A modeling approach for aerosol optical depth

A modeling approach for aerosol optical depth analysis during forest fire events M. Aubé*a,b, N. T. O'Neill, N.Ta, A. Royera, D. Lavouéc a CARTEL, Université de Sherbrooke, 4500 Université, Sherbrooke, Québec, Canada, J1K 2R1; b GRAPHYCS, Collège de Sherbrooke, 475 rue Parc, Sherbrooke, Québec, Canada, J1E 4K1 ; c Meteorological service of Canada, 4905 Dufferin Street, Toronto, Ontario, Canada, M3H5T4 ABSTRACT Measurements of aerosol optical depth (AOD) are important indicators of aerosol particle behavior. Up to now the two standard techniques used for retrieving AOD are; (i) sun photometry which provides measurements of high temporal frequency and sparse spatial frequency, and (ii) satellite based approaches such as DDV (Dense Dark Vegetation) based inversion algorithms which yield AOD over dark targets in remotely sensed imagery. Although the latter techniques allow AOD retrieval over appreciable spatial domains, the irregular spatial pattern of dark targets and the typically low repeat frequencies of imaging satellites exclude the acquisition of AOD databases on a continuous spatiotemporal basis. We attempt to fill gaps in spatiotemporal AOD measurements using a new assimilation methodology that links AOD measurements and the predictions of a particulate matter Transport Model. This modelling package (AODSEM V2.0 for Aerosol Optical Depth Spatiotemporal Evolution Model) uses a size and aerosol type segregated semiLagrangian trajectory algorithm driven by analysed meteorological data. Its novelty resides in the fact that the model evolution may be tied to both ground based and satellite level AOD measurement and all physical processes have been optimized to track this important and robust parameter. We applied this methodology to a significant smoke event that occurred over the eastern part of North America in July 2002. Keywords: aerosol optical depth, transport modeling, remote sensing, forest fires

1. INTRODUCTION The AOD, which represents total aerosol optical attenuation at a given wavelength, is a key parameter in the monitoring of aerosol optical properties. AOD is sensitive to aerosol microphysical characteristics (in particular vertically integrated number density and the size distribution of each aerosol type). The size distribution of hygroscopic aerosols is in turn sensitive to local relative humidity. Many techniques have been developed in order to monitor the spatiotemporal variability of aerosol particles. A well established method consists of observing direct solar radiation using ground based sunphotometer networks such as AERONET (AErosol Robotic NETwork)1. This method provides good temporal information but very sparse spatial information since data are only acquired at about one hundred stations worldwide. A second important technique is based on inversion algorithms, which exploit the atmospherically dominant signal present over dark target pixels in remotely sensed satellite images. This technique has been successfully applied over dense dark vegetation (DDV) and Proceedings of SPIE Volume 5548 Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an EndtoEnd System Perspective, HungLung A. Huang, Hal J. Bloom, Editors, October 2004, pp. 417426

ocean pixels using satellite based sensors such as AVHRR (Advanced Very High Resolution Radiometer)2 and MODIS (Moderate Resolution Imaging Spectroradiometer)3. Satellite based inversion techniques give more comprehensive spatial information but are typically limited to daily sampling frequencies and are typically inferior in accuracy. Although the two techniques are somewhat complementary they do not allow AOD acquisition on a continuous basis. In this paper, we attempt to fill gaps in spatiotemporal AOD measurements using a new assimilation methodology that links AOD measurements and the predictions of a particulate matter Transport Model (TM). This model, which is a part of the AODSEMV2.0 package, uses a size and aerosol type segregated semiLagrangian trajectory algorithm driven by analysed meteorological data. Its novelty resides in its being tied to AOD measurements and thus being effectively optimised in terms of this important and meteorologically robust parameter. For the work presented in this paper, we applied this methodology to the complex but challenging situation which prevailed during the unusually strong Québec forest fire and smoke event of July 2002. AERONET dataset was used as tie down points or weighted observations for the assimilation of AOD data. A degree of model evaluation was exercised by comparing the assimilated model computations with observations not employed in the assimilation scheme.

2. MODEL SUMMARY 2.1 Aerosol transport model AODSEM uses a regional particulate transport model based on a semilagrangian trajectory scheme. In the dynamical computing process, a trajectory element is determined for each tracer and for each time step. In AODSEM a tracer is defined as a particular aerosol species (sea salt, soil dust, black carbon (BC), organic carbon (OC) or sulphate) present in a particle radius range (12 logscaled bins between 0.00520.48 µm) at a specific position on a fixed 3D grid. Trajectory elements define new positions for tracers which, after a linear spatial interpolation on the model grid, give the aerosol concentration distribution at the beginning of the following time step. Both for the horizontal and vertical axes, the typical relaxation time of the wind drag force (typically less than 1 second) is many times smaller than the computing time step (typically 3 hours) so that particle speed may be considered equal to the aerodynamical speed limit. On the horizontal plane, we simply assumed that particles behavior was governed by horizontal wind speed while on the vertical axis, the aerodynamical speed is defined by an equilibrium equation involving gravitational force, buoyant force and vertical wind drag force. The numerical integration scheme used in both directions is based on Kreyszig's approach 4; this is a modified version of f the very common Euler scheme. In Kreyszig's scheme a first guess position x i 1 is computed before advecting the tracer. x if1 =x i u i  x i   t (1) This first guess position is then used to determine a more representative wind speed to apply to the trajectory element. x i 1=

 u  x u i

i

f

i 1

2

 x i 1



t

(2)

Proceedings of SPIE Volume 5548 Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an EndtoEnd System Perspective, HungLung A. Huang, Hal J. Bloom, Editors, October 2004, pp. 417426

x denotes the position in one of the 3 possible directions, u is the corresponding component of the aerodynamical speed limit, i denotes the actual time step number and  t represents the time step. For each tracer trajectory element, we computed a puff size dispersion which is formulated in terms of the turbulent velocity components. At the end of the time step, aerosol concentrations are resampled accordingly to puff sizes assuming “top hat” shapes, that is, a constant concentration inside the puff and zero outside the puff. Horizontal resolution is determined by the user but it is typically of the order of 0.5 deg. The vertical layer thicknesses were chosen as equiAOD layers for standard atmospheric conditions. We used 10 layers between 0 and 30 km. Layers 1 to 8 are situated within a nominal Planetary Boundary Layer (PBL) of 02 km (where aerosols are generally more concentrated). Dynamical computations require inputting an offline 3D meteorological database. These analyzed meteorological 5 databases are provided by the Canadian meteorological centre Global Environmental Multiscale model (GEM) at a resolution of 1 by 1 degree. For dynamical purpose, we used horizontal winds, temperaturedew point spreads, air temperature and and geopotential height. Cumulative precipitation, temperaturedew point spreads and air temperature were also used to compute microphysical processes (wet scavenging, brownian coagulation and hygroscopic growth). AODSEM is executed in a two step dynamical process. In the first step, the model operates at a coarser resolution but on a larger geographical domain (coarse resolution mode). The objective is to generate a realistically nested aerosol concentration database which will be used to fill a fine resolution model buffer region (fine resolution mode). The buffer region (typically 2 degrees wide) is contiguous with the horizontal domain boundaries. Within the region, dynamically inline generated aerosol concentrations are replaced by offline nested concentrations at the end of each time step. This buffering action addresses the problem that, for regional models, source information outside the domain is excluded from model dynamics. The coarse resolution mode buffer region is filled with the Global Aerosol Data Set (GADS 6) which is a seasonal aerosol climatology. 2.2 Emission database Emission of aerosol particles is a fundamental model process which must be accurately simulated in a geographical as well as temporal sense. In AODSEM we have chosen to update emissions on a daily basis. Emission rates are determined at 12h00 UTC and are afterward linearly interpolated to the current time. There are three main source categories in the model: 1 low frequency offline sources (monthly or seasonally); 2 high frequency offline sources like forest fires, volcanoes, pollution events, etc.; 3 high frequency inline sources which depend on near surface wind speed (e.g. Sea salt). Category 1 and 3 are included by default in the model but high frequency offline sources have to be defined by the user. All emission databases are defined at a fixed geographical grid resolution of 1x1 degrees. Table 1 is a summary of sources available in AODSEM. Note that some of the emission flux databases come from the Global Emission Inventory Activity (GEIA). The emission coded B3 and C2 are subsets of the B2 and C1 emissions respectively. The low altitude emissions of the B2 and C1 codes are attributed to agricultural fires, domestic fires and to fossil fuel combustion 7 which are less variable in space and time than other OC and BC sources .


Table 1: Summary of emission inventories integrated to the AODSEMV2.0 package Particle species

Code

Temporal resolution

Vertical injection profile

8

2 levels (80 m, 250 m) 8

Sulphate

A1

Seasonal (GEIA)

Black Carbon

B1

Monthly biomass burning and Yearly fossil fuel (GEIA)9

Organic Carbon

7

1 level (80 m)

B2

Monthly biomass burning and fossil fuel

B3

Monthly low altitude biomass burning and fossil fuel7

1 level (250 m)

B4

Daily ATSR derived sources10

9 levels (0 m to 12 km)

B5

Daily FLAMBEABBA11, 12

C1

Monthly biomass burning and fossil fuel


9 levels (0 m to 12 km) 7

8 levels (250 m to 12 km) 7

C2

Monthly low altitude biomass burning and fossil fuel

C3

Daily ATSR derived sources10


C4

Daily FLAMBEABBA11, 12


Sea salt

D1

Daily

Any

E1

Daily

13

1 levels (250 m)

1 level (80 m) User *

* "User" means that the user may define his own emissions rates and injection heights The Naval Research Laboratory (NRL) Fire Locating and Modeling of Burning Emissions (FLAMBE) smoke flux site provides high frequency forest fire emission data on an operational basis. FLAMBE uses thermal infrared imagery from the GOES satellite to identify fires and to estimates fire area. Knowledge of vegetation type as a function of geographical position, permits the conversion of burned area into an estimate of total particle mass injected into the atmosphere. The assumptions that OC and BC aerosols occupied roughly 60% of that mass and that an OC/BC mass ratio of 19.5 could be employed to characterize boreal forest fires in general12 enabled an estimation of injected mass for each species. Injection height was assumed to be a linear function of burning mass flux. The slope of the function was determined by associating an injection height of 7300 m with the largest injection flux of the database. This method is consistent with the works of Lavoué et al.12 who showed a linear relation between fire frontal intensity and injection height. We chose a unimodal lognormal size distribution with a mode radius of 0.05 µm and a standard deviation (log scale) of 1.7 to characterize the freshly emitted aerosol particles. The database was resampled to compute total fluxes per day on a 1ox1o grid. 2.3 Aerosol microphysics The concept behind AODSEM was to tailor model sophistication to the pragmatic requirement that the level of precision be comparable to the typical accuracy of AOD measurements. We accordingly decided to implement only microphysical processes which would have relatively important optical impact during typical times between assimilation steps (of the order of 1 day). Presently the AODSEM code accounts for dry deposition, incloud and under cloud wet scavenging, hygroscopic growth, and brownian coagulation processes.


2.4 Assimilation scheme A data assimilation system was developed around AODSEM in order to ensure the realistic simulation of AOD spatio temporal variations. Data assimilation is a method of producing gridded analysis products given raw observations and a model capable of predicting the evolving atmospheric state. We used a combination of the Cressman data assimilation and Incremental Analysis Update (IAU) to achieve the assimilation system. In our implementation of Cressman scheme we assumed that model errors are many times larger than observation errors. Figure 1(a) shows the conceptual data assimilation process used in AODSEM. The TM starts with devoid of aerosol content but emissions gradually add particles. For each time step, tracer advection, puff diffusion, and microphysical processes are computed. This results in aerosol background concentrations b from which we can compute the vertically integrated AOD background fields (AODb) as defined by the physical operator HP. The physical operator employs precalculated Mie extinction cross sections stored in a lookup table (LUT). These LUTs are functions of aerosol species, size and ambient relative humidity (which affects particle size and refractive index). We assumed internal mixing for soluble aerosols. The background AOD (AODb) acts as a first guess AOD map. This first guess map is substracted from observations (AODo) to produce the AOD correction  AOD R i  given by

 AOD R  i =

[

n

∑ w i , j  AOD o  j −AOD b  j  j=1

w r i −r j =

n

∑ w i , j  j=1

1 2

C xy∥r i −r j∥

]

(3)

(4)

where w r i −r j  is a distance dependent weight function designed to resample AODo  j − AOD b  j  on all grid points. A correction for spatial correlation between observations is done by the C xy factor which is a measure of the number of proximate observations. We used i to denote an analysis grid point and j to denote an observation or background grid point. Defining an inverse physical operator HP1 , we obtain the equivalent of  AOD R i  in concentration space. This concentration correction, can be written

[

a  i= b  i H −1  AOD R i  P

]

. (5)

In our work HP1 was designed to incorporate simple geographicallyreferenced aerosol models14 and a specific standard vertical profile15. Concentration analyses for each time step are obtained by equally distributing the concentration correction across the background concentrations of all intermediate time steps. This later process is generally called Incremental Analysis Update (IAU). In our implementation of IAU, the proper redistribution fraction for each Proceedings of SPIE Volume 5548 Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an EndtoEnd System Perspective, HungLung A. Huang, Hal J. Bloom, Editors, October 2004, pp. 417426

intermediate time step is given by the ratio of the time elapsed since the first observation over the total time between the two observations. Concentrations analyses are finally transformed into AOD space using the HP operator to give the AOD analyses AODa  i  . AOD a  i = H P [ a  i ]

(6)

The data assimilation process is run iteratively over the whole experiment period using the previous analysis as a new starting point.

tobs1

Measurement (AOD)

tobs2

0,18 0,16

analysis 0,14

∆AODR

HP

HP-1

(a)

IAU

measurement

0,12

AOD

ρb

TM

∆ρ

0,1 0,08 0,06

(b)

0,04

ρa TM

HP

ρb

AODa HP

background

correction

0,02

∆AODR

0 0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

Time (units of time steps)

Figure 1: The AOD data assimilation process. (a) general assimilation concept used in AODSEM; (b) conceptual representation of the Incremental Analysis Update.

3. METHODOLOGY In this study we investigated the usefulness of the AODSEM assimilation approach in terms of its ability to produce AOD analyses of measurement databases. Forest fires smoke events represent a good benchmark for model evaluation since forest fires are highly variable in space and time and since the optical effects of smoke plumes are relatively easy to unambiguously detect and quantify. 3.1 Experimental context We chose, as out first evaluation benchmark, an important smoke emission event which occurred in the beginning of July 2002. During this period, large forest fires were recorded in northern Québec, Canada. Atmospheric circulation during this period steered thick smoke plumes over the east coast of southern Canada and the northern United States. Within that region a relatively large number of groundbased and satellitebased sensors recorded strong plume signatures. In particular, the plume passed over the National Aeronautics and Space Administration (NASA) Goddard o o Space Flight Center (GSFC, 39.02 N, 76.86 W) and large smoke AODs were recorded by an AERONET sunphotometer Proceedings of SPIE Volume 5548 Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an EndtoEnd System Perspective, HungLung A. Huang, Hal J. Bloom, Editors, October 2004, pp. 417426

(largest AODs ever recorded at that site). To conduct this evaluation exercise we chose a geographical domain which included most of North America (see figure 2). In that figure the larger domain, bounded by the black buffer region (4 deg. wide), corresponds to the coarse resolution mode grid. The fine mode grid is bounded by the white buffer region (2 deg. wide). The white stars represent th the approximate positions of fires visible on MODIS image of July 6,7,8 (see figure 4) and the white square represents the location of the GSFC sunphotometer.

Figure 2: Modeling domain for the 2002 northern Québec forest fire evaluation exercise. 3.2 Forecast mode Before proceeding to the assimilation of AOD measurements one must create a realistically nested aerosol concentration database which will be used later to fill the fine resolution model buffer region (white zone in fig. 2). This was done by choosing a larger domain which extended from 25oN, 165oW to 85oN, 45oW and then running AODSEM in coarse resolution mode. We set the spatial resolution to 1 o x 1o and the time step to 6 hours (identical to the GEM database). At each time step, we replaced the buffer content (black zone on fig. 2) with aerosol concentrations prescribed by the GADS climatology. Modeled concentrations were resampled to the grid used for subsequent fine mode runs. In the fine mode, the domain extended from 30oN, 160oW to 80oN, 50oW, the spatial resolution was set to 0.4ox0.4o and the time step to 3 hours. We conducted two fine mode forecast runs: 1A run including only smoke emission fluxes given by FLAMBE (called Fire Forecast Experiment, FFE) 2A run using FLAMBE sources (emission code B5 and C4 of table 1), sulphate sources (emission code A1), low altitude organic and black carbon (emission code B3 and C2) and finally sea salt emissions (code D1) (called Total Forecast Experiment, TFE). The first forecast run was useful for comparing modeled smoke plume shapes with plumes detected by the the MODIS sensor. It allowed us to evaluate the level of accuracy of the advection and source inventories. We used the second Proceedings of SPIE Volume 5548 Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an EndtoEnd System Perspective, HungLung A. Huang, Hal J. Bloom, Editors, October 2004, pp. 417426

forecast run for a temporal evaluation comparison with AERONET sunphotometer data. This allowed us to assess the net improvement delivered by the assimilation scheme in comparison with the model forecast (TFE). 3.3 Assimilation mode The assimilation run was performed on the fine mode grid described above. We used the same nested database as in the fine resolution TFE experiment. The objective of this run was to determine the level of improvement in the AOD prediction afforded by our assimilation process. In the first instance we tried to evaluate the assimilation performance using only AERONET AOD data (called AERONET Assimilation Experiment, AAE). In this exercise, we used only a subset of the total AERONET database, while employing the unused portion as independent data to evaluate model performance. We also used MODIS AOD data to evaluate the spatial improvement delivered by assimilation.

4. DATA In parallel with ground based (AERONET) sun photometry, MODIS images were acquired during this period. The AERONET AOD data comes from the GSFC site situated near Washington DC. We used level 1.0 (non cloudscreened) data to compare with the model outputs. AERONET data are available ~ every 15 minutes during daylight. We used AOD measurements at 500 nm and the Angstrom coefficient for 440 and 675 nm to compute the 550 nm AOD. This was necessary to allow direct comparisons with the 550 nm MODIS AOD product. We assimilated one AERONET AOD for each site situated in our domain in a temporal window of 1 hour centered on 00h00, 12h00, 15h00, 18h00 and 21h00 UTC of each day. Active photometer sites are very sparse in the domain and cloud were often present so that we obtained typically only ~5 data points for each AODSEMAERONET assimilation step. To minimize cloud contamination effects on model evolution we assimilated cloud screened level 1.5 AERONET data instead of level 1. th

th

We also retrieved all MODIS AOD data from June 15 to July 15 2002. MODIS AOD resolution is of the order of 0.1 degree but we resampled the data product to our 0.4 degree domain resolution. This resampled database was used for spatial comparisons. 16 Typical measurement errors associated with AERONET AODs are estimated to be  a=±0.02 . Errors associated with the MODIS AOD product are normally  a=±0.05± 0.2  a 3 over land.

5. RESULTS Examination of FFE results reveal that plume trajectories are similar in shape to plumes observed by the MODIS and GOES sensors. In figure 3, major clouds systems have been highlighted in magenta (c.f. the MODIS image on the right) so that other whitish / hazy regions generally represent the smoke plume. It is interesting to note that the upper right cloud zone was superimposed on a major portion of the modeled smoke plume. The modeled plume of figure 3 is shifted toward the southeast direction relative to the perceived position of the plume in the MODIS and GOES images. This anomaly could be explained by an under estimate of fire fluxes situated in the western part of the main active burning area and by inaccurate wind speed fields. It is important to note that the FLAMBE flux database was created th from satellite fire detection which cannot see fires situated under clouds. In fact figure 4 demonstrate that on July 7 Proceedings of SPIE Volume 5548 Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an EndtoEnd System Perspective, HungLung A. Huang, Hal J. Bloom, Editors, October 2004, pp. 417426

there was a cloud system masking at least the western half of the burning area. This observation highlights the importance of acquiring an accurate smoke source inventory and eventually producing emission flux estimates at the precision levels required to forecast AOD for a given location and time. Satellite derived inventory like FLAMBE can sometimes, as we have seen, be incomplete and misleading.

(a)

(b)

(c)

(d)

Figure 3: Comparison of the FFE experiment output for July 7th at (a) 15h UTC and (b) 18h UTC , with a MODIS color composite at 16h35 UTC (c) and GOES8 visible image at 18h UTC (d).

06/07/2002

07/07/2002

08/07/2002

Figure 4: MODIS color composites where fires appear in red. The large red region corresponds to the location of northern Québec fires which were active during the beginning of July 2002. The magenta zone indicates a bank of clouds that prevented fire detection for that period and region. We created a new smoke emission inventory in which we replaced the July 7 th northernQuébec FLAMBE smoke sources by July 6th FLAMBE sources. AODSEM was run in fine forecast mode with this corrected inventory. The new results, which are presented in figure 5 show no significant change in plume shapes except for locations situated near active fires. It should be noted that with new sources, AOD levels are higher and this is in better agreement with the MODIS AOD product shown in figure 5 (c). However the modeled AOD levels continue to be underestimated. This suggests that smoke mass estimates from FLAMBE are also underestimated. Given the modest improvement afforded by


this new smoke emission inventory we opted to keep the original FLAMBE inventory for use as an input for other experiments.

(a)

(b)

(c)

Figure 5: Comparison of the modified FFE experiment output for July 7 th at (a) 15h UTC and (b) 18h UTC , with the MODIS AOD product for 16h35 UTC. The magenta circle delineates the region around the GSFC site (c.f. Figure 6) As a second model evaluation test, the AODSEM time series over the GSFC site was compared with AERONET AODs in order to verify the reliability of the modeled temporal evolution. The black AAE curve of figure 6 corresponds to a data assimilation run tied to only ~5 AOD values per assimilation step (one for each AERONET active site). Although the AOD measurement dataset has been severely restricted, the temporal comparison with AERONET data which was not employed in the assimilation procedure demonstrated significant correlation. AAE assimilation seem to have reduced AOD values compared to TFE estimates this could indicate systematic overestimates of the standard emission inventories in AODSEM. Further investigation will be necessary to identify the specific nature of the inventory overestimate. This figure clearly shows that for the GSFC site, the AODSEM th approach completely failed to explain the major AOD smoke peek which occurred on day 188 (July 7 ). This can be understood by closer examination of Figure 5 where we have encircled the GSFC location. It is clear that the modeled AOD is comparatively weak in this region. For now is is difficult to say if this problem results from an error in the smoke emission inventory or from bad trajectory computations.


7 6

AOD (550 nm)

5 4 3 2 1 0 172

174

176

178

180

182

184

186

188

190

192

194

196

Day of Year (2002)

Figure 6: Comparison of modeled time series with level 1.0 AERONET data (points) at GSFC from June 21th to July 15th . Dashed curve represents FFE, gray curve the TFE and black curve is the AAE.

6. CONCLUSION Results derived from this study indicate the usefulness and potential of the AODSEM approach as an intelligent physical interpolation engine for producing systematic AOD analyses from ground based AOD data. This is especially true for regions or temporal periods weakly affected by forest fire smoke. Assimilation improves AODSEM AOD analysis in those cases. This result is not really surprising since AOD spatial variation is low frequency and thus only requires a sparse ensemble of tiedown points to adequately constrain the assimilation process. For high spatial frequency sources like forest fires, AERONET data may not contain enough spatial information to constrains the model adequately. Assimilation of MODIS AOD should better constrain AOD in such difficult cases as long as the smoke plumes are captured by the satellite images. However this is not always the case due to orbital constraints and/or cloud presence. For regions and periods highly affected by forest fire smoke, accurate smoke emission inventories are required. As we have illustrated above, satellite derived inventory can be inaccurate so that some investigations are needed to develop better source determination processes. A possible approach would be to merge together fire detection capabilities from multiple sensors and platforms. This could exploit the fact that data from each sensor are not acquired at the same time and thus reduce unwanted cloud masking effects.

ACKNOWLEDGMENTS


We are thankful to B. N. Holben and L.B. Pham from NASA GSFC for acces to AERONET data and to for the delivery of MODIS AOD images. We also want to thank R. Hogue and L. Poulin from the Canadian Meteorological Center for GEM data access, E. Prins (National Oceanic and Atmospheric Administration) and J. Reid (NRL) for giving us access to FLAMBEABBA database. Special thanks go to M. N. Nguyen for the processing of MODIS images and to JD. Giguère for his contribution to AODSEM development. Funding for this research was provided by the Canadian Institute for Climate Studies (CICS), the National Sciences and Engineering Research Council of Canada (NSERC), and the Fonds québécois de la recherche sur la nature et les technologies (FQRNT).

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Geophysics Laboratory, MA. 15. R.A. McClatchey, H.J. Bolle, and K. Ya (1982) A Preliminary Cloudless Standard Atmosphere for Radiative Computation, SRA report, International Association for Meteorology and Atmospheric Physics, U.S.A. 16. M. Aubé (2003) Modélisation de l'évolution spatiale et temporelle de l'épaisseur optique des aérosols à l'échelle régionale, PhD thesis, Unversité de Sherbrooke, Canada. *

[email protected]; phone 18195646350 6232; fax 18195641579; www.graphycs.qc.ca/martinaube.html
