Model-independent retrieval of stratospheric temperature from GNSS radio occultation data
Abstract
Radio occultation (RO) using the transmitters of the Global Navigation Satellite Systems (GNSS) holds great power and promise as a climate monitoring system in that it is calibrated fundamentally by atomic clocks. Retrieval of geophysical variables such as temperature and pressure, however, are subject to null-space error: both the Abel integral, which converts observed profiles bending angles to microwave refractivity, and the hydrostatic integral, which converts profiles of microwave refractivity to pressure, require initial values in the vicinity of the stratopause which are invisible to RO. Various approaches to initialize these integrals have been implemented, including importation from atmospheric models and analyses, assumption of an isothermal atmosphere, and adaptation of RO-based long-term climatologies, but all such approaches introduce biases in temperature that are noticeable in long-term trend analyses. Here I will present an alternative approach to initializing both integrals self-consistently. Markov chain Monte-Carlo is used to determine a best fit bending angle profile to observations under the assumption of a constant temperature lapse rate atmosphere in the upper stratosphere. The algorithm depends solely on the latter assumption, is independent of GNSS receiver signal-to-noise ratio performance. The results of the application of this algorithm for one month of CHAMP and COSMIC-1 data will be shown with a comparison to the results published by two independent retrieval systems in the U.S.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.A13J2932L
- Keywords:
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- 0305 Aerosols and particles;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0319 Cloud optics;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0321 Cloud/radiation interaction;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0360 Radiation: transmission and scattering;
- ATMOSPHERIC COMPOSITION AND STRUCTURE