Abstract. Ground-based comparison of satellite atmospheric products is essential for ensuring data quality and algorithm performance. We present a comparison approach for Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) cloud fraction products using a multi-spectral ground station (DG2MCM-15) located in Kempten, Bavaria, Germany. The ground observatory combines calibration-informed measurement protocols with low-cost commercial sensors, creating a citizen science comparison capability. Our comparison dataset comprises 276 temporally matched observations between Sentinel-5P overpasses and ground measurements over a four-week period (11 January–8 February 2026). Ground-based cloud detection using an MLX90614 infrared pyrometer yields a Pearson correlation of R=0.879 (N=27 after quality filtering) with Sentinel-5P cloud fraction retrievals. However, this correlation is driven substantially by two high-cloud-fraction observations; the effective degrees of freedom are lower than N would suggest, and the method is better characterised as a three-state classifier (clear/partly cloudy/overcast) than as a continuous cloud fraction retrieval. The root mean square error of 29.1 % cloud fraction reflects a systematic positive bias from spatial scale mismatch between the ground sensor field of view and satellite pixel dimensions. The method reliably distinguishes between clear, partially cloudy, and overcast conditions, though the derived cloud fraction values exhibit clustering due to the temperature-ratio approach used. Exploratory comparison with TROPOMI aerosol index products yielded negligible correlation due to the absence of UV spectral coverage in the ground sensor, identifying a clear instrumentation requirement for future aerosol validation work. Temporal matching between satellite overpasses and ground observations achieved a mean time difference of 2.7 min, with 95 % of matches within 8 min of satellite observation time. Spatial co-location analysis confirms all comparison points fall within the nominal TROPOMI pixel footprint (3.5 km ×5.5 km at nadir), though the spatial scale mismatch between the ground sensor field of view and satellite pixel dimensions remains the primary source of comparison uncertainty. Our results demonstrate that low-cost infrared sensors, when operated with calibration-informed measurement protocols, can provide scientifically useful satellite cloud product screening data, reliably distinguishing between clear, partially cloudy, and overcast conditions. The quasi-discrete nature of the derived cloud fraction highlights the need for improved cloud detection algorithms in future work. This approach offers a scalable pathway for expanding ground-based validation networks in regions lacking dedicated atmospheric monitoring infrastructure.
Wolfgang Schneider (Wed,) studied this question.