Ground-based data network: a key tool for satellite validation

Chemical composition and climate
A homogenized formaldehyde (HCHO) data set from FTIR measurements has been developed including more than 25 stations over the world, covering clean air regions (Arctic or oceanic sites) and high emissions source regions (cities and forests). This enabled a thorough assessment of the quality of TROPOMI satellite data: TROPOMI is overestimating the HCHO amounts under clean conditions while underestimating them over polluted sites. Such specific results can only be obtained by using a harmonized data set from international networks like NDACC.

Building a homogenized ground-based data set from an international network

BIRA-IASB is very active in international networks of ground-based measurements. Among these is the Fourier Transform Infrared (FTIR) group of the Network for the Detection of Atmospheric Composition Change (NDACC), measuring many traces gases such as ozone, carbon monoxide, methane...

One of the main goals of ground-based measurements is to assess the quality of satellite data. It is crucial to perform this satellite validation at different places over the world to ensure that the satellite data are of good quality under different atmospheric and sensing conditions. Furthermore, to obtain robust validation assessments, the ground-based data must be harmonized among the network stations, to become a reference data set with internal consistency.

In view of the TROPOMI formaldehyde (HCHO) validation, BIRA-IASB has initiated and led the harmonization of the HCHO ground-based FTIR measurements across the network. We have been able to build a consistent HCHO data set including more than 25 stations over the world (see Fig. 1), covering clean air regions (Arctic or oceanic sites) and high emissions source regions (cities and forests).

Assessing the quality of the TROPOMI satellite measurements

This ground-based HCHO data set has been used for TROPOMI validation and has shown very interesting results: TROPOMI is overestimating the HCHO amounts under clean conditions while underestimating them over polluted sites, as can be observed in Fig. 2. Such specific behavior of satellite data can only be detected by using carefully harmonized data sets from a globally distributed network.

A robust linear relationship can be derived between the ground-based and the satellite measurements, as shown in Fig. 3. This linear equation can then be used to correct the satellite data at any place over the globe, which is crucial in inverse modeling studies that aim at improving our knowledge of HCHO emission sources.

 

References:

  • Vigouroux, C., Bauer Aquino, C.A., Bauwens, M., Becker, C., Blumenstock, T., De Mazière, M., García, O., Grutter, M., Guarin, C., Hannigan, J., Hase, F., Jones, N., Kivi, R., Koshelev, D., Langerock, B., Lutsch, E., Makarova, M., Metzger, J.-M., Müller, J.-F., Notholt, J., Ortega, I., Palm, M., Paton-Walsh, C., Poberovskii, A., Rettinger, M., Robinson, J., Smale, D., Stavrakou, T., Stremme, W., Strong, K., Sussmann, R., Té, Y., and Toon, G. (2018). NDACC harmonized formaldehyde time series from 21 FTIR stations covering a wide range of column abundances. Atmospheric Measurement Techniques, 11(9), 5049-5073. https://doi.org/10.5194/amt-11-5049-2018 Open Access Logo
     

  • Vigouroux, C., Langerock, B., Bauer Aquino, C.A., Blumenstock, T., Cheng, Z., De Mazière, M., De Smedt, I., Grutter, M., Hannigan, J.W., Jones, N., Kivi, R., Loyola, D., Lutsch, E., Mahieu, E., Makarova, M., Metzger, J.-M., Morino, I., Murata, I., Nagahama, T., Notholt, J., Ortega, I., Palm, M., Pinardi, G., Röhling, A., Smale, D., Stremme, W., Strong, K., Sussmann, R., Té, Y., Van Roozendael, M., Wang, P., and Winkler, H. (2020). TROPOMI–Sentinel-5 Precursor formaldehyde validation using an extensive network of ground-based Fourier-transform infrared stations. Atmospheric Measurement Techniques, 13(7), 3715-3767. https://doi.org/10.5194/amt-13-3751-2020 Open Access Logo

world map ground-based FTIR stations and HCHO data
Figure 1: Network of ground-based FTIR stations providing HCHO total column data. The background is the September 2018 monthly mean of TROPOMI HCHO tropospheric columns.
Graph Tropomi Bias per Station
Figure 2: TROPOMI bias (TROPOMI – FTIR / FTIR) in percentage at each station as a function of the mean FTIR total column (molec. cm−2). The grey bars indicate the systematic uncertainty of the differences, and the colored error bars are the 2σ-error of the bias.
TROPOMI versus FTIR data scatter plot
Figure 3: Scatter plot of TROPOMI versus FTIR data for individual co-located pairs. The linear relationship between TROPOMI and FTIR obtained with the robust Theil-Sen estimator is given by the red line and text, and can be used to correct TROPOMI HCHO data.