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The operational cloud retrieval algorithms from TROPOMI on board Sentinel-5 Precursor.

Authors :
Loyola, Diego G.
Gimeno García, Sebastián
Lutz, Ronny
Argyrouli, Athina
Romahn, Fabian
Spurr, Robert J. D.
Pedergnana, Mattia
Doicu, Adrian
Molina García, Víctor
Schüssler, Olena
Source :
Atmospheric Measurement Techniques. 2018, Vol. 11 Issue 1, p409-427. 19p.
Publication Year :
2018

Abstract

This paper presents the operational cloud retrieval algorithms for the TROPOspheric Monitoring Instrument (TROPOMI) on board the European Space Agency Sentinel-5 Precursor (S5P) mission scheduled for launch in 2017. Two algorithms working in tandem are used for retrieving cloud properties: OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks). OCRA retrieves the cloud fraction using TROPOMI measurements in the ultraviolet (UV) and visible (VIS) spectral regions, and ROCINN retrieves the cloud top height (pressure) and optical thickness (albedo) using TROPOMI measurements in and around the oxygen A-band in the near infrared (NIR). Cloud parameters from TROPOMI/S5P will be used not only for enhancing the accuracy of trace gas retrievals but also for extending the satellite data record of cloud information derived from oxygen A-band measurements, a record initiated with the Global Ozone Monitoring Experiment (GOME) on board the second European Remote-Sensing Satellite (ERS-2) over 20 years ago. The OCRA and ROCINN algorithms are integrated in the S5P operational processor UPAS (Universal Processor for UV/VIS/NIR Atmospheric Spectrometers), and we present here UPAS cloud results using the Ozone Monitoring Instrument (OMI) and GOME-2 measurements. In addition, we examine anticipated challenges for the TROPOMI/S5P cloud retrieval algorithms, and we discuss the future validation needs for OCRA and ROCINN. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18671381
Volume :
11
Issue :
1
Database :
Academic Search Index
Journal :
Atmospheric Measurement Techniques
Publication Type :
Academic Journal
Accession number :
127807954
Full Text :
https://doi.org/10.5194/amt-11-409-2018