18 May 2020

18 May 2020

Review status: a revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

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

Holger Sihler1, Steffen Beirle1, Steffen Dörner1, Marloes Gutenstein-Penning de Vries1,a, Christoph Hörmann1,b, Christian Borger1, Simon Warnach1, and Thomas Wagner1 Holger Sihler et al.
  • 1Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, Germany
  • anow at: Deutscher Wetterdienst, Offenbach, Germany
  • bnow at: Volume Graphics GmbH, Heidelberg, Germany

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.

Holger Sihler et al.

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Holger Sihler et al.

Holger Sihler et al.


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Short summary
MICRU is an algorithm for the retrieval of effective cloud fractions (CF) from satellite measurements. CF describe the amount of clouds, which have a significant impact on the vertical sensitivity profile of trace-gases like NO2 and HCHO. MICRU retrieves small CF with an accuracy of 0.04 over the entire satellite swath. It features an empirical surface reflectivity model accounting for physical anisotropy (BRDF, sun glitter) and instrumental effects. MICRU is also applicable to imager data.