Empirical analysis and modeling of errors of atmospheric profiles from GPS radio occultation

Abstract

Abstract. The utilization of radio occultation (RO) data in atmospheric studies requires precise knowledge of error characteristics. We present results of an empirical error analysis of GPS radio occultation (RO) bending angle, refractivity, dry pressure, dry geopotential height, and dry temperature. We find very good agreement between data characteristics of different missions (CHAMP, GRACE-A, and Formosat-3/COSMIC (F3C)). In the global mean, observational errors (standard deviation from "true" profiles at mean tangent point location) agree within 0.3 % in bending angle, 0.1 % in refractivity, and 0.2 K in dry temperature at all altitude levels between 4 km and 35 km. Above ≈20 km, the observational errors show a strong seasonal dependence at high latitudes. Larger errors occur in hemispheric wintertime and are associated mainly with background data used in the retrieval process. The comparison between UCAR and WEGC results (both data centers have independent inversion processing chains) reveals different magnitudes of observational errors in atmospheric parameters, which are attributable to different background fields used. Based on the empirical error estimates, we provide a simple analytical error model for GPS RO atmospheric parameters and account for vertical, latitudinal, and seasonal variations. In the model, which spans the altitude range from 4 km to 35 km, a constant error is adopted around the tropopause region amounting to 0.8 % for bending angle, 0.35 % for refractivity, 0.15 % for dry pressure, 10 m for dry geopotential height, and 0.7 K for dry temperature. Below this region the observational error increases following an inverse height power-law and above it increases exponentially. The observational error model is the same for UCAR and WEGC data but due to somewhat different error characteristics below about 10 km and above about 20 km some parameters have to be adjusted. Overall, the observational error model is easily applicable and adjustable to individual error characteristics.