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.

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Interpretation of FRESCO cloud retrievals in case of absorbing aerosol events

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-32685-2011 ◽

2011 ◽

Vol 11 (12) ◽

pp. 32685-32721 ◽

Cited By ~ 1

Author(s):

P. Wang ◽

O. N. E. Tuinder ◽

L. G. Tilstra ◽

P. Stammes

Keyword(s):

Volcanic Ash ◽

Trace Gas ◽

Aerosol Layer ◽

Satellite Measurements ◽

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 pressures contain information on aerosol layer pressure. For cloud-free scenes, the derived FRESCO cloud pressures are close to those of the aerosol layer for optically thick aerosols. For cloudy scenes, if the strongly absorbing aerosols are located above the clouds, then the retrieved FRESCO cloud pressures may represent the height of the aerosol layer rather than the height of the clouds. Combining FRESCO cloud data and AAI, an estimate for the aerosol layer pressure can be given, which can be beneficial for aviation safety and operations in case of e.g. volcanic ash plumes.

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

10.5194/egusphere-egu21-5811 ◽

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

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.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&#8217;s Sentinel-5p Mission Performance Centre (MPC) with support from AO validation projects focusing on the respective atmospheric gases.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&#8217;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&#8217;s for low clouds.&#160; The radiometric cloud fraction and cloud (top) height are compared to those of other satellite cloud products like Aura OMI O2-O2. While the latitudinal variation is often similar, offsets are encountered.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.<div></div>

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A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances

Atmospheric Chemistry and Physics ◽

10.5194/acp-15-10597-2015 ◽

2015 ◽

Vol 15 (18) ◽

pp. 10597-10618 ◽

Cited By ~ 16

Author(s):

M. J. M. Penning de Vries ◽

S. Beirle ◽

C. Hörmann ◽

J. W. Kaiser ◽

P. Stammes ◽

...

Keyword(s):

Classification Algorithm ◽

Atmospheric Composition ◽

Trace Gas ◽

Aerosol Composition ◽

Ångström Exponent ◽

Aerosol Classification ◽

Aerosol Index ◽

Aerosol Types ◽

Good Agreement ◽

Aerosol Type

Abstract. Detecting the optical properties of aerosols using passive satellite-borne measurements alone is a difficult task due to the broadband effect of aerosols on the measured spectra and the influences of surface and cloud reflection. We present another approach to determine aerosol type, namely by studying the relationship of aerosol optical depth (AOD) with trace gas abundance, aerosol absorption, and mean aerosol size. Our new Global Aerosol Classification Algorithm, GACA, examines relationships between aerosol properties (AOD and extinction Ångström exponent from the Moderate Resolution Imaging Spectroradiometer (MODIS), UV Aerosol Index from the second Global Ozone Monitoring Experiment, GOME-2) and trace gas column densities (NO2, HCHO, SO2 from GOME-2, and CO from MOPITT, the Measurements of Pollution in the Troposphere instrument) on a monthly mean basis. First, aerosol types are separated based on size (Ångström exponent) and absorption (UV Aerosol Index), then the dominating sources are identified based on mean trace gas columns and their correlation with AOD. In this way, global maps of dominant aerosol type and main source type are constructed for each season and compared with maps of aerosol composition from the global MACC (Monitoring Atmospheric Composition and Climate) model. Although GACA cannot correctly characterize transported or mixed aerosols, GACA and MACC show good agreement regarding the global seasonal cycle, particularly for urban/industrial aerosols. The seasonal cycles of both aerosol type and source are also studied in more detail for selected 5° × 5° regions. Again, good agreement between GACA and MACC is found for all regions, but some systematic differences become apparent: the variability of aerosol composition (yearly and/or seasonal) is often not well captured by MACC, the amount of mineral dust outside of the dust belt appears to be overestimated, and the abundance of secondary organic aerosols is underestimated in comparison with GACA. Whereas the presented study is of exploratory nature, we show that the developed algorithm is well suited to evaluate climate and atmospheric composition models by including aerosol type and source obtained from measurements into the comparison, instead of focusing on a single parameter, e.g., AOD. The approach could be adapted to constrain the mix of aerosol types during the process of a combined data assimilation of aerosol and trace gas observations.

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

10.5194/amt-2020-182 ◽

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.

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A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-13551-2015 ◽

2015 ◽

Vol 15 (9) ◽

pp. 13551-13605

Author(s):

M. J. M. Penning de Vries ◽

S. Beirle ◽

C. Hörmann ◽

J. W. Kaiser ◽

P. Stammes ◽

...

Keyword(s):

Classification Algorithm ◽

Atmospheric Composition ◽

Trace Gas ◽

Aerosol Composition ◽

Ångström Exponent ◽

Aerosol Classification ◽

Aerosol Index ◽

Aerosol Types ◽

Good Agreement ◽

Aerosol Type

Abstract. Detecting the optical properties of aerosols using passive satellite-borne measurements alone is a difficult task due to the broad-band effect of aerosols on the measured spectra and the influences of surface and cloud reflection. We present another approach to determine aerosol type, namely by studying the relationship of aerosol optical depth (AOD) with trace gas abundance, aerosol absorption, and mean aerosol size. Our new Global Aerosol Classification Algorithm, GACA, examines relationships between aerosol properties (AOD and extinction Ångström exponent from the Moderate Resolution Imaging Spectroradiometer (MODIS), UV Aerosol Index from the second Global Ozone Monitoring Experiment, GOME-2) and trace gas column densities (NO2, HCHO, SO2 from GOME-2, and CO from MOPITT, the Measurements of Pollution in the Troposphere instrument) on a monthly mean basis. First, aerosol types are separated based on size (Ångström exponent) and absorption (UV Aerosol Index), then the dominating sources are identified based on mean trace gas columns and their correlation with AOD. In this way, global maps of dominant aerosol type and main source type are constructed for each season and compared with maps of aerosol composition from the global MACC (Monitoring Atmospheric Composition and Climate) model. Although GACA cannot correctly characterize transported or mixed aerosols, GACA and MACC show good agreement regarding the global seasonal cycle, particularly for urban/industrial aerosols. The seasonal cycles of both aerosol type and source are also studied in more detail for selected 5° × 5° regions. Again, good agreement between GACA and MACC is found for all regions, but some systematic differences become apparent: the variability of aerosol composition (yearly and/or seasonal) is often not well captured by MACC, the amount of mineral dust outside of the dust belt appears to be overestimated, and the abundance of secondary organic aerosols is underestimated in comparison with GACA. Whereas the presented study is of exploratory nature, we show that the developed algorithm is well suited to evaluate climate and atmospheric composition models by including aerosol type and source obtained from measurements into the comparison, instead of focusing on a single parameter, e.g. AOD. The approach could be adapted to constrain the mix of aerosol types during the process of a combined data assimilation of aerosol and trace gas observations.

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

Atmospheric Measurement Techniques ◽

10.5194/amt-14-3989-2021 ◽

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.

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

10.5194/egusphere-egu2020-7989 ◽

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

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).We consider here three S5p TROPOMI-based cloud products: (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 (Clouds-as Reflecting Boundaries), and (iii) the S5p support product FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands). 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&#8217;s Sentinel-5p Mission Performance Centre (MPC) with support from AO validation projects focusing on the respective trace gases.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,&#8230;) is investigated.

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Improving cloud information over deserts from SCIAMACHY O2 A-band

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-5-6013-2005 ◽

2005 ◽

Vol 5 (4) ◽

pp. 6013-6039 ◽

Cited By ~ 1

Author(s):

N. Fournier ◽

P. Stammes ◽

M. de Graaf ◽

R. van der A ◽

A. Piters ◽

...

Keyword(s):

Surface Albedo ◽

Recognition Algorithm ◽

Cloud Fraction ◽

Trace Gas ◽

Retrieval Scheme ◽

Scanning Imaging ◽

Cloud Top Pressure ◽

Atmospheric Trace Gas ◽

Aerosol Index ◽

Level 2

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 both the SCIAMACHY Level 2 operational cloud fraction of OCRA (Optical Cloud Recognition Algorithm) and cloud information deduced from the MODIS 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 a GOME Absorbing Aerosol Index. It is shown that this approach succeeds well in producing more accurate cloud information over the Sahara.

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

Remote Sensing ◽

10.3390/rs13010152 ◽

2021 ◽

Vol 13 (1) ◽

pp. 152

Author(s):

Haklim Choi ◽

Xiong Liu ◽

Gonzalo Gonzalez Abad ◽

Jongjin Seo ◽

Kwang-Mog Lee ◽

...

Keyword(s):

Scattered Light ◽

Trace Gas ◽

Retrieval Algorithm ◽

The Novel ◽

Cloud Parameters ◽

Retrieval Scheme ◽

The Difference ◽

The Look ◽

Time Required ◽

Cloud Top Pressure

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.

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

10.5194/egusphere-egu21-4946 ◽

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

The first European Sentinel satellite for monitoring the composition of the Earth&#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.In this study, different cloud products available for NO2 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,&#160; c) the cloud fraction from the NO2 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.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.

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Interpretation of FRESCO cloud retrievals in case of absorbing aerosol events