A new dynamic approach for statistical optimization of GNSS radio occultation bending angles for optimal climate monitoring utility

TLDR

GNSS‑based radio occultation provides accurate atmospheric profiles, but above 30 km statistical optimization of bending angles is essential for maximizing climate‑monitoring utility. The study introduces a dynamic statistical optimization algorithm that uses multi‑day ECMWF forecast and analysis bending angles, along with averaged observations, to generate daily background profiles and error covariance matrices, and aims to extend this approach to full months and long‑term climate records. The algorithm is evaluated against OPSv5.4 using simulated MetOp and observed CHAMP/COSMIC data for January and July, demonstrating its performance. The new method halves random errors, maintains comparable systematic biases, and improves refractivity and temperature profile errors above 30.

Abstract

Abstract Global Navigation Satellite System (GNSS)‐based radio occultation (RO) is a satellite remote sensing technique providing accurate profiles of the Earth's atmosphere for weather and climate applications. Above about 30 km altitude, however, statistical optimization is a critical process for initializing the RO bending angles in order to optimize the climate monitoring utility of the retrieved atmospheric profiles. Here we introduce an advanced dynamic statistical optimization algorithm, which uses bending angles from multiple days of European Centre for Medium‐range Weather Forecasts (ECMWF) short‐range forecast and analysis fields, together with averaged‐observed bending angles, to obtain background profiles and associated error covariance matrices with geographically varying background uncertainty estimates on a daily updated basis. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.4 (OPSv5.4) algorithm, using several days of simulated MetOp and observed CHAMP and COSMIC data, for January and July conditions. We find the following for the new method's performance compared to OPSv5.4: 1.) it significantly reduces random errors (standard deviations), down to about half their size, and leaves less or about equal residual systematic errors (biases) in the optimized bending angles; 2.) the dynamic (daily) estimate of the background error correlation matrix alone already improves the optimized bending angles; 3.) the subsequently retrieved refractivity profiles and atmospheric (temperature) profiles benefit by improved error characteristics, especially above about 30 km. Based on these encouraging results, we work to employ similar dynamic error covariance estimation also for the observed bending angles and to apply the method to full months and subsequently to entire climate data records.