Aerosol retrievals from METEOSAT-8 - CM SAF
Aerosol retrievals from METEOSAT-8 - CM SAF 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 - 1 -
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<strong>SAF</strong> on Climate Monitoring Visiting Scientists Report Doc. No: 1.0<br />
Issue : 1.0<br />
Date : 4 October 2006<br />
<strong>Aerosol</strong> <strong>retrievals</strong> <strong>from</strong> <strong>METEOSAT</strong>-8<br />
Visiting Scientists Report within the<br />
<strong>SAF</strong> on Climate Monitoring of EUMETSAT<br />
Dominique Jolivet 1 , Didier Ramon 1 , Jerome Riedi 2 and Rob Roebeling 3<br />
1<br />
Hydrogéologie et Observation Satellitaire (Hygeos), 5 rue Héloïse,<br />
59650 Villeneuve d'Ascq, France<br />
2<br />
Laboratoire d'Optique Atmosphérique (LOA – University of Lille),<br />
USTL, Bât. P5; 59655 Villeneuve d'Ascq Cedex, France<br />
3 Royal Netherlands Meteorological Institute (KNMI)<br />
P.O. Box 201, 3730 AE De Bilt, the Netherlands<br />
September 2006<br />
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1 Contents<br />
1 CONTENTS ..........................................................................................................................................2<br />
2 ABSTRACT...........................................................................................................................................3<br />
3 INTRODUCTION ................................................................................................................................3<br />
4 USER REQUIREMENTS....................................................................................................................5<br />
4.1 NEEDS FROM CLIMATE RESEARCHES .......................................................................................................5<br />
4.2 NEEDS FROM <strong>SAF</strong>S..................................................................................................................................5<br />
4.2.1 <strong>CM</strong>-<strong>SAF</strong> ............................................................................................................................................5<br />
4.2.2 O3M <strong>SAF</strong>...........................................................................................................................................6<br />
4.2.3 LAND-<strong>SAF</strong> ........................................................................................................................................6<br />
4.2.4 O&SI <strong>SAF</strong> .........................................................................................................................................7<br />
4.3 COOPERATION WITH OTHER <strong>SAF</strong>S...........................................................................................................7<br />
5 OVERVIEW OF EXISTING AEROSOL RETRIEVAL ALGORITHMS .....................................9<br />
5.1 BACKGROUND .........................................................................................................................................9<br />
5.1.1 Algorithms using angular information ............................................................................................10<br />
5.1.2 Algorithms using spectral indices ...................................................................................................10<br />
5.1.3 GOES ..............................................................................................................................................13<br />
5.1.4 MSG/SEVIRI ...................................................................................................................................15<br />
6 MSG/SEVIRI METHODOLOGY.....................................................................................................17<br />
6.1.1 Introduction.....................................................................................................................................17<br />
6.1.2 Method description..........................................................................................................................17<br />
6.1.3 Reference Surface Reflectance Map................................................................................................18<br />
Cloud Masking.................................................................................................. 18<br />
Rayleigh correction........................................................................................... 18<br />
Surface contribution.......................................................................................... 18<br />
6.1.4 MSG/SEVIRI aerosol optical thickness retrieval algorithm............................................................23<br />
6.1.5 Results and Comparison with AERONET and MODIS ...................................................................27<br />
6.1.6 Assumptions we made to accelerate development of the algorithm and calculations.....................31<br />
7 CONCLUSIONS AND PERSPECTIVES.........................................................................................32<br />
REFERENCES ....................................................................................................................................................34<br />
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2 Abstract<br />
A user requirements and feasibility study is performed to assess the applicability of the Spinning<br />
Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) for<br />
retrieving aerosol properties and follow their evolution during the day. An overview is given of<br />
state of the art algorithms that are used to retrieve aerosol properties <strong>from</strong> today’s operational<br />
satellites, such as MODIS and MERIS. The applicability of SEVIRI for aerosol <strong>retrievals</strong> is<br />
demonstrated with <strong>Aerosol</strong> Optical Thickness (AOT) <strong>retrievals</strong> <strong>from</strong> a simple algorithm. Fifteen<br />
days of SEVIRI observations is used to derive clear sky surface reflectance maps, taking advantage<br />
of the high temporal resolution of MSG. The SEVIRI <strong>retrievals</strong> of AOT are compared to groundbased<br />
observations of AOT <strong>from</strong> AERONET.<br />
The results show the potential of SEVIRI to retrieve AOT and follow the diurnal evolution of AOT.<br />
The high temporal resolution and stable viewing geometry of MSG allows for the retrieval of very<br />
accurate surface reflectance maps. Due to these maps the aerosol properties can be retrieved both<br />
over ocean and land surfaces. The latter is an improvement compared to AOT <strong>retrievals</strong> <strong>from</strong> polar<br />
orbiting satellites, which can only retrieve AOT over dark surfaces because of uncertainties in<br />
surface reflectance maps. The SEVIRI and AERONET retrieved AOT values are in the same order<br />
and show similar diurnal variations, provided the aerosol type is known.<br />
3 Introduction<br />
<strong>Aerosol</strong> properties are an important parameter to be considered by Satellite Application Facility on<br />
Climate Monitoring (<strong>CM</strong>-<strong>SAF</strong>). Observation of the daily cycle of the aerosol and more frequent<br />
observations of the aerosol properties over regions with a high percentage of cloud cover is<br />
becoming an important requirement <strong>from</strong> users of Earth Observation community, which are<br />
scientific users (climate research, data assimilation) or people more oriented toward applications<br />
(public health, aviation, military). For example, over rural and urban sites where dust is not the<br />
main aerosol type the aerosol optical thickness may deviate 10 to 40% <strong>from</strong> the daily mean<br />
(Smirnov et al. 2002).<br />
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Previous generations of geostationary meteorological satellites, such as <strong>METEOSAT</strong> and GOES<br />
have been widely used to monitor aerosol properties over oceans (Moulin et al., 1997). However,<br />
the spectral channels of the first generation <strong>METEOSAT</strong> were rather limited for accurate <strong>retrievals</strong><br />
of aerosol parameters. More advanced aerosol <strong>retrievals</strong> have been done with polar orbiting<br />
satellites such as NOAA-AVHRR, MERIS, SEAWIFS and MODIS (Ramon 2001, Ramon<br />
2004,and Kaufman and Tanre, 1997). The Spinning Enhanced Visible and Infrared Imager<br />
(SEVIRI) onboard Meteosat Second Generation operates channels in the visible and near IR<br />
wavelength regions that are similar to e.g. NOAA-AVHRR and MODIS. Therefore, the launch of<br />
the MSG family is a great opportunity to test new ideas for filling the gap between <strong>retrievals</strong> <strong>from</strong><br />
geostationary and polar orbiting satellites, as SEVIRI combines the specific advantages of the<br />
geostationary orbit and geometric, radiometric and spectroscopic capabilities of the<br />
NOAA/AVHRR family.<br />
The objective of this Visiting Scientist activity is to perform a user requirement and feasibility study<br />
to catch the main steps of a future operational algorithm for retrieving the aerosol optical properties<br />
over land <strong>from</strong> the MSG/SEVIRI instrument. For the feasibility study we kept the shortest time<br />
resolution available <strong>from</strong> MSG i.e. 15 minutes. This constraint could be relaxed in the future but we<br />
wanted to start with the strongest constraint. The short time for this study forced us to restrict<br />
ourselves to the main difficulty of all aerosol retrieval algorithms that are applied over land<br />
surfaces, i.e. the removal of the surface contribution to the satellite signal. This is needed for all<br />
existing sensors and aerosol retrieval algorithms. In this study our approach is to demonstrate,<br />
mainly through real data analysis, that the core idea of this future algorithm is correct and that<br />
significant results are achievable with very simple assumptions. However, the output of this study<br />
should not be taken neither as an Algorithm Theoretical Basis Document nor an algorithm<br />
specification document. A lot of crude assumptions have to be refined in order to reach a complete<br />
and robust algorithm. A very recent paper under press and not available at the beginning of the<br />
work draws very similar conclusions as ours but for the GOES sensor (Knapp et al. 2005).<br />
The user requirements for an aerosol product over land <strong>from</strong> SEVIRI are detailed in chapter 3. In<br />
chapter 4 the main algorithm classes and performances are briefly reviewed. Then we will explain<br />
in chapter 5 our methodology and show some first results. We will end in chapter 6 with giving<br />
some research directions and list all potential improvements we foresee at the moment.<br />
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4 User requirements<br />
4.1 Needs <strong>from</strong> Climate researches<br />
Tropospheric aerosols are important components of the earth-atmosphere and ocean system. They<br />
affect climate through three primary processes. First, direct radiative forcing results when radiation<br />
is scattered or absorbed by the aerosols itself. Second, indirect radiative forcing results when<br />
enhanced concentrations of aerosols modify cloud properties. And finally, aerosols can have an<br />
indirect effect on heterogeneous chemistry, which in turn can influence climate by modifying<br />
concentration of climate-influencing constituents such as greenhouse gases. Frequent observations<br />
during the day can improve characterization of aerosols contents because of their temporal and<br />
dynamical variability.<br />
Frequent observations of aerosols are also desirable for aviation, air pollution and quality and health<br />
applications. In spite of advances in aerosol remote sensing (King et al., 1999), most <strong>retrievals</strong> are<br />
limited to twice per day, by using the morning and afternoon passes of the orbiting polar satellites.<br />
<strong>Aerosol</strong>s, however, show diurnal variations that are missed by such sparse observations. For<br />
example, to monitor air quality and for human health goals it is important to understand aerosol<br />
plume movement to track and forecast the plume movement and its temporal evolution.<br />
4.2 Needs <strong>from</strong> <strong>SAF</strong>s<br />
4.2.1 <strong>CM</strong>-<strong>SAF</strong><br />
The Satellite Application Facility on Climate Monitoring (<strong>CM</strong>-<strong>SAF</strong>) generates and archives high<br />
quality data sets satellite products using EUMETSAT and National Oceanic and Atmospheric<br />
Administration (NOAA) satellites for climate research. The <strong>CM</strong>-<strong>SAF</strong> products are generated in<br />
near-real time and comprise surface albedo, humidity, clouds and radiation products.<br />
The surface albedo and radiation product algorithms correct for atmospheric distortions caused by<br />
aerosols. For the aerosol, model computed single scattering albedo, asymmetry factor and aerosol<br />
optical thickness are used. The information on the spatial variations in aerosol properties is taken<br />
<strong>from</strong> OPAC/GADS climatology (Hess et al. 1998, Koepke et al. 1997). The cloud detection and<br />
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cloud property algorithms do not take into account spatial variations in aerosol properties.<br />
Within the <strong>CM</strong>-<strong>SAF</strong> there is a need for aerosol optical thickness and type information derived <strong>from</strong><br />
satellite. Firstly, to improve the cloud detection, cloud property and surface irradiance <strong>retrievals</strong>.<br />
Secondly, to provide climate researchers with aerosol optical thickness and type products. Through<br />
this Visiting Scientist study the <strong>CM</strong>-<strong>SAF</strong> is assessing the potential of MSG/SEVIRI to retrieve<br />
aerosol properties. Moreover, Steven Dewitte of RMIB has a PhD. student who works on aerosol<br />
retrieval algorithms <strong>from</strong> MSG over ocean using the thermal channels. More information about the<br />
<strong>CM</strong>-<strong>SAF</strong> can be found on http://www.cmsaf.dwd.de.<br />
4.2.2 O3M <strong>SAF</strong><br />
The Satellite Application Facility on Ozone Monitoring (O3M <strong>SAF</strong>) provides products and services<br />
related to global ozone and ground surface UV monitoring. In near-real time the O3M-<strong>SAF</strong><br />
provides total ozone, ozone profiles and clear-sky UV fields, while off-line total columns of ozone,<br />
NO2, OClO, BrO, ozone profiles, aerosols, and surface UV are computed.<br />
The aerosol product consists of the Absorbing <strong>Aerosol</strong>s Indicator (AAI) and <strong>Aerosol</strong> Optical Depth<br />
(AOD) and is based on GOME data. AAI is sensitive to absorbing aerosol (dust smoke).<br />
O3M-<strong>SAF</strong> users are interested in the chemical composition of the atmosphere in relation to climate<br />
and air quality studies. <strong>Aerosol</strong>s are currently the most interesting constituents in these topics.<br />
There is considerable interest to combine aerosol information <strong>from</strong> GOME or METOP with<br />
MSG/SEVIRI derived aerosol information.<br />
Due to the spatial resolution of 40*320 km 2 and the repetition time of up to 3 days GOME based<br />
products cannot be used to monitor diurnal and regional changes in aerosol properties. The GOME<br />
AOD <strong>retrievals</strong> will be improved by combining them with MSG/SEVIRI <strong>retrievals</strong>. More<br />
information about the O3M-<strong>SAF</strong> can be found on http://o3saf.fmi.fi/.<br />
4.2.3 LAND-<strong>SAF</strong><br />
The Land-<strong>SAF</strong> provides MSG and EPS related products to support land, land-atmosphere<br />
interactions and biophysical research. Currently the LAND-<strong>SAF</strong> does not provide an aerosol<br />
optical thickness and aerosol type products. However, aerosols play an important role in the<br />
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<strong>retrievals</strong> of two LAND-<strong>SAF</strong> products i.e.: the surface albedo product and the Down Welling<br />
Shortwave Radiation Flux product.<br />
The Albedo and the Down Welling surface short-wave radiation flux (DSSF) product are generated<br />
each day at the full spatial resolution of the MSG/SEVIRI instrument. Both products are based on<br />
the three short-wave channels (VIS 0.6µm, NIR 0.8µm, SWIR 1.6µm). For cloud screening the<br />
cloud masks derived with the Nowcasting and Very Short Range Forecasting Satellite Application<br />
Facility (NWC <strong>SAF</strong>) software are used. Dynamic information on the atmospheric pressure and<br />
water vapour content comes <strong>from</strong> the E<strong>CM</strong>WF numerical weather prediction model, whereas<br />
climatologic values are used for ozone concentration and aerosol optical thickness.<br />
The largest uncertainties in the albedo and the DSSF flux product are non-detected clouds and<br />
systematic errors in the aerosol optical thickness values. Since the aerosol optical thicknesses are<br />
obtained <strong>from</strong> a climatologic database, the quality of both products can be improved by using a<br />
satellite derived aerosol product. More information on the Land-<strong>SAF</strong> can be found on<br />
https://landsaf.meteo.pt/.<br />
4.2.4 O&SI <strong>SAF</strong><br />
The Ocean & Sea Ice Satellite Application Facility (O&SI <strong>SAF</strong>) provides information for the ocean<br />
on surface temperatures, ice coverage, radiation fluxes and surface winds. This <strong>SAF</strong> needs<br />
information on aerosols for mapping of ocean surface reflectances and radiation fluxes. More<br />
information on the O&SI <strong>SAF</strong> can be found on http://www.osi-saf.org.<br />
4.3 Cooperation with other <strong>SAF</strong>s<br />
The <strong>CM</strong>-<strong>SAF</strong> aims to continue developing retrieval algorithms for parameters that are considered<br />
important for climate monitoring. A new development could be an aerosol product <strong>from</strong> SEVIRI.<br />
Such a development could be a federate <strong>SAF</strong> activity.<br />
At the <strong>CM</strong>-<strong>SAF</strong> data user conference in Nurnberg, Germany, September 2005 the author of this<br />
report presented the preliminary results of the study on aerosol <strong>retrievals</strong> <strong>from</strong> <strong>METEOSAT</strong>-8.<br />
Based on the results of this presentation the plan came up to develop aerosol optical thickness and<br />
aerosol type products <strong>from</strong> MSG/SEVIRI and GOME in the framework of a federate <strong>SAF</strong> activity.<br />
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The reasons for performing aerosol <strong>retrievals</strong> within the <strong>CM</strong>-<strong>SAF</strong> is that aerosol is a nuisance<br />
parameter in many other <strong>CM</strong>-<strong>SAF</strong>-products, such as (thin) cloud products, surface radiation<br />
parameters and surface / land albedo products. Moreover, aerosol properties are an important<br />
climate product.<br />
To develop a mature aerosol product within a federate <strong>SAF</strong> activity the following developments are<br />
anticipated:<br />
- Further development of the <strong>METEOSAT</strong>-8/SEVIRI aerosol product,<br />
- Development of a scheme to separate absorbing <strong>from</strong> non-absorbing aerosols,<br />
- Development of a single usable product for the research community by integrating different<br />
research methods (<strong>METEOSAT</strong>, GOME and ATSR-2),<br />
- Sophisticated co-location is needed to ensure that all products look at the same place and time,<br />
- Validation of the output product with in situ and other measurements.<br />
EUMETSAT would favour a federate <strong>SAF</strong> activity on aerosols. In preparation for the Continued<br />
Development and Operational Phase (CDOP) the participating <strong>SAF</strong>s should include their<br />
contribution to such a federate <strong>SAF</strong> activity in their CDOP proposals. Even though such activity<br />
may have a reasonably low priority in each proposal, the fact that more proposals have the same<br />
activity would raise the priority of this activity considerably.<br />
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5 Overview of existing AEROSOL retrieval algorithms<br />
5.1 Background<br />
We recall briefly the status of the techniques for aerosol remote sensing over land. A good starting<br />
point is the review of King et al. 1999 <strong>from</strong> which we extract one table summarizing main classes<br />
of algorithm (see Table 1).<br />
Table 1: Techniques for remote sensing of global aerosol properties <strong>from</strong> space. Here τ a denotes<br />
aerosol optical thickness, ω 0 the single scattering albedo, r e the aerosol particle effective radius,<br />
and n c (r) the columnar aerosol size distribution.<br />
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5.1.1 Algorithms using angular information<br />
The POLDER aerosol retrieval over land is based on a Bidirectional Reflectance Distribution<br />
Function (BRDF) model deduced <strong>from</strong> polarized and multidirectional radiances measurements in<br />
the red and the NIR channels. This model has empirical coefficients adjusted for different classes of<br />
land surfaces (Deuzé et al. 2001). Because of the geometry dependence of the surface reflectance,<br />
multidirectional measurements are a promising method for the aerosol property <strong>retrievals</strong> over land.<br />
Veefkind et al. (2000) developed a method based on the dual-view image radiometer of the Along<br />
Tracking Scanning Radiometer 2 (ATSR-2). The method is based on the wavelength-independent<br />
ratio between forward and nadir view surface reflection that depends only on the sun/satellite<br />
geometry. The value of the ratio is first estimated in the Mid-IR channel of ATSR-2 where the<br />
atmospheric contribution is neglected. It can be combined with another sensor with high spectral<br />
resolution to obtain information about the aerosol model to use. For example an algorithm<br />
combining AASTR and SCHIAMACHY aboard ENVISAT or ATSR-2 and GOME aboard ERS-2<br />
has been developed and provides AOT and aerosol types (Holzer-Popp et al. 2002). Another<br />
multidirectional measurements algorithm has been developed for MISR. The algorithm uses the<br />
presence of spatial contrast to derive empirical function representation of the angular variation of<br />
the scene reflectance, which is then used to estimate the path radiance (Martonchik et al. 1998).<br />
Figure 1 and 2 present examples of AASTR/SCHIAMACHY and POLDER-3/PARASOL<br />
<strong>retrievals</strong>, respectively.<br />
5.1.2 Algorithms using spectral indices<br />
MODIS uses a classical approach to predict the surface reflectance, which relates the TOA<br />
radiances in the IR at 2.13 µm to the visible surface reflectance in the blue (0.47 µm) and in the red<br />
(0.66 µm). <strong>Aerosol</strong> retrieval (spectral optical thickness in two channels) is performed for targets as<br />
bright as 0.25 in reflectance unit in the IR and an average is performed in a 20x20 box after<br />
rejecting some outliers, thus resulting in a 10x10km spatial resolution for the aerosol product<br />
(Kaufman and Tanré, 1998). After intensive validation with AERONET data, Remer et al. (2005)<br />
show that the AOTs accuracy is within ∆τ = ±0.05 ±0.15τ. The surface coverage is good as can be<br />
seen in Fig. 3. This product is available twice a day (Terra, ~10:30 p.m. and Aqua, ~1:30 p.m.).<br />
MERIS aerosol property <strong>retrievals</strong> over land are based on the use of pixels covered by vegetation.<br />
Dense Dark Vegetation pixels are selected using the Atmospheric Resistant Vegetation Index<br />
(ARVI) as spectral index, which uses Rayleigh corrected reflectances at 443, 670 and 865 nm.<br />
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Reflectance in the blue and in the red for less dark pixels may be predicted empirically using a<br />
linear relationship between ARVI and surface reflectance as noticed <strong>from</strong> MOS, SeaWIFS and<br />
MERIS sensors (Borde et al. 2003 and Santer et al. 2005).<br />
Figure 1: Example of the daily aerosol optical thickness product derived <strong>from</strong> the synergistic use<br />
of AATSR and SCHIAMACHY for the 14 th of July 2005. Image taken <strong>from</strong> the GMES Service<br />
Element PROMOTE Website (http://www.gse-promote.org/).<br />
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Figure 2: Example of <strong>Aerosol</strong> Optical Thickness level 2 product over land at 865 nm <strong>from</strong> the<br />
POLDER-PARASOL instrument on 13 th of July 2005. (source: ICARE web server http://wwwicare.univ-lille1.fr/)<br />
Figure 3: Example of the daily aerosol optical thickness product over land and ocean derived<br />
<strong>from</strong> MODIS Aqua<br />
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Figure 4: Example of the daily aerosol optical thickness product derived <strong>from</strong> MISR on 14 th of<br />
July 2005. The colour scale ranges <strong>from</strong> 0 to 0.5. source: EOS LARC MISR web server<br />
5.1.3 GOES<br />
The GOES <strong>Aerosol</strong>-Smoke Product (GASP) provides aerosol optical depth <strong>retrievals</strong> over the U.S.<br />
at 4 km spatial resolution and 30 minute intervals. The retrieval is performed over ocean and land<br />
and is available once per day for the full disk of the Earth <strong>from</strong> GOES-East (providing information<br />
for South America). The algorithm is described in Knapp et al. (2002, 2005). The main approach is<br />
to build a surface reflectance reference by selecting, for each pixel, the second darkest image in the<br />
composite time period and then to perform aerosol retrieval over land based on Look Up Tables of<br />
gaseous and Rayleigh corrected reflectance for various surface reflectance and aerosol loading (see<br />
an example of the AOT map over the US East coast for the 19 April 2005 in Fig. 5). The second<br />
darkest pixel is chosen to reduce the effect of cloud shadows (shadowed pixel reflectance is<br />
generally darker than surface -alone- reflectance). The reflectance reference is computed for a<br />
composite time period varying along the year <strong>from</strong> 7 days to 28 days. The length of the time period<br />
depends on the accuracy expected on the reflectance reference. More observations increase the<br />
chance of observing cloud-free and aerosol-free day, but if too many days of observations are used,<br />
the surface reflectance may change. For the eastern US, Knapp et al. found that 14-days period was<br />
a good compromise. The main limitation is that it requires that the TOA reflectances increase with<br />
increasing aerosol loading, and thus it does not work for a combination of bright surface and<br />
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absorbing aerosols as one can see in Fig. 6 (For example, for a surface albedo of 0.4 and aerosol<br />
model with a single scattering albedo of 0.81, the TOA reflectance decreases with the AOT) The<br />
aerosol model is assumed to be continental and the accuracy of the retrieved AOT is ±0.13 when<br />
compared to AERONET data. A global analysis for one year of data suggests that the optimal<br />
composite time period is around 14 days and that a continental aerosol model is most of the time<br />
well adapted for the retrieval of an AOT. Almost no bias is found between GOES and AERONET<br />
AOT's when the surface reflectance reference is found after applying a residual aerosol correction<br />
using a background AOT of 0.02.<br />
Figure 5: Example of <strong>Aerosol</strong> Optical Thickness level 2 product over land and ocean retrieved<br />
<strong>from</strong> the GOES 8 instrument on 19 th of April 2005 over US East Coast. (University of Maryland at<br />
Baltimore County server : http://alg.umbc.edu/usaq/archives/000474.html)<br />
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Figure 6 : The reflection function as a function of aerosol optical thickness and surface<br />
reflectance for a Junge size distribution given by n(r) ∝r 4 , where λ = 0.61 µm and θ 0 = 40°.<br />
Panels (a) and (b) apply to θ = 60° and φ = 0° but for different single scattering albedo; (c) and<br />
(d) to nadir observations (θ = 0°) (adapted <strong>from</strong> Fraser and Kaufman 1985).<br />
5.1.4 MSG/SEVIRI<br />
The MSG/SEVIRI sensor is unique and although it is not optimised for observation over land<br />
surfaces, because of the lack of a blue channel, this sensor has several advantages for aerosol<br />
<strong>retrievals</strong> over land. The time sampling of 15 minutes gives access to the diurnal cycle of the<br />
aerosol properties and allows the detection of rapid changes in the atmosphere. Moreover, it<br />
increases the chance of having cloud-free observations. The thermal IR channels allow the<br />
determination of a good cloud mask. The constant viewing geometry and target localisation allow<br />
an easy co registration of images. And finally, the variable solar angles allow some angular<br />
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sampling of the surface BRDF and/or the aerosol phase function<br />
Beside the above-mentioned new aspects that are especially dedicated to MSG/SEVIRI, it is also<br />
possible to use more classical methods. First, for MERIS a method is used that links the vegetation<br />
index empirically to surface reflectance. The lack of a blue channel on SEVIRI will reduce the<br />
vegetation index mostly to an NDVI, which is not independent of atmospheric turbidity. Thus<br />
NDVI is not a robust index for the prediction of the surface reflectance if it is computed <strong>from</strong> a<br />
single observation. However, a spectral index built with multiple views, and using information <strong>from</strong><br />
the blue part of the spectrum of the High Resolution Visible (HRV) channel should not be<br />
discarded. Nevertheless, the use of the HRV channel is not trivial and studies are needed to<br />
investigate methods to separate the reflectances in blue <strong>from</strong> the HRV channel reflectances. Second,<br />
the MODIS like approach that uses visible surface reflectance at 670 nm <strong>from</strong> the channel at 1600<br />
nm (King et al. 1992). This approach has been described in an early stage of the operational<br />
algorithm for the Land<strong>SAF</strong>. To our knowledge no application of this method on real MSG data has<br />
been published. Finally, in particular conditions where one can assume that the aerosol model and<br />
loading vary little in time, it is possible to use consecutive observations with different sun zenith<br />
angles and, according to the reciprocity principle, retrieve aerosol properties. This approach follows<br />
the principles AATSR or MISR algorithms that are designed for multiple view angles. Note that the<br />
paper devoted to Meteosat by Pinty et al., (2000) describes a method to derive surface albedo by<br />
decoupling surface and aerosol contribution <strong>from</strong> a set of images acquired during the day, following<br />
a method close to the MISR algorithm. However, the main constraint of this method is that the<br />
geophysical system shall not vary much during the day, which contradicts with the wish to use<br />
MSG as a tool to measure the rapidly changing aerosol fields.<br />
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6 MSG/SEVIRI methodology<br />
6.1.1 Introduction<br />
This chapter presents the preliminary results of MSG/SEVIRI <strong>retrievals</strong> of aerosol properties over<br />
land. We decided to demonstrate the potential MSG/SEVIRI for aerosol <strong>retrievals</strong> by developing a<br />
simple algorithm to retrieve the AOT <strong>from</strong> SEVIRI. We focused on the aerosol optical thickness<br />
(AOT), which is a key parameter for measuring the aerosol loading in the atmosphere. Most of the<br />
assumptions used in the algorithm were made to support the development of an aerosol product<br />
<strong>from</strong> MSG/SEVIRI in a short time. From this prototype, a robust algorithm can easily be obtained<br />
after implementation of radiative transfer calculations.<br />
In short, the AOT is retrieved <strong>from</strong> the reflectances at one wavelength (i.e. 670nm) in two steps,<br />
using a method similar to the one Knapp et al. (2005) developed for GOES-8. The first step is the<br />
creation of a 3D images data set (for a period of 15 days), <strong>from</strong> which we built a mosaic<br />
(reflectance surface maps of reference) where the darkest pixel of the time period is chosen as the<br />
“clear sky” condition. In the second step these surface reflectance map are used to retrieve AOT for<br />
each images.<br />
6.1.2 Method description<br />
The method is based on the unique capabilities of the MSG/SEVIRI measurements. We take<br />
advantage the high temporal resolution (15 minutes) and fixed viewing geometry of SEVIRI. We<br />
can reasonably assume that for given acquisition time (i.e. 8/00 UTC) within a period of 15 days:<br />
• The solar zenith angle and the relative azimuth angle do not vary significantly. So for one pixel,<br />
the viewing geometry is stable. For example, at 12:00 UTC and over the fifteen first day of July<br />
2005, the solar zenith angle varies of a value between 0.5° and 1.6° depending on the Earth's<br />
locations. The variation in azimuth angle is between -1.2 and 4.5°.<br />
• The surface properties do not change (except for particular and exceptional cases such as floods,<br />
fires and snowfall).<br />
Measurements that are made every day for the same pixel at the same time can then be easily<br />
compared. After screening the clouds and assuming no change in surface reflectivity, the<br />
differences in the measurements come <strong>from</strong> differences in the state of the atmosphere or changes in<br />
aerosols properties. In a first step we will explain how we get ride of the surface by monitoring the<br />
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surface using characteristics of SEVIRI. We will present the retrieval of the surface contribution<br />
and define the surface reflectance map of reference. Knowing the reflectance of the surface for each<br />
pixel and observation time, we will correct measurements for Rayleigh scattering. Using simple<br />
assumptions, aerosols optical thickness at a visible wavelength will be retrieved. The results will be<br />
compared with some AERONET data <strong>from</strong> stations available in the area of interest. Finally, we will<br />
go back to the assumptions we made in the retrieval scheme to explain the weakness of the method.<br />
6.1.3 Reference Surface Reflectance Map<br />
The signal measured by the instruments is a mix of different contribution of the Earth-Atmosphere<br />
system: surface, molecules, aerosols, gases and clouds.<br />
Cloud Masking<br />
First of all, cloudy pixels have to be removed for aerosol retrieval. Because of the short time we had<br />
to develop our methodology we used a simple cloud mask. It is based on threshold tests on the<br />
reflectance at 670 nm, 865nm and 1.6µm and brightness temperature at 11µm (see Appendix C). To<br />
avoid pixels containing cloud border, we also remove neighbouring pixels. Finally, the cloud mask<br />
we used does not detect cloud shadows, which produce pixels with lower reflectances than the<br />
surface reflectance alone.<br />
Rayleigh correction<br />
Once the cloud masking is done, the contribution of the Rayleigh scattering at 670nm is removed<br />
<strong>from</strong> the signal using classical method adapted to the spectral properties of the channel. The<br />
remaining quantity (R ray_corrected ) is then the contribution of the surface and the aerosols.<br />
Surface contribution<br />
The surface reflectance map is built <strong>from</strong> the 3D data set of the Rayleigh corrected reflectances<br />
corresponding to the cloud-free cases. For each time slot (<strong>from</strong> 5:00 UTC till 19:00 UTC, every 15<br />
minutes) we have, at maximum, a set of 14 values of the Rayleigh corrected pixel reflectances. The<br />
three dimensions of the data set are the pixel longitude, latitude and the date (1 to 14 July). The<br />
retrieval method assumes that the minimum value corresponds to the zero aerosol case, or at least,<br />
the case with the least aerosol contamination. In another words, the minimum is the value of the<br />
surface reflectance (R surf_ref ) seen in the correct geometry. The value obtained is assumed to<br />
correspond with the reflectance for a lambertian surface as simulated in radiative transfer<br />
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calculations. This assumption is mainly true when neglecting interaction between surface and<br />
molecules and for viewing geometries far <strong>from</strong> the “hot spot” of the vegetation (Knapp et al.,<br />
2002). The errors due to this assumption will be much lower for geostationary satellites because the<br />
bulk of observations are geometrically far <strong>from</strong> the “hot spot” (Knapp et al., 2005). The reference<br />
map of surface reflectance is generated for each observation time by applying above described<br />
method to all the image pixels. Finally, Fig. 7 summarises in a diagram the method to retrieve the<br />
reference surface reflectance map.<br />
Fig. 8 shows for every hour the reference surface reflectance map for the period 1-14 th July. The<br />
reference area covers almost all the continental areas. Noise appears for low solar elevations in the<br />
morning. This can be due to the cloud masking method, which is not robust for such sun-satellite<br />
geometries. Around 13:00 UTC some high reflectances appear in middle-Europe but there are large<br />
doubts about the accuracy of cloud detection in this area (lots of partly cloudy situations and<br />
scattered clouds).<br />
Fig. 9 gives the time series of the reference maps of surface reflectance for several locations: the<br />
Flandres in Belgium, the Landes forest in South-West of France, the Andalusia in the South of<br />
Spain and the north of Algeria. Temporal variation of the surface reflectance seems reasonable with<br />
regards to vegetation reflectance and biomass models used in MERIS Look-up-tables, i.e., variation<br />
of the surface reflectance with the scattering angle is similar to that used in MERIS LUT. The<br />
erroneous values of the surface reflectance for the Flandres area early in the morning is due to<br />
clouds, which are not detected by our cloud mask. Independently of such erroneous values, the<br />
curves are affected by a noise of small amplitude. The retrieval of ground reflectances is altered by<br />
differences in the residual aerosol loading within the composite period and differences in the<br />
observation date that corresponded to the minimum surface reflectance at the observation time. In<br />
the near future, one can find a solution to minimize the noise that is based on the following facts:<br />
• The surface BRDF varies smoothly with the solar angles along the day. One is allowed to apply<br />
a fit to the surface BRDF versus time.<br />
• A high reflectance value once or two times during the day indicates either a thin cloud or high<br />
aerosol load that was not detected by cloud masking or because no cloud free observations<br />
occurred during the composite time period at that particular time and locations. Those points<br />
should be discarded. In the data sample we used for this study, less than 0.01 % of the pixels<br />
were detected cloudy during the entire 14 days period.<br />
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• A low reflectance value that occurs once or twice a day indicates an anomalous attenuation of<br />
the signal coming <strong>from</strong> that point. In most cases these values are caused by shadowing. It could<br />
also be extinction by absorbing aerosols, but these aerosols would most likely have a darkening<br />
effect on the surface reflectance of adjacent pixels and observation times as well. The overall<br />
methodology is questionable there.<br />
Figure 7: Flow chart of the processing yielding the reference surface reflectance map for one<br />
particular time of the day.<br />
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05:00 UTC 06:00 UTC<br />
07:00 UTC 08:00 UTC<br />
09:00 UTC<br />
0:00 UTC<br />
1<br />
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11:00 UTC 12:00 UTC<br />
13:00 UTC 14:00 UTC<br />
15:00 UTC 16:00 UTC<br />
17:00 UTC<br />
8:00 UTC<br />
1<br />
Figure 8: Reference surface reflectance map over Europa <strong>from</strong> the period 1-14 July 2005 in<br />
SEVIRI channel 1. The color scale is <strong>from</strong> 0.00 (dark blue) to 0.40 (red).<br />
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Figure 9: Example of surface reflectance of reference at 670nm for different geographical<br />
locations <strong>from</strong> the period 1-14 th July 2005.<br />
In conclusion, the reference surface reflectance maps are built <strong>from</strong> the minimum value of the<br />
Rayleigh corrected reflectance over a 14 day period. Although they are contaminated by a residual<br />
aerosol loading but we have neglected this so far. The spatial cover is very good, and only in small<br />
areas very high or missing values are found. It is suggested that these areas were most of the time<br />
covered by clouds during the compositing period. The spatial variations of the reflectance at 670nm<br />
are most of the time smooth. However, in some part of central Europe and Norway large variations<br />
in surface reflectance are observed, which are due to frequent cloud cover.<br />
Although we limited our report to the European continent, it can be easily extended to the disc<br />
observed by MSG/SEVIRI. Thus, the reference surface reflectance map has also been calculated for<br />
the complete northern part of the disc as shown in Appendix A.<br />
6.1.4 MSG/SEVIRI aerosol optical thickness retrieval algorithm<br />
Knowing the surface reflectance in the appropriate geometry, the retrieval of the aerosol optical<br />
thickness is made in two steps. In the first step we estimate the aerosols path reflectance using a<br />
rough approximation. The aerosol path reflectance (R aer ) is the difference between the Rayleigh<br />
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corrected reflectance and the surface reflectance of reference such as:<br />
R aer =R Ray_corrected -R surf_ref<br />
In the second step we assume only single scattering and the aerosol type (and then phase function),<br />
the aerosol optical thickness (AOT) is derived as follows:<br />
AOT=Raer . 4 . cos(θs) cos(θv)/ P(Θ)<br />
where: θs is the solar zenith angle, θv is the viewing zenith angle and P(Θ) is the value of the<br />
aerosol phase function for the scattering angle Θ.<br />
We assume a continental model with an Angstrom coefficient of 1.0, a real refractive index m r of<br />
1.44 and an imaginary index mi of 0.0 which means there is no absorption (This aerosol model is<br />
used in MERIS LUT's). Of course the single scattering assumption in the radiative transfer model<br />
will lead to erroneous retrieval for large value of the AOT.<br />
Fig. 10 resumes the schema of the retrieval method and Fig, 11 shows the aerosol optical thickness<br />
at 670nm for the 14 th July 2005 over Europe and for every hour between 05:00 UTC and 18:00<br />
UTC. The AOT maps are very noisy for partly cloudy areas as seen in Fig.11. Efforts must be<br />
planned to use and apply a better cloud mask. However, the coverage is very good. An aerosol front<br />
crossing the Pyrenees Mountains can be seen and be followed with correct land-sea continuity. A<br />
dust event over North Africa is detected both over land and the Mediterranean Sea.<br />
We also mapped the retrieved aerosol optical thickness over ocean assuming a Lambertian surface<br />
with an albedo of 0.01 at 670nm. But, we plan in the near future to derive the sea reflectance with<br />
the identical method used for the continental surface reflectance. In Appendix B maps of retrieved<br />
AOT are given for the forest fires in Portugal around the 10 th July 2005.<br />
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Figure 10: Flow chart of the aerosol optical thickness retrieval method.<br />
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05:00 UTC 06:00 UTC<br />
07:00 UTC 08:00 UTC<br />
09:00 UTC 10:00 UTC<br />
11:00 UTC 12:00 UTC<br />
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13:00 UTC 14:00 UTC<br />
15:00 UTC 16:00 UTC<br />
17:00 UTC 18:00 UTC<br />
Figure 11: <strong>Aerosol</strong> optical thickness at 670nm over Europe <strong>from</strong> 05:00 UTC till 18:00 UTC the<br />
14 th July 2005 in the SEVIRI channel 1. The color scale is <strong>from</strong> 0.00 (dark blue) till 0.40 (red).<br />
6.1.5 Results and Comparison with AERONET and MODIS<br />
The AOT <strong>retrievals</strong> over the period 1 – 14 July 2005 were compared for several days to AERONET<br />
measurements at Toulouse (France), Ispra (Italy), Blida (Algeria) and El Arenosillo (Spain). AOT's<br />
<strong>from</strong> MSG have been averaged within a 5x5 pixels area around the AERONET stations. In general,<br />
the AERONET and MSG retrieved AOT values for cloud-free areas correlate reasonably well and<br />
are in the same order of magnitude. However, MSG tends to under-estimate AOT, which is<br />
probably due to an overestimation of the surface reflectance. The latter could be caused by residual<br />
aerosol loadings in the surface reflectance values that are not corrected for in the current retrieval<br />
scheme.<br />
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Fig. 12 to 15 present for the 4 AERONET locations the relationship between observed and<br />
MSG/SEVIRI retrieved <strong>Aerosol</strong> Optical Thickness. Big discrepancies appear for some hours of the<br />
day (around 13:00 UTC for Toulouse and 12:00 UTC for Blida), which should be due to the use of<br />
an inappropriate phase function, especially for particular scattering angles. For the El Arenosillo<br />
station, MSG tends to overestimate the AOT between 12:00 and 15:00 UTC. We can remark that<br />
the standard deviation in the box of 5x5 pixels is high during these hours, in the order of 0.2,<br />
whereas the standard deviation is less than 0.06 for the rest of the day. Large standard deviations<br />
can be used as an index of the quality, indicating large spatial variability of the surface reflectance<br />
or the presence of thin clouds. As expected the agreement is better for sites where the surface<br />
reflectance is not too high (Toulouse and Ispra) and when the aerosol is surely non dust. In other<br />
places with high surface reflectance and presence of dust, the quantitative comparison is not yet<br />
satisfactory.<br />
Fig.12: Temporal variation of the aerosol optical thickness at 670nm derived <strong>from</strong> AERONET and<br />
MSG for the 2 nd and 14 th July 2005 over Toulouse (South-West of France)<br />
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Fig.13: Temporal variation of the AOT at 670nm derived <strong>from</strong> AERONET and MSG for the 12 th<br />
and 14 th of July over Ispra (Italy)<br />
Fig.14: Temporal variation of the AOT at 670nm derived <strong>from</strong> AERONET and MSG for the 10 th<br />
and 14 th of July over El Arenosillo (Andalousia-(Spain)<br />
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Fig.15: Temporal variation of the AOT at 670nm derived <strong>from</strong> AERONET and MSG for the 12th<br />
and 14 th of July over Blida (Near Alger - Algeria)<br />
In Fig. 16 we present the comparison between the MSG/SEVIRI and the MODIS aerosol product.<br />
The two instruments capture the main patterns of the aerosol field but quantitative comparison is at<br />
the moment difficult since we do not retrieve the AOT at the same wavelength. The background<br />
AOT seems to agree well and large dust outbreak over North of Algeria and the Mediterranean Sea<br />
is seen by the two instruments. There is a good land/ocean continuity of the aerosol front passing<br />
over Spain and South of France for MSG and it is not so clear for MODIS. However some<br />
discrepancies occur for cloud covered regions, for example in the centre of France where high AOT<br />
are retrieved <strong>from</strong> MSG and MODIS observes mainly clouds instead of aerosols. There is<br />
agreement between the two instruments over England where very high AOT are found. This<br />
emphasizes one more time the fact that our cloud mask still has to be improved.<br />
Finally these results are obtained with lots and sometimes strong assumptions but they are really<br />
promising, especially because changes in aerosol amount over some locations are also seen by<br />
MSG. Better agreements should be obtained using exact radiative transfer calculations and filters on<br />
the number of pixel used for the mean calculation, threshold on the standard deviation and the<br />
proximity of clouds.<br />
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(a)<br />
(b)<br />
(c)<br />
Figure 16: Comparison of AOT derived <strong>from</strong> MSG and MODIS on 14 th of July 2005 around 10:45<br />
UTC. (a) MSG AOT in channel 1, the colour scale is ranging <strong>from</strong> 0 to 0.7; (b) MODIS AOT over<br />
land and ocean at 550 nm, the colour scale is ranging <strong>from</strong> 0 to 0.8; (c) cloud fraction detected by<br />
MODIS<br />
6.1.6 Assumptions we made to accelerate development of the algorithm and calculations<br />
In the framework of this three months “visiting scientist” activity on the feasibility of aerosols<br />
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<strong>retrievals</strong> <strong>from</strong> SEVIRI there was limited time to elaborate a complete and robust algorithm. The<br />
feasibility of MSG/SEVIRI for <strong>retrievals</strong> of aerosol properties over land could be demonstrated, but<br />
with a lot of assumption that could have been avoided if more time would have been available.<br />
We used a simple cloud mask. The algorithm can then be upgraded and improved by using high<br />
performance cloud mask <strong>from</strong> the <strong>CM</strong>-<strong>SAF</strong> or NWC<strong>SAF</strong>. For the reference surface reflectance<br />
map, the cloud mask should include the detection of neighboured cloudy pixels (i.e. cloud free pixel<br />
near cloudy pixels) to exclude effects due to cloud borders. In future algorithm developments it is<br />
preferable to use the official cloud mask and to include a morphological mask that removes<br />
shadowed pixels that have lower reflectances than the surface. In appendix A the results for the<br />
northern part of the disc, as observed by MSG, are given using a more restrictive cloud mask. We<br />
only used one aerosol model: a continental model used in MERIS LUT's. It would be interesting to<br />
use appropriate model just based on simple climatology: desert dust over Africa, maritime model<br />
over ocean and burning biomass over Amazonia. As presented in the next section called “future<br />
improvements”, the use of others channels (0.865 µm and 1.650 µm) should be researched to get<br />
information on the spectral dependence of aerosols and an index of the size distribution which will<br />
be an indicator of the aerosol type.<br />
We used the single scattering assumption to simplify the calculations and to avoid use of LUT's,<br />
which could not be prepared due to the limited time available for this “visiting scientist” activity. Of<br />
course it is a source of uncertainty and systematic bias. Neglecting multiple scattering in the<br />
atmosphere is a strong assumption, especially when large amount of aerosols occurs and in the case<br />
of large solar-viewing zenith angles. This point can be easily solved by using Look Up Tables<br />
computed with an accurate radiative transfer model. The limitation is that a lambertian surface has<br />
to be assumed in those computations. However it is possible to correct for bidirectional effects<br />
when one knows the shape of the BRDF (Vermote et al. 1997, Ramon et al. 2001), which can be<br />
partly measured by SEVIRI. So an iterative approach seems possible here.<br />
7 Conclusions and perspectives<br />
SEVIRI is a powerful instrument for monitoring rapidly changing parameters of the atmosphere,<br />
and tropospheric aerosols fall into this category (see Appendix B for forest fires in Portugal in July<br />
2005). The survey of user requirements showed the need for an aerosol product <strong>from</strong> MSG/SEVIRI<br />
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for both the climate research community and Satellite Application Facilities financed by<br />
EUMETSAT. The application of such a product would either be monitoring of aerosols<br />
climatologies, nowcasting of air quality or the assimilation of aerosol properties in surface and<br />
cloud property retrieval algorithms. For air quality and visibility monitoring it is important to follow<br />
variations in aerosols load and type. For climatological applications it is important to assess the<br />
diurnal amplitude and variability of aerosol properties and verify the added value of geostationary<br />
measurements as compared to polar sensor measurements. In general, it is important to use satellite<br />
data with a high temporal resolution over areas with high cloud cover since it increases the chance<br />
to see “through” the cloud deck As demonstrated in a pioneering study using GOES-8, Knapp et al.<br />
2005 showed an approach that takes advantage of the geostationary viewing geometry to minimise<br />
the uncertainties in the knowledge of the surface reflectance, which is always the main source of<br />
error in aerosol retrieval techniques, i.e. multi spectral, multi angular or using polarization of the<br />
light.<br />
We analysed in this study the possibility to use the same approach described for GOES to SEVIRI.<br />
For that purpose we used a test data set, consisting in every slot acquired <strong>from</strong> 1st to 14th of July<br />
2005 and we built a reference surface reflectance map for channel 1. We decided to go further and<br />
retrieve AOT in a straightforward manner, which showed good agreement for several AERONET<br />
stations located in Europe. This exercise with real data was important because it gave confidence in<br />
the methodology and helped us to clarify where some effort has to be put now:<br />
- There is plenty of room for improving the shadow detection and removal based on geometric<br />
and spectral tests. For example Knapp et al. (2005) just built there reference map using the<br />
second darkest image during the compositing period supposing that a pixel in a shadow twice is<br />
a very rare event.<br />
- Filtering the reference reflectance map using appropriate spatial filter and temporal filter and<br />
extend it for the whole year. Define rules for automatically generating this reference map using<br />
a rolling composite period and a varying composite time depending on season and location<br />
- The surface BRDF estimates can be validated over AERONET sites by applying full<br />
atmospheric corrections based on AOT and aerosol types retrieved using solar radiance<br />
extinction and sky radiances measurements.<br />
- The complementary work is to minimise the bias between AERONET AOT and SEVIRI<br />
retrieved AOT for a set of free parameters of the method, mainly the background aerosol<br />
loading τ res that is used to correct for SEVIRI minimum Rayleigh corrected radiance found in<br />
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the compositing time and the compositing period itself T comp<br />
- Accurate radiative transfer with multiple scattering, including sphericity of the atmosphere for<br />
large angles.<br />
Then a lot of work could be done to improve this one-channel algorithm. Several questions have to<br />
be answered first:<br />
- To what extent can the aerosol properties be considered stable at a particular location within a<br />
couple of hours or an entire day in order to exploit the multi-angular information of SEVIRI<br />
measurement and thus to better constrain the aerosol model<br />
- To what extend can the HRV channel help improve the spatial resolution and the spectral<br />
dependence of the aerosol scattering properties toward the blue<br />
The preliminary results of this study demonstrated the potential of SEVIRI to retrieve aerosol<br />
properties. The comparisons with AERONET data are very promising and showed that SEVIRI<br />
could be used to derived AOT values. In general the variability is well reproduced, although some<br />
stations show biases between MSG and AERONET. There are several suggestions for improving<br />
the presented aerosol retrieval algorithm, and develop a mature algorithm for accurate monitoring of<br />
the diurnal variability (15 minutes time sampling) in aerosol load at a spatial resolution of around 5<br />
km. This would be a unique data set that cannot be derived <strong>from</strong> polar platforms. MSG/SEVIRI<br />
aerosol <strong>retrievals</strong> are possible both over dark targets (ocean and forest) and brighter targets over<br />
land (limitations must be taken into account for very bright target such as desert), because surface<br />
reflectances can be determined with a high accuracy. The latter is an improvement as compared to<br />
<strong>retrievals</strong> <strong>from</strong> polar orbiters such as MODIS.<br />
References<br />
Borde, R., Ramon, D., Schmechtig, C., and Santer, R., “Extension of the DDV concept to<br />
retrieve aerosol properties over land <strong>from</strong> the Modular Optoelectronic Scanner sensor”,<br />
International Journal of Remote Sensing, 24, 1439-1467, 2003.<br />
Hess, M., Koepke, P., Schult, I. Optical properties of aerosols and clouds: the software package<br />
OPAC. Bull. Amer. Meteor. Soc. 79, 831-844, 1998.<br />
- 34 -
<strong>SAF</strong> on Climate Monitoring Visiting Scientists Report Doc. No: 1.0<br />
Issue : 1.0<br />
Date : 4 October 2006<br />
Holzer-Popp, T., M. Schroedter, and G., Gesell, “Retrieving aerosol optical depth and type in the<br />
boundary layer over land and ocean <strong>from</strong> simultaneous GOME spectrometer and ATSR-2<br />
radiometer measurements, 1, Method description”, J. Geophys. Res., 107, D24, 2002.<br />
Kaufman and Tanre, 1998: Algorithm for Remote Sensing of Tropospheric <strong>Aerosol</strong> <strong>from</strong><br />
MODIS. Products: MOD04_L2, MOD08_D3, MOD08_E3, MOD08_M3. ATBD Reference<br />
Number: ATBD-MOD-02. (see http://modis-atmos.gsfc.nasa.gov/MOD04_L2/atbd.html)<br />
Knapp, K. R., R. Frouin, S. Kondragunta and A. Prados, “Toward aerosol optical depth<br />
<strong>retrievals</strong> over land <strong>from</strong> GOES visible radiances: determining surface reflectance”, Int. J. Remote<br />
Sensing, 2005, in press. (doi: 10.1080/01431160500099329)<br />
Knapp, K. R., T. H. Vonder Haar, and Y. J. Kaufman, “<strong>Aerosol</strong> optical depth retrieval <strong>from</strong><br />
GOES-8: Uncertainty study and retrieval validation over South America”, J. Geophys. Res., 107<br />
(D7), 4055, 2002, (doi:10.1029/2001JD000505).<br />
Koepke, P., Hess, M., Schult, I., Shettle, E. Global aerosol data set. Technical Report 243, MPI<br />
Meteorologie, Hamburg, 1997.<br />
King, M. D., Y. J. Kaufman, D. Tanré and T. Nakajima, “Remote sensing of tropospheric<br />
aerosols <strong>from</strong> space: past, present, and future”, Bulletin of the American Meteorological Society,<br />
Vol. 80, No. 11, 1999.<br />
Martonchik, J.V., Diner, D.J., Kahn, R.A., Ackerman, T.P., Verstraete, M.M., Pinty, B., and<br />
Gordon, H.R., “Techniques for the retrieval of aerosol properties over land and ocean using<br />
multiangle imaging”. IEEE Transactions on Geoscience and Remote Sensing, 36, 1212-1227, 1998.<br />
Pinty, B., F. Roveda, M. M. Verstraete, N. Gobron, Y. Govaerts, J. V. Martonchik, D. J. Diner<br />
and R. A. Kahn, “Surface albedo retrieval <strong>from</strong> Meteosat, 1. theory”, JGR, Vol. 105, D14, 18,099-<br />
18112, 2000.<br />
Ramon, D., and R. Santer, “Operational remote sensing of aerosols over land to account for<br />
directional effects”. Applied Optics, 40, 3060-3075, 2001.<br />
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<strong>SAF</strong> on Climate Monitoring Visiting Scientists Report Doc. No: 1.0<br />
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Date : 4 October 2006<br />
Ramon, D., R.Santer, E. Dilligeard, D. Jolivet and J. Vidot, 2004. ”MERIS land product<br />
validation”, Proc. MERIS User Workshop, Frascati, Italy, 10 -13 November 2003 (ESA SP-549,<br />
May 2004).<br />
Remer, L. A., Y. J. Kaufman, D. Tanré, S. Mattoo, D. A. Chiu, J. V. Martins, R.-R. Li, C.<br />
Ichoku, R. C. Levy, R. G. Kleidman, T. F. Eck, E. Vermote, and B. N. Holben, “ The MODIS<br />
aerosol algorithm, products and validation”, J. Atmosph. Science, Vol 62, 947-973, 2005.<br />
Santer, R., D. Ramon, J. Vidot, and E. Dilligeard, “A surface reflectance model for aerosol<br />
remote sensing over land”, Int. J. Remote Sensing, 2005, accepted for publication.<br />
Smirnov, A., B. N. Holben, T. F. Eck, I. Slutsker, B. Chatenet,and R. T. Pinker, “Diurnal<br />
variability of aerosol optical depth observed at AERONET (<strong>Aerosol</strong> Robotic Network) sites”,<br />
Geophysical Res. Lett., Vol. 29, No. 23, 2115, 2002, (doi:10.1029/2002GL016305).<br />
Tanre, D., Y. K. Kaufman, M. Herman, and S. Mattoo, 1997. "Remote sensing of aerosol<br />
properties over ocean using the MODIS/EOS spectral radiances," J. Geophys. Res., 102, 17051-<br />
17067.<br />
Vermote, E., N. El Saleous, C. O Justice, Y. J. Kaufman, J. L. Privette, L. Remer, J.-C. Roger<br />
and D. Tanré, “ Atmospheric correction of visible to middle-infrared EOS-MODIS data over land<br />
surfaces: background, operational algorithm and validation”, JGR, 102, 17,731-17,141, 1997.<br />
Watts, P.D., M.R. Allen, C.T. Mutlow, 2000, <strong>Aerosol</strong> Properties derived <strong>from</strong> Meteosat Second<br />
Generation Observations, RAL/TN/EUM/004<br />
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Appendix A: Over the whole northern disk observed by<br />
MSG/SEVIRI<br />
In this appendix we show SEVIRI retrieved surface reflectance maps for the half-disc (northern<br />
hemisphere) observed by SEVIRI/MSG. To simplify calculations and data computing we did not<br />
correct <strong>from</strong> Rayleigh scattering. Compared to results in the rapport, we also applied a more<br />
restrictive cloud mask which explains the lack of data in middle Europe for example.<br />
Figure A1 presents examples of the map of the surface and molecular reflectances of reference<br />
(R surf+molec ) for Europe and Africa.<br />
Figure A2 presents the map of the AOT assuming that the aerosol path length, R aer , is equal to the<br />
TOA reflectance minus the surface and molecular reflectance of reference (R aer =R TOA - R surf+molec ).<br />
Over the sea a unique value of 0.01 has been used as surface and molecular reflectances. That is<br />
certainly an underestimated value. It explains also the high value of AOT over the seas.<br />
As expected, without Rayleigh corrections, results are much noisier. However, independent of the<br />
assumptions and the quality or the sensitivity of the cloud mask, a good spatial coverage is still<br />
expected over the disc observed by MSG.<br />
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06:00 UTC 14:00 UTC<br />
10:00 UTC 18:00 UTC<br />
Fig A1: Minimum reflectance over the period 1-14 th July 2005 in SEVIRI channel 1. It can be<br />
considered as the reference of the surface and molecular reflectance. The color scale is <strong>from</strong> 0.00<br />
(dark blue) to 0.40 (red).<br />
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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<br />
mask Clouds are in white and ocean in magenta. The colour scale is <strong>from</strong> 0.0 (dark blue) to 0.4<br />
(red)<br />
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Appendix B: Fires in Portugal during July 2005<br />
Several forest fires happened in Portugal in July 2005. The maximum of fires was reached the 12 th<br />
July as observed by the MODIS rapid response fire system (see fig. B1 and<br />
http://rapidfire.sci.gsfc.nasa.gov). Such events have been also observed with our method using<br />
MSG/SEVIRI. Maps of aerosol optical thickness over Portugal are given for some dates in Fig. B2<br />
and Fig. B3.<br />
Fig. B1: MODIS rapid response fire image for the 12 th July 2005 over Portugal. Red squares<br />
indicate areas in fire.<br />
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8 July, 14:00UTC 9 July, 08:00UTC 9 July, 14:00UTC<br />
10 July, 14:00 UTC 10 July, 16:00UTC 11 July, 11:00 UTC<br />
11 July, 14:00UTC 12 July, 11:00 UTC 12 July, 14:00 UTC<br />
Fig. B2: Map of aerosol over Portugal as retrieved <strong>from</strong> MSG/SEVIRI for several dates between<br />
the 8 th and 12 th July 2005. Color scale is <strong>from</strong> 0.0 (dark blue) till 1.0 (dark red).<br />
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Fig. B3: SEVIRI aerosol optical thickness at 670nm at 14:00 UTC for an area with fires in the<br />
North of Porto.<br />
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Appendix C<br />
Clear sky observations and contribution of the temporal<br />
resolution of MSG/SEVIRI<br />
Dominique Jolivet and Didier Ramon (HYGEOS)<br />
&<br />
Jérôme Riedi and Jean-Marc Nicolas (LOA)<br />
The algorithm to separate cloud free <strong>from</strong> cloud contaminated or cloud filled pixels has been<br />
developed with the intention of being applicable globally without requiring ancillary data such as<br />
surface temperature or atmospheric profiles. Each test implemented within the algorithm has been<br />
designed based on fundamental physical differences (emissivity, spectral behaviour) between<br />
clouds and surface.<br />
Many lessons learned <strong>from</strong> the Moderate Resolution Imaging Spectroradiometer (MODIS),<br />
regarding discrimination between clouds and heavy aerosols events or sunglint for instance (spatial<br />
variability, spectral behaviour), have been taken into account in the design of this cloud detection<br />
scheme. However, the present algorithm shall not be considered as just a simplified MODIS-like<br />
cloud mask because : (i) some tests have been adapted and modified to account for the differences<br />
in spectral channels, calibration and/or spatial resolution and make them applicable to SEVIRI, (ii)<br />
the number of tests used is currently much smaller than the one used in the operational MODIS<br />
algorithm (Ackerman et al. 1998; Platnick et al. 2003) and (iii) the decision logic differs<br />
significantly <strong>from</strong> the one used for MODIS (http://www-loa.univ-lille1.fr/~riedi/).<br />
The input to the SEVIRI cloud detection algorithm consists of normalized reflectances <strong>from</strong> the<br />
visible (0.6 and 0.8 µm) and near-infrared (1.6 µm) channels, whereas brightness temperatures are<br />
used <strong>from</strong> the thermal infrared channels (3.8, 8.7, 10.8 and 12.0 µm). Additionally, the algorithm<br />
uses ancillary data on solar and viewing geometry and a land/sea map but none is required <strong>from</strong><br />
model reanalysis. There are spectral threshold and spatial coherence cloud detection tests that are<br />
different for land and ocean surfaces. The various tests implemented are grouped together in such a<br />
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way that the group will identify unambiguously specific cloudy or clear sky condition. Each group<br />
may contain tests aimed at detecting clouds or rejecting unphysical clear sky conditions. The<br />
different groups are then arranged in such a way that the independence between the tests is<br />
maximized. A different weight is assigned to each group of cloud detection tests. Additionally,<br />
groups have been implemented to specifically detect clear sky conditions and a similar weighting is<br />
applied. Finally, the results of all the tests and the sum of the weights are used to generate a<br />
summarized cloud mask that includes four confident levels: clear certain, clear uncertain, cloud<br />
uncertain and cloudy certain (similar to the cloud mask summary provided by the operational<br />
MODIS cloud mask product MOD35).<br />
Statistics on clouds and then on clear sky pixels available for aerosols detection are presented in this<br />
appendix based on results <strong>from</strong> this cloud detection scheme.<br />
Over the entire disk as observed by SEVIRI, the cloud mask of MODIS Aqua at 1kmx1km<br />
resolution (overpass at 14:30 UT) has been built <strong>from</strong> the composite of different orbits (see Fig. 1).<br />
In this map four classes of pixels can be found: clear confident in white, probably clear in red,<br />
probably cloudy in purple and confident cloudy in black. Only pixels flagged as clear confident are<br />
kept for aerosols <strong>retrievals</strong>. The histogram presented in Figure 2 shows the number of pixels which<br />
can be used for MODIS to derive aerosols properties. Almost 38% of the pixels are flagged as clear<br />
confident.<br />
Figure 3 shows the cloud mask <strong>from</strong> SEVIRI for the same day at 14:30 UT over the entire disk.<br />
Four classes are possible: clear certain (in light blue), probably clear (in yellow), probably cloudy<br />
(in red) and confident cloudy (black). Whereas MODIS AQUA observes only once a location the<br />
afternoon at 14:30UTC (MODIS TERRA overpass is morning at 10:30UTC), MSG/SEVIRI can<br />
observe every 15 minutes the same location. Taking into account the time period 12:00-16:00 UTC,<br />
16 observations of the same location are then available. One can note that we can compare with<br />
MODIS TERRA using the period 08:00-12:00 UTC. Figure 4 shows the number of clear sky<br />
observations for each SEVIRI pixels.<br />
Essential information is the spatial coverage of the product. The spatial coverage of the aerosols<br />
products <strong>from</strong> MODIS-AQUA and SEVIRI can be compared in Figure 5. These figures also<br />
indicate how rich is the set of useful measurements available <strong>from</strong> SEVIRI to derive aerosols<br />
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properties. As an example, areas in green are areas found cloudy with MODIS where at least one<br />
clear sky observations is obtained with SEVIRI. For these cases, an aerosol product can be derived<br />
<strong>from</strong> SEVIRI but not <strong>from</strong> MODIS. It implies that the spatial coverage increases with SEVIRI<br />
compared to MODIS. Areas in light blue are areas where the retrieval of an aerosol product is<br />
possible with MODIS and SEVIRI but SEVIRI can offer better information because more than 7<br />
clear sky observations can be used. Here SEVIRI is able to get a sample of the aerosol retrieval. In<br />
opposite, areas in purple are areas found always cloudy by SEVIRI whereas it is cloud free using<br />
MODIS. These areas are mainly at the border of the disk for large values of the viewing angles. It is<br />
more important for the east part because of the afternoon time (it will be the opposite corner -east<br />
border - when taking the morning period 08:00-12:00 UT).<br />
The difference in cloud contamination comes mainly <strong>from</strong> two reasons:<br />
1 MODIS has a better spatial resolution and it can be easier to see through fields of clouds. In<br />
particular that is the case for the scattered clouds (fields of very small clouds) in the south<br />
hemisphere on the west of South Africa.<br />
2 SEVIRI and its temporal resolution allow the observations of moving clouds. An area covered<br />
by clouds at one time can be then clear few minutes or hours later.<br />
Finally the cumulative histogram of Figure 7 shows the statistical distribution of clear sky<br />
observations available <strong>from</strong> SEVIRI for the entire disk. About 42% of the disk is always cloudy. So<br />
at least, one and more clear sky observations are available for 57% of the pixels. One can also see<br />
that 16 clear sky observations are available for about 7% of the pixels.<br />
Based on this comparison with MODIS-AQUA, the spatial coverage of an aerosol product would be<br />
about 57% for SEVIRI when it would be only 38% for MODIS. Moreover, targeting the same<br />
spatial coverage as MODIS (i.e. 38%), SEVIRI would be able to provide 5 times more observations<br />
than MODIS.<br />
As a preliminary conclusion (we only studied the 17 th August 2005), MSG/SEVIRI looks to be a<br />
real contribution for an aerosol product for the areas around the centre of disk whereas it looks<br />
tricky to use it for areas near the borders of the disk.<br />
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Figure 1: Cloud mask <strong>from</strong> MODIS -AQUA composite images (orbits at 14:30 UT ) over the MSG<br />
observed disk for the 17 th August 2005. White pixels are pixels flagged as clear confident. Red are<br />
probably clear, purple are probably cloudy and black are confident cloudy.<br />
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Figure 2: Ratio of the pixels of the entire disk which are not observed, flagged as confident cloudy,<br />
flagged as probably cloudy, flagged as probably clear and flagged as confident clear <strong>from</strong> the<br />
MODUS-AQUA cloud mask for the 17 th August 2005.<br />
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Figure 3: Cloud mask <strong>from</strong> MSG/SEVIRI for the 17 th August 2005 at 14:30 UTC. The color scale<br />
is the following: light blue is clear confident, yellow is probably clear, red is probably cloudy and<br />
black is confident cloudy.<br />
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Figure 4: Map of the number of clear sky observations <strong>from</strong> MSG/SEVIRI between 12:15 and<br />
16:00 UTC for the 17 th August 2005. Black means that no clear sky observations were found.<br />
Areas with a number of clear sky observations between 1 and 3 are in dark blue, between 4 and 7<br />
are in blue, between 8 and 10 are in light blue. Areas in white are areas where more than 10 clear<br />
sky observations were found.<br />
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Figure 5: Map of the comparison between clear sky observation <strong>from</strong> MODIS-AQUA and<br />
MSG/SEVIRI (16 slots between 12:15 and 14:00 UT) taken into account the number of clear sky<br />
observations.<br />
Color scale<br />
Black : no clear sky observations <strong>from</strong> MODIS and SEVIRI<br />
Purple : Clear sky observation <strong>from</strong> MODIS but nothing <strong>from</strong> SEVIRI<br />
Yellow : Not observed by MODIS<br />
Green : No clear sky <strong>from</strong> MODIS but at least one clear sky observations <strong>from</strong> SEVIRI<br />
between 12:00-16:00 UTC<br />
Dark blue : MODIS and SEVIRI have both only one clear sky observation<br />
Light blue : Clear sky observed by MODIS and more than 7 clear sky observations available <strong>from</strong><br />
SEVIRI during 12:15 – 14:00 UTC.<br />
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Figure 6: Cumulative histogram of the number of clear sky observations available for each pixel<br />
of the entire disk observed by SEVIRI the 17 th August 2005 between 12:00 and 16:00 UT<br />
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