Level 1 processing  algorithms of MicroCarb  microsatellite : Performance assessment of ISRF in-flight estimation through new algorithms

Detta är en Master-uppsats från KTH/Skolan för elektroteknik och datavetenskap (EECS)

Författare: Quentin Taupin; [2021]

Nyckelord: ;

Sammanfattning: Facing global warming and its dramatic consequences, it is now of the utmost urgency to improve our understanding of the carbon cycle mechanisms through which global climate change unfolds. The MicroCarb mission will allow to accurately monitor CO2 global sources and sinks over the entire surface of the Earth. It is the first space mission of its kind in Europe, and it gained support from the French government during COP21 in Paris in December 2015. MicroCarb's payload includes a passive SWIR (Short-Wave lnfraRed} echelle grating spectrometer that measures the solar flux reflected by the Earth. By observing specific high-resolution narrow band corresponding to CO2 and 02 absorption lines, one can estimate the carbon column integrated volume mixing ratio in the atmosphere with a random error up to 1 ppm (<0.250/o} and a regional bias up to 0.1 ppm (<0.0250/o}. This accuracy is limited by measurement errors, among them the ISRF (Instrument Spectral Response Function) knowledge error. ISRF are characterized through ground measurements but may evolve with mechanical vibrations during launch or thermal conditions changes along the orbit. The MicroCarb instrument design trade-off also induced a sensitivity of ISRF to the non-uniformity of the observed scene on Earth. ISRF are then re-estimated at each scientific measurement using ground characterized ISRF (Multi-reading ISRF processing). A way to mitigate errors of the Multi-Reading algorithm is to re-estimate ground characterized ISRF from flight data. The objective of this thesis is to assess the performance of in-flight estimation of Multi-Reading or ground ISRF through new algorithms. This thesis begins by showing the performances of the Optimal Matching Pursuit algorithm for approximating these ISRF. Indeed, it allows to reduce the data volume that will be used for ground segment processing. In a second part, this thesis evaluates the performances of new algorithms for estimating in-flight ISRF on different simulated data sets. Nevertheless, it is rather difficult to conclude on these performances without real datasets. The last part is devoted to the preparation of a cross-validation test using EM27 and MicroCarb sensors which is supposed to take place in April 2021. Part of this test aims to characterize ground ISRF using uniform illumination as obtained by looking at the sun. In particular, it is shown that the accuracy of this test clearly depends on EM27's spectral resolution which must be at least 0.1 cm-1, to almost reach the expected accuracy on the ISRF estimation.  

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