An assessment of differences in lower stratospheric temperature records from (A)MSU, radiosondes, and GPS radio occultation

Abstract

Abstract. Uncertainties for upper-air trend patterns are still substantial. Observations from the radio occultation (RO) technique offer new opportunities to assess the existing observational records there. Long-term time series are available from radiosondes and from the (Advanced) Microwave Sounding Unit (A)MSU. None of them were originally intended to deliver data for climate applications. Demanding intercalibration and homogenization procedures are required to account for changes in instrumentation and observation techniques. In this comparative study three (A)MSU anomaly time series and two homogenized radiosonde records are compared to RO data from the CHAMP, SAC-C, GRACE-A and F3C missions for September 2001 to December 2010. Differences of monthly anomalies are examined to assess the differences in the datasets due to structural uncertainties. The difference of anomalies of the (A)MSU datasets relative to RO shows a statistically significant trend within about (−0.2 ± 0.1) K/10 yr (95 % confidence interval) at all latitudes. This signals a systematic deviation of the two datasets over time. The radiosonde network has known deficiencies in its global coverage, with sparse representation of most of the Southern Hemisphere, the tropics and the oceans. In this study the error that results from sparse sampling is estimated and accounted for by subtracting it from radiosonde and RO datasets. Surprisingly the sampling error correction is also important in the Northern Hemisphere (NH), where the radiosonde network is dense over the continents but does not capture large atmospheric variations in NH winter. Considering the sampling error, the consistency of radiosonde and RO anomalies is improving substantially; the trend in the anomaly differences is generally very small. Regarding (A)MSU, its poor vertical Correspondence to: F. Ladstädter (florian.ladstaedter@uni-graz.at) resolution poses another problem by missing important features of the vertical atmospheric structure. This points to the advantage of homogeneously distributed measurements with high vertical resolution.

A perspective on the fundamental quality of GPS radio occultation data

“…By analyzing the time-frequency content in the occulted signals, a profile of the ray's bending angle and subsequent profiles of atmospheric refractivity, pressure, and temperature can be derived. In past decades, numerous studies have demonstrated the unique strengths of GPS RO, which include high accuracy and vertical resolution, global coverage, all-weather capability, and self-calibration aptitude (e.g., Kursinski et al, 1997;Hajj et al, 2002;Wickert et al, 2004;Kuo et al, 2004). The data are accepted as an operationally reliable source of information by NWP centers worldwide (Poli et al, 2010), and have shown clear positive impacts on weather forecasting (e.g., Healy, 2008;Buontempo et al, 2008;Cucurull and Derber, 2008;Aparicio et al, 2009;Rennie, 2010) and merits in atmospheric reanalysis projects (Saha et al, 2010;Dee et al, 2011).…”

“…It is therefore appropriate to regard the phase path as the minimally processed data (hereafter simply referred to as measurement, in contrast to derived RO data or retrievals) amongst all data types for which geophysical interpretation is possible. The error of the phase path (measurement error hereafter) depends on many factors such as thermal noise, clock instability, local multipath, receiver performance, observational geometry, and atmospheric condition (Kursinski et al, 1997;Hajj et al, 2002). Depending on the standpoint, errors in orbit ephemerides and those in ionospheric correction can be also considered as a part of the measurement error.…”

“…Thus, the availability of high-quality observations is of the utmost importance, carrying broad socioeconomic implications. Recently, Global Positioning System (GPS) radio occultation (RO) (Melbourne et al, 1994;Ware et al, 1996;Kursinski et al, 1997;Anthes et al, 2008) has been receiving a great deal of attention as a promising source of data for both weather and climate applications. The primary observable of RO is the phase path of GPS signals received by an accurate receiver onboard a low Earth orbiting (LEO) satellite.…”

“…In particular, RO data are assimilated without any bias correction. RO data offer a great potential for weather and climate research (e.g., Kursinski et al, 1997;Anthes et al, 2000;Hajj et al, 2000;Ladstädter et al, 2011), and are recognized as a promising contribution to the climate data record (GCOS, 2010(GCOS, , 2011.…”

“…However, RO data possess retrieval errors (occurring in the course of imperfect atmospheric data processing) as well as measurement errors (those in the primary observable). A number of studies analyzed the error sources and propagation of the errors through the retrieval process (e.g., Kursinski et al, 1997;Feng and Herman, 1999;Syndergaard, 1999;Rieder and Kirchengast, 2001;Hajj et al, 2002;Kuo et al, 2004;. Some of the error sources are particularly hard to tackle including ionospheric residuals (e.g, Syndergaard, 2000), the influence of a priori error on the statistical optimization of bending angle (e.g., Wee and Kuo, 2014), horizontal inhomogeneity in the atmosphere (e.g., Poli and Joiner, 2004), abnormal propagations of radio waves (e.g., Sokolovskiy, 2003), strong multipath effects in the lower troposphere (e.g., Gorbunov et al, 2006), and the ambiguity in separating moisture and dry air density from the bending angle or the refractivity (e.g., Kursinski et al, 1997).…”