Evaluation and application of TROPOMI data: cloud retrieval algorithms and global shipping NO2 detection

Satellite-based atmospheric measurements provide an important data basis for monitoring air pollution over long periods. Using the DOAS technique, tropospheric columns of nitrogen dioxide (NO2), a key air pollutant, can be retrieved from these observations. The TROPOspheric Monitoring Instrument (TROPOMI), launched on Sentinel-5P in October 2017, provides daily global measurements, significantly enhancing the ability to detect small-scale emissions like shipping plumes due to its high signal-to-noise ratio and spatial resolution. Clouds have a substantial impact on satellite measurements of tropospheric trace gases in the UV, VIS, and NIR spectral ranges. Therefore, NO2 retrievals rely on information on cloud fraction and cloud height from satellite cloud products. In the first part of this thesis, the cloud parameters from different cloud retrieval algorithms for TROPOMI are compared. Overall, the cloud products show qualitative consistency in processor version 1.x and reasonable agreement in processor version 2.x, except for the VIIRS cloud fraction. Differences between the cloud retrievals are found especially for low cloud heights and small cloud fractions, i.e., clouds that are particularly relevant for tropospheric trace gas retrievals. Cloud fractions primarily differ over snow- and ice-covered pixels and scenes with sun glint, for which only MICRU includes an explicit treatment. All cloud parameters exhibit systematic issues related to across-track dependence, and the consistency among these parameters depends strongly on how the data is filtered. In summary, clear differences were found between the results of various algorithms, but these differences are reduced in the most recent versions of the cloud data. Shipping is an important source of NOx emissions worldwide, negatively affecting marine environments and human health. The second part of the thesis uses the TROPOMI tropospheric NO2 slant columns (tSCDs) to qualitatively identify global shipping routes. Preprocessing techniques, including iterative high-pass and Fourier filtering, markedly improve the detection of shipping lanes, revealing many previously undetectable routes. The analysis examines the impact of high-pass filter box sizes, demonstrating that smaller sizes enhance the visibility of narrow shipping features, whereas larger box sizes increase overall NO2 signals. Additionally, various flagging criteria are investigated that affect NO2 signal distribution, highlighting the critical importance of careful selection for accurate emission monitoring. Filtered TROPOMI NO2 tSCDs over oceans show a strong correlation with shipping activities, as confirmed by comparison with the CAMS-GLOB-SHIP inventory, and reveal unknown shipping routes. TROPOMI also effectively captures NO2 emissions from oil and gas platforms. Finally, the filtered TROPOMI tropospheric NO2 vertical columns (tVCDs) are compared with those from the CAMS global model. While both datasets show NO2 enhancements over global shipping lanes, the CAMS NO2 values are significantly larger than the TROPOMI measurements in the North Atlantic and strongly depend on the masking threshold in the high-pass filtering method in the South Atlantic.