ROTrends

Project Description

The RO Trends Intercomparison Working Group was established in 2006. It is an international collaboration of RO processing centres focused on intercomparisons of RO multi-year data records for a systematic assessment of accuracy and data quality. The aim is to validate RO as a climate benchmark by demonstrating that trends in RO products are essentially independent of retrieval centre. A first step to reach this aim is the quantification of structural uncertainty (SU) in RO products arising from different processing schemes. Quantification of SU as one property of a climate benchmark data type is regarded an essential advance towards a multi-year RO climate record with complete error description.

A first intercomparison study was performed for refractivity data provided by four different processing centres, GFZ, JPL, UCAR, and WEGC, for the CHAMP record 2002 to 2006 [2]. SU was found <0.03% per 5 years for refractivity trends in large-scale monthly and zonal-mean refractivity fields.

In a second intercomparison six processing centres, DMI/ROM SAF, EUMETSAT, GFZ, JPL, UCAR, and WEGC, contributed re-processed data products for the common CHAMP period 09/2001 to 09/2008. SU was quantified for the full set of atmospheric RO variables including bending angle, refractivity, dry pressure, dry geopotential height, and dry temperature. Since RO products are available as individual profiles and as gridded fields we carried out two studies each focusing on one product.

In a profile-to-profile intercomparison (PPC) [3] we used exactly the same set of profiles from each data centre, i.e., a subset of profiles from each centre’s profile products (Data centres basically use the same raw measurements as input but they provide a different number of output profiles due to different quality control procedures in their processing). A detailed description of the processing schemes of the different centres is provided to motivate further study of SU and how it varies for the different processing steps, from bending angle to temperature profile retrievals.

In the intercomparison study of RO gridded climate records, the full set of profiles provided by each centre was used and then averaged to monthly and zonal-mean climatological fields (MMC) [1]. The sampling error due to discrete sampling in space and time was estimated and subtracted from the climatologies. SU was quantified for the fields at full altitude resolution (200m vertical grid) as well as for altitude layer averages. A simple table representation of the all-center mean standard deviation with 100m resolution for Bending Angle, Refractivity, Pressure, Geopotential Height, Temperature
of [1] is available here for download.

SU was found lowest within 50°S to 50°N at 8 km to 25 km for all inspected RO variables. In this region, the SU in trends over 7 years is <0.03% for bending angle, refractivity, and pressure, <3 m for geopotential height of pressure levels, and <0.06 K for temperature. SU increases above 25 km and at high latitudes, mainly due to different bending angle initialisation (which is required for further processing to refractivity, temperature, geopotential height, pressure) in the centres’ processing schemes. The more retrieval steps are required for the RO variables, the lower is the altitude where larger differences occur. At high latitudes larger residual sampling error due to higher atmospheric variability is also a factor in the analysis.

The results for gridded climate records [1] are consistent with those for individual profiles [3] and with former results [2], indicating that residual sampling error is small to negligible over the regions with a lower atmospheric variability. The consistency of findings from the different studies, based on different data versions and methodological approaches, underpins the quality of RO data and their utility for climate studies.

Currently GPS RO can be used for climate trend assessments within the region from 50°S to 50°N and below 25 km altitude, where SU meets stability requirements for air temperature as defined by the Global Climate Observing System (GCOS) program [4] as well as corresponding estimated requirements for the other RO variables. Since RO processing systems undergo continuous development, further improvements and reductions in SU are expected. The RO Trends project accepts as its goal a reduction of SU based on improved understanding of the different contributing error sources. Reduction of SU per se is not the goal of RO Trends.

The data records used in [3] and [1] will be made available to the broader science community, including:

• profile data, gridded climatologies and sampling error estimates (netCDF files);
• bending angle, refractivity, dry pressure, dry geopotential height, dry temperature;
• of the centres DMI, EUM (only bending angle), GFZ, JPL, UCAR, WEGC.

Future plans of the RO Trends working group include studies on:

• multi-satellite consistency, to analyse SU across different instruments and missions as revealed by different processing centres;
• upper stratosphere retrievals, by analyzing intermediate processing products, to determine how SU may be introduced in this critical initialization region;
• quality control, to understand why there are significant differences (~50%) in the number of quality controlled profiles generated at the different processing centres, providing further information on processing differences and SU.

The ROTrends activity is now also hosted under the SCOPE-CM project RO-CLIM.

Websites of ROTrends Collaborators

References

[1] Steiner, A. K., D. Hunt, S.-P. Ho, G. Kirchengast, A. J. Mannucci, B. Scherllin-Pirscher, H. Gleisner, A. von Engeln, T. Schmidt, C. Ao, S. S. Leroy, E. R. Kursinski, U. Foelsche, M. Gorbunov, Y.-H. Kuo, K. B. Lauritsen, C. Marquardt, C. Rocken, W. Schreiner, S. Sokolovskiy, S. Syndergaard, S. Heise, and J. Wickert (2013), Quantification of structural uncertainty in climate data records from GPS radio occultation, Atmos. Chem. Phys., 13, 1469-1484, doi:10.5194/acp-13-1469-2013, http://www.atmos-chem-phys.net/13/1469/2013.

[2] Ho, S.-P., G. Kirchengast, S. Leroy, J. Wickert, T. Mannucci, A. K. Steiner, D. Hunt, W. Schreiner, S. V. Sokolovskiy, C.O. Ao, M. Borsche, A. von Engeln, U. Foelsche, S. Heise, B. Iijima, Y.-H. Kuo, E. R. Kursinski, B. Pirscher, M. Ringer, C. Rocken, and T. Schmidt (2009), Estimating the uncertainty of using GPS radio occultation data for climate monitoring: Inter-comparison of CHAMP refractivity climate records 2002-2006 from different data centers, J. Geophys. Res., 114, D23107, doi:10.1029/2009JD011969.

[3] Ho, S.-P., D. Hunt, A. K. Steiner, A. J. Mannucci, G. Kirchengast, H. Gleisner, S. Heise, A. von Engeln, C. Marquardt, S. Sokolovskiy, W. Schreiner, B. Scherllin-Pirscher, C. Ao, J. Wickert, S. Syndergaard, K. Lauritsen, S. Leroy, E. R. Kursinski, Y.-H. Kuo, U. Foelsche, T. Schmidt, M. Gorbunov (2012), Reproducibility of GPS radio occultation data for climate monitoring: Profile-to-profile inter-comparison of CHAMP climate records 2002 to 2008 from six data centers, J. Geophys. Res., 117, D18111, doi:10.1029/2012JD017665.

[4] GCOS (2006), Systematic observation requirements for satellite-based products for climate, GCOS-107 (WMO/TD No. 1338).