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2 results on '"Keppens, Arno"'

1. Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O2-O2, MODIS, and Suomi-NPP VIIRS

Author

Compernolle, Steven, Argyrouli, Athina, Lutz, Ronny, Sneep, Maarten, Lambert, Jean-Christopher, Fjæraa, Ann Mari, Hubert, Daan, Keppens, Arno, Loyola, Diego, O'Connor, Ewan, Romahn, Fabian, Stammes, Piet, Verhoelst, Tijl, and Wang, Ping

Subjects
Remote Sensing, Clouds, Validation, TROPOMI, Atmosphärenprozessoren
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.

Published
2021

2. Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature

Author

Karlsruhe Institute of Technology, von Clarmann, Thomas, Degenstein, Douglas A., Livesey, Nathaniel J., Bender, Stefan, Braverman, Amy, Butz, André, Compernolle, Steven, Damadeo, Robert P., Dueck, Seth, Eriksson, Patrick, Funke, Bernd, Johnson, Margaret C., Kasai, Yasuko, Keppens, Arno, Kleinert, Anne, Kramarova, Natalya A., Laeng, Alexandra, Langerock, Bavo, Payne, Vivienne H., Rozanov, Alexei, Sato, Tomohiro O., Schneider, Matthias, Sheese, Patrick, Sofieva, Viktoria, Stiller, Gabriele P., von Savigny, Christian, Zawada, Daniel, Karlsruhe Institute of Technology, von Clarmann, Thomas, Degenstein, Douglas A., Livesey, Nathaniel J., Bender, Stefan, Braverman, Amy, Butz, André, Compernolle, Steven, Damadeo, Robert P., Dueck, Seth, Eriksson, Patrick, Funke, Bernd, Johnson, Margaret C., Kasai, Yasuko, Keppens, Arno, Kleinert, Anne, Kramarova, Natalya A., Laeng, Alexandra, Langerock, Bavo, Payne, Vivienne H., Rozanov, Alexei, Sato, Tomohiro O., Schneider, Matthias, Sheese, Patrick, Sofieva, Viktoria, Stiller, Gabriele P., von Savigny, Christian, and Zawada, Daniel

Abstract

Remote sensing of atmospheric state variables typically relies on the inverse solution of the radiative transfer equation. An adequately characterized retrieval provides information on the uncertainties of the estimated state variables as well as on how any constraint or a priori assumption affects the estimate. Reported characterization data should be intercomparable between different instruments, empirically validatable, grid-independent, usable without detailed knowledge of the instrument or retrieval technique, traceable and still have reasonable data volume. The latter may force one to work with representative rather than individual characterization data. Many errors derive from approximations and simplifications used in real-world retrieval schemes, which are reviewed in this paper, along with related error estimation schemes. The main sources of uncertainty are measurement noise, calibration errors, simplifications and idealizations in the radiative transfer model and retrieval scheme, auxiliary data errors, and uncertainties in atmospheric or instrumental parameters. Some of these errors affect the result in a random way, while others chiefly cause a bias or are of mixed character. Beyond this, it is of utmost importance to know the influence of any constraint and prior information on the solution. While different instruments or retrieval schemes may require different error estimation schemes, we provide a list of recommendations which should help to unify retrieval error reporting. © Author(s) 2020.

Published
2020

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