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. In this paper, we discuss the method for validation of random uncertaintiesin the remote sensing measurements based on evaluation of the structurefunction, i.e., root-mean-square differences as a function of increasingspatiotemporal separation of the measurements. The limit at the zeromismatch provides the experimental estimate of random noise in the data. Atthe same time, this method allows probing of the natural variability of themeasured parameter. As an illustration, we applied this method to theclear-sky total ozone measurements by the TROPOspheric Monitoring Instrument(TROPOMI) on board the Sentinel-5P satellite. We found that the random uncertainties reported by the TROPOMI inversionalgorithm, which are in the range 1–2 DU, agree well with the experimentaluncertainty estimates by the structure function. Our analysis of the structure function has shown the expected results ontotal ozone variability: it is significantly smaller in the tropics comparedto mid-latitudes. At mid-latitudes, ozone variability is much larger inwinter than in summer. The ozone structure function is anisotropic (beinglarger in the latitudinal direction) at horizontal scales larger than10–20 km.The structure function rapidly grows with the separation distance. Atmid-latitudes in winter, the ozone values can differ by 5 % atseparations 300–500 km. The method discussed is a powerful tool in experimental estimates of therandom noise in data and studies of natural variability, and it can be usedin various applications.

atmospheric measurement techniques

A method for random uncertainties validation and probing the natural variability with application to TROPOMI on board Sentinel-5P total ozone measurements