Retrieval methods of effective cloud cover from the GOME instrument: an intercomparison

The radiative scattering by clouds leads to errors in the retrieval of column densities and concentration profiles of atmospheric trace gas species from satellites. Moreover, the presence of clouds changes the UV actinic flux and the photo-dissociation rates of various species significantly. The Global Ozone Monitoring Experiment (GOME) instrument on the ERS-2 satellite, principally designed to retrieve trace gases in the atmosphere, is also capable of detecting clouds. Four cloud fraction retrieval methods for GOME data that have been developed are discussed in this paper (the Initial Cloud Fitting Algorithm, the PMD Cloud Recognition Algorithm, the Optical Cloud Recognition Algorithm (an in-house version and the official implementation) and the Fast Retrieval Scheme for Clouds from the Oxygen A-band). Their results of cloud fraction retrieval are compared to each-other and also to synoptic surface observations. It is shown that all studied retrieval methods calculate an effective cloud fraction that is related to a cloud with a high optical thickness. Generally, we found ICFA to produce the lowest cloud fractions, followed by our in-house OCRA implementation, FRESCO, PC2K and finally the official OCRA implementation along four processed tracks (+2%, +10%, +15% and +25% compared to ICFA respectively). Synoptical surface observations gave the highest absolute cloud fraction when compared with individual PMD sub-pixels of roughly the same size

Retrieval methods of effective cloud cover for the GOME instrument: an intercomparison

International audienceThe radiative scattering by clouds leads to errors in the retrieval of column densities and concentration profiles of atmospheric trace gas species from satellites. Moreover, the presence of clouds changes the UV actinic flux and the photo-dissociation rates of various species significantly. The Global Ozone Monitoring Experiment (GOME) instrument on the ERS-2 satellite, principally designed to retrieve trace gases in the atmosphere is also capable of detecting clouds. Four cloud fraction retrieval methods for GOME data that have been developed are discussed in this paper (the Initial Cloud Fitting Algorithm, the PMD Cloud Retrieval Algorithm, the Optical Cloud Recognition Algorithm and the Fast Retrieval Scheme for Cloud Observables). Their results of cloud fraction retrieval are compared to each-other and also to synoptic surface observations. It is shown that all studied retrieval methods calculate an effective cloud fraction that is related to a cloud with a high optical thickness. Generally, we found ICFA to produce the lowest cloud fractions, followed by OCRA, then FRESCO and PC2K along four processed tracks (+2%, +10% and +15% compared to ICFA respectively). Synoptical surface observations gave the highest absolute cloud fraction when compared with individual PMD sub-pixels of roughly the same size

Evaluation of SCIAMACHY Level-1 data versions using nadir ozone profile retrievals in the period 2003–2011

Ozone profile retrieval from nadir-viewing satellite instruments operating in the ultraviolet–visible range requires accurate calibration of Level-1 (L1) radiance data. Here we study the effects of calibration on the derived Level-2 (L2) ozone profiles for three versions of SCanning Imaging Absorption spectroMeter for Atmospheric ChartograpHY (SCIAMACHY) L1 data: version 7 (v7), version 7 with m-factors (v7mfac) and version 8 (v8). We retrieve nadir ozone profiles from the SCIAMACHY instrument that flew on board Envisat using the Ozone ProfilE Retrieval Algorithm (OPERA) developed at KNMI with a focus on stratospheric ozone. We study and assess the quality of these profiles and compare retrieved L2 products from L1 SCIAMACHY data versions from the years 2003 to 2011 without further radiometric correction. From validation of the profiles against ozone sonde measurements, we find that the v8 performs better than v7 and v7mfac due to correction for the scan-angle dependency of the instrument's optical degradation. Validation for the years 2003 and 2009 with ozone sondes shows deviations of SCIAMACHY ozone profiles of 0.8–15 % in the stratosphere (corresponding to pressure range ∼ 100–10 hPa) and 2.5–100 % in the troposphere (corresponding to pressure range ∼ 1000–100 hPa), depending on the latitude and the L1 version used. Using L1 v8 for the years 2003–2011 leads to deviations of ∼ 1–11 % in stratospheric ozone and ∼ 1–45 % in tropospheric ozone. The SCIAMACHY L1 v8 data can still be improved upon in the 265–330 nm range used for ozone profile retrieval. The slit function can be improved with a spectral shift and squeeze, which leads to a few percent residue reduction compared to reference solar irradiance spectra. Furthermore, studies of the ratio of measured to simulated reflectance spectra show that a bias correction in the reflectance for wavelengths below 300 nm appears to be necessary

Ozone ProfilE Retrieval Algorithm (OPERA) for nadir-looking satellite instruments in the UV–VIS

For the retrieval of the vertical distribution of ozone in the atmosphere the Ozone ProfilE Retrieval Algorithm (OPERA) has been further developed. The new version (1.26) of OPERA is capable of retrieving ozone profiles from UV–VIS observations of most nadir-looking satellite instruments like GOME, SCIAMACHY, OMI and GOME-2. The setup of OPERA is described and results are presented for GOME and GOME-2 observations. The retrieved ozone profiles are globally compared to ozone sondes for the years 1997 and 2008. Relative differences between GOME/GOME-2 and ozone sondes are within the limits as specified by the user requirements from the Climate Change Initiative (CCI) programme of ESA (20% in the troposphere, 15% in the stratosphere). To demonstrate the performance of the algorithm under extreme circumstances, the 2009 Antarctic ozone hole season was investigated in more detail using GOME-2 ozone profiles and lidar data, which showed an unusual persistence of the vortex over the Río Gallegos observing station (51° S, 69.3° W). By applying OPERA to multiple instruments, a time series of ozone profiles from 1996 to 2013 from a single robust algorithm can be created

Evaluation of the operational Aerosol Layer Height retrieval algorithm for Sentinel-5 Precursor: application to O₂ A band observations from GOME-2A

An algorithm setup for the operational Aerosol Layer Height product for TROPOMI on the Sentinel-5 Precursor mission is described and discussed, applied to GOME-2A data, and evaluated with lidar measurements. The algorithm makes a spectral fit of reflectance at the O₂ A band in the near-infrared and the fit window runs from 758 to 770 nm. The aerosol profile is parameterised by a scattering layer with constant aerosol volume extinction coefficient and aerosol single scattering albedo and with a fixed pressure thickness. The algorithm's target parameter is the height of this layer. In this paper, we apply the algorithm to observations from GOME-2A in a number of systematic and extensive case studies, and we compare retrieved aerosol layer heights with lidar measurements. Aerosol scenes cover various aerosol types, both elevated and boundary layer aerosols, and land and sea surfaces. The aerosol optical thicknesses for these scenes are relatively moderate. Retrieval experiments with GOME-2A spectra are used to investigate various sensitivities, in which particular attention is given to the role of the surface albedo. <br><br> From retrieval simulations with the single-layer model, we learn that the surface albedo should be a fit parameter when retrieving aerosol layer height from the O₂ A band. Current uncertainties in surface albedo climatologies cause biases and non-convergences when the surface albedo is fixed in the retrieval. Biases disappear and convergence improves when the surface albedo is fitted, while precision of retrieved aerosol layer pressure is still largely within requirement levels. Moreover, we show that fitting the surface albedo helps to ameliorate biases in retrieved aerosol layer height when the assumed aerosol model is inaccurate. Subsequent retrievals with GOME-2A spectra confirm that convergence is better when the surface albedo is retrieved simultaneously with aerosol parameters. However, retrieved aerosol layer pressures are systematically low (i.e., layer high in the atmosphere) to the extent that retrieved values no longer realistically represent actual extinction profiles. When the surface albedo is fixed in retrievals with GOME-2A spectra, convergence deteriorates as expected, but retrieved aerosol layer pressures become much higher (i.e., layer lower in atmosphere). The comparison with lidar measurements indicates that retrieved aerosol layer heights are indeed representative of the underlying profile in that case. Finally, subsequent retrieval simulations with two-layer aerosol profiles show that a model error in the assumed profile (two layers in the simulation but only one in the retrieval) is partly absorbed by the surface albedo when this parameter is fitted. This is expected in view of the correlations between errors in fit parameters and the effect is relatively small for elevated layers (less than 100 hPa). If one of the scattering layers is near the surface (boundary layer aerosols), the effect becomes surprisingly large, in such a way that the retrieved height of the single layer is above the two-layer profile. <br><br> Furthermore, we find that the retrieval solution, once retrieval converges, hardly depends on the starting values for the fit. Sensitivity experiments with GOME-2A spectra also show that aerosol layer height is indeed relatively robust against inaccuracies in the assumed aerosol model, even when the surface albedo is not fitted. We show spectral fit residuals, which can be used for further investigations. Fit residuals may be partly explained by spectroscopic uncertainties, which is suggested by an experiment showing the improvement of convergence when the absorption cross section is scaled in agreement with Butz et al. (2013) and Crisp et al. (2012), and a temperature offset to the a priori ECMWF temperature profile is fitted. Retrieved temperature offsets are always negative and quite large (ranging between −4 and −8 K), which is not expected if temperature offsets absorb remaining inaccuracies in meteorological data. Other sensitivity experiments investigate fitting of stray light and fluorescence emissions. We find negative radiance offsets and negative fluorescence emissions, also for non-vegetated areas, but from the results it is not clear whether fitting these parameters improves the retrieval. <br><br> Based on the present results, the operational baseline for the Aerosol Layer Height product currently will not fit the surface albedo. The product will be particularly suited for elevated, optically thick aerosol layers. In addition to its scientific value in climate research, anticipated applications of the product for TROPOMI are providing aerosol height information for aviation safety and improving interpretation of the Absorbing Aerosol Index

Attribution of the Arctic ozone column deficit in March 2011

Arctic column ozone reached record low values (∼310 DU) during March of 2011, exposing Arctic ecosystems to enhanced UV-B. We identify the cause of this anomaly using the Oslo CTM2 atmospheric chemistry model driven by ECMWF meteorology to simulate Arctic ozone from 1998 through 2011. CTM2 successfully reproduces the variability in column ozone, from week to week, and from year to year, correctly identifying 2011 as an extreme anomaly over the period. By comparing parallel model simulations, one with all Arctic ozone chemistry turned off on January 1, we find that chemical ozone loss in 2011 is enhanced relative to previous years, but it accounted for only 23% of the anomaly. Weakened transport of ozone from middle latitudes, concurrent with an anomalously strong polar vortex, was the primary cause of the low ozone When the zonal winds relaxed in mid-March 2011, Arctic column ozone quickly recovered

Attribution of the Arctic ozone column deficit in March 2011

Arctic column ozone reached record low values (∼310 DU) during March of 2011, exposing Arctic ecosystems to enhanced UV-B. We identify the cause of this anomaly using the Oslo CTM2 atmospheric chemistry model driven by ECMWF meteorology to simulate Arctic ozone from 1998 through 2011. CTM2 successfully reproduces the variability in column ozone, from week to week, and from year to year, correctly identifying 2011 as an extreme anomaly over the period. By comparing parallel model simulations, one with all Arctic ozone chemistry turned off on January 1, we find that chemical ozone loss in 2011 is enhanced relative to previous years, but it accounted for only 23% of the anomaly. Weakened transport of ozone from middle latitudes, concurrent with an anomalously strong polar vortex, was the primary cause of the low ozone When the zonal winds relaxed in mid-March 2011, Arctic column ozone quickly recovered