Validation of Sentinel-5p TROPOMI cloud data with ground-based Cloudnet and other satellite data products

2021 ◽  
Author(s):  
Steven Compernolle ◽  
Athina Argyrouli ◽  
Ronny Lutz ◽  
Maarten Sneep ◽  
Jean-Christopher Lambert ◽  
...  
Keyword(s):  
Major Change ◽  
Recognition Algorithm ◽  
Atmospheric Composition ◽  
Trace Gas ◽  
Oxygen Absorption ◽  
Absorption Bands ◽  
Cloud Data ◽  
Cloud Height ◽  
Data Products ◽  
Retrieval Scheme

<p>Space-born atmospheric composition measurements, like those from Sentinel-5p TROPOMI, are strongly affected by the presence of clouds. Dedicated cloud data products, typically retrieved with the same sensor, are therefore an important tool for the provider of atmospheric trace gas retrievals. Cloud products are used for filtering and modification of the modelled radiative transfer.</p><p>In this work, we assess the quality of the cloud data derived from Copernicus Sentinel-5 Precursor TROPOMI radiance measurements. Three cloud products are considered: (i) L2_CLOUD OCRA/ROCINN CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks; Clouds-As-Layers), (ii) L2_CLOUD OCRA/ROCINN CRB (same; Clouds-as Reflecting Boundaries), and (iii) the S5p support product FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands for Sentinel). These cloud products are used in the retrieval of several S5p trace gas products (e.g., ozone columns and profile, total and tropospheric nitrogen dioxide, sulfur dioxide, formaldehyde). The quality assessment of these cloud products is carried out within the framework of ESA’s Sentinel-5p Mission Performance Centre (MPC) with support from AO validation projects focusing on the respective atmospheric gases.</p><p>Cloud height data from the three S5p cloud products is compared to radar/lidar based cloud profile information from the ground-based networks CLOUDNET and ARM. The cloud height from S5p CLOUD CRB and S5p FRESCO are on average 0.6 km below the cloud mid-height of CLOUDNET measurements, and the cloud top height from S5p CLOUD CAL is on average 1 km below CLOUDNET’s cloud top height. However, the comparison is different for low and high clouds, with S5p CLOUD CAL cloud top height being only 0.3 km below CLOUDNET’s for low clouds.  The radiometric cloud fraction and cloud (top) height are compared to those of other satellite cloud products like Aura OMI O<sub>2</sub>-O<sub>2</sub>. While the latitudinal variation is often similar, offsets are encountered.</p><p>Recently, major S5p cloud product upgrades were released for S5p OCRA/ROCINN (July 2020) and for S5p FRESCO (December 2020), leading to a decrease of the ROCINN CRB cloud height and an increase of the FRESCO cloud height on average. Moreover, a major change in the ROCINN surface albedo treatment leads to a clear improvement of the comparison with CLOUDNET at the complicated sea/land/ice/snow site Ny-Alesund.</p><div></div>

Validation of Sentinel-5p retrieved cloud height data using ground-based radar/lidar measurements from the CLOUDNET network

2020 ◽  
Author(s):  
Steven Compernolle ◽  
Athina Argyrouli ◽  
Ronny Lutz ◽  
Maarten Sneep ◽  
José Granville ◽  
...  
Keyword(s):  
Recognition Algorithm ◽  
Cloud Fraction ◽  
Trace Gas ◽  
Oxygen Absorption ◽  
Absorption Bands ◽  
Cloud Data ◽  
Cloud Properties ◽  
Cloud Height ◽  
Retrieval Scheme ◽  
A Site

<p>Satellite measurements of tropospheric or total column trace gas species, including those from Sentinel-5p TROPOMI, are affected by the presence of clouds. Therefore, cloud data products retrieved with the same sensor play an essential role, as they allow the data provider to take an estimated cloud impact on the trace gas retrieval into account. Examples are the modification of the radiative transfer and associated quantities such as the air mass factor, and the partial masking of the measurement scene. Evidently, the accuracy of these corrections relies on the accuracy of the retrieved cloud properties, like radiometric cloud fraction (CF), cloud top height (CTH) or cloud height (CH), and cloud optical thickness (COT) or cloud albedo (CA).</p><p>We consider here three S5p TROPOMI-based cloud products: (i) L2_CLOUD OCRA/ROCINN CAL (<span>Optical Cloud Recognition Algorithm</span>/<span>Retrieval of Cloud Information using Neural Networks;</span> Clouds-As-Layers), (ii) L2_CLOUD OCRA/ROCINN CRB (Clouds-as Reflecting Boundaries), and (iii) the S5p support product FRESCO-S (<span>Fast Retrieval Scheme for Clouds from Oxygen absorption bands</span>). These are input to the S5p operational processors for several trace gas products, including ozone columns and profile, total and tropospheric NO2, formaldehyde, sulfur dioxide. The quality assessment of these cloud products is carried out within the framework of ESA’s Sentinel-5p Mission Performance Centre (MPC) with support from AO validation projects focusing on the respective trace gases.</p><p>In this work, cloud height (from S5p CLOUD CRB and S5p FRESCO algorithms) and cloud top height (from S5p CLOUD CAL) S5p data is validated with radar/lidar-based cloud profile information from the ground-based networks CLOUDNET and ARM at 17 sites. For some sites the comparison is difficult due to e.g., orography or snow/ice cover. S5P and CLOUDNET report similar cloud height variations at several sites, and the correlation between the S5p cloud products and CLOUDNET can be high (Pearson R up to 0.9). However, there is a site-dependent negative bias of the S5p cloud (top) height with respect to the CLOUDNET data: up to -2.5 km for S5p CLOUD CAL cloud top height and -1.5 km for S5p CLOUD CRB and S5p FRESCO cloud height. The dependence on other parameters measured by S5p and CLOUDNET (e.g., radiometric cloud fraction, cloud phase,…) is investigated.</p>

scholarly journals 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

2021 ◽  
Vol 14 (3) ◽  
pp. 2451-2476
Author(s):  
Steven Compernolle ◽  
Athina Argyrouli ◽  
Ronny Lutz ◽  
Maarten Sneep ◽  
Jean-Christopher Lambert ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Recognition Algorithm ◽  
Atmospheric Composition ◽  
Oxygen Absorption ◽  
Absorption Bands ◽  
Cloud Data ◽  
Geographical Patterns ◽  
Cloud Properties ◽  
Cloud Height ◽  
Cloud Optical Thickness

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.

Intercomparison of Cloud Products based on S5P/TROPOMI Level 1b Data Version 1 and the updated Version 2

2021 ◽  
Author(s):  
Miriam Latsch ◽  
Andreas Richter ◽  
John P. Burrows ◽  
Thomas Wagner ◽  
Holger Sihler ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Recognition Algorithm ◽  
Cloud Fraction ◽  
Trace Gas ◽  
Oxygen Absorption ◽  
Data Sets ◽  
Absorption Bands ◽  
Data Set ◽  
Trace Species ◽  
Retrieval Scheme

&lt;p&gt;The first European Sentinel satellite for monitoring the composition of the Earth&amp;#8217;s atmosphere, the Sentinel 5 Precursor (S5p), carries the TROPOspheric Monitoring Instrument (TROPOMI) to map trace species of the global atmosphere at high spatial resolution. Retrievals of tropospheric trace gas columns from satellite measurements are strongly influenced by clouds. Thus, cloud retrieval algorithms were developed and implemented in the trace gas processing chain to consider this impact.&lt;/p&gt;&lt;p&gt;In this study, different cloud products available for NO&lt;sub&gt;2&lt;/sub&gt; retrievals based on the TROPOMI level 1b data version 1 and an updated TROPOMI level 1b test data set of version 2 (Diagnostic Data Set 2B, DDS2B) are analyzed. The data sets include a) the TROPOMI level 2 OCRA/ROCINN (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks) cloud products CRB (cloud as reflecting boundaries) and CAL (clouds as layers), b) the FRESCO (Fast Retrieval Scheme for Clouds from Oxygen absorption bands) cloud product,&amp;#160; c) the cloud fraction from the NO&lt;sub&gt;2&lt;/sub&gt; fitting window, d) the VIIRS (Visible Infrared Imaging Radiometer Suite) cloud product, and e) the MICRU (Mainz Iterative Cloud Retrieval Utilities) cloud fraction. The cloud products are compared with regard to cloud fraction, cloud height, cloud albedo/optical thickness, flagging and quality indicators in all 4 seasons. In particular, the differences of the cloud products under difficult situations such as snow or ice cover and sun glint are investigated.&lt;/p&gt;&lt;p&gt;We present results of a statistical analysis on a limited data set comparing cloud products from the current and the upcoming lv2 data versions and their approaches. The aim of this study is to better understand TROPOMI cloud products and their quantitative impacts on trace gas retrievals.&lt;/p&gt;

Evaluation of TROPOMI cloud products for NO2 retrievals

2020 ◽  
Author(s):  
Miriam Latsch ◽  
Andreas Richter ◽  
John P. Burrows ◽  
Thomas Wagner ◽  
Holger Silher ◽  
...  
Keyword(s):  
Recognition Algorithm ◽  
Trace Gas ◽  
Oxygen Absorption ◽  
Absorption Bands ◽  
Data Set ◽  
Gas Processing ◽  
Trace Species ◽  
Retrieval Scheme ◽  
First Results ◽  
Reflecting Boundaries

&lt;p&gt;The first European Sentinel satellite for monitoring the composition of the Earth&amp;#8217;s atmosphere, the Sentinel 5 Precursor (S5p), carries the TROPOspheric Monitoring Instrument (TROPOMI) on board to map trace species of the global atmosphere at high spatial resolution. Retrievals of tropospheric trace gas columns from satellite measurements are strongly influenced by clouds. Thus, cloud retrieval algorithms were developed and implemented in the trace gas processing chain to consider this impact.&lt;/p&gt;&lt;p&gt;In this study, different cloud products available for NO&lt;sub&gt;2&lt;/sub&gt; retrievals from TROPOMI data are analyzed. The TROPOMI level 2 OCRA/ROCINN (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks) cloud products CRB (cloud as reflecting boundaries) and CAL (clouds as layers) as well as the FRESCO (Fast Retrieval Scheme for Clouds from Oxygen absorption bands) cloud product are compared with regard to e. g. cloud fraction, cloud height, cloud albedo/optical thickness, flagging and quality indicators. In particular, difficult situations such as snow or ice, sun glint, and high aerosol load are investigated.&lt;/p&gt;&lt;p&gt;The eventual aim of this study is to better understand TROPOMI cloud products and their quantitative impacts on trace gas retrievals. Here, we present first results of a statistical analysis on a limited data set comparing currently existing cloud products and their approaches focusing on NO&lt;sub&gt;2&lt;/sub&gt;.&lt;/p&gt;

scholarly journals Interpretation of FRESCO cloud retrievals in case of absorbing aerosol events

2012 ◽  
Vol 12 (19) ◽  
pp. 9057-9077 ◽  
Author(s):  
P. Wang ◽  
O. N. E. Tuinder ◽  
L. G. Tilstra ◽  
M. de Graaf ◽  
P. Stammes
Keyword(s):  
Trace Gas ◽  
Aerosol Layer ◽  
Satellite Measurements ◽  
Dominant Effect ◽  
Cloud Data ◽  
Retrieval Scheme ◽  
Absorbing Aerosols ◽  
Aerosol Effects ◽  
Aerosol Index ◽  
Aerosol Types

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.

scholarly journals MICRU background map and effective cloud fraction algorithms designed for UV/vis satellite instruments with large viewing angles

2020 ◽  
Author(s):  
Holger Sihler ◽  
Steffen Beirle ◽  
Steffen Dörner ◽  
Marloes Gutenstein-Penning de Vries ◽  
Christoph Hörmann ◽  
...  
Keyword(s):  
Recognition Algorithm ◽  
Trace Gas ◽  
Satellite Measurements ◽  
Measuring Device ◽  
Spectral Bands ◽  
Retrieval Scheme ◽  
Systematic Effects ◽  
Physical Effects ◽  
Surface Reflectivity ◽  
Spectral Ranges

Abstract. Clouds impact the radiative transfer of the Earth's atmosphere and strongly influence satellite measurements in the UV visible and 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 (CF) in particular. The accuracy of MICRU is governed by an empirical parametrisation 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 measuring device (PMD) data collected between April 2007 and June 2013 by the GOME-2A instrument onboard the MetOp-A satellite. CF are retrieved at different spectral bands between 374 and 758 nm. The MICRU results for MSC and PMD at different wavelengths are inter-compared 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 CF 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. CF 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 TROPOMI/Sentinel-5P, but is also applicable to UV/vis imagers like, for example, AVHRR, MODIS, VIIRS, and Sentinel-2.

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

2004 ◽  
Vol 4 (1) ◽  
pp. 255-273 ◽  
Author(s):  
O. N. E. Tuinder ◽  
R. de Winter-Sorkina ◽  
P. J. H. Builtjes
Keyword(s):  
Recognition Algorithm ◽  
Cloud Fraction ◽  
Trace Gas ◽  
Actinic Flux ◽  
Fitting Algorithm ◽  
Retrieval Scheme ◽  
Ozone Monitoring ◽  
Radiative Scattering ◽  
Atmospheric Trace Gas ◽  
Surface Observations

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.

scholarly journals The Cluster Low-Streams Regression Method for Fast Computations of Top-of-the-Atmosphere Radiances in Absorption Bands

2020 ◽  
pp. short25-1-short25-9
Author(s):  
Ana del Aguila ◽  
Dmitry Efremenko
Keyword(s):  
Performance Enhancement ◽  
Regression Method ◽  
Atmospheric Composition ◽  
Trace Gas ◽  
Huge Amount ◽  
Absorption Bands ◽  
Clear Sky ◽  
Median Error ◽  
On Line ◽  
Aerosol Types

Atmospheric composition sensors provide a huge amount of data. A key component of trace gas retrieval algorithms are radiative transfer models (RTMs), which are used to simulate the spectral radiances in the absorption bands. Accurate RTMs based on line-by-line techniques are time-consuming. In this paper we analyze the efficiency of the cluster low-streams regression (CLSR) technique to accelerate computations in the absorption bands. The idea of the CLRS method is to use the fast two-stream RTM model in conjunction with the line-by-line model and then to refine the results by constructing the regression model between two- and multi-stream RTMs. The CLSR method is applied to the Hartley-Huggins, O2 A-, water vapour and CO2 bands for the clear sky and several aerosol types. The median error of the CLSR method is below 0.001 %, the interquartile range (IQR) is below 0.1 %, while the performance enhancement is two orders of magnitude.

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

2021 ◽  
Vol 14 (6) ◽  
pp. 3989-4031
Author(s):  
Holger Sihler ◽  
Steffen Beirle ◽  
Steffen Dörner ◽  
Marloes Gutenstein-Penning de Vries ◽  
Christoph Hörmann ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Recognition Algorithm ◽  
Trace Gas ◽  
Satellite Measurements ◽  
Spectral Bands ◽  
Retrieval Scheme ◽  
Systematic Effects ◽  
Physical Effects ◽  
Surface Reflectivity ◽  
Moderate Resolution Imaging Spectroradiometer

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.

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

2002 ◽  
Vol 2 (3) ◽  
pp. 623-668 ◽  
Author(s):  
O. N. E. Tuinder ◽  
R. de Winter-Sorkina ◽  
P. J. H. Builtjes
Keyword(s):  
Recognition Algorithm ◽  
Cloud Fraction ◽  
Trace Gas ◽  
Retrieval Algorithm ◽  
Fitting Algorithm ◽  
Retrieval Scheme ◽  
Ozone Monitoring ◽  
Radiative Scattering ◽  
Atmospheric Trace Gas ◽  
Surface Observations

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 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.

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