Sentinel-5P products in Terrascope

The Terrascope ecosytem

Data from satellites observing the atmosphere of the Earth have become available in ever-increasing quantities. It can often be hard for users to find the most suitable information, data volumes may appear too large to download, or the data format is difficult to read for non-expert users.

Terrascope addresses these issues by providing a full ecosystem where both new and experienced users can view and analyse a range of satellite data products. This way, Terrascope intends to attract not only users from the scientific community but also policy makers, health authorities and the general public.

Terrascope-S5P

Within the ESA-supported project Terrascope-S5P, BIRA-IASB has prepared a series of data products for Terrascope that are based on observations from the Tropospheric Monitoring Instrument (TROPOMI) on-board the Sentinel-5 Precursor (S5P) satellite.

S5P has delivered vertical column products of atmospheric constituents at high spatial resolution since early 2018.

The data products for Terrascope come as Level-3 (L3) data: resampled on a rectangular spatial grid (0.05°×0.05° or less) for easy comparison and averaging. Care was taken to come up with products that are thought to be of added value to the user community in comparison to products already available elsewhere.

The suite of L3 products added to Terrascope is:

• Global formaldehyde (HCHO)

  Formaldehyde (De Smedt et al., 2021) is a tracer of hydrocarbon emissions and leads to the formation of tropospheric ozone, which makes it interesting for air quality and climate studies.
   

• Global methane (CH₄)

  Methane is the second most important greenhouse gas. The CH₄ product is used, for example, in chemistry-transport models and for improving greenhouse gas emission inventories (Sha et al., 2021).
   

• Global COBRA sulphur dioxide (SO₂)

  By using the COBRA algorithm (Theys et al., 2021) instead of a traditional DOAS scheme, the SO₂ distribution reveals many more weak sources. As such, the product is highly relevant to policy makers and health authorities.

   

• Improved nitrogen dioxide (NO₂) over Europe

  By using improved a priori profile data (Douros et al., 2023), this product provides more accurate estimates of NO₂ concentrations, with overall higher values over hotspots.

   

• Near-surface NO₂ concentration over Western Europe.

  For this product a machine-learning algorithm was developed (Sun et al., 2024) that uses the aforementioned improved TROPOMI NO₂ columns and additional parameters like meteorology and emissions. The model infers daily NO₂ surface concentration, a direct indicator of air quality.

References

• De Smedt, I., Pinardi, G., Vigouroux, C., Compernolle, S., Bais, A., Benavent, N., Boersma, F., Chan, K.-L., Donner, S., Eichmann, K.-U., Hedelt, P., Hendrick, F., Irie, H., Kumar, V., Lambert, J.-C., Langerock, B., Lerot, C., Liu, C., Loyola, D., Piters, A., Richter, A., Rivera Cárdenas, C., Romahn, F., Ryan, R. G., Sinha, V., Theys, N., Vlietinck, J., Wagner, T., Wang, T., Yu, H., and Van Roozendael, M.: Comparative assessment of TROPOMI and OMI formaldehyde observations and validation against MAX-DOAS network column measurements, Atmos. Chem. Phys., 21, 12561–12593, https://doi.org/10.5194/acp-21-12561-2021, 2021.
   
• Douros, J., Eskes, H., Van Geffen, J., Boersma, K.F., Compernolle, S., Pinardi, G., Blechschmidt, A.-M., Peuch, V.-H., Colette, A., Veefkind, P., Comparing Sentinel-5P TROPOMI NO2 column observations with the CAMS regional air quality ensemble, Geoscientific Model Development, Vol: 16, issue: 2, 509-534, DOI: 10.5194/gmd-16-509-2023, 2023.
   
• Sha, M. K., Langerock, B., Blavier, J.-F. L., Blumenstock, T., Borsdorff, T., Buschmann, M., Dehn, A., De Mazière, M., Deutscher, N. M., Feist, D. G., García, O. E., Griffith, D. W. T., Grutter, M., Hannigan, J. W., Hase, F., Heikkinen, P., Hermans, C., Iraci, L. T., Jeseck, P., Jones, N., Kivi, R., Kumps, N., Landgraf, J., Lorente, A., Mahieu, E., Makarova, M. V., Mellqvist, J., Metzger, J.-M., Morino, I., Nagahama, T., Notholt, J., Ohyama, H., Ortega, I., Palm, M., Petri, C., Pollard, D. F., Rettinger, M., Robinson, J., Roche, S., Roehl, C. M., Röhling, A. N., Rousogenous, C., Schneider, M., Shiomi, K., Smale, D., Stremme, W., Strong, K., Sussmann, R., Té, Y., Uchino, O., Velazco, V. A., Vigouroux, C., Vrekoussis, M., Wang, P., Warneke, T., Wizenberg, T., Wunch, D., Yamanouchi, S., Yang, Y., and Zhou, M.: Validation of methane and carbon monoxide from Sentinel-5 Precursor using TCCON and NDACC-IRWG stations, Atmos. Meas. Tech., 14, 6249–6304, https://doi.org/10.5194/amt-14-6249-2021, 2021.
   
• Sun, W., Tack, F., Clarisse, L., Schneider, R., Stavrakou, T., & Van Roozendael, M. (2024). Inferring surface NO2 over Western Europe: A machine learning approach with uncertainty quantification. Journal of Geophysical Research: Atmospheres, 129, e2023JD040676. https://doi.org/10.1029/2023JD040676.
   
• Theys, N., Lerot, C., Brenot, H., van Gent, J., De Smedt, I., Clarisse, L., Burton, M., Varnam, M., Hayer, C., Esse, B., and Van Roozendael, M.: Improved retrieval of SO₂ plume height from TROPOMI using an iterative Covariance-Based Retrieval Algorithm, Atmos. Meas. Tech., 15, 4801–4817, https://doi.org/10.5194/amt-15-4801-2022, 2022.