Homogenization of the global radiosonde temperature and wind dataset using
innovation statistics from reanalyses
Leopold Haimberger1, Christine Gruber1, Stefan Sperka1 and Christina Tavolato2
1

Department of Meteorology and Geophysics, University of Vienna, Austria
2
European Centre for Medium-Range Weather Forecasts, Reading, UK
Leopold.haimberger@univie.ac.at

Radiosonde temperature and wind data have been the backbone of the global upper air observing
system at least until the 1990s. However, shifts in the raw radiosonde records due to changes inobserving practice limit their use for climate analysis or as input in reanalyses.
During the past four years an automatic homogenization method (RAdiosonde OBservation
COrrection using REanalyses, RAOBCORE, Haimberger 2007a, current version is 1.4) for the global
radiosonde dataset has been developed. RAOBCORE analyzes time series of differences between
observations and ERA-40 and the operational ECMWF background (BG) forecasts. These differences
are called innovations and are used for detection and adjustment of inhomogeneities in temperatureand wind. The ERA-40 background is therefore used as reference for homogenization.

Fig. 1: Time series of 100 hPa wind direction innovations (obs-ERA-40 up to 2000, obs-ECMWF operational modelfrom 2001 onwards, red curve) at Russian station Aktjubinsk and test statistic of Standard Normal Homogeneity Test
(blue curve, right axis, values above 20 significant). Breaks are caused by erroneous north alignment of station
between 1973 and 1991.

Fig. 2: Vertical profiles of break estimates in wind direction at breakpoints in 1973, 1991 detected in Fig. 1. Black:00GMT, Red: 12GMT, Symbols: Values at individual levels, Lines: vertical averages.

Fig. 3: Map of radiosonde stations with detected wind inhomogeneities during period 1958-2001. Black: Stationswith breaks in north alignment, Grey: Stations with breaks in other wind parameters, e.g. wind speed. From Gruberand Haimberger (2008)

Recently we have also tried to homogenize the global radiosonde wind dataset (see Gruber andHaimberger, 2008 for details). Figs. 1 and 2 illustrate an example of breaks in observed radiosonde
wind data. The direction bias estimate shown in Fig. 2 is vertically constant, which indicates that theshifts are caused due to changes in the station north alignment. More than 200 cases of stations northalignment shifts of at least 3 degrees could be detected and adjusted during the period 1958-2001.
Breaks in u,v or wind speed could be also detected (in more than 1000 cases, see Fig. 3) and adjusted.
There are only few indications of inhomogeneities in the ERA-40 background wind, so that we havemuch confidence in the wind homogenization. Despite the relatively large number of breaks, nosystematic effect of the adjustments on the global mean wind climatology could be detected so far.
Wind direction errors seem a good candidate for online bias estimation since vertically constantsystematic forecast model errors in wind direction are unlikely.
When applied to temperature, RAOBCORE leads to improved spatiotemporal consistency of thehomogenized observations, but care is needed during some periods when the homogeneity of the
background forecast time series is questionable, especially around 1975, in the early 1980s and 1986.
Nevertheless large and pervasive biases in the radiosonde temperature dataset, especially before themid-1990s, could be safely detected and adjusted. Comparisons with MSU-derived layer mean
temperature records also show the robustness of the method (Haimberger et al, 2008). The MSU-4 andMSU-3 equivalent anomaly difference time series of station Yap (91413) in Fig. 4 are an example.
They show a large break at the end of 1995 caused by a change from VIZ-B to Vaisala RS80.
RAOBCORE v1.4 adjusted time series shows good agreement with ERA-40 bg and satellite timeseries. In order to eliminate the formal dependency of the break size estimation on the ERA-40
background, a neighbor intercomparison method (RICH = Radiosonde innovation composite
homogenization, see HTS08) has also been implemented.

Fig. 4: Time series of monthly mean 00GMT temperature anomaly differences with respect to RSS MSU satellitedata, a) for the LS (MSU4) and b) for the TS (MSU3) layer at station Yap (91413, tropical west Pacific).
Differences are: RAOBCORE-RSS (light blue), UAH-RSS (orange), BG-RSS (green), RICH-RSS (pink),
HadAT2-RSS (dark red) and unadjusted (RS) data-RSS (dark blue). Triangles at bottom of panels indicate
documented radiosonde instrumentation changes. Corresponding decadal trend differences 1979-2006 (LS) and1987-2006 (TS) are plotted in the right panels. From Haimberger et al. (2008).

Fig. 5: Estimates of break profile caused by change from VIZ-B to Vaisala RS80 at Pacific station Yap in the western
tropical Pacific. Blue curve in both panels: RAOBCORE v1.4 estimate, Orange curve: Weighted mean of breakestimates gained by comparison of temperature anomalies at Yap with neighboring reference stations (WMO IDs inupper right corner). Green curve: Weighted mean of break estimates gained by comparison of temperature
innovations (obs-ERA-40 background) with the same neighboring stations. Black lines are individual break size
estimates from neighboring stations. Note smaller variance in right panel.

Fig. 6: Time series of tropical (20S-20N) mean MSU LS/TS temperature anomaly differences RSSRAOBCORE (red), UAH-RAOBCORE (orange), ERA-40 bg-RAOBCORE (green), RICH-RAOBCORE(pink), HadAT2-RAOBCORE (dark red) and unadjusted radiosonde (RS) minus RAOBCORE (dark blue).
Corresponding trend differences are shown in the right panels. All means are from data subsampled atradiosonde stations. RAOBCORE trends for the LS and TS layers are -0.4K/decade and -0.04K/decade,respectively.

Fig. 5 gives an example how comparison with temperature anomalies or temperature innovations
from neighboring stations can yield a sensible break estimate. Using innovations yields smaller
variance but is not strictly independent of BG. Therefore anomalies are used in all other figures
shown. RICH estimates show good agreement with the RAOBCORE estimates in the case of Fig. 4but also for most other individual stations, except in the southern extratropcics. As is shown in Figs 6,the RAOBCORE and RICH estimates agree much better with satellite data than unadjusted
radiosonde data and also better than HadAT in the MSU4 and MSU3 equivalent layers. The agreement
is also better in the MSU2 and MSU2LT layers (not shown). The vertical profiles of temperature
trends derived from both RAOBCORE and RICH also show better agreement with climate model
predictions than earlier radiosonde datasets (Santer et al. 2008, to be submitted to Int. J. Climatology).
The present temperature adjustment dataset (RAOBCORE v1.4) is available for climate studies andas input in future reanalyses from 1958 onwards. It is successfully used in ERA-Interim and MERRA(see articles by Uppala and Schubert in this volume). Fig. 7 summarizes the tropical mean departure
statistics of radiosonde temperature compared to the ERA-Interim background forecasts in January
1989. Mean as well as rms departures are smaller when using RAOBCORE adjustments. Also thenumber of assimilated observations is increased. Using RAOBCORE adjustments leads to about
0.3-0.4K cooler analyses in the lower stratosphere (not shown, see Haimberger, 2007b).

Fig. 7: Radiosonde temperature background departure (=innovation, solid) and analysis departure (dotted) statistics
from ERA-Interim assimilation experiments 1151 (red, with RAOBCORE bias correction) and 1166 (black, without
radiosonde temperature bias correction). “nobsexp” is number of observations accepted by the DA system, “exp-ref”
is negative if more observations are assimilated by 1151.

The RAOBCORE/RICH approach needs a pilot climate data assimilation that generates
background forecasts before it can be applied to the pre-1958 period. Surface pressure only reanalysis(Compo, this volume) background forecasts could be used as reference for this purpose. This would
allow homogenization of upper air data from the 1938-1958 period as provided by NCAR, IGRA, orBronnimann (2003). A homogenized upper air dataset could be quite useful for a future full extended
reanalysis from 1938 onwards. It is also intended to use ERA-Interim and JRA25 (Onogi et al. 2007)
innovation to check the sensitivity of RAOBCORE to the background forecast time series used.
References:Bronnimann, S., 2003: A historical upper air-data set for the 1939-44 period. Int. J. Climatol., 23, 769–791.
Gruber, C. and L. Haimberger, 2008: On the homogeneity of radiosonde wind time series. Meteorol. Z.,
accepted with minor revisions.
Haimberger, L., 2007a: Homogenization of radiosonde temperature time series using innovation statistics. J.
Climate. 20 1377-1403.
Haimberger, L., 2007b: Interim report 2007 of special project ”Homogenization of the global radiosondetemperature and wind dataset”. Available from ECMWF via
http://www.ecmwf.int/about/special_projects/haimberger_temp_wind_dataset/report_2007.pdf.
Haimberger, L., C. Tavolato and S. Sperka, 2008: Towards elimination of the warm bias in historic radiosondetemperature records - some new results from a comprehensive intercomparison of upper air data, J. Climate
21, in press
Onogi, K., J. Tsutsui, H. Koide, M. Sakamoto, S. Kobayashi, H. Hatsushika, T. Matsumoto, N.Yamazaki, H.
Kamahori, K. Takahashi, S. Kadokura, K. Wada, K. Kato, R. Oyama, T. Ose, N. Mannoji, and R. Taira,2007: The JRA-25 reanalysis. J. Met. Soc. Japan, 85, 369–432. DOI: 10.2151/jmsj.85.369.
Santer, B.D., P.W. Thorne, L. Haimberger, K.E. Taylor, T.M.L. Wigley, J.R. Lanzante, S. Solomon, M.
Free, P.J. Gleckler, P.D. Jones, T.R. Karl, S.A. Klein, C. Mears, G.A. Schmidt, D.J. Seidel, S.C.
Sherwood and F.J. Wentz, 2008: Consistency of Modelled and Observed Temperature Trends in the
Tropical Troposphere, to be submitted to Int. J. Climatology
Uppala, S.M., et al. 2005: The ERA-40 Re-analysis. Quart. J. Roy. Meteor. Soc., 131, 2961–3012.