Abstract

The dominant error source in radio occultation soundings of the upper stratosphere and the mesosphere is residual ionospheric noise. This noise can be significantly reduced by using statistical optimization, in which measured bending angle profiles are combined with a priori or First Guess bendin... Show moreThe dominant error source in radio occultation soundings of the upper stratosphere and the mesosphere is residual ionospheric noise. This noise can be significantly reduced by using statistical optimization, in which measured bending angle profiles are combined with a priori or First Guess bending angle profiles in a statistical optimal manner. First Guess profiles are normally derived from a climate model. In order for this technique to work optimally, the error covariance of the observations and the error covariance of the model must be known, which is generally not the case. It is common practice to assume that each of these errors is uncorrelated. In this study it is shown that if this assumption is applied together with dynamical error estimation, it is important to account for the fact that the First Guess and the observational bending angle errors are not damped equally when refractivity profiles are computed through the Abel transform. It is demonstrated, that the difference in noise damping can be accounted for by simply scaling the ratio of the observational to the First Guess error variances. It is shown that the scaling factor can be related to the ratio between the error correlation lengths of the observational errors and the model errors. We present a simple procedure where variances are estimated dynamically and scaled as described above. This approach is applied to one month of CHAMP occultations and the retrieved refractivity profiles are compared with corresponding profiles derived from ECMWF operational analysis. It is found that in the height range from 30 to 40 km the relative refractivity errors is reduced by approximately 0.25-1 percentage point compared to a ‘standard’ statistical optimization scheme where the relative error in the First Guess bending angle profile is assumed to be 20%. Show less