Quantifying trace gases and particles: the atmospheric retrieval process

Extracted from Chimot, J., Global mapping of atmospheric composition from space – Retrieving aerosol height and tropospheric NO2 from OMI, PhD book, Delft University of Technology (TU Delft), The Royal Netherlands Meteorological Institute (KNMI), July 2018.


Estimation principle and forward model

“Since an atmospheric satellite measurement is a light spectrum containing the information on our atmospheric composition, it is necessary to convert it into a geophysical parameter: e.g. estimate the abundance of pollutant gases in the troposphere. Such a process is commonly named…” More here


Inverse model approach and challenges

“The atmospheric retrieval problem from a spectral satellite measurement generally faces, at least, three fundamental challenges: , 1) the choice of the forward model itself leads to systematic errors, 2) the retrieval problem is ill-posed, 3) required analytical equations of the geophysical retrieval do not exist. A forward model can be chosen based on…” More here


Big data challenge and operational processing needs

“State-of-the-art retrieval algorithms are facing a continuous increase of the amount of observations: 1 million spectra per day from OMI (Levelt et al., 2006), 20 million spectra per day from the future TROPOMI on-board Sentinel-5 Precursormission (Veefkind et al., 2012). The next operational Sentinel-4, Sentinel-5 and the proposed greenhouse gas Sentinel-7 missions will add even more…” More here

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