Cross‐validation of MIPAS/ENVISAT and GPS‐RO/CHAMP temperature profiles

Abstract

[1] The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on board the ENVISAT and the Global Positioning System (GPS) receiver on the Challenging Mini-Satellite Payload (CHAMP) provide temperature profiles by limb-viewing midinfrared emission and radio occultation (RO) measurements, respectively. The MIPAS temperatures retrieved at the Institut für Meteorologie und Klimaforschung (IMK) are compared with the GPS-RO/CHAMP observations derived at Jet Propulsion Laboratory (JPL) and GeoForschungsZentrum (GFZ) Potsdam. The three data sets show generally good agreement. The global mean differences averaged between 8 and 30 km in 14 days of September/October 2002 are À0.44 ± 0.02 K and 0.07 ± 0.02 K for MIPAS/GPS-RO JPL and GFZ comparisons, respectively. The MIPAS global mean temperatures below 25 km are slightly lower than those of GPS-RO JPL and GFZ by less than 1 K and 0.2 K, respectively. Above 25 km, the MIPAS temperatures are higher than the JPL and GFZ data, in particular near both poles and the equator, with maxima of 1 K for JPL and 1.5 K for GFZ at 30 km. The standard deviations are $2-4 K. Possible explanations for the observed differences include (1) effect of spatial and temporal mismatch between the correlative measurements on the observed standard deviations, in particular in regions and episodes of enhanced wave activity; (2) a negative bias in GPS-RO/CHAMP temperatures in regions of increased humidity; (3) a mapping of initialization temperature profiles on GPS-RO/CHAMP retrievals at altitudes where low refraction contains no information on air density; and (4) measurement errors of both instruments, particularly the errors due to insufficient knowledge of the instrument line shape and spectroscopy in current MIPAS retrievals.

GPS radio occultation with CHAMP and SAC-C: global monitoring of thermal tropopause parameters

“…A similar study was performed by Kuo et al (2005). A cross-validation of MI-PAS/ENVISAT and CHAMP RO temperature profiles for 2 weeks in September/October 2002 shows global mean differences averaged between 8 and 30 km of less than 0.1 K (Wang et al, 2004). For the data quality of SAC-C temperature we refer to an intercomparison study for CHAMP and SAC-C (Hajj et al, 2004) that showed for nearby profiles (distance d≤200 km and time delay t ≤30 min) an agreement within 0.1 K in the mean between 5 and 15 km altitude.…”

Retrieval of temperature profiles from CHAMP for climate monitoring: intercomparison with Envisat MIPAS and GOMOS and different atmospheric analyses

“…Based on these measures, CCR aims to extend the altitude range of reliable, accurate retrieval results (currently up to about 25 km; see Sect. 4; Wang et al, 2004;Wickert et al, 2004;von Engeln, 2006) towards the upper stratosphere (up to about 35 km). The development of the algorithmic basis and careful performance assessment via end-to-end simulation studies by Gobiet and Kirchengast (2004) and Steiner and Kirchengast (2005) have demonstrated that this improvement potential exists.…”

“…Evaluation studies showed that RO temperatures are generally derived with an accuracy of better than 1 K (e.g., Hajj et al, 2002;Wickert et al, 2004;Steiner et al, 2004) though it has also been found that RO retrieval results of major processing centres show, compared to other remote sensing instruments, radiosondes, and amongst themselves, biases exceeding 1 K above 25 km (e.g., Wang et al, 2004;Wickert et al, 2004;von Engeln, 2006) which is mainly due to the methodology of integrating a priori information in the highaltitude initialisation of the retrieval process as will be discussed in this study. Biases induced by downward propagated a priori information are a particular concern for climate applications like, e.g., long-term trend studies, since a priori data (often meteorological analyses) are on one hand particularly prone to model errors due to sparse observational data at high altitudes, and on the other hand they are far from long-term stable due to frequent model, observational data, and data assimilation system changes and improvements.…”

“…Particularly the high vertical resolution and accuracy in regions where so far predominantly rather low vertical resolution satellite-based data from nadir looking instruments are available (e.g., over remote oceanic areas and in polar regions) opens new possibilities for the evaluation and advancement of analyses and forecasts of numerical weather prediction (NWP) systems (e.g., Gobiet et al, 2005;Healy et al, 2005;Borsche et al, 2007;Healy and Thépaut, 2006) and for stratospheric process studies (e.g., Alfred et al, 2006). In addition, its "self-calibrating" nature makes it ideal for providing benchmark datasets for the validation of other remote sensing systems (Schroeder et al, 2003;Wang et al, 2004Wang et al, , 2005Steiner et al, 2007 1 ) the evaluation and advancement of climate models (Goody et al, 1998;Leroy et al, 2006) and for climate monitoring in general .…”

Comparison of ECMWF analyses with GPS radio occultations from CHAMP

“…Steiner et al, 2007;Gobiet et al, 2007;Wickert, 2004a;Wang et al, 2004;Hajj et al, 2004). Special comparisons of tropopause parameters between CHAMP and ECMWF can be found in, e.g.…”