In the stratosphere, the method globally reduces standard deviation (StD) with respect to a well-tried ozone climatology by around 40%. Tropospheric ozone can also be retrieved in many cases, reducing the StD by 10-30% globally. The neural network was found capable of correcting for instrument degradation, pixel cloudiness and scan angle effects on its own, based on the input data provided. Remaining inhomogeneities in the geographical distribution of training data, combined with differing ozone field variability causes retrieval quality to vary with latitude and season.
Since retrieval only requires one forward propagation through the network, NNORSY is about 103-105 times faster than classical, local retrieval techniques like, for instance, Optimal Estimation. Therefore the method is well suited for real-time application and fast data reprocessing. An operational near-real-time prototype of the system is already running successfully at three GOME receiving stations.
In order to better characterize single output ozone profiles, a number of local error estimation methods has been investigated. Vertical resolution of the profiles was assessed empirically in comparisons with the high-resolution collocations, and seems to be in the order of 4-6 km.
Further developments of NNORSY could involve improvement of training data composition, input parameter optimization, more sophisticated network error functions and training methods, as well as adaptation to other sensors.