Articles | Volume 8, issue 1
Research article
09 Jan 2015
Research article |  | 09 Jan 2015

An evaluation of COSMIC radio occultation data in the lower atmosphere over the Southern Ocean

L. B. Hande, S. T. Siems, M. J. Manton, and D. H. Lenschow

Abstract. The global positioning system (GPS) radio occultation (RO) method is a relatively new technique for taking atmospheric measurements for use in both weather and climate studies. As such, this technique needs to be evaluated for all parts of the globe. Here, we present an extensive evaluation of the performance of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) GPS RO observations of the Southern Ocean boundary layer. The two COSMIC products used here are the "wetPrf" product, which is based on 1-D variational analysis with European Centre for Medium-Range Weather Forecasts (ECMWF), and the "atmPrf" product, which contains the raw measurements from COSMIC. A direct comparison of temporally and spatially co-located COSMIC profiles and high resolution radiosonde profiles from Macquarie Island (54.62° S, 158.85° E) highlights weaknesses in the ability of both COSMIC products to identify the boundary layer structure, as identified by break points in the refractivity profile. In terms of reproducing the temperature and moisture profile in the lowest 2.5 km, the "wetPrf" COSMIC product does not perform as well as an analysis product from the ECMWF. A further statistical analysis is performed on a large number of COSMIC profiles in a region surrounding Macquarie Island. This indicates that, statistically, COSMIC performs well at capturing the heights of main and secondary break points. However, the frequency of break points detected is lower than the radiosonde profiles suggest, but this could be simply due to the long horizontal averaging in the COSMIC measurements. There is also a weak seasonal cycle in the boundary layer height similar to that observed in the radiosonde data, providing some confidence in the ability of COSMIC to detect an important boundary layer variable.

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