Dynamic statistical optimization of GNSS radio occultation bending angles: an advanced algorithm and its performance analysis.

We introduce a new dynamic statistical optimization algorithm to initialize ionospherecorrected bending angles of Global Navigation Satellite System (GNSS) based radio occultation (RO) measurements. The new algorithm estimates background and obser-vation error covariance matrices with geographically-varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform re trieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAMP and COSMIC measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant 15 reduction in random errors (standard deviations) of optimized bending angles down to about two-thirds of their size or more; (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3) improved retrieval of refractivity and temperature profiles; (4) produces realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Over all the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well characterized and high-quality atmospheric profiles over the entire stratosphere. [ABSTRACT FROM AUTHOR]