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The 'statistically\udoptimal' approach to smoothing bending angles derived from radio occultation\ud(RO) measurements is outlined. This combines a measured bending angle profile\udwith an <i>a priori </i>or background estimate derived from climatology, in\udorder to obtain the most probable bending angle profile. However, the method is\udonly optimal if the error statistics of both the measured and background\udprofiles are known and applied accurately. In this work it is shown that\udcorrelations in the background estimate have a significant role in determining\udthe degree of smoothing in the solution. We find that smooth profiles,\udconsistent with the measured values, can be derived if the correlations are\udapproximated analytically with a Gaussian, assuming a scale length of 6km. In\udregions where the observed and background error levels are comparable, the\udsolutions take the general shape from the background estimate, centred on the\udobservation data. The effects of correlated observation errors are also\udconsidered. It is shown that the quality of the temperature retrievals can be\udsignificantly affected by the choice of climatology used for background\udestimate.  <b>Key words. </b>Atmosphere composition\udand structure (pressure, density and temperature) – Radio science (remote\udsensing

Smoothing radio occultation bending angles above 40 km