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Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

DOI:10.5194/amt-11-111-2018 期刊:Atmospheric Measurement Techniques 出版年份:2018 更新时间:2025-09-10 09:29:36
摘要: A new reference occultation processing system (rOPS) will include a Global Navigation Satellite System (GNSS) radio occultation (RO) retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle (BA) retrieval in the lower troposphere and introduce (1) an empirically estimated boundary layer bias (BLB) model then employed to reduce the systematic uncertainty of excess phases and bending angles in about the lowest 2 km of the troposphere and (2) the estimation of (residual) systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF) analysis fields. It is formulated in terms of predictors and adaptive functions (powers and cross products of predictors), where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform (CT) amplitude, and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction and capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower bounded by the uncertainty from the (indirect) use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The systematic and random uncertainties are propagated from excess phase to bending angle profiles, using a perturbation approach and the wave-optical method recently introduced by Gorbunov and Kirchengast (2015), starting with estimated excess phase uncertainties. The results are encouraging and this uncertainty propagation approach combined with BLB correction enables a robust reduction and quantification of the uncertainties of excess phases and bending angles in the lower troposphere.
作者: Michael E. Gorbunov,Gottfried Kirchengast
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To develop a new reference occultation processing system (rOPS) with integrated uncertainty propagation for GNSS radio occultation (RO) retrieval, focusing on wave-optics bending angle (BA) retrieval in the lower troposphere, including the introduction of an empirically estimated boundary layer bias (BLB) model and the estimation and propagation of systematic and random uncertainties from excess phase to bending angle profiles.

The study successfully developed a regression-based approach for modeling and propagating the atmospheric boundary layer biases and associated systematic uncertainties within the wave-optical retrieval chain. The BLB correction was found to be very effective for bias reduction, capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The results are encouraging for follow-on work to provide a refined BLB model design and a detailed inspection and validation of the complete wave-optical retrieval and uncertainty propagation.

The study acknowledges that the BLB model cannot be looked at as a complete explanation of the bias and that the systematic uncertainty propagation does not work sufficiently well due to the large-scale nature of such profiles not transforming smoothly under FIO operations. Additionally, the model uses ECMWF fields as a reference, which have their own systematic deviations from the truth.

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