Fast Retrieval Scheme For Clouds Research Articles

Improved monitoring of shipping NO<sub>2</sub> with TROPOMI: decreasing NO<sub><i>x</i></sub> emissions in European seas during the COVID-19 pandemic

Abstract. TROPOMI (TROPOspheric Monitoring Instrument) measurements of tropospheric NO2 columns provide powerful information on emissions of air pollution by ships on open sea. This information is potentially useful for authorities to help determine the (non-)compliance of ships with increasingly stringent NOx emission regulations. We find that the information quality is improved further by recent upgrades in the TROPOMI cloud retrieval and an optimal data selection. We show that the superior spatial resolution of TROPOMI allows for the detection of several lanes of NO2 pollution ranging from the Aegean Sea near Greece to the Skagerrak in Scandinavia, which have not been detected with other satellite instruments before. Additionally, we demonstrate that under conditions of sun glint TROPOMI's vertical sensitivity to NO2 in the marine boundary layer increases by up to 60 %. The benefits of sun glint are most prominent under clear-sky situations when sea surface winds are low but slightly above zero (±2 m s−1). Beyond spatial resolution and sun glint, we examine for the first time the impact of the recently improved cloud algorithm on the TROPOMI NO2 retrieval quality, both over sea and over land. We find that the new FRESCO+ (Fast Retrieval Scheme for Clouds from the Oxygen A band) wide algorithm leads to 50 hPa lower cloud pressures, correcting a known high bias, and produces 1–4×1015 molec. cm−2 higher retrieved NO2 columns, thereby at least partially correcting for the previously reported low bias in the TROPOMI NO2 product. By training an artificial neural network on the four available periods with standard and FRESCO+ wide test retrievals, we develop a historic, consistent TROPOMI NO2 data set spanning the years 2019 and 2020. This improved data set shows stronger (35 %–75 %) and sharper (10 %–35 %) shipping NO2 signals compared to co-sampled measurements from OMI. We apply our improved data set to investigate the impact of the COVID-19 pandemic on ship NO2 pollution over European seas and find indications that NOx emissions from ships reduced by 10 %–20 % during the beginning of the COVID-19 pandemic in 2020. The reductions in ship NO2 pollution start in March–April 2020, in line with changes in shipping activity inferred from automatic identification system (AIS) data on ship location, speed, and engine.

MICRU: an effective cloud fraction algorithm designed for UV–vis satellite instruments with large viewing angles

Abstract. Clouds impact the radiative transfer of the Earth's atmosphere and strongly influence satellite measurements in the ultraviolet–visible (UV–vis) and infrared (IR) spectral ranges. For satellite measurements of trace gases absorbing in the UV–vis spectral range, particularly clouds ultimately determine the vertical sensitivity profile, mainly by reducing the sensitivity for trace-gas columns below the cloud. The Mainz iterative cloud retrieval utilities (MICRU) algorithm is specifically designed to reduce the error budget of trace-gas retrievals, such as those for nitrogen dioxide (NO2), which strongly depends on the accuracy of small cloud fractions (CFs) in particular. The accuracy of MICRU is governed by an empirical parameterisation of the viewing-geometry-dependent background surface reflectivity taking instrumental and physical effects into account. Instrumental effects are mainly degradation and polarisation effects; physical effects are due to the anisotropy of the surface reflectivity, e.g. shadowing of plants and sun glitter. MICRU is applied to main science channel (MSC) and polarisation measurement device (PMD) data collected between April 2007 and June 2013 by the Global Ozone Monitoring Experiment 2A (GOME-2A) instrument aboard the MetOp-A satellite. CFs are retrieved at different spectral bands between 374 and 758 nm. The MICRU results for MSC and PMD at different wavelengths are intercompared to study CF precision and accuracy, which depend on wavelength and spatial correlation. Furthermore, MICRU results are compared to FRESCO (fast retrieval scheme for clouds from the oxygen A band) and OCRA (optical cloud recognition algorithm) operational cloud products. We show that MICRU retrieves small CFs with an accuracy of 0.04 or better for the entire 1920 km wide swath with a potential bias between −0.01 and −0.03. CFs retrieved at shorter wavelengths are less affected by adverse surface heterogeneities. The comparison to the operational CF algorithms shows that MICRU significantly reduces the dependence on viewing angle, time, and sun glitter. Systematic effects along coasts are particularly small for MICRU due to its dedicated treatment of land and ocean surfaces. The MICRU algorithm is designed for spectroscopic instruments ranging from the GOME to Sentinel-5P/Tropospheric Monitoring Instrument (TROPOMI) but is also applicable to UV–vis imagers like the Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and Sentinel-2.

Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O<sub>2</sub>–O<sub>2</sub>, MODIS, and Suomi-NPP VIIRS

Abstract. Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data from three Copernicus Sentinel-5 Precursor (S5P) TROPOMI cloud products: (i) S5P OCRA/ROCINN_CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks;Clouds-As-Layers), (ii) S5P OCRA/ROCINN_CRB (Clouds-as-Reflecting Boundaries), and (iii) S5P FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands – Sentinel). Target properties of this work are cloud-top height and cloud optical thickness (OCRA/ROCINN_CAL), cloud height (OCRA/ROCINN_CRB and FRESCO-S), and radiometric cloud fraction (all three algorithms). The analysis combines (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS, and OMI O2–O2), and (iii) ground-based validation with respect to correlative observations (30 April 2018 to 27 February 2020) from the Cloudnet network of ceilometers, lidars, and radars. Zonal mean latitudinal variation of S5P cloud properties is similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud-top height and cloud optical thickness and with Cloudnet cloud-top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud-top height is below the cloud-top height of VIIRS and of Cloudnet, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the Cloudnet cloud mean height for the low clouds but match on average better with the Cloudnet cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the Cloudnet observations. Peculiar geographical patterns are identified in the cloud products and will be mitigated in future releases of the cloud data products.

A Fast Retrieval of Cloud Parameters Using a Triplet of Wavelengths of Oxygen Dimer Band around 477 nm

Clouds act as a major reflector that changes the amount of sunlight reflected to space. Change in radiance intensity due to the presence of clouds interrupts the retrieval of trace gas or aerosol properties from satellite data. In this paper, we developed a fast and robust algorithm, named the fast cloud retrieval algorithm, using a triplet of wavelengths (469, 477, and 485 nm) of the O2–O2 absorption band around 477 nm (CLDTO4) to derive the cloud information such as cloud top pressure (CTP) and cloud fraction (CF) for the Geostationary Environment Monitoring Spectrometer (GEMS). The novel algorithm is based on the fact that the difference in the optical path through which light passes with regard to the altitude of clouds causes a change in radiance due to the absorption of O2–O2 at the three selected wavelengths. To reduce the time required for algorithm calculations, the look-up table (LUT) method was applied. The LUT was pre-constructed for various conditions of geometry using Vectorized Linearized Discrete Ordinate Radiative Transfer (VLIDORT) to consider the polarization of the scattered light. The GEMS was launched in February 2020, but the observed data of GEMS have not yet been widely released. To evaluate the performance of the algorithm, the retrieved CTP and CF using observational data from the Global Ozone Monitoring Experiment-2 (GOME-2), which cover the spectral range of GEMS, were compared with the results of the Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO) algorithm, which is based on the O2 A-band. There was good agreement between the results, despite small discrepancies for low clouds.

FRESCO-B: a fast cloud retrieval algorithm using oxygen B-band measurements from GOME-2

Abstract. The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A band) algorithm is a simple, fast and robust algorithm used to retrieve cloud information in operational satellite data processing. It has been applied to GOME-1 (Global Ozone Monitoring Experiment), SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography), GOME-2 and more recently to TROPOMI (Tropospheric Monitoring Instrument). FRESCO retrieves effective cloud fraction and cloud pressure from measurements in the oxygen A band around 761 nm. In this paper, we propose a new version of the algorithm, called FRESCO-B, which is based on measurements in the oxygen B band around 687 nm. Such a method is interesting for vegetated surfaces where the surface albedo is much lower in the B band than in the A band, which limits the ground contribution to the top-of-atmosphere reflectances. In this study we first perform retrieval simulations. These show that the retrieved cloud pressures from FRESCO-B and FRESCO differ only between −10 and +10 hPa, except for high, thin clouds over vegetation where the difference is larger (about +15 to +30 hPa), with FRESCO-B yielding higher pressure. Next, inter-comparison between FRESCO-B and FRESCO retrievals over 1 month of GOME-2B data reveals that the effective cloud fractions retrieved in the O2 A and B bands are very similar (mean difference of 0.003), while the cloud pressures show a mean difference of 11.5 hPa, with FRESCO-B retrieving higher pressures than FRESCO. This agrees with the simulations and is partly due to deeper photon penetrations of the O2 B band in clouds compared to the O2 A-band photons and partly due to the surface albedo bias in FRESCO. Finally, validation with ground-based measurements shows that the FRESCO-B cloud pressure represents an altitude within the cloud boundaries for clouds that are not too far from the Lambertian reflector model, which occurs in about 50 % of the cases.

Surface reflectivity climatologies from UV to NIR determined from Earth observations by GOME‐2 and SCIAMACHY

AbstractThe primary goal of this paper is to introduce two new surface reflectivity climatologies. The two databases contain the Lambertian‐equivalent reflectivity (LER) of the Earth's surface, and they are meant to support satellite retrieval of trace gases and of cloud and aerosol information. The surface LER databases are derived from the Global Ozone Monitoring Experiment (GOME)‐2 and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instruments and can be considered as improved and extended descendants of earlier surface LER climatologies based on the Total Ozone Mapping Spectrometer (TOMS), GOME‐1, and Ozone Monitoring Instrument (OMI) instruments. The GOME‐2 surface LER database consists of 21 wavelength bands that span the wavelength range from 335 to 772 nm. The SCIAMACHY surface LER database covers the wavelength range between 335 and 1670 nm in 29 wavelength bands. The two databases are made for each month of the year, and their spatial resolution is 1° × 1°. In this paper we present the methods that are used to derive the surface LER; we analyze the spatial and temporal behavior of the surface LER fields and study the amount of residual cloud contamination in the databases. For several surface types we analyze the spectral surface albedo and the seasonal variation. When compared to the existing surface LER databases, both databases are found to perform well. As an example of possible application of the databases we study the performance of the Fast Retrieval Scheme for Clouds from the Oxygen A‐band (FRESCO) cloud information retrieval when it is equipped with the new surface albedo databases. We find considerable improvements. The databases introduced here can not only improve retrievals from GOME‐2 and SCIAMACHY but also support those from other instruments, such as TROPOspheric Monitoring Instrument (TROPOMI), to be launched in 2017.

The direct fitting approach for total ozone column retrievals: a sensitivity study on GOME-2/MetOp-A measurements

Abstract. We present a sensitivity study of the direct fitting approach to retrieve total ozone columns from the clear sky Global Ozone Monitoring Experiment 2/MetOp-A (GOME-2/MetOp-A) measurements between 325 and 335 nm in the period 2007–2010. The direct fitting of the measurement is based on adjusting the scaling of a reference ozone profile and requires accurate simulation of GOME-2 radiances. In this context, we study the effect of three aspects that introduce forward model errors if not addressed appropriately: (1) the use of a clear sky model atmosphere in the radiative transfer demanding cloud filtering, (2) different approximations of Earth's sphericity to address the influence of the solar zenith angle, and (3) the need of polarization in radiative transfer modeling. We conclude that cloud filtering using the operational GOME-2 FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A band) cloud product, which is part of level 1B data, and the use of pseudo-spherical scalar radiative transfer is fully sufficient for the purpose of this retrieval. A validation with ground-based measurements at 36 stations confirms this showing a global mean bias of −0.1 % with a standard deviation (SD) of 2.7 %. The regularization effect inherent to the profile scaling approach is thoroughly characterized by the total column averaging kernel for each individual retrieval. It characterizes the effect of the particular choice of the ozone profile to be scaled by the inversion and is part of the retrieval product. Two different interpretations of the data product are possible: first, regarding the retrieval product as an estimate of the true column, a direct comparison of the retrieved column with total ozone columns from ground-based measurements can be done. This requires accurate a priori knowledge of the reference ozone profile and the column averaging kernel is not needed. Alternatively, the retrieval product can be interpreted as an effective column defined by the total column averaging kernel. This interpretation relies much less on the a priori knowledge of the reference ozone profile; however, for its validation, measurements of the vertical ozone distribution are needed. The different manners of data interpretation are demonstrated for simulated and real measurements using on-ground ozone column and ozonesonde measurements for validation.

Evaluation of SCIAMACHY Oxygen A band cloud heights using Cloudnet measurements

Abstract. Two SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) O2 A band cloud height products are evaluated using ground-based radar/lidar measurements between January 2003 and December 2011. The products are the ESA (European Space Agency) Level 2 (L2) version 5.02 cloud top height and the FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A band) version 6 cloud height. The radar/lidar profiles are obtained at the Cloudnet sites of Cabauw and Lindenberg, and are averaged for 1 h centered at the SCIAMACHY overpass time. In total we have 217 cases of single-layer clouds and 204 cases of multilayer clouds. We find that the ESA L2 cloud top height has a better agreement with the Cloudnet cloud top height than the Cloudnet cloud middle height. The ESA L2 cloud top height is on average 0.4 km higher than the Cloudnet cloud top height, with a standard deviation of 3.1 km. The FRESCO cloud height is closer to the Cloudnet cloud middle height than the Cloudnet cloud top height. The mean difference between the FRESCO cloud height and the Cloudnet cloud middle height is −0.1 km with a standard deviation of 1.9 km. The ESA L2 cloud top height is higher than the FRESCO cloud height. The differences between the SCIAMACHY cloud (top) height and the Cloudnet cloud top height are linked to cloud optical thickness. The SCIAMACHY cloud height products are further compared to the Cloudnet cloud top height and the Cloudnet cloud middle height in 1 km bins. For single-layer clouds, the difference between the ESA L2 cloud top height and the Cloudnet cloud top height is less than 1 km for each cloud bin at 3–7 km. The difference between the FRESCO cloud height and the Cloudnet cloud middle height is less than 1 km for each cloud bin at 0–6 km. The results are similar for multilayer clouds, but the percentage of cases having a bias within 1 km is smaller than for single-layer clouds. We may conclude that the FRESCO cloud height is accurate for low and middle level clouds, whereas the ESA L2 cloud top height is more accurate for middle level clouds. Both products are less accurate for high clouds.

Interpretation of FRESCO cloud retrievals in case of absorbing aerosol events

Abstract. Cloud and aerosol information is needed in trace gas retrievals from satellite measurements. The Fast REtrieval Scheme for Clouds from the Oxygen A band (FRESCO) cloud algorithm employs reflectance spectra of the O2 A band around 760 nm to derive cloud pressure and effective cloud fraction. In general, clouds contribute more to the O2 A band reflectance than aerosols. Therefore, the FRESCO algorithm does not correct for aerosol effects in the retrievals and attributes the retrieved cloud information entirely to the presence of clouds, and not to aerosols. For events with high aerosol loading, aerosols may have a dominant effect, especially for almost cloud free scenes. We have analysed FRESCO cloud data and Absorbing Aerosol Index (AAI) data from the Global Ozone Monitoring Experiment (GOME-2) instrument on the Metop-A satellite for events with typical absorbing aerosol types, such as volcanic ash, desert dust and smoke. We find that the FRESCO effective cloud fractions are correlated with the AAI data for these absorbing aerosol events and that the FRESCO cloud pressure contains information on aerosol layer pressure. For cloud free scenes, the derived FRESCO cloud pressure is close to the aerosol layer pressure, especially for optically thick aerosol layers. For cloudy scenes, if the strongly absorbing aerosols are located above the clouds, then the retrieved FRESCO cloud pressure may represent the height of the aerosol layer rather than the height of the clouds. Combining FRESCO and AAI data, an estimate for the aerosol layer pressure can be given.

Total ozone column derived from GOME and SCIAMACHY using KNMI retrieval algorithms: Validation against Brewer measurements at the Iberian Peninsula

[1] This article focuses on the validation of the total ozone column (TOC) data set acquired by the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite remote sensing instruments using the Total Ozone Retrieval Scheme for the GOME Instrument Based on the Ozone Monitoring Instrument (TOGOMI) and Total Ozone Retrieval Scheme for the SCIAMACHY Instrument Based on the Ozone Monitoring Instrument (TOSOMI) retrieval algorithms developed by the Royal Netherlands Meteorological Institute. In this analysis, spatially colocated, daily averaged ground-based observations performed by five well-calibrated Brewer spectrophotometers at the Iberian Peninsula are used. The period of study runs from January 2004 to December 2009. The agreement between satellite and ground-based TOC data is excellent (R2 higher than 0.94). Nevertheless, the TOC data derived from both satellite instruments underestimate the ground-based data. On average, this underestimation is 1.1% for GOME and 1.3% for SCIAMACHY. The SCIAMACHY-Brewer TOC differences show a significant solar zenith angle (SZA) dependence which causes a systematic seasonal dependence. By contrast, GOME-Brewer TOC differences show no significant SZA dependence and hence no seasonality although processed with exactly the same algorithm. The satellite-Brewer TOC differences for the two satellite instruments show a clear and similar dependence on the viewing zenith angle under cloudy conditions. In addition, both the GOME-Brewer and SCIAMACHY-Brewer TOC differences reveal a very similar behavior with respect to the satellite cloud properties, being cloud fraction and cloud top pressure, which originate from the same cloud algorithm (Fast Retrieval Scheme for Clouds from the Oxygen A-Band (FRESCO+)) in both the TOSOMI and TOGOMI retrieval algorithms.

Surface solar irradiance from SCIAMACHY measurements: algorithm and validation

Abstract. Broadband surface solar irradiances (SSI) are, for the first time, derived from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) satellite measurements. The retrieval algorithm, called FRESCO (Fast REtrieval Scheme for Clouds from the Oxygen A band) SSI, is similar to the Heliosat method. In contrast to the standard Heliosat method, the cloud index is replaced by the effective cloud fraction derived from the FRESCO cloud algorithm. The MAGIC (Mesoscale Atmospheric Global Irradiance Code) algorithm is used to calculate clear-sky SSI. The SCIAMACHY SSI product is validated against globally distributed BSRN (Baseline Surface Radiation Network) measurements and compared with ISCCP-FD (International Satellite Cloud Climatology Project Flux Dataset) surface shortwave downwelling fluxes (SDF). For one year of data in 2008, the mean difference between the instantaneous SCIAMACHY SSI and the hourly mean BSRN global irradiances is −4 W m−2 (−1 %) with a standard deviation of 101 W m−2 (20 %). The mean difference between the globally monthly mean SCIAMACHY SSI and ISCCP-FD SDF is less than −12 W m−2 (−2 %) for every month in 2006 and the standard deviation is 62 W m−2 (12 %). The correlation coefficient is 0.93 between SCIAMACHY SSI and BSRN global irradiances and is greater than 0.96 between SCIAMACHY SSI and ISCCP-FD SDF. The evaluation results suggest that the SCIAMACHY SSI product achieves similar mean bias error and root mean square error as the surface solar irradiances derived from polar orbiting satellites with higher spatial resolution.

MERIS albedo climatology for FRESCO+ O<sub>2</sub> A-band cloud retrieval

Abstract. A new global albedo climatology for Oxygen A-band cloud retrievals is presented. The climatology is based on MEdium Resolution Imaging Spectrometer (MERIS) Albedomap data and its favourable impact on the derivation of cloud fraction is demonstrated for the FRESCO+ (Fast Retrieval Scheme for Clouds from the Oxygen A-band) algorithm. To date, a relatively coarse resolution (1° × 1°) surface reflectance dataset from GOME (Global Ozone Monitoring Experiment) Lambert-equivalent reflectivity (LER) is used in FRESCO+. The GOME LER climatology does not account for the usually higher spatial resolution of UV/VIS instruments designed for trace gas remote sensing which introduces several artefacts, e.g. in regions with sharp spectral contrasts like coastlines or over bright surface targets. Therefore, MERIS black-sky albedo (BSA) data from the period October 2002 to October 2006 were aggregated to a grid of 0.25° × 0.25° for each month of the year and for different spectral channels. In contrary to other available surface reflectivity datasets, MERIS includes channels at 754 nm and 775 nm which are located close to the spectral windows required for O2 A-band cloud retrievals. The MERIS BSA in the near-infrared compares well to Moderate Resolution Imaging Spectroradiometer (MODIS) derived BSA with an average difference lower than 1% and a correlation coefficient of 0.98. However, when relating MERIS BSA to GOME LER a distinctly lower correlation (0.80) and enhanced scatter is found. Effective cloud fractions from two exemplary months (January and July 2006) of Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) data were subsequently derived with FRESCO+ and compared to those from the Heidelberg Iterative Cloud Retrieval Utilities (HICRU) algorithm. The MERIS climatology generally improves FRESCO+ effective cloud fractions. In particular small cloud fractions are in better agreement with HICRU. This is of importance for atmospheric trace gas retrieval which relies on accurate cloud information at small cloud fractions. In addition, overestimates along coastlines and underestimates in the Intertropical Convergence Zone introduced by the GOME LER were eliminated. While effective cloud fractions over the Saharan desert and the Arabian peninsula are successfully reduced in January, they are still too high in July relative to HICRU due to FRESCO+'s large sensitivity to albedo inaccuracies of highly reflecting targets and inappropriate aerosol information which hampers an accurate albedo retrieval. Finally, NO2 tropospheric vertical column densities and O3 total columns were derived with the FRESCO+ cloud parameters from the new dataset and it is found that the MERIS BSA climatology has a pronounced and beneficial effect on regional scale. Apart from FRESCO+, the new MERIS albedo dataset is applicable to any cloud retrieval algorithms using the O2 A-band or the O2-O2 absorption band around 477 nm. Moreover, the by-product of BSA at 442 nm can be used in NO2 remote sensing and the BSA at 620 nm, 665 nm, and 681 nm could be integrated in current H2O retrievals.

FRESCO+: an improved O<sub>2</sub> A-band cloud retrieval algorithm for tropospheric trace gas retrievals

Abstract. The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) algorithm has been used to retrieve cloud information from measurements of the O2 A-band around 760 nm by GOME, SCIAMACHY and GOME-2. The cloud parameters retrieved by FRESCO are the effective cloud fraction and cloud pressure, which are used for cloud correction in the retrieval of trace gases like O3 and NO2. To improve the cloud pressure retrieval for partly cloudy scenes, single Rayleigh scattering has been included in an improved version of the algorithm, called FRESCO+. We compared FRESCO+ and FRESCO effective cloud fractions and cloud pressures using simulated spectra and one month of GOME measured spectra. As expected, FRESCO+ gives more reliable cloud pressures over partly cloudy pixels. Simulations and comparisons with ground-based radar/lidar measurements of clouds show that the FRESCO+ cloud pressure is about the optical midlevel of the cloud. Globally averaged, the FRESCO+ cloud pressure is about 50 hPa higher than the FRESCO cloud pressure, while the FRESCO+ effective cloud fraction is about 0.01 larger. The effect of FRESCO+ cloud parameters on O3 and NO2 vertical column density (VCD) retrievals is studied using SCIAMACHY data and ground-based DOAS measurements. We find that the FRESCO+ algorithm has a significant effect on tropospheric NO2 retrievals but a minor effect on total O3 retrievals. The retrieved SCIAMACHY tropospheric NO2 VCDs using FRESCO+ cloud parameters (v1.1) are lower than the tropospheric NO2VCDs which used FRESCO cloud parameters (v1.04), in particular over heavily polluted areas with low clouds. The difference between SCIAMACHY tropospheric NO2 VCDs v1.1 and ground-based MAXDOAS measurements performed in Cabauw, The Netherlands, during the DANDELIONS campaign is about −2.12×1014molec cm−2.

Improving cloud information over deserts from SCIAMACHY Oxygen A-band measurements

Abstract. The retrieval of column densities and concentration profiles of atmospheric trace gas species from satellites is sensitive to light scattered by clouds. The SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on the Envisat satellite, principally designed to retrieve trace gases in the atmosphere, is also capable of detecting clouds. FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) is a fast and robust algorithm providing cloud information from the O2 A-band for cloud correction of ozone. FRESCO provides a consistent set of cloud products by retrieving simultaneously effective cloud fraction and cloud top pressure. The FRESCO retrieved values are compared with the SCIAMACHY Level 2 operational cloud fraction of OCRA (Optical Cloud Recognition Algorithm) but, also, with cloud information from HICRU (Heidelberg Iterative Cloud Retrieval Utilities), SACURA (SemiAnalytical CloUd Retrieval Algorithm) and the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument. The results correlate well, but FRESCO overestimates cloud fraction over deserts. Thus, to improve retrievals at these locations, the FRESCO surface albedo databases are decontaminated from the presence of desert dust aerosols. This is achieved by using the GOME Absorbing Aerosol Index. It is shown that this approach succeeds well in producing more accurate cloud information over the Sahara.

Aerosol optical thickness retrieval over land and water using Global Ozone Monitoring Experiment (GOME) data

An algorithm for the retrieval of the aerosol optical thickness over land and over water from Global Ozone Monitoring Experiment (GOME) data is presented. The cloud fraction in the GOME pixels is determined using the Fast Retrieval Scheme for Clouds From the Oxygen A Band (FRESCO) algorithm. Surface contributions to the top of atmosphere reflectance are determined from the GOME surface reflectance database. The aerosol retrieval algorithm uses lookup tables that were created using the radiative transfer model 6S. The algorithm allows retrieving the aerosol types characterized by Ångstrom coefficients in the range from −0.1 to 2.8; i.e., the range of values observed by the AERONET ground‐based measurements. Validation of the algorithm done using the AERONET Sun photometer data for 12 sites in Europe and Africa, for the year 1997, shows very good agreement. The correlation coefficient between the satellite retrieval and AERONET data for the wavelength of 440 nm is 97%, and for 670 nm it is 94%. Validation of the algorithm for the year 2000 was done for a few sites with similar results. The algorithm has been successfully tested over an island influenced by Saharan dust (i.e., Cape Verde, 16°N, 22°W) and over a site located near the Saharan desert (i.e., Bondoukoui, 11°N, 3°W). For other sites located near the Saharan desert such as Bidi Bahn (14°N, 2°W) and Banizombou (13°N, 2°E), the agreement was very good at 440 nm. The algorithm has not been tested over other bright surfaces such as ice‐covered regions. Examples of the spatial distribution of the aerosol optical thickness over Europe, north Africa, and the North Atlantic for the year 1997 and 2000 are presented.

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

Abstract. 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.

Variability in tropical tropospheric ozone: Analysis with Global Ozone Monitoring Experiment observations and a global model

Tropical tropospheric ozone columns (TTOCs) have been determined with a convective‐cloud‐differential (CCD) method, using ozone column and cloud measurements from the Global Ozone Monitoring Experiment (GOME) instrument. GOME cloud top pressures, derived with the Fast Retrieval Scheme for Clouds from the Oxygen A‐band (FRESCO) method, indicate that most convective cloud top levels are between 300 and 500 hPa and do not extend to the tropical tropopause. The new GOME‐CCD method takes this tropical transition layer below the tropopause into account and uses above‐cloud and clear‐sky ozone column measurements to derive a monthly mean TTOC below 200 hPa. Validation of the GOME‐TTOCs with seven Southern Hemisphere Additional Ozonesondes (SHADOZ) sites shows good agreement, with an RMS difference of about 5 Dobson units. In the northern tropics the GOME‐TTOC compares most of the time well with in situ measurements at Paramaribo (6°N, 55°W) and Abidjan (5°N, 4°W). Analysis of the GOME‐TTOCs for 2000 and 2001, with the aid of the chemistry‐transport model TM3, illustrates that the variability in the TTOC depends on a complex interaction of several processes, including biomass burning, lightning, and large‐scale transport. The much larger extent of the South Atlantic TTOC maximum in September–October 2001, compared to September–October 2000, can be attributed to differences in large‐scale transport. An exceptional situation in the northern tropics occurred during the biomass burning season December 2001 to January 2002, when there were almost no fires over northern Africa. This resulted in strongly reduced TTOCs over the Atlantic between the equator and 10°N.

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

The Global Ozone Monitoring Experiment (GOME) on board the ERS‐2 is designed 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. This algorithm, called Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO), makes use of reflectivities as measured by GOME inside and outside the oxygen A band (758–778 nm). For validation, the results of FRESCO are compared to effective cloud fractions and cloud top pressures derived with standard methods from colocated measurements made by the Along Track Scanning Radiometer‐2 (ATSR‐2). The brightness temperatures of the cloudy pixels as measured by ATSR‐2 are related to cloud top pressures using temperature profiles from the European Center for Medium‐range Weather Forecasts model. Generally, the results from FRESCO and ATSR‐2 agree reasonably well. For the effective cloud fractions the average difference (based on a comparison of 322 points) is 0.04; the standard deviation is 0.09. For the cloud top pressures, only points with an effective cloud fraction larger than 0.1 have been compared. For these 236 points the average difference in cloud top pressure is 65 hPa, and the standard deviation is 92 hPa.