ADS-CO2

Automated Data Selection / Filtering methodologies for the case of CO₂

 

The Global Atmosphere Watch (GAW) Programme of the World Meteorological Organization (UNO/WMO) stands for a worldwide database of high-quality atmospheric data with best possible comparability. It is of crucial interest to find a best suitable set of automated data selection/filtering methodologies for flagging non-representative data in the measurements, which can be applied for a larger number of measurement stations. A statistical data selection method called Adaptive Diurnal Minimum Variation (ADMV) has been developed and applied to atmospheric CO₂ measurements at six GAW mountain stations in Europe for evaluation. The corresponding paper has been submitted and currently under review by the journal Atmospheric Measurement Techniques (AMT).

Yuan, Y., Ries, L., Petermeier, H., Steinbacher, M., Gómez-Peláez, A.J., Leuenberger, M.C., Schumacher, M., Trickl, T., Couret, C., Meinhardt, F., Menzel, A., 2017. Adaptive Baseline Finder, a statistical data selection strategy to identify atmospheric CO2; baseline levels and its application to European elevated mountain stations. Atmos. Meas. Tech. Discuss., 1–27. 10.5194/amt-2017-316.

Meanwhile, more researches are carried out on the long-term measurements of CO₂ and other trace gases at GAW global station Zugspitze-Schneefernerhaus. Further researches will focus on evaluation and strategical combination of different data selection methods.

 

This work (or project) is supported by the scholarship from China Scholarship Council (CSC) under the Grant CSC No. 201508080110

Ansprechpartner:

Ye Yuan

Laufzeit:

2015 - 2018