Aerosol retrievals from METEOSAT-8 - CM SAF

SAF on Climate Monitoring Visiting Scientists Report Doc. No: 1.0 

Issue : 1.0 

Date : 4 October 2006 

Aerosol retrievals from METEOSAT-8 

Visiting Scientists Report within the 

SAF on Climate Monitoring of EUMETSAT 

Dominique Jolivet 1 , Didier Ramon 1 , Jerome Riedi 2 and Rob Roebeling 3 

1 

Hydrogéologie et Observation Satellitaire (Hygeos), 5 rue Héloïse, 

59650 Villeneuve d'Ascq, France 

2 

Laboratoire d'Optique Atmosphérique (LOA – University of Lille), 

USTL, Bât. P5; 59655 Villeneuve d'Ascq Cedex, France 

3 Royal Netherlands Meteorological Institute (KNMI) 

P.O. Box 201, 3730 AE De Bilt, the Netherlands 

September 2006 

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1 Contents 

1 CONTENTS ..........................................................................................................................................2 

2 ABSTRACT...........................................................................................................................................3 

3 INTRODUCTION ................................................................................................................................3 

4 USER REQUIREMENTS....................................................................................................................5 

4.1 NEEDS FROM CLIMATE RESEARCHES .......................................................................................................5 

4.2 NEEDS FROM SAFS..................................................................................................................................5 

4.2.1 CM-SAF ............................................................................................................................................5 

4.2.2 O3M SAF...........................................................................................................................................6 

4.2.3 LAND-SAF ........................................................................................................................................6 

4.2.4 O&SI SAF .........................................................................................................................................7 

4.3 COOPERATION WITH OTHER SAFS...........................................................................................................7 

5 OVERVIEW OF EXISTING AEROSOL RETRIEVAL ALGORITHMS .....................................9 

5.1 BACKGROUND .........................................................................................................................................9 

5.1.1 Algorithms using angular information ............................................................................................10 

5.1.2 Algorithms using spectral indices ...................................................................................................10 

5.1.3 GOES ..............................................................................................................................................13 

5.1.4 MSG/SEVIRI ...................................................................................................................................15 

6 MSG/SEVIRI METHODOLOGY.....................................................................................................17 

6.1.1 Introduction.....................................................................................................................................17 

6.1.2 Method description..........................................................................................................................17 

6.1.3 Reference Surface Reflectance Map................................................................................................18 

Cloud Masking.................................................................................................. 18 

Rayleigh correction........................................................................................... 18 

Surface contribution.......................................................................................... 18 

6.1.4 MSG/SEVIRI aerosol optical thickness retrieval algorithm............................................................23 

6.1.5 Results and Comparison with AERONET and MODIS ...................................................................27 

6.1.6 Assumptions we made to accelerate development of the algorithm and calculations.....................31 

7 CONCLUSIONS AND PERSPECTIVES.........................................................................................32 

REFERENCES ....................................................................................................................................................34 

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2 Abstract 

A user requirements and feasibility study is performed to assess the applicability of the Spinning 

Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) for 

retrieving aerosol properties and follow their evolution during the day. An overview is given of 

state of the art algorithms that are used to retrieve aerosol properties from today’s operational 

satellites, such as MODIS and MERIS. The applicability of SEVIRI for aerosol retrievals is 

demonstrated with Aerosol Optical Thickness (AOT) retrievals from a simple algorithm. Fifteen 

days of SEVIRI observations is used to derive clear sky surface reflectance maps, taking advantage 

of the high temporal resolution of MSG. The SEVIRI retrievals of AOT are compared to groundbased 

observations of AOT from AERONET. 

The results show the potential of SEVIRI to retrieve AOT and follow the diurnal evolution of AOT. 

The high temporal resolution and stable viewing geometry of MSG allows for the retrieval of very 

accurate surface reflectance maps. Due to these maps the aerosol properties can be retrieved both 

over ocean and land surfaces. The latter is an improvement compared to AOT retrievals from polar 

orbiting satellites, which can only retrieve AOT over dark surfaces because of uncertainties in 

surface reflectance maps. The SEVIRI and AERONET retrieved AOT values are in the same order 

and show similar diurnal variations, provided the aerosol type is known. 

3 Introduction 

Aerosol properties are an important parameter to be considered by Satellite Application Facility on 

Climate Monitoring (CM-SAF). Observation of the daily cycle of the aerosol and more frequent 

observations of the aerosol properties over regions with a high percentage of cloud cover is 

becoming an important requirement from users of Earth Observation community, which are 

scientific users (climate research, data assimilation) or people more oriented toward applications 

(public health, aviation, military). For example, over rural and urban sites where dust is not the 

main aerosol type the aerosol optical thickness may deviate 10 to 40% from the daily mean 

(Smirnov et al. 2002). 

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Previous generations of geostationary meteorological satellites, such as METEOSAT and GOES 

have been widely used to monitor aerosol properties over oceans (Moulin et al., 1997). However, 

the spectral channels of the first generation METEOSAT were rather limited for accurate retrievals 

of aerosol parameters. More advanced aerosol retrievals have been done with polar orbiting 

satellites such as NOAA-AVHRR, MERIS, SEAWIFS and MODIS (Ramon 2001, Ramon 

2004,and Kaufman and Tanre, 1997). The Spinning Enhanced Visible and Infrared Imager 

(SEVIRI) onboard Meteosat Second Generation operates channels in the visible and near IR 

wavelength regions that are similar to e.g. NOAA-AVHRR and MODIS. Therefore, the launch of 

the MSG family is a great opportunity to test new ideas for filling the gap between retrievals from 

geostationary and polar orbiting satellites, as SEVIRI combines the specific advantages of the 

geostationary orbit and geometric, radiometric and spectroscopic capabilities of the 

NOAA/AVHRR family. 

The objective of this Visiting Scientist activity is to perform a user requirement and feasibility study 

to catch the main steps of a future operational algorithm for retrieving the aerosol optical properties 

over land from the MSG/SEVIRI instrument. For the feasibility study we kept the shortest time 

resolution available from MSG i.e. 15 minutes. This constraint could be relaxed in the future but we 

wanted to start with the strongest constraint. The short time for this study forced us to restrict 

ourselves to the main difficulty of all aerosol retrieval algorithms that are applied over land 

surfaces, i.e. the removal of the surface contribution to the satellite signal. This is needed for all 

existing sensors and aerosol retrieval algorithms. In this study our approach is to demonstrate, 

mainly through real data analysis, that the core idea of this future algorithm is correct and that 

significant results are achievable with very simple assumptions. However, the output of this study 

should not be taken neither as an Algorithm Theoretical Basis Document nor an algorithm 

specification document. A lot of crude assumptions have to be refined in order to reach a complete 

and robust algorithm. A very recent paper under press and not available at the beginning of the 

work draws very similar conclusions as ours but for the GOES sensor (Knapp et al. 2005). 

The user requirements for an aerosol product over land from SEVIRI are detailed in chapter 3. In 

chapter 4 the main algorithm classes and performances are briefly reviewed. Then we will explain 

in chapter 5 our methodology and show some first results. We will end in chapter 6 with giving 

some research directions and list all potential improvements we foresee at the moment. 

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4 User requirements 

4.1 Needs from Climate researches 

Tropospheric aerosols are important components of the earth-atmosphere and ocean system. They 

affect climate through three primary processes. First, direct radiative forcing results when radiation 

is scattered or absorbed by the aerosols itself. Second, indirect radiative forcing results when 

enhanced concentrations of aerosols modify cloud properties. And finally, aerosols can have an 

indirect effect on heterogeneous chemistry, which in turn can influence climate by modifying 

concentration of climate-influencing constituents such as greenhouse gases. Frequent observations 

during the day can improve characterization of aerosols contents because of their temporal and 

dynamical variability. 

Frequent observations of aerosols are also desirable for aviation, air pollution and quality and health 

applications. In spite of advances in aerosol remote sensing (King et al., 1999), most retrievals are 

limited to twice per day, by using the morning and afternoon passes of the orbiting polar satellites. 

Aerosols, however, show diurnal variations that are missed by such sparse observations. For 

example, to monitor air quality and for human health goals it is important to understand aerosol 

plume movement to track and forecast the plume movement and its temporal evolution. 

4.2 Needs from SAFs 

4.2.1 CM-SAF 

The Satellite Application Facility on Climate Monitoring (CM-SAF) generates and archives high 

quality data sets satellite products using EUMETSAT and National Oceanic and Atmospheric 

Administration (NOAA) satellites for climate research. The CM-SAF products are generated in 

near-real time and comprise surface albedo, humidity, clouds and radiation products. 

The surface albedo and radiation product algorithms correct for atmospheric distortions caused by 

aerosols. For the aerosol, model computed single scattering albedo, asymmetry factor and aerosol 

optical thickness are used. The information on the spatial variations in aerosol properties is taken 

from OPAC/GADS climatology (Hess et al. 1998, Koepke et al. 1997). The cloud detection and 

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cloud property algorithms do not take into account spatial variations in aerosol properties. 

Within the CM-SAF there is a need for aerosol optical thickness and type information derived from 

satellite. Firstly, to improve the cloud detection, cloud property and surface irradiance retrievals. 

Secondly, to provide climate researchers with aerosol optical thickness and type products. Through 

this Visiting Scientist study the CM-SAF is assessing the potential of MSG/SEVIRI to retrieve 

aerosol properties. Moreover, Steven Dewitte of RMIB has a PhD. student who works on aerosol 

retrieval algorithms from MSG over ocean using the thermal channels. More information about the 

CM-SAF can be found on http://www.cmsaf.dwd.de. 

4.2.2 O3M SAF 

The Satellite Application Facility on Ozone Monitoring (O3M SAF) provides products and services 

related to global ozone and ground surface UV monitoring. In near-real time the O3M-SAF 

provides total ozone, ozone profiles and clear-sky UV fields, while off-line total columns of ozone, 

NO2, OClO, BrO, ozone profiles, aerosols, and surface UV are computed. 

The aerosol product consists of the Absorbing Aerosols Indicator (AAI) and Aerosol Optical Depth 

(AOD) and is based on GOME data. AAI is sensitive to absorbing aerosol (dust smoke). 

O3M-SAF users are interested in the chemical composition of the atmosphere in relation to climate 

and air quality studies. Aerosols are currently the most interesting constituents in these topics. 

There is considerable interest to combine aerosol information from GOME or METOP with 

MSG/SEVIRI derived aerosol information. 

Due to the spatial resolution of 40*320 km 2 and the repetition time of up to 3 days GOME based 

products cannot be used to monitor diurnal and regional changes in aerosol properties. The GOME 

AOD retrievals will be improved by combining them with MSG/SEVIRI retrievals. More 

information about the O3M-SAF can be found on http://o3saf.fmi.fi/. 

4.2.3 LAND-SAF 

The Land-SAF provides MSG and EPS related products to support land, land-atmosphere 

interactions and biophysical research. Currently the LAND-SAF does not provide an aerosol 

optical thickness and aerosol type products. However, aerosols play an important role in the 

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retrievals of two LAND-SAF products i.e.: the surface albedo product and the Down Welling 

Shortwave Radiation Flux product. 

The Albedo and the Down Welling surface short-wave radiation flux (DSSF) product are generated 

each day at the full spatial resolution of the MSG/SEVIRI instrument. Both products are based on 

the three short-wave channels (VIS 0.6µm, NIR 0.8µm, SWIR 1.6µm). For cloud screening the 

cloud masks derived with the Nowcasting and Very Short Range Forecasting Satellite Application 

Facility (NWC SAF) software are used. Dynamic information on the atmospheric pressure and 

water vapour content comes from the ECMWF numerical weather prediction model, whereas 

climatologic values are used for ozone concentration and aerosol optical thickness. 

The largest uncertainties in the albedo and the DSSF flux product are non-detected clouds and 

systematic errors in the aerosol optical thickness values. Since the aerosol optical thicknesses are 

obtained from a climatologic database, the quality of both products can be improved by using a 

satellite derived aerosol product. More information on the Land-SAF can be found on 

https://landsaf.meteo.pt/. 

4.2.4 O&SI SAF 

The Ocean & Sea Ice Satellite Application Facility (O&SI SAF) provides information for the ocean 

on surface temperatures, ice coverage, radiation fluxes and surface winds. This SAF needs 

information on aerosols for mapping of ocean surface reflectances and radiation fluxes. More 

information on the O&SI SAF can be found on http://www.osi-saf.org. 

4.3 Cooperation with other SAFs 

The CM-SAF aims to continue developing retrieval algorithms for parameters that are considered 

important for climate monitoring. A new development could be an aerosol product from SEVIRI. 

Such a development could be a federate SAF activity. 

At the CM-SAF data user conference in Nurnberg, Germany, September 2005 the author of this 

report presented the preliminary results of the study on aerosol retrievals from METEOSAT-8. 

Based on the results of this presentation the plan came up to develop aerosol optical thickness and 

aerosol type products from MSG/SEVIRI and GOME in the framework of a federate SAF activity. 

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The reasons for performing aerosol retrievals within the CM-SAF is that aerosol is a nuisance 

parameter in many other CM-SAF-products, such as (thin) cloud products, surface radiation 

parameters and surface / land albedo products. Moreover, aerosol properties are an important 

climate product. 

To develop a mature aerosol product within a federate SAF activity the following developments are 

anticipated: 

- Further development of the METEOSAT-8/SEVIRI aerosol product, 

- Development of a scheme to separate absorbing from non-absorbing aerosols, 

- Development of a single usable product for the research community by integrating different 

research methods (METEOSAT, GOME and ATSR-2), 

- Sophisticated co-location is needed to ensure that all products look at the same place and time, 

- Validation of the output product with in situ and other measurements. 

EUMETSAT would favour a federate SAF activity on aerosols. In preparation for the Continued 

Development and Operational Phase (CDOP) the participating SAFs should include their 

contribution to such a federate SAF activity in their CDOP proposals. Even though such activity 

may have a reasonably low priority in each proposal, the fact that more proposals have the same 

activity would raise the priority of this activity considerably. 

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5 Overview of existing AEROSOL retrieval algorithms 

5.1 Background 

We recall briefly the status of the techniques for aerosol remote sensing over land. A good starting 

point is the review of King et al. 1999 from which we extract one table summarizing main classes 

of algorithm (see Table 1). 

Table 1: Techniques for remote sensing of global aerosol properties from space. Here τ a denotes 

aerosol optical thickness, ω 0 the single scattering albedo, r e the aerosol particle effective radius, 

and n c (r) the columnar aerosol size distribution. 

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5.1.1 Algorithms using angular information 

The POLDER aerosol retrieval over land is based on a Bidirectional Reflectance Distribution 

Function (BRDF) model deduced from polarized and multidirectional radiances measurements in 

the red and the NIR channels. This model has empirical coefficients adjusted for different classes of 

land surfaces (Deuzé et al. 2001). Because of the geometry dependence of the surface reflectance, 

multidirectional measurements are a promising method for the aerosol property retrievals over land. 

Veefkind et al. (2000) developed a method based on the dual-view image radiometer of the Along 

Tracking Scanning Radiometer 2 (ATSR-2). The method is based on the wavelength-independent 

ratio between forward and nadir view surface reflection that depends only on the sun/satellite 

geometry. The value of the ratio is first estimated in the Mid-IR channel of ATSR-2 where the 

atmospheric contribution is neglected. It can be combined with another sensor with high spectral 

resolution to obtain information about the aerosol model to use. For example an algorithm 

combining AASTR and SCHIAMACHY aboard ENVISAT or ATSR-2 and GOME aboard ERS-2 

has been developed and provides AOT and aerosol types (Holzer-Popp et al. 2002). Another 

multidirectional measurements algorithm has been developed for MISR. The algorithm uses the 

presence of spatial contrast to derive empirical function representation of the angular variation of 

the scene reflectance, which is then used to estimate the path radiance (Martonchik et al. 1998). 

Figure 1 and 2 present examples of AASTR/SCHIAMACHY and POLDER-3/PARASOL 

retrievals, respectively. 

5.1.2 Algorithms using spectral indices 

MODIS uses a classical approach to predict the surface reflectance, which relates the TOA 

radiances in the IR at 2.13 µm to the visible surface reflectance in the blue (0.47 µm) and in the red 

(0.66 µm). Aerosol retrieval (spectral optical thickness in two channels) is performed for targets as 

bright as 0.25 in reflectance unit in the IR and an average is performed in a 20x20 box after 

rejecting some outliers, thus resulting in a 10x10km spatial resolution for the aerosol product 

(Kaufman and Tanré, 1998). After intensive validation with AERONET data, Remer et al. (2005) 

show that the AOTs accuracy is within ∆τ = ±0.05 ±0.15τ. The surface coverage is good as can be 

seen in Fig. 3. This product is available twice a day (Terra, ~10:30 p.m. and Aqua, ~1:30 p.m.). 

MERIS aerosol property retrievals over land are based on the use of pixels covered by vegetation. 

Dense Dark Vegetation pixels are selected using the Atmospheric Resistant Vegetation Index 

(ARVI) as spectral index, which uses Rayleigh corrected reflectances at 443, 670 and 865 nm. 

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Reflectance in the blue and in the red for less dark pixels may be predicted empirically using a 

linear relationship between ARVI and surface reflectance as noticed from MOS, SeaWIFS and 

MERIS sensors (Borde et al. 2003 and Santer et al. 2005). 

Figure 1: Example of the daily aerosol optical thickness product derived from the synergistic use 

of AATSR and SCHIAMACHY for the 14 th of July 2005. Image taken from the GMES Service 

Element PROMOTE Website (http://www.gse-promote.org/). 

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Figure 2: Example of Aerosol Optical Thickness level 2 product over land at 865 nm from the 

POLDER-PARASOL instrument on 13 th of July 2005. (source: ICARE web server http://wwwicare.univ-lille1.fr/) 

Figure 3: Example of the daily aerosol optical thickness product over land and ocean derived 

from MODIS Aqua 

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Figure 4: Example of the daily aerosol optical thickness product derived from MISR on 14 th of 

July 2005. The colour scale ranges from 0 to 0.5. source: EOS LARC MISR web server 

5.1.3 GOES 

The GOES Aerosol-Smoke Product (GASP) provides aerosol optical depth retrievals over the U.S. 

at 4 km spatial resolution and 30 minute intervals. The retrieval is performed over ocean and land 

and is available once per day for the full disk of the Earth from GOES-East (providing information 

for South America). The algorithm is described in Knapp et al. (2002, 2005). The main approach is 

to build a surface reflectance reference by selecting, for each pixel, the second darkest image in the 

composite time period and then to perform aerosol retrieval over land based on Look Up Tables of 

gaseous and Rayleigh corrected reflectance for various surface reflectance and aerosol loading (see 

an example of the AOT map over the US East coast for the 19 April 2005 in Fig. 5). The second 

darkest pixel is chosen to reduce the effect of cloud shadows (shadowed pixel reflectance is 

generally darker than surface -alone- reflectance). The reflectance reference is computed for a 

composite time period varying along the year from 7 days to 28 days. The length of the time period 

depends on the accuracy expected on the reflectance reference. More observations increase the 

chance of observing cloud-free and aerosol-free day, but if too many days of observations are used, 

the surface reflectance may change. For the eastern US, Knapp et al. found that 14-days period was 

a good compromise. The main limitation is that it requires that the TOA reflectances increase with 

increasing aerosol loading, and thus it does not work for a combination of bright surface and 

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absorbing aerosols as one can see in Fig. 6 (For example, for a surface albedo of 0.4 and aerosol 

model with a single scattering albedo of 0.81, the TOA reflectance decreases with the AOT) The 

aerosol model is assumed to be continental and the accuracy of the retrieved AOT is ±0.13 when 

compared to AERONET data. A global analysis for one year of data suggests that the optimal 

composite time period is around 14 days and that a continental aerosol model is most of the time 

well adapted for the retrieval of an AOT. Almost no bias is found between GOES and AERONET 

AOT's when the surface reflectance reference is found after applying a residual aerosol correction 

using a background AOT of 0.02. 

Figure 5: Example of Aerosol Optical Thickness level 2 product over land and ocean retrieved 

from the GOES 8 instrument on 19 th of April 2005 over US East Coast. (University of Maryland at 

Baltimore County server : http://alg.umbc.edu/usaq/archives/000474.html) 

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Figure 6 : The reflection function as a function of aerosol optical thickness and surface 

reflectance for a Junge size distribution given by n(r) ∝r 4 , where λ = 0.61 µm and θ 0 = 40°. 

Panels (a) and (b) apply to θ = 60° and φ = 0° but for different single scattering albedo; (c) and 

(d) to nadir observations (θ = 0°) (adapted from Fraser and Kaufman 1985). 

5.1.4 MSG/SEVIRI 

The MSG/SEVIRI sensor is unique and although it is not optimised for observation over land 

surfaces, because of the lack of a blue channel, this sensor has several advantages for aerosol 

retrievals over land. The time sampling of 15 minutes gives access to the diurnal cycle of the 

aerosol properties and allows the detection of rapid changes in the atmosphere. Moreover, it 

increases the chance of having cloud-free observations. The thermal IR channels allow the 

determination of a good cloud mask. The constant viewing geometry and target localisation allow 

an easy co registration of images. And finally, the variable solar angles allow some angular 

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sampling of the surface BRDF and/or the aerosol phase function 

Beside the above-mentioned new aspects that are especially dedicated to MSG/SEVIRI, it is also 

possible to use more classical methods. First, for MERIS a method is used that links the vegetation 

index empirically to surface reflectance. The lack of a blue channel on SEVIRI will reduce the 

vegetation index mostly to an NDVI, which is not independent of atmospheric turbidity. Thus 

NDVI is not a robust index for the prediction of the surface reflectance if it is computed from a 

single observation. However, a spectral index built with multiple views, and using information from 

the blue part of the spectrum of the High Resolution Visible (HRV) channel should not be 

discarded. Nevertheless, the use of the HRV channel is not trivial and studies are needed to 

investigate methods to separate the reflectances in blue from the HRV channel reflectances. Second, 

the MODIS like approach that uses visible surface reflectance at 670 nm from the channel at 1600 

nm (King et al. 1992). This approach has been described in an early stage of the operational 

algorithm for the LandSAF. To our knowledge no application of this method on real MSG data has 

been published. Finally, in particular conditions where one can assume that the aerosol model and 

loading vary little in time, it is possible to use consecutive observations with different sun zenith 

angles and, according to the reciprocity principle, retrieve aerosol properties. This approach follows 

the principles AATSR or MISR algorithms that are designed for multiple view angles. Note that the 

paper devoted to Meteosat by Pinty et al., (2000) describes a method to derive surface albedo by 

decoupling surface and aerosol contribution from a set of images acquired during the day, following 

a method close to the MISR algorithm. However, the main constraint of this method is that the 

geophysical system shall not vary much during the day, which contradicts with the wish to use 

MSG as a tool to measure the rapidly changing aerosol fields. 

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6 MSG/SEVIRI methodology 

6.1.1 Introduction 

This chapter presents the preliminary results of MSG/SEVIRI retrievals of aerosol properties over 

land. We decided to demonstrate the potential MSG/SEVIRI for aerosol retrievals by developing a 

simple algorithm to retrieve the AOT from SEVIRI. We focused on the aerosol optical thickness 

(AOT), which is a key parameter for measuring the aerosol loading in the atmosphere. Most of the 

assumptions used in the algorithm were made to support the development of an aerosol product 

from MSG/SEVIRI in a short time. From this prototype, a robust algorithm can easily be obtained 

after implementation of radiative transfer calculations. 

In short, the AOT is retrieved from the reflectances at one wavelength (i.e. 670nm) in two steps, 

using a method similar to the one Knapp et al. (2005) developed for GOES-8. The first step is the 

creation of a 3D images data set (for a period of 15 days), from which we built a mosaic 

(reflectance surface maps of reference) where the darkest pixel of the time period is chosen as the 

“clear sky” condition. In the second step these surface reflectance map are used to retrieve AOT for 

each images. 

6.1.2 Method description 

The method is based on the unique capabilities of the MSG/SEVIRI measurements. We take 

advantage the high temporal resolution (15 minutes) and fixed viewing geometry of SEVIRI. We 

can reasonably assume that for given acquisition time (i.e. 8/00 UTC) within a period of 15 days: 

• The solar zenith angle and the relative azimuth angle do not vary significantly. So for one pixel, 

the viewing geometry is stable. For example, at 12:00 UTC and over the fifteen first day of July 

2005, the solar zenith angle varies of a value between 0.5° and 1.6° depending on the Earth's 

locations. The variation in azimuth angle is between -1.2 and 4.5°. 

• The surface properties do not change (except for particular and exceptional cases such as floods, 

fires and snowfall). 

Measurements that are made every day for the same pixel at the same time can then be easily 

compared. After screening the clouds and assuming no change in surface reflectivity, the 

differences in the measurements come from differences in the state of the atmosphere or changes in 

aerosols properties. In a first step we will explain how we get ride of the surface by monitoring the 

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surface using characteristics of SEVIRI. We will present the retrieval of the surface contribution 

and define the surface reflectance map of reference. Knowing the reflectance of the surface for each 

pixel and observation time, we will correct measurements for Rayleigh scattering. Using simple 

assumptions, aerosols optical thickness at a visible wavelength will be retrieved. The results will be 

compared with some AERONET data from stations available in the area of interest. Finally, we will 

go back to the assumptions we made in the retrieval scheme to explain the weakness of the method. 

6.1.3 Reference Surface Reflectance Map 

The signal measured by the instruments is a mix of different contribution of the Earth-Atmosphere 

system: surface, molecules, aerosols, gases and clouds. 

Cloud Masking 

First of all, cloudy pixels have to be removed for aerosol retrieval. Because of the short time we had 

to develop our methodology we used a simple cloud mask. It is based on threshold tests on the 

reflectance at 670 nm, 865nm and 1.6µm and brightness temperature at 11µm (see Appendix C). To 

avoid pixels containing cloud border, we also remove neighbouring pixels. Finally, the cloud mask 

we used does not detect cloud shadows, which produce pixels with lower reflectances than the 

surface reflectance alone. 

Rayleigh correction 

Once the cloud masking is done, the contribution of the Rayleigh scattering at 670nm is removed 

from the signal using classical method adapted to the spectral properties of the channel. The 

remaining quantity (R ray_corrected ) is then the contribution of the surface and the aerosols. 

Surface contribution 

The surface reflectance map is built from the 3D data set of the Rayleigh corrected reflectances 

corresponding to the cloud-free cases. For each time slot (from 5:00 UTC till 19:00 UTC, every 15 

minutes) we have, at maximum, a set of 14 values of the Rayleigh corrected pixel reflectances. The 

three dimensions of the data set are the pixel longitude, latitude and the date (1 to 14 July). The 

retrieval method assumes that the minimum value corresponds to the zero aerosol case, or at least, 

the case with the least aerosol contamination. In another words, the minimum is the value of the 

surface reflectance (R surf_ref ) seen in the correct geometry. The value obtained is assumed to 

correspond with the reflectance for a lambertian surface as simulated in radiative transfer 

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calculations. This assumption is mainly true when neglecting interaction between surface and 

molecules and for viewing geometries far from the “hot spot” of the vegetation (Knapp et al., 

2002). The errors due to this assumption will be much lower for geostationary satellites because the 

bulk of observations are geometrically far from the “hot spot” (Knapp et al., 2005). The reference 

map of surface reflectance is generated for each observation time by applying above described 

method to all the image pixels. Finally, Fig. 7 summarises in a diagram the method to retrieve the 

reference surface reflectance map. 

Fig. 8 shows for every hour the reference surface reflectance map for the period 1-14 th July. The 

reference area covers almost all the continental areas. Noise appears for low solar elevations in the 

morning. This can be due to the cloud masking method, which is not robust for such sun-satellite 

geometries. Around 13:00 UTC some high reflectances appear in middle-Europe but there are large 

doubts about the accuracy of cloud detection in this area (lots of partly cloudy situations and 

scattered clouds). 

Fig. 9 gives the time series of the reference maps of surface reflectance for several locations: the 

Flandres in Belgium, the Landes forest in South-West of France, the Andalusia in the South of 

Spain and the north of Algeria. Temporal variation of the surface reflectance seems reasonable with 

regards to vegetation reflectance and biomass models used in MERIS Look-up-tables, i.e., variation 

of the surface reflectance with the scattering angle is similar to that used in MERIS LUT. The 

erroneous values of the surface reflectance for the Flandres area early in the morning is due to 

clouds, which are not detected by our cloud mask. Independently of such erroneous values, the 

curves are affected by a noise of small amplitude. The retrieval of ground reflectances is altered by 

differences in the residual aerosol loading within the composite period and differences in the 

observation date that corresponded to the minimum surface reflectance at the observation time. In 

the near future, one can find a solution to minimize the noise that is based on the following facts: 

• The surface BRDF varies smoothly with the solar angles along the day. One is allowed to apply 

a fit to the surface BRDF versus time. 

• A high reflectance value once or two times during the day indicates either a thin cloud or high 

aerosol load that was not detected by cloud masking or because no cloud free observations 

occurred during the composite time period at that particular time and locations. Those points 

should be discarded. In the data sample we used for this study, less than 0.01 % of the pixels 

were detected cloudy during the entire 14 days period. 

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• A low reflectance value that occurs once or twice a day indicates an anomalous attenuation of 

the signal coming from that point. In most cases these values are caused by shadowing. It could 

also be extinction by absorbing aerosols, but these aerosols would most likely have a darkening 

effect on the surface reflectance of adjacent pixels and observation times as well. The overall 

methodology is questionable there. 

Figure 7: Flow chart of the processing yielding the reference surface reflectance map for one 

particular time of the day. 

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05:00 UTC 06:00 UTC 

07:00 UTC 08:00 UTC 

09:00 UTC 

0:00 UTC 

1 

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11:00 UTC 12:00 UTC 

13:00 UTC 14:00 UTC 

15:00 UTC 16:00 UTC 

17:00 UTC 

8:00 UTC 

1 

Figure 8: Reference surface reflectance map over Europa from the period 1-14 July 2005 in 

SEVIRI channel 1. The color scale is from 0.00 (dark blue) to 0.40 (red). 

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Figure 9: Example of surface reflectance of reference at 670nm for different geographical 

locations from the period 1-14 th July 2005. 

In conclusion, the reference surface reflectance maps are built from the minimum value of the 

Rayleigh corrected reflectance over a 14 day period. Although they are contaminated by a residual 

aerosol loading but we have neglected this so far. The spatial cover is very good, and only in small 

areas very high or missing values are found. It is suggested that these areas were most of the time 

covered by clouds during the compositing period. The spatial variations of the reflectance at 670nm 

are most of the time smooth. However, in some part of central Europe and Norway large variations 

in surface reflectance are observed, which are due to frequent cloud cover. 

Although we limited our report to the European continent, it can be easily extended to the disc 

observed by MSG/SEVIRI. Thus, the reference surface reflectance map has also been calculated for 

the complete northern part of the disc as shown in Appendix A. 

6.1.4 MSG/SEVIRI aerosol optical thickness retrieval algorithm 

Knowing the surface reflectance in the appropriate geometry, the retrieval of the aerosol optical 

thickness is made in two steps. In the first step we estimate the aerosols path reflectance using a 

rough approximation. The aerosol path reflectance (R aer ) is the difference between the Rayleigh 

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corrected reflectance and the surface reflectance of reference such as: 

R aer =R Ray_corrected -R surf_ref 

In the second step we assume only single scattering and the aerosol type (and then phase function), 

the aerosol optical thickness (AOT) is derived as follows: 

AOT=Raer . 4 . cos(θs) cos(θv)/ P(Θ) 

where: θs is the solar zenith angle, θv is the viewing zenith angle and P(Θ) is the value of the 

aerosol phase function for the scattering angle Θ. 

We assume a continental model with an Angstrom coefficient of 1.0, a real refractive index m r of 

1.44 and an imaginary index mi of 0.0 which means there is no absorption (This aerosol model is 

used in MERIS LUT's). Of course the single scattering assumption in the radiative transfer model 

will lead to erroneous retrieval for large value of the AOT. 

Fig. 10 resumes the schema of the retrieval method and Fig, 11 shows the aerosol optical thickness 

at 670nm for the 14 th July 2005 over Europe and for every hour between 05:00 UTC and 18:00 

UTC. The AOT maps are very noisy for partly cloudy areas as seen in Fig.11. Efforts must be 

planned to use and apply a better cloud mask. However, the coverage is very good. An aerosol front 

crossing the Pyrenees Mountains can be seen and be followed with correct land-sea continuity. A 

dust event over North Africa is detected both over land and the Mediterranean Sea. 

We also mapped the retrieved aerosol optical thickness over ocean assuming a Lambertian surface 

with an albedo of 0.01 at 670nm. But, we plan in the near future to derive the sea reflectance with 

the identical method used for the continental surface reflectance. In Appendix B maps of retrieved 

AOT are given for the forest fires in Portugal around the 10 th July 2005. 

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Figure 10: Flow chart of the aerosol optical thickness retrieval method. 

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05:00 UTC 06:00 UTC 

07:00 UTC 08:00 UTC 

09:00 UTC 10:00 UTC 

11:00 UTC 12:00 UTC 

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13:00 UTC 14:00 UTC 

15:00 UTC 16:00 UTC 

17:00 UTC 18:00 UTC 

Figure 11: Aerosol optical thickness at 670nm over Europe from 05:00 UTC till 18:00 UTC the 

14 th July 2005 in the SEVIRI channel 1. The color scale is from 0.00 (dark blue) till 0.40 (red). 

6.1.5 Results and Comparison with AERONET and MODIS 

The AOT retrievals over the period 1 – 14 July 2005 were compared for several days to AERONET 

measurements at Toulouse (France), Ispra (Italy), Blida (Algeria) and El Arenosillo (Spain). AOT's 

from MSG have been averaged within a 5x5 pixels area around the AERONET stations. In general, 

the AERONET and MSG retrieved AOT values for cloud-free areas correlate reasonably well and 

are in the same order of magnitude. However, MSG tends to under-estimate AOT, which is 

probably due to an overestimation of the surface reflectance. The latter could be caused by residual 

aerosol loadings in the surface reflectance values that are not corrected for in the current retrieval 

scheme. 

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Fig. 12 to 15 present for the 4 AERONET locations the relationship between observed and 

MSG/SEVIRI retrieved Aerosol Optical Thickness. Big discrepancies appear for some hours of the 

day (around 13:00 UTC for Toulouse and 12:00 UTC for Blida), which should be due to the use of 

an inappropriate phase function, especially for particular scattering angles. For the El Arenosillo 

station, MSG tends to overestimate the AOT between 12:00 and 15:00 UTC. We can remark that 

the standard deviation in the box of 5x5 pixels is high during these hours, in the order of 0.2, 

whereas the standard deviation is less than 0.06 for the rest of the day. Large standard deviations 

can be used as an index of the quality, indicating large spatial variability of the surface reflectance 

or the presence of thin clouds. As expected the agreement is better for sites where the surface 

reflectance is not too high (Toulouse and Ispra) and when the aerosol is surely non dust. In other 

places with high surface reflectance and presence of dust, the quantitative comparison is not yet 

satisfactory. 

Fig.12: Temporal variation of the aerosol optical thickness at 670nm derived from AERONET and 

MSG for the 2 nd and 14 th July 2005 over Toulouse (South-West of France) 

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Fig.13: Temporal variation of the AOT at 670nm derived from AERONET and MSG for the 12 th 

and 14 th of July over Ispra (Italy) 

Fig.14: Temporal variation of the AOT at 670nm derived from AERONET and MSG for the 10 th 

and 14 th of July over El Arenosillo (Andalousia-(Spain) 

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Fig.15: Temporal variation of the AOT at 670nm derived from AERONET and MSG for the 12th 

and 14 th of July over Blida (Near Alger - Algeria) 

In Fig. 16 we present the comparison between the MSG/SEVIRI and the MODIS aerosol product. 

The two instruments capture the main patterns of the aerosol field but quantitative comparison is at 

the moment difficult since we do not retrieve the AOT at the same wavelength. The background 

AOT seems to agree well and large dust outbreak over North of Algeria and the Mediterranean Sea 

is seen by the two instruments. There is a good land/ocean continuity of the aerosol front passing 

over Spain and South of France for MSG and it is not so clear for MODIS. However some 

discrepancies occur for cloud covered regions, for example in the centre of France where high AOT 

are retrieved from MSG and MODIS observes mainly clouds instead of aerosols. There is 

agreement between the two instruments over England where very high AOT are found. This 

emphasizes one more time the fact that our cloud mask still has to be improved. 

Finally these results are obtained with lots and sometimes strong assumptions but they are really 

promising, especially because changes in aerosol amount over some locations are also seen by 

MSG. Better agreements should be obtained using exact radiative transfer calculations and filters on 

the number of pixel used for the mean calculation, threshold on the standard deviation and the 

proximity of clouds. 

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(a) 

(b) 

(c) 

Figure 16: Comparison of AOT derived from MSG and MODIS on 14 th of July 2005 around 10:45 

UTC. (a) MSG AOT in channel 1, the colour scale is ranging from 0 to 0.7; (b) MODIS AOT over 

land and ocean at 550 nm, the colour scale is ranging from 0 to 0.8; (c) cloud fraction detected by 

MODIS 

6.1.6 Assumptions we made to accelerate development of the algorithm and calculations 

In the framework of this three months “visiting scientist” activity on the feasibility of aerosols 

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retrievals from SEVIRI there was limited time to elaborate a complete and robust algorithm. The 

feasibility of MSG/SEVIRI for retrievals of aerosol properties over land could be demonstrated, but 

with a lot of assumption that could have been avoided if more time would have been available. 

We used a simple cloud mask. The algorithm can then be upgraded and improved by using high 

performance cloud mask from the CM-SAF or NWCSAF. For the reference surface reflectance 

map, the cloud mask should include the detection of neighboured cloudy pixels (i.e. cloud free pixel 

near cloudy pixels) to exclude effects due to cloud borders. In future algorithm developments it is 

preferable to use the official cloud mask and to include a morphological mask that removes 

shadowed pixels that have lower reflectances than the surface. In appendix A the results for the 

northern part of the disc, as observed by MSG, are given using a more restrictive cloud mask. We 

only used one aerosol model: a continental model used in MERIS LUT's. It would be interesting to 

use appropriate model just based on simple climatology: desert dust over Africa, maritime model 

over ocean and burning biomass over Amazonia. As presented in the next section called “future 

improvements”, the use of others channels (0.865 µm and 1.650 µm) should be researched to get 

information on the spectral dependence of aerosols and an index of the size distribution which will 

be an indicator of the aerosol type. 

We used the single scattering assumption to simplify the calculations and to avoid use of LUT's, 

which could not be prepared due to the limited time available for this “visiting scientist” activity. Of 

course it is a source of uncertainty and systematic bias. Neglecting multiple scattering in the 

atmosphere is a strong assumption, especially when large amount of aerosols occurs and in the case 

of large solar-viewing zenith angles. This point can be easily solved by using Look Up Tables 

computed with an accurate radiative transfer model. The limitation is that a lambertian surface has 

to be assumed in those computations. However it is possible to correct for bidirectional effects 

when one knows the shape of the BRDF (Vermote et al. 1997, Ramon et al. 2001), which can be 

partly measured by SEVIRI. So an iterative approach seems possible here. 

7 Conclusions and perspectives 

SEVIRI is a powerful instrument for monitoring rapidly changing parameters of the atmosphere, 

and tropospheric aerosols fall into this category (see Appendix B for forest fires in Portugal in July 

2005). The survey of user requirements showed the need for an aerosol product from MSG/SEVIRI 

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for both the climate research community and Satellite Application Facilities financed by 

EUMETSAT. The application of such a product would either be monitoring of aerosols 

climatologies, nowcasting of air quality or the assimilation of aerosol properties in surface and 

cloud property retrieval algorithms. For air quality and visibility monitoring it is important to follow 

variations in aerosols load and type. For climatological applications it is important to assess the 

diurnal amplitude and variability of aerosol properties and verify the added value of geostationary 

measurements as compared to polar sensor measurements. In general, it is important to use satellite 

data with a high temporal resolution over areas with high cloud cover since it increases the chance 

to see “through” the cloud deck As demonstrated in a pioneering study using GOES-8, Knapp et al. 

2005 showed an approach that takes advantage of the geostationary viewing geometry to minimise 

the uncertainties in the knowledge of the surface reflectance, which is always the main source of 

error in aerosol retrieval techniques, i.e. multi spectral, multi angular or using polarization of the 

light. 

We analysed in this study the possibility to use the same approach described for GOES to SEVIRI. 

For that purpose we used a test data set, consisting in every slot acquired from 1st to 14th of July 

2005 and we built a reference surface reflectance map for channel 1. We decided to go further and 

retrieve AOT in a straightforward manner, which showed good agreement for several AERONET 

stations located in Europe. This exercise with real data was important because it gave confidence in 

the methodology and helped us to clarify where some effort has to be put now: 

- There is plenty of room for improving the shadow detection and removal based on geometric 

and spectral tests. For example Knapp et al. (2005) just built there reference map using the 

second darkest image during the compositing period supposing that a pixel in a shadow twice is 

a very rare event. 

- Filtering the reference reflectance map using appropriate spatial filter and temporal filter and 

extend it for the whole year. Define rules for automatically generating this reference map using 

a rolling composite period and a varying composite time depending on season and location 

- The surface BRDF estimates can be validated over AERONET sites by applying full 

atmospheric corrections based on AOT and aerosol types retrieved using solar radiance 

extinction and sky radiances measurements. 

- The complementary work is to minimise the bias between AERONET AOT and SEVIRI 

retrieved AOT for a set of free parameters of the method, mainly the background aerosol 

loading τ res that is used to correct for SEVIRI minimum Rayleigh corrected radiance found in 

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the compositing time and the compositing period itself T comp 

- Accurate radiative transfer with multiple scattering, including sphericity of the atmosphere for 

large angles. 

Then a lot of work could be done to improve this one-channel algorithm. Several questions have to 

be answered first: 

- To what extent can the aerosol properties be considered stable at a particular location within a 

couple of hours or an entire day in order to exploit the multi-angular information of SEVIRI 

measurement and thus to better constrain the aerosol model 

- To what extend can the HRV channel help improve the spatial resolution and the spectral 

dependence of the aerosol scattering properties toward the blue 

The preliminary results of this study demonstrated the potential of SEVIRI to retrieve aerosol 

properties. The comparisons with AERONET data are very promising and showed that SEVIRI 

could be used to derived AOT values. In general the variability is well reproduced, although some 

stations show biases between MSG and AERONET. There are several suggestions for improving 

the presented aerosol retrieval algorithm, and develop a mature algorithm for accurate monitoring of 

the diurnal variability (15 minutes time sampling) in aerosol load at a spatial resolution of around 5 

km. This would be a unique data set that cannot be derived from polar platforms. MSG/SEVIRI 

aerosol retrievals are possible both over dark targets (ocean and forest) and brighter targets over 

land (limitations must be taken into account for very bright target such as desert), because surface 

reflectances can be determined with a high accuracy. The latter is an improvement as compared to 

retrievals from polar orbiters such as MODIS. 

References 

Borde, R., Ramon, D., Schmechtig, C., and Santer, R., “Extension of the DDV concept to 

retrieve aerosol properties over land from the Modular Optoelectronic Scanner sensor”, 

International Journal of Remote Sensing, 24, 1439-1467, 2003. 

Hess, M., Koepke, P., Schult, I. Optical properties of aerosols and clouds: the software package 

OPAC. Bull. Amer. Meteor. Soc. 79, 831-844, 1998. 

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Holzer-Popp, T., M. Schroedter, and G., Gesell, “Retrieving aerosol optical depth and type in the 

boundary layer over land and ocean from simultaneous GOME spectrometer and ATSR-2 

radiometer measurements, 1, Method description”, J. Geophys. Res., 107, D24, 2002. 

Kaufman and Tanre, 1998: Algorithm for Remote Sensing of Tropospheric Aerosol from 

MODIS. Products: MOD04_L2, MOD08_D3, MOD08_E3, MOD08_M3. ATBD Reference 

Number: ATBD-MOD-02. (see http://modis-atmos.gsfc.nasa.gov/MOD04_L2/atbd.html) 

Knapp, K. R., R. Frouin, S. Kondragunta and A. Prados, “Toward aerosol optical depth 

retrievals over land from GOES visible radiances: determining surface reflectance”, Int. J. Remote 

Sensing, 2005, in press. (doi: 10.1080/01431160500099329) 

Knapp, K. R., T. H. Vonder Haar, and Y. J. Kaufman, “Aerosol optical depth retrieval from 

GOES-8: Uncertainty study and retrieval validation over South America”, J. Geophys. Res., 107 

(D7), 4055, 2002, (doi:10.1029/2001JD000505). 

Koepke, P., Hess, M., Schult, I., Shettle, E. Global aerosol data set. Technical Report 243, MPI 

Meteorologie, Hamburg, 1997. 

King, M. D., Y. J. Kaufman, D. Tanré and T. Nakajima, “Remote sensing of tropospheric 

aerosols from space: past, present, and future”, Bulletin of the American Meteorological Society, 

Vol. 80, No. 11, 1999. 

Martonchik, J.V., Diner, D.J., Kahn, R.A., Ackerman, T.P., Verstraete, M.M., Pinty, B., and 

Gordon, H.R., “Techniques for the retrieval of aerosol properties over land and ocean using 

multiangle imaging”. IEEE Transactions on Geoscience and Remote Sensing, 36, 1212-1227, 1998. 

Pinty, B., F. Roveda, M. M. Verstraete, N. Gobron, Y. Govaerts, J. V. Martonchik, D. J. Diner 

and R. A. Kahn, “Surface albedo retrieval from Meteosat, 1. theory”, JGR, Vol. 105, D14, 18,099- 

18112, 2000. 

Ramon, D., and R. Santer, “Operational remote sensing of aerosols over land to account for 

directional effects”. Applied Optics, 40, 3060-3075, 2001. 

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Ramon, D., R.Santer, E. Dilligeard, D. Jolivet and J. Vidot, 2004. ”MERIS land product 

validation”, Proc. MERIS User Workshop, Frascati, Italy, 10 -13 November 2003 (ESA SP-549, 

May 2004). 

Remer, L. A., Y. J. Kaufman, D. Tanré, S. Mattoo, D. A. Chiu, J. V. Martins, R.-R. Li, C. 

Ichoku, R. C. Levy, R. G. Kleidman, T. F. Eck, E. Vermote, and B. N. Holben, “ The MODIS 

aerosol algorithm, products and validation”, J. Atmosph. Science, Vol 62, 947-973, 2005. 

Santer, R., D. Ramon, J. Vidot, and E. Dilligeard, “A surface reflectance model for aerosol 

remote sensing over land”, Int. J. Remote Sensing, 2005, accepted for publication. 

Smirnov, A., B. N. Holben, T. F. Eck, I. Slutsker, B. Chatenet,and R. T. Pinker, “Diurnal 

variability of aerosol optical depth observed at AERONET (Aerosol Robotic Network) sites”, 

Geophysical Res. Lett., Vol. 29, No. 23, 2115, 2002, (doi:10.1029/2002GL016305). 

Tanre, D., Y. K. Kaufman, M. Herman, and S. Mattoo, 1997. "Remote sensing of aerosol 

properties over ocean using the MODIS/EOS spectral radiances," J. Geophys. Res., 102, 17051- 

17067. 

Vermote, E., N. El Saleous, C. O Justice, Y. J. Kaufman, J. L. Privette, L. Remer, J.-C. Roger 

and D. Tanré, “ Atmospheric correction of visible to middle-infrared EOS-MODIS data over land 

surfaces: background, operational algorithm and validation”, JGR, 102, 17,731-17,141, 1997. 

Watts, P.D., M.R. Allen, C.T. Mutlow, 2000, Aerosol Properties derived from Meteosat Second 

Generation Observations, RAL/TN/EUM/004 

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Appendix A: Over the whole northern disk observed by 

MSG/SEVIRI 

In this appendix we show SEVIRI retrieved surface reflectance maps for the half-disc (northern 

hemisphere) observed by SEVIRI/MSG. To simplify calculations and data computing we did not 

correct from Rayleigh scattering. Compared to results in the rapport, we also applied a more 

restrictive cloud mask which explains the lack of data in middle Europe for example. 

Figure A1 presents examples of the map of the surface and molecular reflectances of reference 

(R surf+molec ) for Europe and Africa. 

Figure A2 presents the map of the AOT assuming that the aerosol path length, R aer , is equal to the 

TOA reflectance minus the surface and molecular reflectance of reference (R aer =R TOA - R surf+molec ). 

Over the sea a unique value of 0.01 has been used as surface and molecular reflectances. That is 

certainly an underestimated value. It explains also the high value of AOT over the seas. 

As expected, without Rayleigh corrections, results are much noisier. However, independent of the 

assumptions and the quality or the sensitivity of the cloud mask, a good spatial coverage is still 

expected over the disc observed by MSG. 

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06:00 UTC 14:00 UTC 

10:00 UTC 18:00 UTC 

Fig A1: Minimum reflectance over the period 1-14 th July 2005 in SEVIRI channel 1. It can be 

considered as the reference of the surface and molecular reflectance. The color scale is from 0.00 

(dark blue) to 0.40 (red). 

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Fig A2: AOT maps at 10:00 UTC and 08:00 UTC for the 14 th July 2005 using a very restrictive 

mask Clouds are in white and ocean in magenta. The colour scale is from 0.0 (dark blue) to 0.4 

(red) 

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Appendix B: Fires in Portugal during July 2005 

Several forest fires happened in Portugal in July 2005. The maximum of fires was reached the 12 th 

July as observed by the MODIS rapid response fire system (see fig. B1 and 

http://rapidfire.sci.gsfc.nasa.gov). Such events have been also observed with our method using 

MSG/SEVIRI. Maps of aerosol optical thickness over Portugal are given for some dates in Fig. B2 

and Fig. B3. 

Fig. B1: MODIS rapid response fire image for the 12 th July 2005 over Portugal. Red squares 

indicate areas in fire. 

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8 July, 14:00UTC 9 July, 08:00UTC 9 July, 14:00UTC 

10 July, 14:00 UTC 10 July, 16:00UTC 11 July, 11:00 UTC 

11 July, 14:00UTC 12 July, 11:00 UTC 12 July, 14:00 UTC 

Fig. B2: Map of aerosol over Portugal as retrieved from MSG/SEVIRI for several dates between 

the 8 th and 12 th July 2005. Color scale is from 0.0 (dark blue) till 1.0 (dark red). 

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Fig. B3: SEVIRI aerosol optical thickness at 670nm at 14:00 UTC for an area with fires in the 

North of Porto. 

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Appendix C 

Clear sky observations and contribution of the temporal 

resolution of MSG/SEVIRI 

Dominique Jolivet and Didier Ramon (HYGEOS) 

& 

Jérôme Riedi and Jean-Marc Nicolas (LOA) 

The algorithm to separate cloud free from cloud contaminated or cloud filled pixels has been 

developed with the intention of being applicable globally without requiring ancillary data such as 

surface temperature or atmospheric profiles. Each test implemented within the algorithm has been 

designed based on fundamental physical differences (emissivity, spectral behaviour) between 

clouds and surface. 

Many lessons learned from the Moderate Resolution Imaging Spectroradiometer (MODIS), 

regarding discrimination between clouds and heavy aerosols events or sunglint for instance (spatial 

variability, spectral behaviour), have been taken into account in the design of this cloud detection 

scheme. However, the present algorithm shall not be considered as just a simplified MODIS-like 

cloud mask because : (i) some tests have been adapted and modified to account for the differences 

in spectral channels, calibration and/or spatial resolution and make them applicable to SEVIRI, (ii) 

the number of tests used is currently much smaller than the one used in the operational MODIS 

algorithm (Ackerman et al. 1998; Platnick et al. 2003) and (iii) the decision logic differs 

significantly from the one used for MODIS (http://www-loa.univ-lille1.fr/~riedi/). 

The input to the SEVIRI cloud detection algorithm consists of normalized reflectances from the 

visible (0.6 and 0.8 µm) and near-infrared (1.6 µm) channels, whereas brightness temperatures are 

used from the thermal infrared channels (3.8, 8.7, 10.8 and 12.0 µm). Additionally, the algorithm 

uses ancillary data on solar and viewing geometry and a land/sea map but none is required from 

model reanalysis. There are spectral threshold and spatial coherence cloud detection tests that are 

different for land and ocean surfaces. The various tests implemented are grouped together in such a 

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way that the group will identify unambiguously specific cloudy or clear sky condition. Each group 

may contain tests aimed at detecting clouds or rejecting unphysical clear sky conditions. The 

different groups are then arranged in such a way that the independence between the tests is 

maximized. A different weight is assigned to each group of cloud detection tests. Additionally, 

groups have been implemented to specifically detect clear sky conditions and a similar weighting is 

applied. Finally, the results of all the tests and the sum of the weights are used to generate a 

summarized cloud mask that includes four confident levels: clear certain, clear uncertain, cloud 

uncertain and cloudy certain (similar to the cloud mask summary provided by the operational 

MODIS cloud mask product MOD35). 

Statistics on clouds and then on clear sky pixels available for aerosols detection are presented in this 

appendix based on results from this cloud detection scheme. 

Over the entire disk as observed by SEVIRI, the cloud mask of MODIS Aqua at 1kmx1km 

resolution (overpass at 14:30 UT) has been built from the composite of different orbits (see Fig. 1). 

In this map four classes of pixels can be found: clear confident in white, probably clear in red, 

probably cloudy in purple and confident cloudy in black. Only pixels flagged as clear confident are 

kept for aerosols retrievals. The histogram presented in Figure 2 shows the number of pixels which 

can be used for MODIS to derive aerosols properties. Almost 38% of the pixels are flagged as clear 

confident. 

Figure 3 shows the cloud mask from SEVIRI for the same day at 14:30 UT over the entire disk. 

Four classes are possible: clear certain (in light blue), probably clear (in yellow), probably cloudy 

(in red) and confident cloudy (black). Whereas MODIS AQUA observes only once a location the 

afternoon at 14:30UTC (MODIS TERRA overpass is morning at 10:30UTC), MSG/SEVIRI can 

observe every 15 minutes the same location. Taking into account the time period 12:00-16:00 UTC, 

16 observations of the same location are then available. One can note that we can compare with 

MODIS TERRA using the period 08:00-12:00 UTC. Figure 4 shows the number of clear sky 

observations for each SEVIRI pixels. 

Essential information is the spatial coverage of the product. The spatial coverage of the aerosols 

products from MODIS-AQUA and SEVIRI can be compared in Figure 5. These figures also 

indicate how rich is the set of useful measurements available from SEVIRI to derive aerosols 

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properties. As an example, areas in green are areas found cloudy with MODIS where at least one 

clear sky observations is obtained with SEVIRI. For these cases, an aerosol product can be derived 

from SEVIRI but not from MODIS. It implies that the spatial coverage increases with SEVIRI 

compared to MODIS. Areas in light blue are areas where the retrieval of an aerosol product is 

possible with MODIS and SEVIRI but SEVIRI can offer better information because more than 7 

clear sky observations can be used. Here SEVIRI is able to get a sample of the aerosol retrieval. In 

opposite, areas in purple are areas found always cloudy by SEVIRI whereas it is cloud free using 

MODIS. These areas are mainly at the border of the disk for large values of the viewing angles. It is 

more important for the east part because of the afternoon time (it will be the opposite corner -east 

border - when taking the morning period 08:00-12:00 UT). 

The difference in cloud contamination comes mainly from two reasons: 

1 MODIS has a better spatial resolution and it can be easier to see through fields of clouds. In 

particular that is the case for the scattered clouds (fields of very small clouds) in the south 

hemisphere on the west of South Africa. 

2 SEVIRI and its temporal resolution allow the observations of moving clouds. An area covered 

by clouds at one time can be then clear few minutes or hours later. 

Finally the cumulative histogram of Figure 7 shows the statistical distribution of clear sky 

observations available from SEVIRI for the entire disk. About 42% of the disk is always cloudy. So 

at least, one and more clear sky observations are available for 57% of the pixels. One can also see 

that 16 clear sky observations are available for about 7% of the pixels. 

Based on this comparison with MODIS-AQUA, the spatial coverage of an aerosol product would be 

about 57% for SEVIRI when it would be only 38% for MODIS. Moreover, targeting the same 

spatial coverage as MODIS (i.e. 38%), SEVIRI would be able to provide 5 times more observations 

than MODIS. 

As a preliminary conclusion (we only studied the 17 th August 2005), MSG/SEVIRI looks to be a 

real contribution for an aerosol product for the areas around the centre of disk whereas it looks 

tricky to use it for areas near the borders of the disk. 

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Figure 1: Cloud mask from MODIS -AQUA composite images (orbits at 14:30 UT ) over the MSG 

observed disk for the 17 th August 2005. White pixels are pixels flagged as clear confident. Red are 

probably clear, purple are probably cloudy and black are confident cloudy. 

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Issue : 1.0 


Figure 2: Ratio of the pixels of the entire disk which are not observed, flagged as confident cloudy, 

flagged as probably cloudy, flagged as probably clear and flagged as confident clear from the 

MODUS-AQUA cloud mask for the 17 th August 2005. 

- 47 -


Issue : 1.0 


Figure 3: Cloud mask from MSG/SEVIRI for the 17 th August 2005 at 14:30 UTC. The color scale 

is the following: light blue is clear confident, yellow is probably clear, red is probably cloudy and 

black is confident cloudy. 

- 48 -


Issue : 1.0 


Figure 4: Map of the number of clear sky observations from MSG/SEVIRI between 12:15 and 

16:00 UTC for the 17 th August 2005. Black means that no clear sky observations were found. 

Areas with a number of clear sky observations between 1 and 3 are in dark blue, between 4 and 7 

are in blue, between 8 and 10 are in light blue. Areas in white are areas where more than 10 clear 

sky observations were found. 

- 49 -


Issue : 1.0 


Figure 5: Map of the comparison between clear sky observation from MODIS-AQUA and 

MSG/SEVIRI (16 slots between 12:15 and 14:00 UT) taken into account the number of clear sky 

observations. 

Color scale 

Black : no clear sky observations from MODIS and SEVIRI 

Purple : Clear sky observation from MODIS but nothing from SEVIRI 

Yellow : Not observed by MODIS 

Green : No clear sky from MODIS but at least one clear sky observations from SEVIRI 

between 12:00-16:00 UTC 

Dark blue : MODIS and SEVIRI have both only one clear sky observation 

Light blue : Clear sky observed by MODIS and more than 7 clear sky observations available from 

SEVIRI during 12:15 – 14:00 UTC. 

- 50 -


Issue : 1.0 


Figure 6: Cumulative histogram of the number of clear sky observations available for each pixel 

of the entire disk observed by SEVIRI the 17 th August 2005 between 12:00 and 16:00 UT 

- 51 -

Aerosol retrievals from METEOSAT-8 - CM SAF

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