transfer model [De Haan et al., 1987; Stareroes, 1994].

In (13) we assume that each pixel consists of a clear and a cloudy part and that their refiectivities may be added, weighted with the cloud fraction, to yield the reflectivity of a partly cloudy pixel. To calculate Rclear, Rayleigh scattering and ozone absorption were assumed accord- ing to the midlatitude summer atmosphere [Anderson et al., 1986]. No aerosol was included. The surface albedo at 660 nm was chosen 0.05 over land and 0.02 over sea.

To calculate Rcloud, the C1 cloud model of Deirmend- jian [1969, p. 82] was assumed, corresponding to liquid water droplets with a two-parameter gamma size distri- bution with an effective particle radius of 6 •m. Con- sistent with the assumption in ICFA, the cloud optical thickness was chosen equal to 20. The cloud particles were inserted between 3 and 4 km height. We note, however, that at 0.66 •m, the reflectivity of a cloudy atmosphere is almost insensitive to the assumed cloud top height. After the calculation of the cloud fraction for each ATSR-2 pixel using (13), the ATSR-2 pixels collocated with a GONE pixel are selected, and the av- erage cloud fraction is calculated for each GOME pixel. This quantity is an effective cloud fraction which should be similar to the ICFA cloud fractions.

4.2. Comparison of ICFA and ATSR-2 Cloud Fractions

The data presented here were acquired on July 23, 1995, over western Europe and are obtained during ERS-2 orbits 1336 and 1337 (Nadir pixels only). Or- bit 1336 is over northern Scandinavia, the Baltic Sea, and central Europe; orbit 1337 is over the North Sea, part of Ireland, and the Atlantic Ocean. The data were obtained during the GONE validation period and have a pixel size of 40 x 80 km 2.

Figure 6 shows the effective cloud fractions from ICFA and ATSR-2 for both orbits. Although some cor- relation between the curves is present, large differences between the cloud fractions exist over extended parts of the orbits, especially for orbit 1337. Cloud fraction differences as large as 0.5-0.7 occur. The mean and standard deviation of the difference between the ATSR-

KOELEMEIJER AND STAMMES: VALIDATION OF GOME CLOUD FRACTIONS 18,809

1.0 (A) .-

0.8

i! ... ATSR 0.6 !! ....... ICFA

0.4, ,,

• .

0.0i

40 45 50 55 60 65 70 75

latitude (degrees)

1.0

0.8

0.6

0.4

0.a

o.o•-

45

....... .

v'

//%,./•'•._ ATSR

...... ICFA

50 55 60 65 70 75 80

latitude (degrees)

Figure 6. Effective cloud fractions from ATSR-2 and ICFA as a function of latitude. Data acquired on July 23, 1995. (a) Data from ERS-2 orbit 1336, acquired between 1006 and 1015 UT; (b) data from ERS-2 orbit 1337, acquired between 1146 and 1154 UT.

2 and ICFA cloud fractions (ATSR-ICFA) are given in Table 2, as well as the linear correlation coefficient be- tween the ICFA and ATSR-2 cloud fractions. The av-

erage difference in cloud fraction between ATSR-2 and ICFA is 0.18, and the standard deviation of the differ- ence is 0.23.

To investigate the cause of this difference in detail, we made a local implementation of the ICFA scheme at the Royal Netherlands Meteorological Institute. With

Table 2. Comparison Between Effective Cloud Frac- tions From ATSR-2 and ICFA, and ATSR-2 and Mod- ified ICFA, for ERS-2 Orbits 1336 and 1337

ATSR-ICFA 0.18 0.23 0.75 ATSR-Modified ICFA -0.04 0.09 0.97

Variables are i, mean difference; (r, standard deviation of difference; r, linear correlation coefficient.

this local implementation we were able to reproduce the operational ICFA results. However, not only cloud frac- tion results were stored, but also the surface albedo val- ues obtained from the fits (compare (12); unfortunately, these values are not supplied in the GOME level 2 prod- uct). Figure 7 shows the difference between ATSR-2 and ICFA cloud fractions for both orbits, as well as the fitted surface albedo. The fitted surface albedo corre-

lates well with the difference in cloud fraction between

ATSR-2 and ICFA. Apparently, if the ICFA cloud frac- tion is too low compared to ATSR-2, the fitted surface albedo values are erroneously high. This suggests that for these cases, part of the measured radiance is in- correctly assigned by ICFA to the surface instead of the cloud; this may happen when the actual cloud top pres- sure is higher than the climatological cloud top pressure.

To verify the explanation of the difference between the cloud fractions of ATSR-2 and ICFA, we performed an experiment with our ICFA version, in which we added 150 mbar to the cloud top pressures in orbit 1337. In this experiment we found an improved agreement be- tween the ICFA and ATSR-2 cloud fractions (not shown

(A) 0.8

0.6

0.4

0.2

0.0

-0.4

• ___ fitted surface albedo

i '"'t .... difference ATSR-ICFA

40 45 50 55 60 65 70 75

latitude (degrees)

1.0

0.8'd

0.6 ©

o.4'E

0.2•

0.0"'-"

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

(13) fitted surface albedo

.... difference ATSR-ICFA

!./

-0.4 ............................. '

45 50 55 60 65 70 75 80

latitude (degrees)

1.0

o

0.8•

0.6 ©

0.4 •

0.2x•

0.0 '•

Figure 7. Difference between the effective cloud frac- tions from ATSR-2 and ICFA, as well as the surface albedo fitted by ICFA, as a function of latitude for ERS- 2 orbits (a) 1336 and (b) 1337.

18,810 KOELEMEIJER AND STAMMES: VALIDATION OF GOME CLOUD FRACTIONS

here). This indicates that in the operational ICFA ver- sion errors in the assumed cloud top pressure lead to errors in the derived cloud fraction and surface albedo

values.

4.3. Modified ICFA

Regarding the effect of errors in the assumed cloud top pressure on the cloud fraction derived by ICFA, we may distinguish three possible cases: (1) actual cloud top pressure is higher than climatological cloud top pressure; (2) actual cloud top pressure is equal to climatological cloud top pressure; (3) actual cloud top pressure is lower than climatological cloud top pressure. In case (1), ICFA misinterprets part of the measured ra- diance as being reflected by the surface rather than by the cloud. As mentioned above, this leads to an over- estimation of surface albedo, and an underestimation of cloud fraction. In case (2) the fits of ICFA are ex- pected to be good, thus yielding correct cloud fractions and surface albedos. In case (3) the opposite of case (1) will occur, thus overestimation of cloud fraction and underestimation of surface albedo. However, if the real surface albedo is close to zero, such as over the ocean, the surface albedo cannot be underestimated much, and thus the cloud fraction cannot be overestimated much.

Thus, whereas in cases (2) and (3) the cloud fractions derived by ICFA are expected to be reasonably good, large errors may occur in case (1).

To overcome this problem, we changed the interpreta- tion of the fitting parameter fl in our local ICFA version. Thereto we fixed the surface albedo to As=0.02 over ocean and As=0.25 over land for the spectral fitting interval 758-778 nm, and we derived a cloud fraction for low-altitude clouds, Clow, using a modified version of (12):

• - ClowAc q- (1 - c - Clow)As, (14)

1.0

0.8

0.6

O.4

0.2

!i ..... ATSR

•! modified ICFA

0.0

40 45 50 55 60 65 70 75

latitude (degrees)

1.0

0.8

0.6

0.4

0.2

(B) /• ;.

rl

ATSR

....... modified ICFA- , , , , , I , , , I .... I • • • i I • , , , I , , , , I

45 50 55 60 65 70 75 80

latitude (degrees)

Figure 8. Effective cloud fractions derived with the modified ICFA version (fixed surface albedo) and those derived from ATSR-2 data, as a function of latitude for ERS-2 orbits (a) 1336 and (b) 1337.

where As is the fixed surface albedo. In this equation the "excess reflectance," which is erroneously assigned to the surface in the operational ICFA version, is now only partly assigned to the surface, and partly to clouds close to the surface. The cloud fraction of our modified

ICFA version is obtained by adding the cloud fractions of the operational ICFA version c calculated by (11) and Clow calculated by (14).

In Figure 8 the cloud fractions from our modified ICFA version and those from ATSR-2 are shown for

both orbits. Clearly, the correlation between the modi- fied ICFA version and ATSR-2 is much better than the

correlation between the operational ICFA version and ATSR-2. As shown in Table 2, the mean and standard deviation of the difference are reduced considerably us- ing the modified ICFA version. We may conclude that the modified ICFA version performs better than the op- erational ICFA version, and that the cloud fractions from the modified ICFA version are less sensitive to the

assumed cloud top height.

5. Comparison of Monthly Average and Individual Pixel Validations

As shown in section 3, the global monthly average July 1995 cloud fractions of ICFA correlate reasonably well with global monthly average ISCCP/C2 cloud frac- tions for July, averaged over the 1984-1990 period. The correlation was worse for global monthly average ICFA cloud fractions for January 1998 (compared with global monthly average ISCCP/C2 cloud fractions for January, averaged over the 1984-1990 period), probably due to the E1 Nifio conditions in January 1998.

From the previous section, however, it appeared that for individual pixels large differences in effective cloud fraction between ICFA and ATSR-2 occur. The errors in the derived cloud fraction and surface albedo in the operational ICFA are probably due to errors in the as- sumed cloud top pressure, as shown in subsections 4.2 and 4.3. We also found that if the real surface albedo is close to zero, such as over ocean, the surface albedo

KOELEMEIJER AND STAMMES: VALIDATION OF GOME CLOUD FRACTIONS 18,811

cannot be underestimated much, and thus the cloud fraction cannot be overestimated much. On the other

hand, the surface albedo over ocean can be overesti- mated considerably, and thus considerable underesti- mations of the cloud fraction may occur. Therefore, we might expect that over ocean ICFA cloud fractions are biased towards lower values. Over more strongly reflect- ing surfaces, such as vegetated land and desert, both underestimation and overestimation of cloud fractions

are possible, and may cancel in monthly average values. Over highly reflecting snow surfaces, underestimation of the surface albedo is more likely than overestimation, and therefore, the ICFA cloud fractions may be biased towards higher values. This may explain the relatively low cloud amounts detected by ICFA as compared to ISCCP over surface types with low albedos, and the in- crease of the ratio ICFA/ISCCP with increasing surface albedo (compare Table 1).

Given the fact that large differences exist between cloud fractions from ICFA and ATSR-2 for individual

pixels, it is surprising that the monthly average maps of ICFA compare reasonably well with ISCCP. This is probably due to the fact that the error in the assumed cloud top pressure averaged over I month is small in ICFA, since the cloud top pressure in ICFA is set equal to the climatological value from the ISCCP database. Therefore the monthly average errors in ICFA are small compared to errors in ICFA results for individual pixel retrievals.

6. Consequences for the GOME Ozone Column

In this section we will investigate how errors in the GOME cloud fraction and cloud top pressure influence the retrieved ozone column. To that end, we use ozone slant column density values of the two orbits 1336 and 1337, which were also considered in section 4, and re- trieve the vertical column density from the slant column density using a similar approach as in the operational GOME data processor. The slant column values are provided in the GOME level-2 data product. The re- trieved vertical colmnn depends on the applied cloud fraction and cloud top pressure. In the operational GOME data processor, the cloud fraction is derived us- ing ICFA, and cloud top pressure is taken from ISCCP. Here we derive the vertical column for different cloud

scenarios, using cloud fraction from ATSR-2 instead of ICFA and using cloud top pressure varied with respect to the ISCCP value.

We make use of results of the sensitivity study de- scribed by Koelemeijer and Stareroes [1999], hereinafter referred to as KS, in which the influence of errors in cloud parameters on the GOME ozone retrieval is in- vestigated. According to that paper, clouds influence the retrieved GOME ozone column in two ways: (1) Clouds generally enhance the reflectivity compared to

clear sky, which leads to a decrease of the effective scat- tering altitude at ultraviolet wavelengths. Therefore, in general, the air mass factor is enhanced because of the presence of clouds. This is referred to as the "reflec- tion effect." (2) Clouds screen part of the tropospheric ozone column. Therefore a "ghost" vertical column is added to the retrieved ozone vertical column to correct

for the ozone below the cloud. This is referred to as

the "ghost column effect." Concerning errors in cloud fraction, keeping cloud top pressure fixed, the reflection effect and ghost column effect work in opposite direc- tion. For low and midlevel clouds, the reflection effect dominates, and an overestimation (underestimation) of the cloud fraction gives an underestimation (overesti- mation) of the retrieved ozone column. For high clouds the ghost column effect dominates; then an overesti- mation (underestimation) of the cloud fraction gives an overestimation (underestimation) of the retrieved ozone column. Concerning errors in cloud top pressure, keep- ing cloud fraction fixed, the reflection effect and ghost column effect work in the same direction. Then an

overestimation (underestimation) of the cloud top pres- sure gives an underestimation (overestimation) of the retrieved ozone column. In KS the cloud albedo is var-

ied between 0.4, 0.6, and 0.8. Here we use a cloud albedo of 0.6, which is close to the cloud albedo used in the operational GOME data processor.

In KS a database of air mass factors and ghost col- umn values is presented for various cloud fractions and cloud top heights. This database is employed here to re- trieve the vertical column density from the slant column density using three different cloud scenarios: (1) cloud fraction from ATSR-2, and cloud top pressure from IS- CCP; (2) cloud fraction from ATSR-2, and cloud top pressure from ISCCP, but 200 mbar added to this; (3) cloud fraction from ATSR-2, and cloud top pressure from ISCCP, but 200 mbar subtracted from this. For comparison we note that the standard deviation of cloud top pressures reported in the ISCCP database is typi- cally 100-150 mbar. Figure 9 shows for each cloud sce- nario the difference of the retrieved ozone column with

the standard ozone column, which is obtained using the cloud fraction from ICFA and cloud top pressure from ISCCP. From comparison of Figures 9 (scenario (1)) and 7, it can be observed that where the ATSR-2 cloud fraction is larger than that of ICFA, the retrieved ozone column is lower than the standard ozone column, and vice versa. The difference between the cloud fractions

from ATSR-2 and ICFA is maximally 0.7; the corre- sponding difference in the retrieved ozone column is -8 DU. This is a rather small difference compared to the error in the cloud fraction and is due to the opposite behavior of the reflection effect and ghost column ef- fect. Therefore errors in cloud fraction partly cancel in the retrieved ozone column. Larger differences in the retrieved ozone column, namely, up to +11 and -16 DU, occur in scenarios (2) and (3), where, in addition,

18,812 KOELEMEIJER AND STAMMES' VALIDATION OF GOME CLOUD FRACTIONS

Z0

lO

o

-10

-20

- (A) cloud fraction ATSR, cloud top pressure ISCCP - _

............ cloud fraction ATSR, cloud top pressure ISCCP + 200 mbar - _

....... cloud fraction ATSR, cloud top pressure ISCCP - 200 mbar - _

I' - '\,/\ - I' '\ .r--.... __

L / \ I -

i•/ \ i - i V \ i _- i "" /\ i _ / \ '•' ' • /'\ I' J' -- ß .. • I \ i'\J ,... , \, ./ ' ,\ / . ',• , _

,

ß , _

ß __

.

40 45 50 55 60 65 70 75

latitude (degrees)

ZO

= 10 o

o

o

• 0

.,•

o

• -10

.,•

!

- (s)

_

_

_

_

_

_

_

cloud fraction ATSR, cloud top pressure ISCCP - _

cloud fraction ATSR, cloud top pressure ISCCP + 200 mbar - _

cloud fraction ATSR, cloud top pressure ISCCP - 200 mbar - _

45 50 55 60 65 70 75 80

latitude (degrees)

Figure 9. Difference between the retrieved ozone column and the standard ozone column, for three different cloud scenarios (see text). The standard ozone column is retrieved with cloud fraction from ICFA and cloud top pressure from ISCCP, as in the operational GOME data processor. The data are obtained during ERS-2 orbits (a) 1336 and (b) 1337.

KOELEMEIJER AND STAMMES: VALIDATION OF GOME CLOUD FRACTIONS 18,813

the cloud top pressure is varied with 200 mbar. Thus if a climatological value for the cloud top pressure is used in the ozone retrieval method, appreciable errors in the retrieved ozone column may occur, since the ac- tual cloud top pressure may be very different from the value deduced from climatology.

7. Summary and Conclusions

In this study, the effective cloud fraction results of the operational GOME cloud retrieval method, ICFA, have been validated in two ways. First, a statistical approach has been followed by comparing global monthly average ICFA results with global monthly average cloud frac- tions from ISCCP. Second, a detailed comparison has been performed for a limited number of data using ef- fective cloud fractions derived from collocated ATSR-2

data.

We found that for 00 > 75 ø, ICFA cloud fractions increase rapidly with increasing 00. This is an artifact which may be due to the breakdown of the radiative transfer assumptions in ICFA. In the comparison with the ISCCP data, only ICFA cloud fractions were con- sidered which were obtained when 00 < 75 ø.

Monthly average global cloud patterns of ICFA for July 1995 and January 1998 compare reasonably well with ISCCP global cloud fractions of July and January averaged over the period 1984-1990. The ICFA cloud patterns over the Pacific Ocean in January 1998 differ from the ISCCP cloud fractions of January averaged over the period 1984-1990. This, however, is due to the E1 Nifio conditions which prevailed in that area during January 1998.

In contrast, from comparison with effective cloud fractions derived from ATSR-2 data, we found that large errors may occur in ICFA cloud fractions for in- dividual orbits. The mean difference between the cloud

fractions of ATSR-2 and ICFA is 0.18; the standard deviation is 0.23. The difference is strongly correlated with the surface albedo values fitted by ICFA. These surface albedo values are unrealistically high when the ICFA cloud fractions are too low compared to ATSR-2. Using a modified version of ICFA, in which the surface albedo was fixed to 0.02 over sea and 0.25 over land

for the spectral interval 758-778 nm, the derived cloud fractions compared well with those from ATSR-2. The errors in the operational ICFA cloud fractions are prob- ably induced by differences between the actual cloud top pressure and the assumed cloud top pressure, which is put equal to the climatological mean value of ISCCP.

The errors in the retrieved ozone column due to er-

rors in the cloud fraction were found to be relatively small, namely, -8 DU, for errors in the cloud fraction as large as 0.7. If in addition the cloud top pressure is changed by -200 and +200 mbar, the error in the re- trieved ozone column can increase to +11 and -16 DU, respectively. Thus if a climatological value for the cloud top pressure is used in the ozone retrieval method, ap-

preciable errors in the retrieved ozone column may oc- cur. Errors in cloud fraction and cloud top pressure may explain part of the difference between GOME ozone columns and ground-based measurements [e.g. Lambert et al., 1997, 1999]. However, monthly average errors in the retrieved ozone column due to errors in cloud frac-

tion and cloud top height are smaller than the errors in individual ozone column retrievals and cannot explain the systematic difference of about 5% between GOME and TOMS ozone vertical column values (J. Gleason, private communication, 1998).

GOME is the first satellite instrument which oper- ationally employs the oxygen A band for cloud detec- tion. At present, cloud top height is chosen a priori, and an effective cloud fraction is derived using ICFA. A similar approach is envisaged for the Scanning Imaging Absorption Spectrometer for Atmospheric Chartogra- phy (SCIAMACHY), scheduled for launch on board the European Space Agency's Envisat in 2000 [DLR, 1998]. In this study we showed that with only a minor mod- ification to the operational ICFA scheme, cloud frac- tion results can be improved significantly. In a recent GOME algorithm improvement study, the possibility was investigated of simultaneous retrieval of both cloud fraction and cloud top height from the radiances around the oxygen A band [Kurosu et al., 1998]. Preliminary results are encouraging, and probably cloud detection for GOME and SCIAMACHY can be improved further following such an approach.

Acknowledgments. We acknowledge the discussions about ICFA with R. Spurr (Harvard Smithsonian Center for Astrophysics) and W. Thomas (Deutsches Zentrum ffir Luft- und Raumfahrt). We would like to thank J. W. Hovenier (Free University Amsterdam) and two anonymous reviewers for their comments on a draft version of this paper. We are grateful to the staff of the Goddard Distributed Active Archive Center, NASA- Goddard Space Flight Center for providing the ISCCP/C2 data and the Earth Probe TOMS reflectivity data. This work was supported by the Space Research Organisation of the Netherlands.

References

Anderson, G. P., S. A. Clough, F. X. Kneizys, J. H. Chet- wynd, and E. P. Shettle, AFGL atmospheric constituent profiles, Tech. Rep. AFGL-TR-86-0110, Air Force Geo- phys. Lab., Hanscorn AFB, Mass., 1986.

Bailey, P., SADIST-2 v100 products, Tech. Rep. ER-TN- RAL-AT-216d, 123 pp., Rurtherford Appleton Labora- tory, Chilton, Didcot, England, 1995.

Burrows, J.P., M. Weber, K.-U. Eichmann, K. Bramstedt, A. Richter, R. Hoogen, A. Ladst/itter-Weiflenmayer, and M. Buchwitz, GOME/ERS-2 total Os and NO2 measure- ments during arctic spring 1996 and 1997, in Polar strato- spheric ozone 1997, pp. 419-422, European commission, Luxembourg, 1997.

Climate Prediction Center, Climate Diagnostics Bulletin, January 1998, Washington, D.C., 1998.

De Haan, J. F., P. B. Bosma, and J. W. Hovenier, The adding method for multiple scattering calculations of po- larized light, Astron. Astrophys., 183, 371-391, 1987.

18,814 KOELEMEIJER AND STAMMES: VALIDATION OF GOME CLOUD FRACTIONS

Deirmendjian, D., Electromagnetic Scattering on Spherical Polydispersions, 290 pp., Elsevier, New York, 1969.

Deutsches Zentrum flit Luft- und Raumfahrt (DLR), GOME level I to 2 algorithms description, Tech. Rep. ER-TN- DLR-GO-0025, 32 pp., Oberpfaffenhofen, 1994.

Deutsches Zentrum ffir Luft- und Raumfahrt (DLR), EN- VISAT D-PAC PHASE C/D SCIAMACHY level lb to 2 off-line processing algorithm technical baseline docu- ment, Tech. Rep. ENV-SA O-ATB-SCI-2200-0003, 67 pp., Oberpfaffenhofen, 1998.

European Space Agency, GOME Users Manual, ESA SP- 1182, 123 pp., Eur. Space Res. and Technol. Cent., Noord- wijk, The Netherlands, 1995.

Fischer, J., and H. Grassl, Detection of cloud top height from backscattered radiances within the oxygen A-band, Part 1, Theoretical study, J. Appl. Meteorol., 30, 1245- 1259, 1991.

Hsu, N. C., R. D. McPeters, C. J. Seftor, and A.M. Thomp- son, Effect of an improved cloud climatology on the total ozone mapping spectrometer total ozone retrieval, J. Geo- phys. Res., 102, 4247-4255, 1997.

Hudson, R. D., J.-H. Kim, and A.M. Thompson, On the derivation of tropospheric column ozone from radiances measured by the total ozone mapping spectrometer, J. Geophys. Res., 100, 11,137-11,145, 1995.

Koelemeijer, R. B. A., and P. Stammes, Effects of clouds on ozone column retrieval from GOME UV measurements, J. Geophys. Res., l O J, 8281-8294, 1999.

Koelemeijer, R. B. A., P. Stammes, and P. D. Watts, Com- parison of visible calibrations of GOME and ATSR-2, Re- mote Sens. Environ., 63, 279-288, 1998.

Kurosu, T. P., K. V. Chance, and R. J. D. Spurt, Cloud re- trieval algorithm for the European Space Agency's Global Ozone Monitoring Experiment, Proc. SPIE Int. Soc. Opt. Eng., 3J95, 17-26, 1998.

Kuze, A., and K. V. Chance, Analysis of cloud top height and cloud coverage from satellites using the O2 A and B bands, J. Geophys. Res., 99, 14,481-14,491, 1994.

Lambert, J.-C., et al., Pole-to-pole validation of the ERS-2 GOME level-2 products with the SAOZ ground-based net- work, in Proceedings of the Third ERS Symposium, Flo- rence, Italy, 1J-21 March 1997, Eur. Space Agency Spec. Publ., ESA SP-JiJ, 629-636, 1997.

Lambert, J.-C., et al., Combined characterization of GOME and TOMS total ozone using ground-based observations from the NDSC, Adv. Space Res., in press, 1999.

McPeters, R. D., et al., Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) Data Products User's Guide, NASA Ref. Publ., 138J, 67 pp., 1996.

Noxon, J. F., Nitrogen dioxide in the stratosphere and troposphere measured by ground-based absorption spec- troscopy, Science, 189, 547-549, 1975.

Olesen, F.-S., and H. Grassl, Cloud detection and classifica- tion over oceans at night with NOAA-7, Int. J. Remote Sens., 6, 1435-1444, 1985.

Philander, S.G.H., El Nifo, La Ni•a, and the Southern Os- cillation, 293 pp., Academic, San Diego, Calif., 1990.

Platt, U., Differential Optical Absorption Spectroscopy (DOAS), in Air Monitoring by Spectroscopic Techniques, edited by M. W. Sigrist, pp. 27-84, John Wiley, New York, 1994.

Platt, U., D. Perner, and H. W. P&tz, Simultaneous mea- surements of atmospheric CH20, 03, and NO2 by differ- ential optical absorption, J. Geophys. Res., 8•, 6329-6335, 1979.

Rossow, W. B., and L. C. Garder, Cloud detection using satellite measurements of infrared and visible radiances

for ISCCP, J. Clim., 6, 2341-2369, 1993. Rossow, W. B., A. W. Walker, and L. C. Garders Com-

parison of ISCCP and other cloud amounts, J. Clim., 6, 2394-2418, 1993.

Saunders, R. W., and K. T. Kriebel, An improved method for detecting clear sky and cloudy radiances from AVHRR data, Int. J. Remote Sens., 9, 123-150, 1988.

Schiffer, R. A., and W. B. Rossow, The international satel- lite cloud climatology project ISCCP: The first project of the world climate research programme, Bull. Am. Mete- orol. Soc., 64, 779-784, 1983.

Stammes, P., Errors in UV reflectivity and albedo calcula- tions due to neglecting polarisation, Proc. SPIE Int. Soc. Opt. Eng., 2311, 227-235, 1994.

Thompson, A.M., D. P. McNamara, K. E. Pickering, and R. D. McPeters, Effect of marine stratocumulus on TOMS ozone, J. Geophys. Res., 98, 23,051-23,057, 1993.

Weng, F., and N. C. Grody, Retrieval of cloud liquid water using the special sensor microwave imager (SSM/I), J. Geophys. Res., 99, 25,535-25,551, 1994.

R. B. A. Koelemeijer and P. Stammes, Royal Nether- lands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE De Bilt, The Netherlands. (koelemei@knmi.nl; stammes@knmi.nl)

(Received December 18, 1998; revised March 19, 1999; accepted April 13, 1999.)