A fast method for retrieval of cloud parameters using oxygen A band measurements from the Global Ozone Monitoring Experiment

Abstract

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.

Tropical tropospheric ozone columns from nadir retrievals of GOME-1/ERS-2, SCIAMACHY/Envisat, and GOME-2/MetOp-A (1996–2012)

“…Both instruments and cloud algorithms agree on the location of the DCCs but not on the number of the DCCs per grid box, mainly due to differences in the cloud algorithms used and the spatial resolution of the instru- The number of counts per grid box with CF greater than 0.8 and CTH greater than 9 km (SCIAMACHY) and CTH greater thanments. For example, several cloud algorithms like FRESCO (Koelemeijer et al, 2001) assume that clouds behave as opaque Lambertian surfaces, resulting usually in retrieving the effective (optical centroid) cloud-top height (CTH; see Fig. 1) which lies below the physical CTH (Ziemke et al, 2008).…”

“…The WFDOAS algorithm fits vertically integrated ozone weighting functions rather than ozone absorption cross sections to the sun-normalised radiances, which enables a direct retrieval of vertical column amounts . The FRESCO algorithm, (Koelemeijer et al, 2001), is used to derive cloud properties such as CTH and CF for GOME. This cloud information is used to correct the retrieved TCOs for ozone below the cloud layer that cannot be measured .…”

Accounting for the effects of surface BRDF on satellite cloud and trace-gas retrievals: a new approach based on geometry-dependent Lambertian equivalent reflectivity applied to OMI algorithms

“…The MLER model typically assumes R c = 0.8. This value of R c was used by McPeters et al (1996) for a UV total column O 3 algorithm and independently derived by Koelemeijer et al (2001) for use in near-infrared O 2 A-band cloud pressure retrievals. The assumption of R c = 0.8 effectively accounts for Rayleigh scattering in partially cloudy scenes (Ahmad et al, 2004;Stammes et al, 2008).…”

Intercomparison of SCIAMACHY and OMI tropospheric NO2 columns: Observing the diurnal evolution of chemistry and emissions from space

“…We compute tropospheric AMFs following the formulation of Palmer et al [2001] and Eskes and Boersma [2003] with the DAK radiative transfer model , accounting for solar and viewing zenith angle (at the surface), and relative azimuth angle, and furthermore take into account the effects of clouds (for SCIAMACHY from the FRESCO [Koelemeijer et al, 2001] and for OMI from the O 2 -O 2 [Acarreta et al, 2004] cloud algorithm), surface albedo (from the Herman and Celarier [1997] and Koelemeijer et al [2002] data sets as described Boersma et al [2004]), and the a priori vertical profile shape from TM4. No independent aerosol correction is applied as it is effectively accounted for in the cloud retrievals as shown in .…”