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Publikationstyp
Wissenschaftlicher Artikel
Erscheinungsjahr
2019
Comparison of continuous in-situ CO2 measurements with co-located column-averaged XCO 2 TCCON/satellite observations and CarbonTracker model over the Zugspitze region
Comparison of continuous in-situ CO2 measurements with co-located column-averaged XCO 2 TCCON/satellite observations and CarbonTracker model over the Zugspitze region
Autor:innen
Herausgeber
Quelle
Remote sensing
11 (2019), Heft 24
11 (2019), Heft 24
Schlagwörter
Kohlendioxid, In-Situ, Fernerkundung, Satellit, Zeitreihenanalyse
Zitation
YUAN, Ye, Ralf SUSSMANN, Ludwig RIES und Markus RETTINGER, 2019. Comparison of continuous in-situ CO2 measurements with co-located column-averaged XCO 2 TCCON/satellite observations and CarbonTracker model over the Zugspitze region. Remote sensing [online]. 2019. Bd. 11 (2019), Heft 24. DOI 10.60810/openumwelt-874. Verfügbar unter: https://openumwelt.de/handle/123456789/4824
Zusammenfassung englisch
Atmospheric CO2 measurements are important in understanding the global carbon cycle and in studying local sources and sinks. Ground and satellite-based measurements provide information on different temporal and spatial scales. However, the compatibility of such measurements at single sites is still underexplored, and the applicability of consistent data processing routines remains a challenge. In this study, we present an inter-comparison among representative surface and column-averaged CO2 records derived from continuous in-situ measurements, ground-based Fourier transform infrared measurements, satellite measurements, and modeled results over the Mount Zugspitze region of Germany. The mean annual growth rates agree well with around 2.2 ppm yr-1 over a 17-year period (2002-2018), while the mean seasonal amplitudes show distinct differences (surface:11.7 ppm/column-averaged: 6.6 ppm) due to differing air masses. We were able to demonstrate that, by using consistent data processing routines with proper data retrieval and gap interpolation algorithms, the trend and seasonality can be well extracted from all measurement data sets. Quelle:https://www.mdpi.com