The Radio Occultation Experiment aboard CHAMP: Operational Data Analysis and Validation of Vertical Atmospheric Profiles

Abstract

The operational data analysis of the GPS radio occultation experiment aboard the German CHAMP (CHAllenging Minisatellite Payload) satellite mission is described. Continuous Near-Real-Time processing with average time delay of @5 hours between measurement and provision of analysis results is demonstrated. A delay of less than 3 hours is reached for individual events. This is made possible by using an operationally operated ground infrastructure, consisting of a polar downlink station, a globally distributed fiducial GPS ground network, a precise orbit determination facility, an automated occultation processing system and an advanced data center (the Information System and Data Center at GFZ, ISDC). The infrastructure was installed within the CHAMP and the German GPS Atmosphere Sounding Project (GASP). More than 120,000 globally distributed occultation measurements were automatically analysed during 2001 and 2002. A set of @46,000 vertical profiles of refractivity, temperature and water vapor is validated with meteorological analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and data from the global radiosonde network. The mean temperature bias in relation to the analyses is less then 0.4 K between 10 and 35 km, the mean deviation of the refractivity is <0.5%. A height dependent standard deviation of @1 K at 10 km and @2 K at 30 km is observed. This result is confirmed by comparing @6,000 CHAMP occultations with corresponding radiosonde measurements. A negative bias of the refractivity in relation to the analyses up to @5% in the Tropics is found in the lower troposphere. It corresponds to mean meridional dry biases of the specific humidity up to @30%. It is shown, that the application of a heuristic retrieval method, based on the Canonical Transform method and the sliding spectral approach, reduces the refractivity bias on average by a factor of @2. The corresponding bias in the specific humidity is reduced by a factor of @3. In mid-latitudes almost no more refractivity bias out of the planetary boundary layer is observed. This is shown by a comparison of

GPS Radio Occultation: Results from CHAMP, GRACE and FORMOSAT-3/COSMIC

“…CHAMP and GRACE-A or bit and occultation data are op er a tion ally an a lyzed by GFZ be gin ning with raw data (GPS ob ser va tions of the ze nith and occultation an tenna) up to higher level data prod ucts, the glob ally dis trib uted ver tical pro files of at mo spheric pa ram e ters (see, e.g., Wickert et al 2004a). Ion o spheric occultation data are an a lyzed at the Ger man Aero space Cen ter (DLR) Neustrelitz, Ger many (e.g., Jakowski 2005).…”

“…The op er a tional ca pa bil ity and in ter play of these com po nents al low for the dem on stra tion of a near-real time (NRT) oc cultation data anal y sis, which is al most con tin u ously dem on strated with CHAMP and GRACE-A (see also sec tion 3.4). A more de tailed over view on the occultation infrastructure and rel e vant ref er ences are given by Wickert et al (2004a).…”

Quantifying residual ionospheric errors in GNSS radio occultation bending angles based on ensembles of profiles from end-to-end simulations

“…The climatological analyses of CHAMP data revealed an accuracy of 0.4 K in the global-mean temperature in the UTLS and a 1 K standard deviation at an altitude of 10 km that increased to 2 K at 30 km (Wickert et al, 2004). A comparison between COSMIC RO results and co-located radiosonde data showed high agreement in temperature profiles, with less than 0.5 K mean differences and less than 2.0 K standard deviations of all the bins (He et al, Figure 1.…”

A comparison of lower stratosphere temperature from microwave measurements with CHAMP GPS RO data

“…CHAMP is a German GPS RO satellite, which has produced stable and accurate measurements of high vertical resolution temperature profiles since 2001 [Wickert et al, 2004]. Since CHAMP sample size is much smaller, and they are taken at different local times in different locations than those of MSU/AMSU data, we binned CHAMP data into the RSS and UAH grid boxes to minimize the temporal, spatial and sampling differences between CHAMP and MSU/AMSU data.…”