Abstract Detail

Radio occultation processing at the Wegener Center: Validation results and first long-term time series of rOPS

Presenter:
Marc Schwärz
Wegener Center, University of Graz
Co-authors:
Gottfried Kirchengast, Veronika Proschek, Josef Innerkofler, Florian Ladstädter, and Armin Leuprecht
Wegener Center, University of Graz

Talk

The new Reference Occultation Processing System (rOPS), developed at the Wegener Center over the last couple of years, aims to process raw Global Navigation Satellite System (GNSS) radio occultation (RO) measurements through a complete retrieval chain into profiles of essential climate variables (ECVs). We base this solution on the SI-traceablity of GNSS RO data, which are available since 2001 from a range of RO missions and scheduled long-term into the future.
rOPS is a core project at the WEGC, supported also by colleagues from EUMETSAT Darmstadt, ECMWF Reading, UCAR Boulder, JPL Pasadena, DMI Copenhagen, AIUB Berne, NSSC Beijing, and others. It aims to rigorously implement the traceability, and the quantification of accuracy and precision, by processing along the full chain from the SI-tied raw data to the ECVs (temperature, pressure, and tropospheric water vapor), integrating a complete uncertainty propagation, both for estimated random and systematic uncertainties.
In this presentation we start with a brief introduction of the design of rOPS including the processing setup of the first reprocessing using rOPS. After that we show the validation results of representative example months using different validation datasets (e.g., radiosondes and ERA-5) as well as different RO missions. The focus of this validation study lies in the evaluation of the consistency between the different datasets utilizing the capabilities of rOPS, i. e., the integrated uncertainty propagation which allows us to track measurement uncertainties through the full processing chain resulting in uncertainty estimates for intermediate retrieved profiles and the final ECV results.
We also present preliminary comparisons between rOPS long-term time series results and other RO and reference datasets.

Presentation in PDF:

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