Satellite measurements of carbon dioxide: analysis and reduction of scattering related retrieval errors

Carbon dioxide, remote sensing, clouds, aerosols, SCIAMACHY, scattering, satellite, retrieval algorithm. - The greenhouse gas carbon dioxide (CO2) is the most important human-made contributor to global warming. Despite its importance, our knowledge about its sources and sinks has large gaps. This li...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
1. Verfasser: Heymann, Jens (VerfasserIn)
Format: Abschlussarbeit Elektronisch E-Book
Sprache:English
Veröffentlicht: 2013
Schlagworte:
Online-Zugang:kostenfrei
Zusammenfassung:Carbon dioxide, remote sensing, clouds, aerosols, SCIAMACHY, scattering, satellite, retrieval algorithm. - The greenhouse gas carbon dioxide (CO2) is the most important human-made contributor to global warming. Despite its importance, our knowledge about its sources and sinks has large gaps. This limits a reliable climate prediction. Satellite observations of atmospheric CO2 combined with modelling can help to reduce these knowledge gaps. However, this requires to meet demanding accuracy and precision requirements for the satellite instrument, the retrieval algorithm and the model. One of the most important error sources for satellite retrievals of CO2 from reflected and backscattered solar radiation is unaccounted scattering by aerosols and clouds. In this context, the objectives of this thesis are to assess the quality of an existing satellite-based CO2 data set focussing on the investigation of these error source and to generate and validate an improved CO2 data set. The CO2 data bases on measurements of the passive remote sensing satellite instrument SCIAMACHY on-board ENVISAT, which has performed more than 10 years of radiance measurements in the short wave infrared spectral region. In the period 2003-2009, SCIAMACHY was the only satellite instrument measuring CO2 with high sensitivity down to the Earth's surface where the sources and sinks of CO2 are located. Therefore, the SCIAMACHY measurements are important in terms of generating an accurate global long-term atmospheric CO2 data set. Starting point for this thesis was an analysis of an existing 7-year (2003-2009) data set of column-averaged dry airmole-fraction of CO2, denoted XCO2, which was generated with version 2.1 of the Weighting Function Modified - Differential Optical Absorption Spectroscopy (WFM-DOAS) retrieval algorithm (WFMDv2.1).