Intercomparison of Sentinel-5P TROPOMI cloud products for tropospheric trace gas retrievals

Abstract. Clouds have a strong impact on satellite measurements of tropospheric trace gases in the ultraviolet, visible, and near-infrared spectral ranges from space. Therefore, trace gas retrievals rely on information on cloud fraction, cloud albedo, and cloud height from cloud products. In this study, the cloud parameters from different cloud retrieval algorithms for the Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) are compared: the Optical Cloud Recognition Algorithm (OCRA) a priori cloud fraction, the Retrieval Of Cloud Information using Neural Networks (ROCINN) CAL (Clouds-As-Layers) cloud fraction and cloud top and base height, the ROCINN CRB (Clouds-as-Reflecting-Boundaries) cloud fraction and cloud height, the Fast Retrieval Scheme for Clouds from the Oxygen A-band (FRESCO) cloud fraction, the interpolated FRESCO cloud height from the TROPOMI NO2 product, the cloud fraction from the NO2 fitting window, the O2–O2 cloud fraction and cloud height, the Mainz Iterative Cloud Retrieval Utilities (MICRU) cloud fraction, and the Visible Infrared Imaging Radiometer Suite (VIIRS) cloud fraction. Two different versions of the TROPOMI cloud products OCRA/ROCINN, FRESCO, and the TROPOMI NO2 product are included in the comparisons (processor version 1.x and 2.x). Overall, the cloud parameters retrieved by the different algorithms show qualitative consistency in version 1.x and good agreement in version 2.x with the exception of the VIIRS cloud fraction, which cannot be directly compared to the other data. Differences between the cloud retrievals are found especially for small cloud heights with a cloud fraction threshold of 0.2, i.e. clouds that are particularly relevant for tropospheric trace gas retrievals. The cloud fractions of the different version 2 cloud products primarily differ over snow- and ice-covered pixels and scenes with sun glint, for which only MICRU includes an explicit treatment. All cloud parameters show some systematic problems related to the across-track dependence, where larger values are found at the edges of the satellite view. The consistency between the cloud parameters from different algorithms depends strongly on how the data are filtered for the comparison, for example, what quality value is used or whether snow- and ice-covered pixels are excluded from the analysis. In summary, clear differences were found between the results of various algorithms, but these differences are reduced in the most recent versions of the cloud data.

[1]  K. F. Boersma,et al.  Sentinel-5P TROPOMI NO2 retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data , 2022, Atmospheric Measurement Techniques.

[2]  K. F. Boersma,et al.  Sentinel-5P TROPOMI NO2 retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data , 2021 .

[3]  S. Beirle,et al.  MICRU: an effective cloud fraction algorithm designed for UV–vis satellite instruments with large viewing angles , 2021 .

[4]  E. O'connor,et al.  Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O2–O2, MODIS, and Suomi-NPP VIIRS , 2021, Atmospheric Measurement Techniques.

[5]  S. Compernolle,et al.  Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O 2 -O 2 , MODIS and Suomi-NPP VIIRS , 2020 .

[6]  Thomas Wagner,et al.  Algorithm theoretical baseline for formaldehyde retrievals from S5P TROPOMI and from the QA4ECV project , 2017 .

[7]  L. G. Tilstra,et al.  The Ozone Monitoring Instrument: overview of 14 years in space , 2017 .

[8]  Adrian Doicu,et al.  The operational cloud retrieval algorithms from TROPOMI on board Sentinel-5 Precursor , 2017 .

[9]  Pieternel F. Levelt,et al.  Improvements to the OMI O 2 –O 2 operational cloud algorithm and comparisons with ground-based radar–lidar observations , 2016 .

[10]  Isabelle De Smedt,et al.  Sulfur dioxide retrievals from TROPOMI onboard Sentinel-5 Precursor: algorithm theoretical basis , 2016 .

[11]  D. Loyola,et al.  OCRA radiometric cloud fractions for GOME-2 on MetOp-A/B , 2015 .

[12]  Michael Eisinger,et al.  The GOME-2 instrument on the Metop series of satellites: instrument design, calibration, and level 1 data processing – an overview , 2015 .

[13]  Henk Eskes,et al.  TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications , 2012 .

[14]  M. V. Roozendael,et al.  FRESCO+: an improved O 2 A-band cloud retrieval algorithm for tropospheric trace gas retrievals , 2008 .

[15]  Piet Stammes,et al.  Effective cloud fractions from the Ozone Monitoring Instrument: Theoretical framework and validation , 2008 .

[16]  Piet Stammes,et al.  Click Here for Full Article , 1989 .

[17]  Michael Buchwitz,et al.  The semianalytical cloud retrieval algorithm for SCIAMACHY II. The application to MERIS and SCIAMACHY data , 2006 .

[18]  Diego G. Loyola,et al.  Automatic cloud analysis from polar-orbiting satellites using neural network and data fusion techniques , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[19]  Piet Stammes,et al.  Cloud pressure retrieval using the O2‐O2 absorption band at 477 nm , 2004 .

[20]  Henk Eskes,et al.  Error analysis for tropospheric NO2 retrieval from space , 2004 .

[21]  James F. Gleason,et al.  An improved retrieval of tropospheric nitrogen dioxide from GOME , 2002 .

[22]  John P. Burrows,et al.  TROPOSPHERIC NO2 FROM GOME MEASUREMENTS , 2002 .

[23]  J. Hovenier,et al.  A fast method for retrieval of cloud parameters using oxygen A band measurements from the Global Ozone Monitoring Experiment , 2001 .

[24]  Michael Eisinger,et al.  The Global Ozone Monitoring Experiment (GOME): Mission Concept and First Scientific Results , 1999 .

[25]  M. Buchwitz,et al.  SCIAMACHY: Mission Objectives and Measurement Modes , 1999 .

[26]  John P. Burrows,et al.  SCIAMACHY—scanning imaging absorption spectrometer for atmospheric chartography , 1992 .

[27]  J. Lampel.  Algorithm theoretical baseline for formaldehyde retrievals from S 5 P TROPOMI and from the QA 4 ECV project , 2018 .

[28]  Jian Xu,et al.  S5P/TROPOMI Total Ozone ATBD , 2018 .

[29]  Isabelle De Smedt,et al.  Sulfur dioxide retrievals from TROPOMI onboard Sentinel-5 Precursor : algorithm theoretical basis , 2017 .

[30]  TROPOMI ATBD of the total and tropospheric NO2 data products , 2017 .

[31]  J. Burrows,et al.  TROPOSPHERIC NO 2 FROM GOME MEASUREMENTS , 2002 .