Abstract
Abstract. We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS)-based radio occultation (RO) measurements. The new algorithm estimates background and observation 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 retrieval 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 CHAllenging Minisatellite Payload (CHAMP) and Constellation Observing System for Meteorology, Ionosphere, and Climate (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 reduction of random errors (standard deviations) of optimized bending angles down to about half 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; and (4) realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall 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.
Highlights
Global Navigation Satellite System (GNSS)-based radio occultation (RO) is a robust atmospheric remote-sensing technique that provides accurate atmospheric profiles of the Earth’s atmosphere (Kursinski et al, 1997; Hajj et al, 2002; Kirchengast, 2004)
For the CHAllenging Minisatellite Payload (CHAMP) and COSMIC data, these co-located reference profiles were extracted from European Centre for Medium-Range Weather Forecasts (ECMWF) analysis fields; for simMetOp data the “true” ECMWF analysis field profiles from the forward modeling were used as a reference
Above 50 km, the differences from both the dynamic algorithm and from COSMIC Data Analysis and Archive Center (CDAAC) are generally larger than those from the OPSv5.6 and b-dynamic approaches. This does not mean that bending angle profiles from the dynamic and CDAAC algorithms are not accurate at high altitudes, ; the result mainly depends on the determination of the weights of the background and observed bending angles in the statistical optimization
Summary
Global Navigation Satellite System (GNSS)-based radio occultation (RO) is a robust atmospheric remote-sensing technique that provides accurate atmospheric profiles of the Earth’s atmosphere (Kursinski et al, 1997; Hajj et al, 2002; Kirchengast, 2004). The observation error covariance matrix is formulated from estimating the observation error at a defined mesospheric altitude range (where the RO signal is weak) and using simple exponential fall-off error correlations (Healy, 2001, Gobiet and Kirchengast, 2004) or again just ignoring the latter These rough estimations generally result in inaccurate error covariance matrices and result in inaccurate optimized bending angles that degrade the accuracy of subsequently retrieved atmospheric profiles. The aim of this study is to obtain even more accurate and reliable atmospheric profiles for optimal climate monitoring by advancing the b-dynamic algorithm to a complete dynamical estimation of both background and observation uncertainties and correlations This is accomplished by employing a realistically estimated observation error covariance matrix in addition to the b-dynamic formulation.
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