, provides an expanding selection of research monographs in all major areas of optics: lasers and quantum optics, ultrafast phenomena, optical spectroscopy techniques, optoelectronics, quantum information, information optics, applied laser technology, industrial applications, and other topics of contemporary interest. With this broad coverage of topics, the series is of use to all research scientists and engineers who need up-to-date reference books.
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.
The retrieval problems arising in atmospheric remote sensing belong to the class of the socalled discrete ill-posed problems. These problems are unstable under data perturbations, and can be solved by numerical regularization methods, in which the solution is stabilized by taking additional information into account.
The functionality and characteristics of six different data processors (i.e., retrieval codes in their actual software and hardware environment) for analysis of high‐resolution limb emission infrared spectra recorded by the space‐borne Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) have been validated by means of a blind test retrieval experiment based on synthetic spectra. For this purpose a self‐consistent set of atmospheric state parameters, including pressure, temperature, vibrational temperatures, and abundances of trace gases and aerosols, has been generated and used as input for radiative transfer calculations for MIPAS measurement geometry and configuration. These spectra were convolved with the MIPAS field of view, spectrally degraded by the MIPAS instrument line shape, and, finally, superimposed with synthetic measurement noise. These synthetic MIPAS measurements were distributed among the participants of the project “Advanced MIPAS level‐2 data analysis” (AMIL2DA), who performed temperature and species abundance profile retrievals by inverse radiative transfer calculations. While the retrieved profiles of atmospheric state parameters reflect some characteristics of the individual data processors, it was shown that all the data processors under investigation are capable of producing reliable results in the sense that deviations of retrieved results from the reference profiles are within the margin that is consistent with analytical error estimation.
Abstract. This study presents two scientific and one operational retrieval algorithms used to obtain vertical distributions of bromine monoxide (BrO) from observations of the scattered solar light performed by the SCIAMACHY instrument in limb viewing geometry. The study begins with a discussion of the theoretical basis of all algorithms followed by an investigation of the retrieval sensitivity. Simulations with three different radiative transfer models allow us to analyze influence of the forward model implementation upon the retrieval results. By means of synthetic retrievals we analyze major sources of uncertainties in the resulting BrO profiles such as different BrO cross sections, their temperature dependence, and stratospheric aerosols. Finally, the reliability of SCIAMACHY BrO profile retrievals is demonstrated comparing results from different algorithms to each other and to balloon-borne observations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.