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

Abstract

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.

A cloud algorithm based on the O2-O2 477 nm absorption band featuring an advanced spectral fitting method and the use of surface geometry-dependent Lambertian-equivalent reflectivity

“…That is why the additional cloud assumptions should be adopted. For instance, if we retrieve the cloud parameters from an oxygen absorption band and assume a model of scattering cloud (Loyola et al, 2018), we have to adopt a priori values of the cloud microphysical parameters and cloud vertical extent assuming a homogeneous cloud layer and to add information to cloud fraction from other measurements.…”

A cloud algorithm based on the O2-O2 477 nm absorption band featuring an advanced spectral fitting method and the use of surface geometry-dependent Lambertian-equivalent reflectivity

“…Here, we generally follow the approach developed by Marchenko et al (2015) for the NO 2 SCD estimates. Instead of simultaneous retrieval of coefficients of multiple parameters as takes place in the classical DOAS formalism, we divide the problem into a series of sequential steps (Fig.…”

“…Step 4, the SCD retrieval, follows the approach described in Marchenko et al (2015). Here, preliminary SCD values are obtained from two algorithms, the Nelder-Mead minimization method and the least-squares Levenberg-Marquardt fit (Press et al, 1992), taking the latter as a default and fitting the normalized (Step 3) O 2 -O 2 profile in the 465-487 nm interval.…”

“…This new cloud algorithm is intended for the operational OMI NO 2 algorithm and is planned as a backup cloud algorithm for the upcoming Tropospheric Emissions: Monitoring Pollution (TEMPO) geostationary mission (Zoogman et al, 2017). Our spectral fitting procedure is similar to that developed by Marchenko et al (2015) for NO 2 slant column density (SCD) retrieval. It relies on the temperature-dependent O 2 -O 2 cross sections (Thalman and Volkamer, 2013) and derives the O 2 -O 2 SCD using O 3 , NO 2 , and H 2 O slant column estimates from independent OMI algorithms.…”

The Global Ozone Monitoring Experiment: Review of in-flight performance and new reprocessed 1995–2011 level 1 product

“…This should enable the application of common reading software to the different atmospheric composition sensors with little or no adaptions required for the various products. In addition to radiance and irradiance data, cloud parameters retrieved with the OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) algorithms (Lutz et al, 2016;Loyola et al, 2018) have been integrated in the new level 1 product which required reprocessing of the data record in several 25 iterations. Following the request from the users, another addition compared to the old product is geolocation information for each single PMD measurement.…”