Figure 1. Global frequency distribution of cloud pressure from SCIAMACHY using the FRESCO O2 A-band algorithm for five months of 2002. Note the bimodal shape, with modes at about 850 hPa and 500 hPa.
Figure 1. Global frequency distribution of cloud pressure from SCIAMACHY using the FRESCO O2 A-band algorithm for five months of 2002. Note the bimodal shape, with modes at about 850 hPa and 500 hPa.
1. Scientific highlights of SCIAMACHY
SCIAMACHY, launched on board of ESA’s Envisat satellite in 2002, is a spectrometer covering the spectral range from 240 tot 2380 nm with nadir and limb view capabilities. SCIAMACHY has been developed in collaboration between Germany, the Netherlands and Belgium. It not only measures UV-visible absorbing gases, like O3, NO2, SO2, but also near-IR absorbing gases, like CO2, CH4 and CO. The spatial resolution is 30 km x 60 km in the UV-visible, and 30 km x 240 km in the near-IR; in 6 days the entire Earth is covered. With SCIAMACHY already many unique scientific results have been obtained, like global maps of NO2, which is an air pollutant, and CH4, which is an important greenhouse gas. These highlights are presented by Van der A and Kelder, and by Van Weele, elsewhere in this report.

In the period 2005-2006 the SCIAMACHY operational and scientific data products have been validated and matured. A special issue of the journal Atmospheric Chemistry and Physics in 2005 was dedicated to the SCIAMACHY validation. The validation of the SCIAMACHY data products is being coordinated by KNMI. In the Netherlands SRON and KNMI collaborate closely for the calibration, validation and exploitation of SCIAMACHY data. The SCIAMACHY results produced in the Netherlands are available at the TEMIS website, the SCIAMACHY data center, and partly at the KODAC database. Below we focus on two data products from SCIAMACHY, namely clouds and aerosols.

1.1 Cloud detection using oxygen
Detection of clouds from SCIAMACHY and OMI measurements is important for two reasons: (i) clouds affect the detection of trace gases, and (ii) clouds are an important component in the climate system because of their effects on the radiation balance and their role in the hydrological cycle. For SCIAMACHY, GOME and GOME-2 we use the reflectance inside and outside the absorption band of oxygen at 760 nm (the O2 A-band) to detect the pressure and fractional cover of clouds, respectively. For OMI we use the oxygen absorption band at 477 nm. Since oxygen is well-mixed the detected amount of oxygen is a direct measure for the pressure of clouds. From radiative transfer simulations and comparisons with other cloud height detection techniques, it appears that cloud pressure retrieval using oxygen absorption at visible wavelengths produces the pressure of the middle of the cloud. The algorithm which uses the O2 A-band is called FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band). This algorithm has been developed at KNMI since about 20001,2,3). The algorithm fits the measured O2 A-band under the assumption that clouds are Lambertian reflectors with a high albedo.

In Figure 1 the frequency distribution of globally and monthly averaged cloud pressures from SCIAMACHY is shown for 2002. The figure shows a superposition of two modes in the distribution of global cloud pressure: one mode at about 500 hPa (high clouds) and one at about 850 hPa (low clouds). These two modes are not seen in the data from the International Satellite Cloud Climatology Project (ISCCP), which is based on thermal IR satellite data. Apparently, the optical O2 A-band cloud pressures behave differently from IR cloud pressures, which are more dominated by high thin clouds, like cirrus. The SCIAMACHY results agree well with GOME results using the same FRESCO algorithm. This means that we can make a continuous cloud data record from the two instruments.

1.2 Aerosol detection above clouds
Aerosols are important for climate, because of their effects on the radiation balance and their influence on cloud formation. The unique aspect of UV-visible spectrometers like SCIAMACHY and OMI is the capability of detecting absorbing aerosols, like desert dust and smoke, over ocean and land, which are both dark in the UV. Even in the presence of clouds the UV technique of detection of absorbing aerosols works well. Most satellite imagers, like MODIS, cannot detect aerosols in bright cloudy scenes. However, when using the UV spectrum the so-called Absorbing Aerosol Index (AAI) can be determined which indicates UV-absorbing aerosols. The AAI is the colour of the scene in the UV (at 340-380 nm) as compared to the colour of a purely Rayleigh scattering atmosphere. An example is shown in Figure 2, where the SCIAMACHY AAI is shown on top of a MODIS image of biomass burning aerosol off the west coast of Africa4,5). The reflectance spectrum of a cloudy pixel with smoke on top of it is shown in Figure 3, which illustrates the deviation in the spectral shape of the polluted pixel.
Figure 2. Biomass burning event over Africa, as detected by MODIS imagery (underlying picture) and by the SCIAMACHY absorbing aerosol index (overlying data blocks). The absorbing aerosol index (AAI) can detect absorbing aerosols even over clouds, since it does not use the brightness but the colour of the scene in the UV.
Figure 2. Biomass burning event over Africa, as detected by MODIS imagery (underlying picture) and by the SCIAMACHY absorbing aerosol index (overlying data blocks). The absorbing aerosol index (AAI) can detect absorbing aerosols even over clouds, since it does not use the brightness but the colour of the scene in the UV.

Figure 3. Reflectance spectra of a biomass burning aerosol (BBA) scene with clouds and a clear sky scene, measured by SCIAMACHY (see Figure 2). The absorbing aerosols cause a much lower reflectance in the UV-visible part of the spectrum as compared to a normal cloudy scene, which is spectrally flat. The measured spectrum can be simulated (solid line) by assuming a thin layer of absorbing aerosols (optical thickness τ=0.3) on top of a non-absorbing cloud (τ =20).
Figure 3. Reflectance spectra of a biomass burning aerosol (BBA) scene with clouds and a clear sky scene, measured by SCIAMACHY (see Figure 2). The absorbing aerosols cause a much lower reflectance in the UV-visible part of the spectrum as compared to a normal cloudy scene, which is spectrally flat. The measured spectrum can be simulated (solid line) by assuming a thin layer of absorbing aerosols (optical thickness τ=0.3) on top of a non-absorbing cloud (τ =20).

2. Scientific highlights of the Ozone Monitoring Instrument
The Ozone Monitoring Instrument (OMI) is a UV/VIS nadir solar backscatter spectrometer onboard NASA's EOS-Aura satellite, which was launched in July 2004. OMI measurements constitute a major contribution to our understanding of ozone trends, the global impact of air pollution and the interaction between atmospheric chemistry and climate by virtue of the unique capability of measuring important trace gases with daily global coverage and a small footprint (13 km x 24 km at nadir)6,7). OMI measures trace gases including O3, NO2, SO2, HCHO, BrO and OClO as well as aerosols, clouds, and UV irradiance at the surface. All anticipated OMI algorithms have been published and the products are produced operationally. Most products are publicly available via the NASA Data and Information Services Center. Currently the final round of a large validation effort takes place, which will result in a special issue of the Journal of Geophysical Research on Aura validation to be published in 2007.

OMI monitors high-latitude ozone depletion events and the stratospheric ozone recovery predicted by chemical models. Figure 4 shows the high-latitude ozone record measured by various satellite instruments including the Total Ozone Mapping Spectrometer (TOMS). This time series is continued by OMI’s ozone measurements starting from the year 2005. OMI’s ozone measurements have been delivered to the IPCC8). OMI’s high spatial resolution enables monitoring of tropospheric pollution including four of the US EPA’s (Environmental Protection Agency) criteria air pollutants. Below, we discuss two OMI products developed by KNMI, namely nitrogen dioxide and aerosols.
Figure 4. Average column ozone poleward of 63º latitude in the springtime of each hemisphere (March for the NH and October for the SH), in Dobson units, based on data from various satellite instruments as indicated. The figure is updated from the IPCC/TEAP Special Report 2005 by including the data points for 2006 from OMI measurements.
Figure 4. Average column ozone poleward of 63º latitude in the springtime of each hemisphere (March for the NH and October for the SH), in Dobson units, based on data from various satellite instruments as indicated. The figure is updated from the IPCC/TEAP Special Report 2005 by including the data points for 2006 from OMI measurements.

2.1 Air Pollution above the Netherlands measured by OMI
KNMI developed a tropospheric nitrogen dioxide product from OMI measurements which can be run in Near-Real-Time (NRT). NO2 is a well known atmospheric pollutant, of which in the Netherlands 60 % is produced by road traffic. NO2 also plays a prominent role as precursor of the greenhouse gases tropospheric ozone and CO. The NRT products are available within 3 hours after measurement on the TEMIS website and are part of the air quality forecast developed by KNMI, RIVM and TNO. The algorithm was already developed for SCIAMACHY NO2 retrieval and has been adapted to OMI9). The high spatial resolution from OMI is unprecedented in atmospheric measurements from space and enables for the first time detection of air pollution at urban scales on a daily basis with global coverage. In Figure 5 a set of OMI measurements of nitrogen dioxide is shown for continuous cloud-free days above the Netherlands. These measurements were taken during the earliest summer days in the Netherlands ever recorded. High temperatures in combination with low wind-speeds lead to the formation of smog, which can be seen in the high NO2 values. Also the characteristic low NO2 on Sunday, April 15, due to less traffic, is clearly visible.

OMI’s high spatial resolution increases the likelihood of observing cloud-free scenes which facilitates the monitoring of tropospheric pollution. Whereas OMI can measure day-to-day variations in NO2, GOME and SCIAMACHY are limited to monthly or yearly averages of NO2 pollution because of their low spatial resolution and limited coverage. Also high regional dependency of pollution can be seen by OMI, as is shown in Figure 5.
Figure 5. Tropospheric NO2 observations by OMI for 12-15 April 2007. High concentrations of NO2 are observed for densely populated areas such as the Randstad and the Ruhr area. On Sundays (e.g. 15 April 2007, lower right panel) the concentration of NO2 is significantly lower due to the less road traffic.
Figure 5. Tropospheric NO2 observations by OMI for 12-15 April 2007. High concentrations of NO2 are observed for densely populated areas such as the Randstad and the Ruhr area. On Sundays (e.g. 15 April 2007, lower right panel) the concentration of NO2 is significantly lower due to the less road traffic.

2.2 New aerosol algorithm from OMI improves distinction of aerosol type
A multi-wavelength aerosol algorithm has been developed at KNMI to retrieve aerosol properties from OMI spectral reflectance measurements. This algorithm uses spectral information from both the visible and the UV parts of the spectrum, which improves the distinction between weakly absorbing, biomass burning aerosols and desert dust aerosol types. In particular desert dust aerosol can be distinguished from other aerosol types due to the strong absorption of mineral dust in the UV. The multi-wavelength algorithm provides information about the aerosol optical thickness, which is a measure of the amount of aerosols, and the aerosol type10). Aerosol climate forcing depends strongly on the aerosol radiative effect as well as on aerosol-cloud interactions, which can differ significantly for different aerosol types. In Figure 6 the retrieved aerosol optical thickness is shown for Central Europe for July 14, 2006. The strong contrast on that day between clean air over England, the Benelux and Northern Germany, and aerosol loaded air further south was verified by ground based measurements. In conclusion, the new aerosol product from OMI will provide information on the tropospheric composition useful for climate and air quality monitoring.
Figure 6. Aerosol optical thickness retrieved from OMI measurements using the multi-wavelength algorithm. The strong contrast between clean air over England, Benelux and North Germany and aerosol loaded air further south has been observed as well by other aerosol sensors.
Figure 6. Aerosol optical thickness retrieved from OMI measurements using the multi-wavelength algorithm. The strong contrast between clean air over England, Benelux and North Germany and aerosol loaded air further south has been observed as well by other aerosol sensors.


References
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