MULTISPECTRAL CLEAR-SKY BRIGHTNESS TEMPERATURE OVERDESERT FOR AEROSOL RETRIEVAL

Bart De Paepe, Alessandro Ipe, Nicolas Clerbaux, Steven Dewitte

ROYAL METEOROLOGICAL INSTITUTE OF BELGIUM

abstract

The desert surface is characterized by periodical outbreaks of mineral dust storms. These dust aerosolshave an impact on the upwelling infra red radiance. The desert surface has a low emissivity in the 8.7µm channel, compared to the 10.8 µm channel. This characteristic can be used to separate the TOAbrightness temperatures of clear-sky and aerosol events.

In order to do so, we have to take into account the surface contribution to the measured satellite sig-nal. To separate this from the aerosol signal, we calculate the Top Of the Atmosphere (TOA) clear-skyradiances (as brightness temperatures) of the 8.7µm, 10.8 µm and 12.0 µm channels. The clear-skybrightness temperatures in those channels for a fixed time of the day are selected using a commonclear-sky criterion over the different days of a month. Different clear-sky criterions are tested, e.g. takingthe maximum 10.8 - 8.7 µm brightness temperature difference over the month. Empirical analyses ofthe 8.7µm and 10.8 µm channels shows that the satellite measured radiance over desert decreases inthe presence of aerosols or clouds. The brightness temperature value of both channels is very sim-ilar for aerosol pixels, whereas for clear-sky it differs significantly. The resulting clear-sky brightnesstemperatures are valid during both night and day.

During day time we verify this method with the clear-sky detection by the RMIB-team in the contextof the GERB-processing. The RMIB GERB clear-sky detection only uses visible channels. We validatethe clear-sky brightness temperature values derived with the infrared method by comparison with thebrightness temperature values for the times identified as clear-sky by the RMIB GERB method.

The TOA clear-sky brightness temperature for desert will serve as reference image when calculatingthe optical properties of aerosols. It will prove valuable for the implementation of a climatology of Africanmineral dust, based on Meteosat-8 imagery.

1 INTRODUCTION

Aerosols are important for climate studies. They have an impact on the earth radiation budget, directwhen radiation interacts with a particle, but also indirect when aerosols interfere in cloud formation.Desert aerosols in particular have a large influence on climate. Desert aerosols, dust aerosols, consistof mineral dust particles of different size and shape. During dust storms over the desert, large amountsof these particles are lifted up into the troposphere where they form increased aerosol concentrationsthat are transported on relative low altitude. Similar events cause significant aerosol concentrations inthe troposphere that can be easily detected on satellite images. Another reason why desert aerosols areparticularly interesting is their regular occurrence. Mainly three to four times a year, a huge dust stormbreaks out over the desert, bringing large amounts of dust into the troposphere. If we want to calculatethe optical properties of these dust aerosols, we should take into account the surface contribution. Thesignal measured at the satellite is composed both of the surface signal, and the radiance coming fromthe atmosphere as well as from the aerosols.

Figure 1: false color composite (IR12.0-IR10.8,IR10.8-IR8.7,IR10.8), 8 March 2006 12:00 UTC

The observed radiances by the satellite will not only change as a function of surface and atmo-sphere. The radiance leaving the surface varies both on a daily and seasonal basis. The diurnal cycleis charaterized by lower radiances during nighttime and higher radiances during daytime (Legrand etal.[2]). The seasonal cycle has changing radiances according to the variations in sun-earth position.Therefore radiances are not suited to estimate the surface contribution, and instead we should use thesurface emissivity. Surface emissivity depends on surface type, condition, and view angle. Therefore itis less variable in time, and thus it is better suited as an estimation for the surface contribution. Once weknow the radiance coming from the surface, we can separate it from the observed radiance to obtain theaerosol signal.

2 DATA

From 5 to 9 March 2006 there was a dust outbreak to the south of Algeria and over Niger. A cold airmass coming from Europe was responsible for the dust storm that moved from West to East over theSahara. The dust aerosols were visible on the thermal infrared channels and we could follow its spatialand temporal distribution over this time range. Figure 1 illustrates the dust storm over the Sahara.

For this study we used SEVIRI level 1.5 data. We selected the short wave bands at 0.6 µm and 0.8µm, and the long wave channels at 3.9 µm, 8.7 µm, 10.8 µm, and 12.0 µm.

For the comparisons with the MODIS emissivity we used the Terra MOD11B1 and the Aqua MYD11B1emissivity product. Band 31 was used for comparison with the SEVIRI 10.8 µm channel and band 32 forthe 12.0 µm channel. This MODIS data is online available at the EOS Data Gateway (http://delenn.gsfc-.nasa.gov/ imswww/pub/imswelcome/index.html).

The processing of the GERB clear-sky image requires 2 months of SEVIRI data of the correspondingtime, preferable symmetric around the choosen date. Similar the clear-sky brightness temperature imagerequires one month of SEVIRI data.

Figure 2: 8.7 µm - 10.8 µm, 8 March 2006 12:00 UTC

3 METHOD

In order to estimate the surface contribution we calculate the top of atmosphere clear-sky images, thatare free from clouds and aerosols. Over desert we take advantage of a desert surface characteristicto obtain these clear-sky images. The desert surface is characterized by low emissivity in the 8.7 µmchannel. This results in significant lower radiances compared to the 10.8 µm channel over clear-skyregions. In the presence of aerosols or clouds on the other hand, the emissivities at 8.7 µm and 10.8 µmare much more similar. We use this characteristic to calculate the clear-sky radiances for a certain placeand time. We take one month of long wave data for the channels 8.7 µm and 10.8 µm and calculatethe difference of the radiance (as brightness temperature) between 10.8 and 8.7 for each day. This isrepeated for all days over one month. Figure 2 shows the difference between 8.7 and 10.8, here clear-sky correspond with the darkest regions which have a difference value lower than -10 and appear asblack on the image. For these pixels the radiance at 8.7 µm is at its minimum and the radiance at 10.8µm is at its maximum. The maximum difference, corresponding to the minimum 8.7 radiance and themaximum 10.8 radiance, is the best estimation for a clear-sky event. We take then the correspondingradiance for each channel as the clear-sky value for that channel.

We use the GERB clear-sky reflectances to validate our clear-sky radiances. The GERB clear-skyproduct gives clear-sky reflectances in the 0.6 µm, and the 0.8 µm channel. It uses a threshold techniqueover two months of data to estimate the clear-sky reflectances (Ipe et al. [1]). We plot the observedreflectances against the clear-sky reflectances for the same place and time over a one week range. Ifthe observed reflectances coincide with the clear-sky reflectances on a particular day, than we assumethat this day is a good clear-sky estimation. Then we expect that the estimated clear-sky radiances willbe close to the observed radiances. If this is the case, then we have proven that our clear-sky retieval isconsistent with the GERB clear-sky reflectances.

Since surface temperature varies significantly both diurnal and seasonal, we calculate surface emis-sivity instead as surface contribution. Surface emissivity varies with wavelength, soil type and moisturecontent, and vegetation type and condition as well as viewing angles. Because of these dependencies,the spectral variations of the surface emissivity are important for remote sensing of aerosols and re-trievals of their properties. Surface emissivity is calculated by solving a set of equations to obtain the

surface skin temperature, that is required to calculate the surface emissivity (Minnis et al.[3]). We needobservations from both daytime and nighttime over the same area in the channel 3.9 µm and 10.8 µm.First we need to estimate the apparent surface temperature in both channels. The relationship betweenthe TOA and surface radiances can be represented as in equation (1).

Bi(Ti) = εaiBi(Tai) + (1− εai)Bi(Tsi) (1)

Here B is the Planck function, εa and Ta are the atmospheric effective emissivity and effective tem-perature respectively. The radiance for Tsi, the apparent skin radiating temperature for channel i, isdetermined by adjusting the radiance for atmospheric absorption and emission. This is done by meansof the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model to take into accountatmospheric absorption using atmospheric profiles of temperature, relative humidity and ozone takenfrom the European Centre for Medium-Range Weather Forecasts (ECMWF). We selected a datasetconsisting of pixels equally spread every two degrees over the Sahara to calculate the apparent surfacetemperature in the same way as we just described. For the remaining pixels we obtained the apparentsurface temperature by means of a parameterization of total precipitable water (ECMWF) against thedifference of radiance between the surface and the top of atmosphere. At night both T3.9 and T10.8 canbe derived using (1) and the emissivity ratio ε3.9′ can be computed. We assume that the ratio is constantfor a given location during both day and night, what allows to retrieve ε3.9 from daytime data. During day,the apparent channel-3.9 surface temperature is given by equation (2).

B3.9(Ts3.9) = ε3.9[B3.9(Tskin)] + α3.9[χS3.9′ + La3.9] (2)

Here the value of χ is assumed to be constant over desert, α is the surface albedo and S3.9, thesolar radiation reaching the surface is computed by SBDART as well. According to Kirchoff’s law we getε3.9 = (1 − α3.9) and after substitution for the emitted component and the albedo and rearranging givesequation (3).

ε3.9 = 1− {B3.9(Ts3.9)− ε3.9′ [B3.9(Ts10.8)]− (1− ε3.9′)La3.9}χS3.9′

(3)

Using this equation ε3.9 is derived, Tskin can now easily be solved using equation (4).

Tskin = B−1i [{Bi(Tsi)− Lai}/εi + Lai] (4)

If we know Tskin we can compute the emissivity for the remaining channels using equation (5).

εi =[Bi(Ti)− Lai]

[Bi(Tskin)− Lai](5)

4 RESULTS

We estimated the top of atmosphere clear-sky radiances for the thermal channels at 8.7 µm, 10.8 µm,and 12.0 µm for 8 March 2006. The method works both during daytime as well as during nighttime.During day aerosols cause a decrease of the radiance outgoing to space while during night aerosolsare responsible for an increase of this radiance. A desert pixel without significant aerosols and withoutclouds has a higher radiance at 10.8 µm compared to 8.7 µm. In the presence of aerosols this differencedisappears, and both radiances are much more similar. For clouds we even observe the inverse situation,with higher radiances for the 8.7 µm. For all channels the clear-sky radiances are higher during day andlower during night. Figure 3 illustrates the clear-sky brightness temperatures for channel 8.7 and channel10.8 during day and night.

We retrieve surface emissivity over desert in the 8.7 µm, 10.8 µm, and 12.0 µm for 8 March 2006.The 8.7 µm emissivity is significantly lower compared to the others. Figure 4 illustrates the surfaceemissivity for channel 8.7, channel 10.8, and channel 12.0.

5 VALIDATION

The Terra/MODIS and Aqua/MODIS surface emissivity products, MOD11B1 and MYD11B1 respectively,are used to compare with our emissivity estimation. The MODIS Band 31 is compared with the 10.8 µm

Figure 3: clear-sky brightness temperatures : (a) 8.7 µm 8 March 2006 12:00 UTC, (b) 10.8 µm 8March 2006 12:00 UTC, (c) 8.7 µm 9 March 2006 00:00 UTC, (d) 10.8 µm 9 March 2006 00:00 UTC

Figure 4: surface emissivity 8 March 2006 12:00 UTC : (a) 8.7 µm, (b) 10.8 µm, (c) 12.0 µm

channel and Band 32 with the 12.0 µm channel. We selected the data of 8 March 2006 at 12:00 UTC forvalidation. The Terra overpass over the Sahara that lies closest to this time was at 10:25 UTC, and theAqua overpass was at 13:30 UTC. We selected a match-up pixel dataset for both satellites that couldbe used for statistical analysis. Figure 5 shows the difference statistics we obtained for the match-updatasets. The graphs are histograms of the difference between MODIS and our emissivity. For all caseswe observe that the MODIS emissivities are higher. The Terra Band 31 comparison shows a meandifference of 0.047, the Terra Band 32 has a mean difference of 0.043. The differences with Aqua are0.065 and 0.068 for Band 31 and Band 32 respectively.

Figure 5: emissivity difference statistics 8 March 2006 : (a) MOD11B1 band31-IR10.8, (b)MOD11B1 band32-IR12.0, (c) MYD11B1 band31-IR10.8, (d) MYD11B1 band31-IR12.0

These differences can be explained by the parameterization we used, and in which we didn’t takeinto account the total ozone concentration, neither the viewing angle. Secondly we assumed that theanisotropic correction factor over desert is constant.

6 CONCLUSION

An accurate estimation of the surface emissivity is required to retrieve aerosol properties. Good knowl-edge of the geographical, temporal, and angular variabilities of emissivity is needed to separate theaerosol signal from the surface contribution over land. We presented a straightforward method to obtainsurface emissivity over desert for SEVIRI in the three thermal channels. The desert surface is charac-terized by a low emissivity in the 8.7 µm channel. This results in low radiance at this wavelength forclear-sky events. The clear-sky radiance in the 10.8 µm channel is significantly higher. In the presenceof clouds or aerosols the radiance in both channels is similar. This is valid during day as well as during

night, although for the latter the difference is smaller. We calculate clear-sky radiances for a given placeand time using this characteristic. The method requires a large amount of satellite input data to calculatethe clear-sky images. Additional atmospheric data is needed to calculate the radiances near the surface.

Surface radiance (as brightness temperature) varies on diurnal and seasonal basis. These variationsmake radiances unsuitable as reference image. Therefore we calculate the surface emissivity. Surfaceemissivity depends on surface type, condition, and view angle. It is less variable in time and place.Minnis et al.[3] presented a method to estimate the surface emissivity based on satellite observationsin the 3.9 µm channel and the 10.8 µm channel during both day and night. The method retrieves thesurface skin temperature using observations in both channels. Once we know the skin temperature wecan calculate the surface emissivity for the remaining channels using the Planck-function.

In order to obtain the surface emissivity we used the clear-sky images as reference image. Usingthe same method we can calculate the aerosol emissivity. Therefore we replace the reference imagesby cloud-screened real-time observations. The emissivity we obtain will be a function of the surface andthe aerosols. Since we know the surface emissivity this will allow us to quantify the aerosol signal. Themain advantage of this method lies in the similar approach that is used to retrieve the emissivities. Thedifferences we measure can only be a result of the aerosol loading.

References

[1] Ipe, A., Bertrand, C., Clerbaux, N., Dewitte, S., and Gonzalez, L. (2003). Pixel-scale composite top-of-the-atmosphere clear-sky reflectances for Meteosat-7 visible data. Journal Geophysical Research,(108(D19)):40612, doi:10.1029/2002JD002771.

[2] Legrand, M., Plana-Fattori, A., and N’doume, C. (2001). Satellite detection of dust using the IR im-agery of Meteosat 1.Infrared Difference Dust Index. Journal Geophysical Research, (106):pp 18251–18274.

[3] Minnis, P. and Young, D. F. (2002). Surface and clear-sky albedos and emissivities from MODIS andVIRS with application to SEVIRI. In EUMETSAT Proceedings, number 36, pages pp 600–607.