AerosolmapsfromGOMEdata - University of Oxfordcarboni/doc/Atm_Env_35... · 2005-11-24 · abletoretrievetheaerosolproﬁlebynadirandoﬀ-nadir view. With the advent of such instruments

Atmospheric Environment 35 (2001) 5079–5091

Aerosol maps from GOME data

Rodolfo Guzzia,*, Giovanni Ballistab, Walter Di Nicolantoniob, Elisa Carbonia

a ISAO-CNR, Via Gobetti 101, 40129 Bologna, ItalybCarlo Gavazzi Space, Via Gobetti 101, 40129 Bologna, Italy

Received 15 September 2000; received in revised form 23 April 2001; accepted 24 April 2001

Abstract

In this paper, we present a methodology to calibrate the surface reflectance seen by satellite and validate the aerosoloptical properties retrieved by the GOME instrument. Data are also visualized in maps by a tool properly developed,

named GOMEView. The validation procedure is based on ground measurements obtained by sunphotometers. Resultsshow that calibration of the surface reflectance is crucial to obtain the best results, i.e. in agreement with the groundmeasurements. Aerosol data have also been classified on the basis of their optical properties evidencing for instance, the

presence of desert aerosol over the sea along the west coast of Sahara. Cloud retrievals were also analyzed in terms oftheir occurrence and amount. r 2001 Elsevier Science Ltd. All rights reserved.

Keywords: Aerosol optical depth; Atmospheric radiative transfer; Earth observation; Remote-sensing calibration; Sea reflectance

1. Introduction

Aerosols scatter and absorb solar radiation. Thescattering and absorption features depend on their

chemical and physical properties. In populated areas,black carbon is responsible for most of the aerosolabsorption while biomass burning prevails in tropical

regions. The aerosol absorption spectrum is uniformlydistributed along the solar spectrum and is proportionalto the aerosol scattering decreasing with wavelength.Several papers have analyzed the influence of aerosols

on the radiative budget (Charlson and Heintzenberg,1995; Haywood and Shine, 1995; Russell and Heintzen-berg, 2000). The interaction between aerosols and

incoming solar radiation may influence the radiativeforcing and explain the difference between the observedand modeled temperature trends. Energy balance

models have shown the effect of aerosols on cooling.Aerosols can modify cloud microphysics by activity ascloud condensation nuclei having a strong impact on

cloud radiative properties (Haywood and Boucher,

2000).Aerosol particles have an important role on the

biogeochemical cycle. Biomass burning produces an

important source of organic particles (Penner et al.,1992), while arid and semi-arid regions create mineraldust. Aerosols also play an important role in tropo-

spheric chemistry. For these reasons, satellite, contain-ing instruments, have been launched to measure theatmospheric aerosol profile and load.Starting from high space resolution and low spectral

resolution like advanced very high resolution radiometer(AVHRR), instruments have become more and moresophisticated reaching the medium spectral resolution

and high spatial resolution of moderate resolutionimaging spectroradiometer (MODIS, see Kaufman andTanr!ee, 1996), polarization and directionality of the

Earth’s reflectance (POLDER, see Deschamps et al.,1994) and global ozone monitoring experiment (GOME,see Burrows et al., 1999); the latter has low space

resolution.A spacecraft named TERRA was launched by NASA

in December 2000. It contains MODIS for gas andparticle studies and an instrument named multiangle

imaging spectroradiometer (MISR, Kahn et al., 1998)

*Corresponding author. Tel.: +39-051-639-8004; fax: +39-

051-639-8132.

E-mail address: [email protected] (R. Guzzi).

1352-2310/01/$ - see front matter r 2001 Elsevier Science Ltd. All rights reserved.

PII: S 1 3 5 2 - 2 3 1 0 ( 0 1 ) 0 0 3 2 4 - 7

able to retrieve the aerosol profile by nadir and off-nadirview.

With the advent of such instruments advancedalgorithms are being developing to retrieve the atmo-spheric components from satellite measurements (Kahn

et al., 1998; Abdou et al., 1997). In general, retrievalmethods are based on relationships existing between theupwelling visible and near-infrared radiances, and theaerosol optical depth (AOD) over the surface of low

albedo (typically oceans) for different aerosol sizedistributions and refractive indices at several differentwavelengths.

Earlier monospectral methods based on single wave-length measurements were soon superseded by multi-channel algorithms. The rationale for the multi-channel

approach is that each channel contains some uniqueinformation about the scattering process in the atmo-sphere. Multi-spectral algorithms use inter-relationships

between radiance measured in different spectral chan-nels. These relationships are effected in a pronouncedmanner by changes in the size distribution function,while they are rather insensitive to changes in the real

and imaginary part of the refractive index of the aerosol.Connections between radiances at different wavelengthsare established when the radiative properties of aerosol

and molecules and the reflectance properties of theunderlying surface are prescribed. In any practicalapplication using measured radiances, this information

may not be available and thus the application result ofthe method will yield radiatively equivalent quantities.Moreover, the upwelling radiances are also dependenton the absorption properties of gaseous components of

the atmosphere and corrections need to be done in orderto remove their effects on different spectral regions.In case of land surface, the surface reflectance in the

visible spectral range prevents aerosols being retrievedbecause the surface spectral signature overlaps theaerosol spectral signature. Some exercises have been

proposed by different authors (King et al., 1992;Martonchik, 1997; Kaufman et al., 1997; Yang andGordon, 1998). Up to now aerosol operational retrieval

has been made over sea water that is black beyond thered wavelengths while it has a small but not negligiblereflectance in the visible where the reflectance increasesin the presence of sea roughness and whitecaps.

In 1995, ERS 2 containing the GOME instrument waslaunched. Total ozone data are currently producedby Deutschen Zentrum fur Luft und RaumfahrtFDeutsches Fernerkundungsdatenzentrum (DLR-DFD)and Koninklijk Nederlands Meteorologisch Instituut(KNMI) as standard products. Other gases are also

available from the University of the Bremen.Recently, aerosol algorithms have been developed by

our group (Guzzi et al., 1998; Torricella et al., 1999) and

applied to GOME data to obtain columnar atmosphericaerosol over the sea.

This paper deals with the methodology used toproduce the AOD and their class, to calibrate the

reflection due to underlying surface and validate thedata using ground stations of the aerosol roboticnetwork (AERONET) (Holben et al., 1998) and ‘‘ad

hoc’’ campaigns. Discussions over a consistent set ofdata and the method to interpolate sparse data in orderto obtain a map are also reported.

2. Retrieval method

In order to derive aerosol spectral features fromGOME reflectance, we use the so-called pseudo-inversion

method. In this method, the radiative transfer is firstdirectly solved for many values of the parameters to beretrieved. Then results are compared with measurementsuntil the best fit is obtained, i.e. a minimum in the

‘‘figure of merit function’’ is reached (the merit functionmeasures the agreement between the data and themodel). Then, the parameter values that led to this

minimum are considered the retrieved parameters.In practice, the GOME measured reflectances spectra

are fitted with a theoretical reflectance function depend-

ing on the AOD and the aerosol class, a termsummarizes the set of chemico-physical properties ofthe aerosol.As a first guess, all parameters driving the atmo-

spheric reflectance, for instance the sea reflectivity or thesea level pressure are considered fixed and are used asinput to this method. The method presented is

considered suitable for cloud-free scenarios and foroceanic low reflecting areas.The outputs of the fitting procedure are a value of the

AOD, ta; at the reference wavelength of 500 nm for eachselected class, ICLASS, the error estimate on theretrieved optical thickness and an index of the goodness

of the fit.The fitting is carried out by means of the Levenberg–

Marquardt fitting method (LMFM) that is a standardand very robust fitting method suited for nonlinear

models (see Press et al., 1994). In our context, the meritfunction w2 is

w2ðtaÞ ¼XNi¼1

RexpðliÞ � Rmodðli; ta; ICLASSÞsexpðliÞ

� �2; ð1Þ

where the independent variables li for i ¼ 1;y; N arethe selected wavelengths. The RexpðliÞ data, for i ¼1;y; N; are the GOME reflectance measurements withtheir own measurement errors sexpðliÞ.RmodðliÞ is the reflectance computed at the top of the

atmosphere (TOA) by means of a radiative transfer

model (RTM) named DOWNSTREAM (Levoni et al.,2001) which is highly accurate and fast. The parameter

R. Guzzi et al. / Atmospheric Environment 35 (2001) 5079–50915080

to be retrieved is a scalar quantity ta: To establishwhether the process reaches convergence, the difference

between the actual value of w2 and the previous one isevaluated after each iteration step. We check if therelative and absolute differences between the two w2 fallbelow the corresponding pre-selected threshold:

jw2ðnÞ � w2ðn�1Þj

w2ðn�1Þoerel and jw2ðnÞ � w2ðn�1Þjoeabs;

n ¼ number of iteration: ð2Þ

The procedure is stopped after n consecutive steps

yielding to w2 values that fulfill these conditions.Let us now take into consideration all the parameters

and variables upon which the computed atmospheric

reflectance depends and describe how they are input toRTM.The following wavelengths li ¼ 364; 373; 385; 394;

424; 754; 780 nm were selected in the GOME spectralrange where gaseous absorption features are not present.With this choice the radiative transfer computations

required for the fitting are greatly simplified.The use of N wavelengths implies that the radiative

transfer code has to be called N times each time w2 has tobe recomputed according to Eq. (1). Moreover, since in

the minimization process the computation of the firstderivative of Rmodðli; ta; ICLASSÞ is also required, itmeans several evaluations of the function itself corre-

sponding to many calls to radiative transfer code andthen the use of a fast RTM, like DOWNSTREAM,appears mandatory.

Between the 15 aerosol classes presented in Table 1 wehave selected the four indicated with the asterix. Thechoice of this set of classes was based on being welldistinguishable from other classes and leading to

acceptable minimum errors during retrieval of theoptical depth.The relevant optical properties to give as input to the

radiative transfer code are: the extinction and scatteringcoefficients, the single scattering phase function and thesingle scattering albedo. The aerosol optical properties

are computed by means of the code described in Levoniet al. (1997) in a suitable format to be used as input toradiative transfer code.

The first guess on the sea water spectral albedo Ag isshown in Fig. 3 (Torricella et al., 1999).Summarizing the retrieval procedure shown in Fig. 1,

GOME measured reflectances for the 7 selected wave-

lengths and the geometrical information for each pixel,i.e. solar zenith angle (SZA) and satellite observationangles, are read from the file corresponding to the

current ground pixel (GOME level 1 data product). Assoon as the fitting procedure starts the LMFM isindependently applied for each of the 4 aerosol classes.

These 4 fitting procedures produce, as output, fourvalues of the best fit parameters AOD, t*a ; the associated

standard deviations st; and the corresponding w2 value,w2ðt*a Þ; called the residual of the fittings. When thesmaller fitting residual among the four available valuesis selected, the corresponding parameters t*a ; st andICLASS are the resulting aerosol characteristic of thepixel under examination.

3. Calibration procedure

Since the first AOD values obtained from satellitedata retrieved by the previously outlined procedure arenot completely comparable with those obtained from

ground measurements, we revised the algorithm usedin our procedure starting from the evaluation of therightness of the boundary conditions used.

As shown in Fig. 3, the sea water reflectivity has awide variability, despite a low reflectance in the spectralband of 300–800 nm. Furthermore, its reflectivity

changes as a function of wind speed (for the foamcontribution, Koepke, 1984) and the angle between winddirection and sun azimuth (Masuda and Takashima,

1986) angle and in presence of the dispersed biomass andtherefore of the pigment concentration over the seasurface (Morel, 1988; Austin and Petzold, 1986). Thewide variability of the models produces uncertainties in

spectral surface albedo which may generate large errorsin the retrieved AOD.Since we cannot choose the right values to apply to

the retrieval process ‘‘a priori’’ we then proceed to makea calibration of the sea surface albedo. Such procedure,whose algorithm is drawn in Fig. 2, uses the spectral

AOD obtained from ground measurements in coasts orisland sites. The related spectral AOD are introduced

Table 1

List of aerosol classes proposed for the test. After the analysis

only the four indicated with * were selected to be used in the

retrieval (Levoni et al., 1997)

Index ICLASS Name of the aerosol class

1 Clean continental

2 Average continental

3 Urban

4 *Clean maritime

5 *Maritime polluted

6 Desert background

7 *Desert wind-carry

8 Maritime

9 Continental

10 Urban industrial

11 Rural

12 Urban (Lowtran)

13 Maritime (Lowtran)

14 *Volcanic 1

15 Volcanic 2

R. Guzzi et al. / Atmospheric Environment 35 (2001) 5079–5091 5081

into the RTM where, in the first run, the surface albedo

is equal to zero. Afterwards the actual spectralreflectance, Rexpl ; measured by GOME instrument foreach pixel, is compared with Rmodl ; obtained by thesimulation model, for each aerosol class, and spectralsurface albedo, Al; that is increased step by step until thereflectance simulated fits the one measured. The mini-mum of the residual reflectance, DRn

l ; is considered andif its value is less than the standard deviation of themeasured value, sexpl ; the sea surface albedo, A*l ; isfound, otherwise the procedure starts again by decreas-

ing the albedo of a certain fraction.

The calibration of the sea surface albedo was carried

out in the following three sites:

* Sagres (Atlantic ocean) coastal site (361590N, 81570W,

Altitude 50m) (Vitale et al., 2000),* Kaashidhoo (Indian ocean) island site (041570N,731270E, Altitude 0m), AERONET,

* Nauru (Pacific ocean) island site (001310S, 1661540E,Altitude 7m), AERONET,

that can be considered representative of the differentareas over the ocean.

Fig. 1. Aerosol retrieval procedure. RexpðliÞ and sexpðliÞ are the GOME reflectance and error measurements, respectively, at theselected wavelengths li : Rmodðli ; taÞ is the TOA reflectance computed by DOWNSTREAM. ta and st are the AOD and associated

standard deviation at 500 nm.


The calibration procedure requires the followingsteps:

1. Select the sun photometer and GOME data with highspace–time coincidence. In other words, the GOME

footprint must cross the ground measurement site assoon as possible at the same time.

2. Select GOME pixel and ground data measurementsin cloud-free scenarios. In particular we consider:* AERONET level 2.0 data (quality assureddata that had tested with cloud screeningprocedure).

* GOME data that are cloud-free 3 h before andafter the GOME pixel selected in item 1. Clouddata presence can be obtained by Meteosat or

GOES images).* Use only GOME data which have a completereflectance spectrum and with no anomalies

(jumps, flags, etc).

For our purpose we selected three days for Sagres (5,

6, 12 July 1997), 8 days for Kaashidhoo (13–29 March1998, 27 December 1998, 23 November 1999, 12December 1999, 22–23 January 2000, 10 May 2000),

and 2 days for Nauru (5 October 1999, 11 January 2000).Results of the calibration procedures of the sea

surface albedo are presented in Fig. 3, where the albedo

A*l ; found for the mentioned sites, are compared withdifferent models and data bases. The mean values and

their standard deviation, shown by errorbars, arereferred to the same site.Looking at the data related to wavelengths 754 and

780 nm we can observe that Sagres’s albedo valuesobtained for GOME pixel near the coast and associatederrorbars are higher than those related to Nauru and

Kaashidhoo. These large errors can be explained by theeffect of a very thin or small cloud in GOME pixel notvisible from the Meteosat image. Since the GOME

repetition rate is three days and GOME pixel is320� 40 km2 only a small number of pixels are suitablefor calibration. In fact, small or thin clouds commonlygive a not negligible contribution to the spectral

radiance measured.Then the source of uncertainties in the calibration of

the parameter A*l can be summarized as

* the size of the GOME pixel that can cause differenttypes of error:* the ground AOD measurements assumed to be thesame in the whole GOME pixel area. This implies

that variation in the GOME pixel due to othersources not measured along the sun path by thesunphotometer are not considered during the

comparison between ground and satellite sensormeasurements.

* Probability to find thin or small clouds in the

GOME pixel. The presence of clouds is a source oferror, as is evident in the case of Sagres site, where

Fig. 2. Algorithm for the sea surface albedo calibration. Rexpl ðRmodl ) is the measured (simulated) reflectance at the top of the

atmosphere at wavelength l: sexpl is the measured error. A*l is the computed sea surface albedo. For the description, see also text.


the spectral albedo is less tilted than that ofKashidoo and Nauru.

* Inhomogeneous GOME pixel due the concomi-tant presence of land and sea. In order to reduce

the effects of such problem we selected islandswith an area less than 18� 18 km2:

* Variability in ocean conditions such as sea rough-

ness, pigment concentration and foam.* The small number of coincidences between satelliteand ground measurements.

* Errors due to polarization of light, mainly for clearpixels where the presence of molecules is prevalent.

4. Data comparison for validation

Inserting the calibrated spectral surface albedo intothe pseudo-inversion method, we are able to obtain thenew aerosol characteristics remote-sensing derived. Once

the processing chain is applied and the AOD and aerosolclass are obtained from GOME data, the retrieved dataneed to be validated by comparison with data obtained

from other satellite and/or ground measurements todisclose the quality of data obtained.We found some pixels, at different periods of the year,

over Nauru and Kaashidhoo islands that satisfied thecondition of calibration.

The AOD at 500 nm retrieved from the measurementsof these GOME pixels, obtained after the introductionof the calibration procedure of the sea surface albedo,are compared with those obtained from ground instru-

ments located in Kaashidoo and Nauru islands. Fromthe results shown in Fig. 4, we see that AOD derivedfrom GOME measurements by new surfaces spectral

albedo (GOME-AOD with calibration) are in agreementwith the related ground data (Sunphotometer-AOD).The maximum discrepancy is about 30% with a

maximum deviation of 0.05.The same figure also shows the AOD data (GOME-

AOD without calibration) obtained by the sea surfacespectral albedo used in Torricella et al. (1999) and

shown in Fig. 3 present a higher disagreement withrelated ground data.Due to the results obtained the analysis can be

extended to the open ocean. Of course, it is obvious thatthe calibration of the sea reflectance is highly reliable asfar as it is, in time–space, close to the site in which it is

done. Then the extension of calibration values obtainedat certain sites to the open ocean need to be regarded asvalues affected by errors due to certain features of the

sea surface ranging from the flat clear sea water toroughness surface where pigment is dispersed as shownin Fig. 3.In the case of Sagres data the extension to open ocean

of the sea albedo obtained by the calibration procedure

Fig. 3. Comparison between sea surface spectral calibrate albedo A*l ; computed from Sagres, Kashidhoo and Nauru, and those

simulated by models: (a) Koepke’s model (Koepke, 1984) at wind speeds=5ms�1; the angle between the wind direction and sunazimuth is 901 and pigment concentration value of 1mg/m3; (b) lake model; and (c) clear water model take from 6S (Vermote et al.,1995); (d) sea surface spectral albedo by Torricella et al. (1999).


cannot be retained because they are affected by theuncertainties defined in Section 3.

5. Mapping

Once the aerosol retrieved has been obtained it is

necessary to plot it on a world map. Such a task requiresmuch caution because satellite data suffer from variousproblems (sparse data set, random sampling grid, data

uncertainties) and interpolation algorithms have to beused to obtain good cartography.Several interpolation methods for environmental

phenomena have been developed but all can be tracedback to the stochastic-interpolation procedure based onKriging (Agterberg, 1970) (mainly in geological and

hydrological problems) and objective analysis (OA,Bretherton et al., 1976) (mainly in meteorological–oceanographical fields). Both methods reproduce theevent under study, basing their approaches on

the covariance of experimental data (variogram) andthe trend of occurrence. The main difference between thetwo methods is in the solution of the minimum. In

Kriging, the solution is obtained by the Lagrangemultiplier, in OA by Gauss–Markov theorem.Comparison studies (Bergamasco et al., 1993) on

the two methods indicate they are substantiallyidentical even though the OA shows more generality

because it could be applied to time-dependentfields.

Our approach is based on the Kriging method and ishere summarized.

* Best variogram model. Since the Kriging method uses

different variogram models (or semivariogram), weevaluated the one we could consider the best. Theprocedure was carried out on data samples (using thenew data) obtained from aerosol retrieved data in

different world zones. Missing data were recon-structed using a procedure of the best fit applied tospherical and exponential models.

* Reconstruction. On the basis of the semivariogramobtained, we reconstructed the world map datainterpolating those pixels with missed data and those

we identified as cloudy. The world map was dividedinto a grid of 11� 11 and the data selected were at adistance twice the effective range of the semivario-gram. In order to render satellite data easy to handle,

it is necessary to visualize them. Visualization ofGOME data is carried out with the GOMEView toolwhose features are presented in the Appendix.

6. Results and discussion

The algorithms we developed are able to evaluate theatmospheric aerosol in terms of any optical depth.

Fig. 4. Comparison between GOME-AOD vs ground AOD measurements. GOME-AOD without calibration refer to AOD derived

from GOME measurements by sea spectral albedo used in Torricella et al. (1999). GOME-AOD with calibration is the AOD derived

from GOME measurements using new sea spectral albedo computed by calibration procedure as indicated in Section 3.


However, it is obvious that when the optical depth is

over a certain value (typically tX3), clouds are presentover the scene. For to3 we classified all the events dueto the presence of atmospheric aerosol (tp1) and as faras 1oto3 we defined the whole set of desert aerosol,biomass burning or data that are affected by clouds thatcover the pixel for less than 10% and that we cannot

classify as cloudy. As shown in Fig. 4, data obtainedwithout any calibration of the underlying surface showhigher values of AOD in disagreement with groundmeasurements. Such result indicates that it is crucial to

calibrate the satellite on the reflecting surface beforeintroducing a new aerosol class or shape (for instancenon-spherical particles as proposed by Mishchenko and

Travis (1997)). The same figure also presents the effect ofthe methodology used, showing that satellite data are inhigher agreement with ground data. Only when the

calibration has been carried out and residual values arelarger than the satellite errors, can new particles beintroduced in the processing algorithms.

Since the GOME repetition rate is of the order of 3days, we have, a lack of data during 1 day orbit.However, making the hypothesis that aerosol spacetransport is very limited and unlikely to change along

two contiguous orbits, we may assemble 3 days of data

without losing any physics information. A typical 3 day

map is shown in Fig. 5 where the aerosol and cloudoptical depth are reported. Fig. 6 also shows the aerosolclass. In this figure, the presence of desert aerosol is

evident, mainly along the Sahara coast on the Atlanticocean.Fig. 5 also shows that clouds cover several pixels

along certain latitudes. In order to reveal the amount ofthe cloud during a month we analyzed the cloud data forsummer and winter seasons of 1997. The analysis forJune 1997 is shown in Fig. 7. The figure shows the cloud

amount extracted from GOME data has the samepattern already found in literature (K.aarner and Kee-vallik, 1993). It also shows that during a month there are

zones with very low or no cloud cover. This means thatwe can obtain the aerosol statistics using monthly meansof aerosol data.

The AOD monthly mean value in the range [0–1] forJune 1997 is shown in Fig. 8. In this figure, someinhomogeneous areas appear in the middle Atlantic

which are also present and more pronounced in Fig. 5.As in this case the pattern follows the overlapping of thesatellite orbits in Fig. 8 inhomogeneity may be due to anoise probably linked to the residual of the cloud cover

fraction inside the GOME pixel.

Fig. 5. Aerosol optical depth at 500 nm obtained by GOME data for 3 days (7, 8, 9 June 1997). The white refers to clouds. Data were

visualized by GOMEView.


The presence of desert storm over the sea, further-more, can be evidenced by the monthly number ofevents occurring in the [1–3] AOD range during the

same month as shown in Fig. 9.

A further analysis over the desert aerosol is shown inFig. 10. The dotted line is the distribution of desert dustover the globe, while the continuous line, limited to

Sahara dust events over the ocean at west coast of

Fig. 6. Aerosol class for the same 3 days of Fig. 5. The desert aerosol over the Atlantic ocean along the Sahara west coast is evident.

Data were visualized by GOMEView.

Fig. 7. Monthly clouds amount for June 1997.


Africa, show that most of the events present AODvalues between 1 and 1.5.

The same exercise was also made on maritime,maritime polluted compared with the distribution ofthe desert dust over the globe (see Fig. 11). It is evidentfrom this figure that there is a shift in the mode of the

aerosol depth depending on the aerosol class.

7. Conclusions

Aerosol retrieval was produced by proper algorithmsdeveloped for the ERS2/GOME instrument. Such datamust be validated by ground data obtained fromsunphotometers. When the comparison from satellite

data and ground data exhibit large differences the first

Fig. 8. Monthly mean of aerosol optical depth at 500 nm in the range [0–1] for June 1997.

Fig. 9. The amount of monthly frequency of events occurring in the range [1–3] of aerosol optical depth at 500 nm for June 1997.


Fig. 10. Comparison between desert aerosol at the west coast of Africa and desert aerosol over the globe in terms of their optical depth

at 500 nm.

Fig. 11. Comparison between maritime, maritime polluted and desertic aerosol (global) in terms of their optical depth at 500 nm.


thing is to calibrate the surface reflectance. The data canbe re-processed and new results which show a better fit

can be obtained. Maps shown by a visualization tooldisclose that GOME is able not only to measureatmospheric gases, but also the aerosol and the cloud

and their frequency occurrence. Findings can be used toanalyze the aerosol and cloud occurrence also in termsof their radiative effect over the climate.

Acknowledgements

We thank C. Tomasi (ISAO-CNR) and B.N. Holben(NASA’s GSFC) for supplying ground data from Sagresand AERONET sites, respectively, and G. Gobbi (IFA-

CNR) for useful discussion.

Appendix A. Visualization tool

GOMEView is developed entirely under the Javalanguage. We made this choice to take advantage ofsome features of this technology:

* very little effort to make a program cross-platform;* quite easy to modify and maintain;* GOMEView can be used to share GOME data over a

network (this is a future release);* GOME data can be shared and viewed acrossWWW.

The current version of GOMEView can be used toshow GOME data locally. Data are written usinga special file format called GCD (GOME CodifiedData) that can be written in ASCII or binary mode.

ASCII format is easy to read and it can be used to readGOME data using other programming languages(e.g. Fortran). The binary format was developed to

save disk space (when data are released on CD-ROM) or time (when data are downloaded from thenetwork). The binary and ASCII GCD formats are

interchangeable.GOMEView displays the output from the Aerosol

GOME data processor and shows the optical depth and/

or the class of the aerosols. GOMEView is able to plotGOME pixels over a world map, or tabulate them toview all the following GOME data for each pixel(number, the optical depth ta and its error, aerosolclass, location and orientation, solar zenith angle, thetime acquisition, flag due to jump and offset of thesignal).

GOMEView also has a ‘‘zoom window’’ function toview some regions of interest. This window is alsoused to identify the pixel features (i.e. pixel acquisition

time, pixel number and all the parameters abovementioned).

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AerosolmapsfromGOMEdata - University of Oxfordcarboni/doc/Atm_Env_35... · 2005-11-24 · abletoretrievetheaerosolproﬁlebynadirandoﬀ-nadir view. With the advent of such instruments