A global database of Lambert‐equivalent reflectivity (LER) of the Earth's surface has been constructed by analyzing observations of the reflectivity at the top of the atmosphere made by the Global Ozone Monitoring Experiment (GOME). Since its launch on board the ERS‐2 satellite in April 1995, the GOME instrument has been measuring spectra of the Earth between 237 and 794 nm, with a spectral resolution between 0.2 and 0.4 nm and a spatial resolution between 40 × 80 and 40 × 320 km2. The LER database covers eleven 1‐nm‐wide wavelength bins centered at 335, 380, 416, 440, 463, 494.5, 555, 610, 670, 758, and 772 nm, which were selected for various retrieval applications. The database has a spatial resolution of 1° × 1°, is made for each month of the year, and pertains to the period June 1995–December 2000. Typical spectra of various surface types are presented. Attention is paid to instrument degradation and residual cloud contamination. We have found satisfactory agreement between our database at 380 nm and the Total Ozone Mapping Spectrometer (TOMS) LER database at 340–380 nm, with negligible average difference and a standard deviation of 0.013. The database presented here can be used to improve retrievals of trace gases, clouds and aerosols from GOME, Scanning Imaging Absorption Spectrometer or Atmospheric Cartography (SCIAMACHY), Ozone Monitoring Instrument (OMI), and GOME‐2.

Abstract. The Global Ozone Monitoring Experiment (GOME) on board the ERS-2 isdesigned to measure trace gas column densities in the Earth's atmosphere. Such retrievals are hindered by the presence of clouds. The most important cloud parameters that are needed to correct trace gas column density retrievals for the disturbing effects of clouds are the (effective) cloud fraction and cloud top pressure. At present, in the operational GOME data processor an effective cloud fraction is derived for each pixel, but cloud top pressure is assumed a priori and is deduced from a climatological database. Here we report an improved cloud retrieval scheme, which simultaneously retrieves the effective cloud fraction and cloud top pressure from GOME data.

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The Absorbing Aerosol Index (AAI) was investigated and used to analyze GOME data and compare it to TOMS data. The physical interpretation of the AAI was studied with an extensive theoretical sensitivity analysis. The dependence of the method on a number of atmospheric, surface, and aerosol properties was studied using a numerical radiative transfer model. It was found to be sensitive to absorbing aerosols with wavelength‐dependent refractive indices and to elevated absorbing aerosols, both with wavelength‐dependent and wavelength‐independent (gray) refractive indices. It was found to be insensitive to clouds, while small size scattering aerosols yield negative values. AAIs were calculated from GOME data for the period July 1995 to December 2000 and compared to TOMS AAI data. In a part of this period, July 1995 to October 1996, no TOMS observations were available, and the GOME data can be used to supplement the TOMS data set. The GOME AAI corresponds very well with known absorbing aerosol events. It suffers from lower spatial resolution and less frequent temporal coverage as compared to TOMS, but is useful as an independent data source of global aerosol measurements.

Abstract. Estimates of PM 2.5 distributions based on satellite data depend critically on an established relation between AOD and ground level PM 2.5 . In this study we performed an experiment at Cabauw to establish a relation between AOD and PM 2.5 for the Netherlands. A first inspection of the AERONET L1.5 AOD and PM 2.5 data showed a low correlation between the two properties. The AERONET L1.5 showed relatively many observations of high AOD values paired to low PM 2.5 values, which hinted cloud contamination. Various methods were used to detect cloud contamination in the AERONET data to substantiate this hypothesis. A cloud screening method based on backscatter LIDAR observations was chosen to detect cloud contaminated observations in the AERONET L1.5 AOD. A later evaluation of AERONET L2.0 showed that the most data that are excluded in the update from L1.5 to L2.0 were also excluded by our cloud screening, which provides confidence in both our cloud-screening method as well as the final screening in the AERONET procedure. The use of LIDAR measurements in conjunction with the CIMEL AOD data is regarded highly beneficial. Contra-intuitively, the AOD to PM 2.5 relationship was shown to be insensitive to inclusion of the mixed layer height. The robustness of the relation improves dependent on the time window during the day towards noon. The final relation found for Cabauw is PM 2.5 =124.5×AOD−0.34 and is valid for fair weather conditions. The relationship found between bias corrected MODIS AOD and PM 2.5 at Cabauw is very similar to the analysis based on the much larger datasetCorrespondence to: M. Schaap (martijn.schaap@tno.nl) from ground based data only. We applied the relationship to a MODIS composite map to assess the PM 2.5 distribution over the Netherlands for the first time. The verification of the derived map is difficult because ground level artefact free PM 2.5 data are lacking. The validity and utility of our proposed mapping methodology should be further investigated.