  The performance of tomographic adaptive optics systems is intrinsically
linked to the vertical profile of optical turbulence. Firstly, a sufficient
number of discrete turbulent layers must be reconstructed to model the true
continuous turbulence profile. Secondly over the course of an observation, the
profile as seen by the telescope changes and the tomographic reconstructor must
be updated. These changes can be due to the unpredictable evolution of
turbulent layers on meteorological timescales as short as minutes. Here we
investigate the effect of changing atmospheric conditions on the quality of
tomographic reconstruction by coupling fast analytical adaptive optics
simulation to a large database of 10 691 high resolution turbulence profiles
measured over two years by the Stereo-SCIDAR instrument at ESO Paranal, Chile.
This work represents the first investigation of these effects with a large,
statistically significant sample of turbulence profiles. The statistical nature
of the study allows us to assess not only the degradation and variability in
tomographic error with a set of system parameters (e.g. number of layers,
temporal update period) but also the required parameters to meet some error
threshold. In the most challenging conditions where the profile is rapidly
changing, these parameters must be far more tightly constrained in order to
meet this threshold. By providing estimates of these constraints for a wide
range of system geometries as well as the impact of different temporal
optimisation strategies we may assist the designers of tomographic AO for the
ELT to dimension their systems.
