Abstract

Cloud shadows are observed by the TROPOMI satellite instrument as a result of its high spatial resolution as compared to its predecessor instruments. These shadows contaminate TROPOMI's air quality measurements, because shadows are generally not taken into account in the models that are used for aerosol and trace gas retrievals. If the shadows are to be removed from the data, or if shadows are to be studied, an automatic detection of the shadow pixels is needed. We present the Detection AlgoRithm for CLOud Shadows (DARCLOS) for TROPOMI, which is the first cloud shadow detection algorithm for a spaceborne spectrometer. DARCLOS raises potential cloud shadow flags (PCSFs), and actual cloud shadow flags (ACSFs). The PCSFs indicate the TROPOMI ground pixels that are potentially affected by cloud shadows based on a geometric consideration with safety margins. The ACSFs are a refinement of the PCSFs using spectral reflectance information of the PCSF pixels, and identify the TROPOMI ground pixels that are confidently affected by cloud shadows. We validate DARCLOS with true color images made by the VIIRS instrument on board of Suomi NPP orbiting in close constellation with TROPOMI on board of Sentinel 5-P. We conclude that the PCSF can be used to exclude cloud shadow contamination from TROPOMI data, while the ACSF can be used to select pixels for the scientific analysis of cloud shadow effects.

Highlights

  • Air quality monitoring from space using satellite spectrometers started in 1978 with the launch of the first Total Ozone Mapping 15 Spectrometer (TOMS) instrument on board the Nimbus-7 satellite

  • The actual cloud shadow flags (ACSFs) are a refinement of the potential cloud shadow flags (PCSFs) using spectral reflectance information of the PCSF pixels, and identify the TROPOspheric Monitoring Instrument (TROPOMI) ground pixels that are confidently affected by cloud shadows

  • The ACSFs are a refinement of the PCSFs using spectral reflectance information of the PCSF pixels, and indicate the TROPOMI ground pixels that are confidently affected by cloud shadows

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Summary

Introduction

Air quality monitoring from space using satellite spectrometers started in 1978 with the launch of the first Total Ozone Mapping 15 Spectrometer (TOMS) instrument on board the Nimbus-7 satellite. The first high-spectral resolution spectrometer was the Global Ozone Monitoring Experiment (GOME) (Burrows et al, 1999) launched in 1995, followed by the SCanning Imaging Absorption spectroMeter for Atmospheric ChartograpHY (SCIAMACHY) (Bovensmann et al, 1999), the Ozone Monitoring Instrument 20 (OMI) (Levelt et al, 2006), the GOME-2 A/B/C instruments (Munro et al, 2016) and, most recently, the TROPOspheric Monitoring Instrument (TROPOMI) (Veefkind et al, 2012), allowing for trace gas retrieval using differential absorption features in the spectra of the Earth’s reflectance (Platt and Stutz, 2008). The spatial resolutions of TOMS, GOME, SCIAMACHY, OMI and GOME-2 have been 50 × 50 km2, 320 × 40 km2, 60 × 30 km2, 24 × 13 km and 80 × 40 km, respectively Those resolutions are too coarse to discern kilometer-scale clouds or 25 cloud shadows.

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