Validation of Atmospheric Precipitable Water in Three Reanalysis
Products using Ground-based GPS Measurements
Junhong Wang1 and Liangying Zhang1
Earth Observing Laboratory, NCAR, P. O. Box 3000, Boulder, CO 80307, U. S. A.
Correspondence: junhong@ucar.edu
INTRODUCTION
Global atmospheric reanalysis products have played an important role in current climate assessment,
diagnostic studies of climate features, seasonal prediction, and climate predictability. Therefore, thevalidations of the reanalysis products are crucial for improving their accuracy and enhancing theirapplications. However, it is hard to find independent data with high quality and accuracy for the validations
because most of datasets are assimilated by the reanalyses. Main outstanding issue in reanalysis as raised by
the Intergovernmental Panel on Climate Change (IPCC, Trenberth et al. 2007) is the inhomogeneity of input
data (both in-situ and satellite). Although various bias corrections have been applied to the input data, theyremain imperfect.
The current reanalysis still has difficulty in simulating the hydrological cycle (e.g., Trenberth andGuillemot 1998). For instance, all three current reanalysis products (NCEP/NCAR, ERA-40 and JRA-25)
overestimate global mean precipitation by more than 10% (Fig. 7 in Onogi et al. 2007). Previous studies also
found that the reanalyses are deficient in reproducing the variability and trends of atmospheric precipitablewater (PW), especially over the tropical oceans (e.g., Allan et al. 2004; Sudradjat et al. 2005; Trenberth et al.
2005). The conventional surface and radiosonde, and satellite observations are all assimilated into thereanalyses. Therefore, other independent PW data are needed. A global, 2-hourly PW dataset has been createdfrom ground-based GPS measurements (Wang et al. 2007). This dataset has various scientific applications,
including monitoring the quality of radiosonde humidity data and studying PW diurnal variations (Wang et al.
2007; Wang and Zhang 2008a and 2008b). The advantages of GPS-derived PW include the availability under
all weather conditions, high accuracy (< 3 mm), high temporal resolution (5 min to 2-hourly) and, more
importantly, its long-term stability. All of these make the GPS PW dataset appealing for validating thereanalysis PW data. In additional, none of the reanalysis products uses the GPS-PW data, which makes it anindependent dataset for validations. This study evaluates the PW data from three reanalysis products(NCEP/NCAR, ERA-40 and JRA-25) by comparing with the 2-hourly GPS PW dataset from diurnal todecadal time scales.
RESULTSIn this study we use the six-hourly PW data from NCEP/NCAR (NNR), ERA-40 and JRA-25 reanalysis
products (Kalnay et al. 1996; Uppala et al. 2005; Onogi et al. 2007). The ERA-40 and JRA-25 have a T106
horizontal resolution (equivalent to ~1.125ºX1.125º), while the NNR is on a ~2.5ºX2.5º grid. We also use theNNR temperature and humidity profile data on the T62 (~1.875X1.875º) horizontal resolution and 28 hybrid
vertical levels. The 2-hourly GPS PW data are available at ~370 International GNSS (Global Navigation
Satellite System) Service (IGS) stations around the globe from February 1997 to December 2006 and ~170
SuomiNet stations in U.S.A. from 2003 to 2006 (see Fig. 1 in Wang and Zhang 2008b). The following
comparisons between the reanalyses (RAs) and the GPS data are made from February 1997 to December
2006 for NNR and JRA-25 at the IGS stations, from February 1997 to August 2002 for ERA-40 at the IGS
stations, and from January 2003 to December 2006 for NNR and JRA-25 at the SuomiNet stations.
Direct comparisons1Geographic distributions of multi-year annual mean PW values are first compared among threereanalyses. The comparison shows some known features, the ERA-40’s excessive water vapor in the tropics,
the NNR’s dry bias in the tropics and the NNR’s wet bias over subtropical anticyclones (e.g., Trenberth et al.
2005). It also shows that the JRA-25 is drier than both NNR and ERA-40 over central Africa. Then thereanalyses and the GPS data are matched horizontally and temporally for direct comparisons. For thehorizontal matching, the reanalysis grid boxes with GPS stations located inside are selected. For the temporal
matching, two GPS data points close to the reanalysis time are averaged. For example, the reanalysis datapoint at 06 UTC is matched with the average of the GPS data points at 05 UTC and 07 UTC. Figure 1 shows
the histogram of annual mean PW differences (RAs – GPS) at all stations. The NNR and JRA-25 are drierthan the GPS data at over 70% of stations, while the ERA-40 has better agreement with the GPS data. Oneproblem with this direct comparison is that the model surface height can be different from the GPS station
height, and PW is very sensitive to the station elevation (Morland et al. 2006). The high correlations between
the PW differences and the surface altitude differences in Fig. 2a confirm this. The surface altitude difference
can be as much as ~3.5 km. The stations in the upper-left quarter in Fig. 2a are mainly island or coast
mountainous stations, and the ones in the lower-right quarter are in the land high mountain regions whereGPS stations are located near the foot of the mountains or in the valleys. There are two ways to limit thisproblem. One is to restrict the comparisons to the stations with the surface altitude differences less than 100m. The histogram of annual mean PW differences at these stations in Fig. 2b still shows the negative bias in
the NNR and JRA-25 at more than 70% of stations. Another way is to correct the surface altitude differenceby calculating the layer PW from the GPS station ground to the model surface. The temperature and humidity
profiles in the layer are interpolated or extrapolated from the reanalysis profiles using the similar method usedin Wang et al. (2005, Fig. 1). The correction is applied to the NNR data. The correction has the largest
impacts on the stations in the tails of Fig. 1 and changes the mode negligibly (not shown). There are still 71%of stations exhibiting drier NNR values after corrections. In conclusion, the direct comparison suggests thatthe NNR and JRA-25 have a negative bias in PW comparing with the GPS PW. More work needs to be done
to find out where and when the dry bias is more profound and why it exists in the NNR and JRA-25.
Comparisons of PW diurnal variations
The diurnal cycle is one of the most obvious and reliable signals of the climate. However, there is a lackof data with high temporal resolution for studying the diurnal cycle on the global scale. The availability of the2-hourly GPS PW data provides an unprecedented opportunity for us to study the water vapor diurnal
variations and validate the reanalyses. The amplitude and phase of the PW diurnal cycle in U.S.A. and Europe
are presented in Fig. 3 and 4, respectively, for the GPS data and three reanalysis products and for theNorthern Hemisphere summer season (June-July-August). All four data sources show similar geographic
patterns in amplitude in U. S. A., such as the larger amplitude over the Rockies, California, the low-level-jetregion, and the east coast. The ERA-40 has the largest amplitude. The NNR has the best agreement with theGPS data in phase, showing the peaks from the late afternoon to early evening in the east, early morning overthe Great Plains, and from 18 LST to 24 LST over the Rockies (Fig. 3). The JRA-25 is unable to reproduce
the spatial variations of the diurnal phase and is dominated by the afternoon to the mid-night maximum overmost of the U.S.A. The land-water contrast (Coast Ocean and lakes) in PW diurnal phase is pronounced in allreanalysis data.
The GPS data show the consistent phase at most of GPS stations (~12-20 LST) in Europe (Fig. 4). As aresult, the diurnal cycle at all stations can be averaged to produce mean diurnal cycle in Europe (see Fig. 5).
The PW diurnal amplitude is smaller in the NNR and JRA-25, especially in the South. The PW in the NNRand ERA-40 peaks several hours latter than the GPS PW. All three reanalyses again show the land-water
contrast in phase, from the afternoon to night over land and in the morning over water. Seasonal variations ofdiurnal and sub-monthly PW variability in Europe are computed by averaging the data at all stations (Fig. 5).
The diurnal anomalies are calculated from the fitted diurnal harmonics using derived amplitude and phase.

2The GPS data shows that the PW diurnal cycle in Europe is strongest in summer with an amplitude of ~0.6mm but weakest in spring; the local time of the maximum value makes a transition from early evening(~2000-2200 LST) in summer to late afternoon (~1600-1800 LST) in autumn and then to before noon (10001200 LST) in winter. The PW diurnal cycle is very weak and poorly represented in amplitude, phase andseasonal variation in the NNR and JRA-25. The ERA-40 has good agreement with the GPS data in summer,
but fails to show the seasonal phase transition evident in the GPS data. The sub-monthly variability is muchlarger than the diurnal variation. That is not surprising because the sub-monthly variability is mainly a resultof weather systems, whereas various competing factors can contribute to the diurnal variation. All threereanalysis datasets perform well in predicting sub-monthly variability and its seasonal and diurnal changes.
Bock et al. (2007) found that the PW diurnal cycle is poorly represented in ERA-40 in Africa.
Comparisons in long-term variations
Thirteen GPS stations are found to have complete 10-year (1997-2006) PW record. Monthly mean PW
anomalies at 13 stations from 1997 to 2006 from three reanalyses are well correlated with ones from the GPS
dataset with correlation coefficients of larger than 0.8 at all stations except at MacQuarie Island. The timeseries show very good agreements on inter-annual variability among all four data sources. Among thirteenstations, three of them have nearby stations, so the comparisons between RAs or radiosondes and GPS aremade (not shown). The PW differences between the radiosonde and the GPS show some known biases inradiosonde data and the discontinuities due to the changes of radiosonde types. The radiosonde biases areevident in RAs at one station, but not obvious at other two sites. The dry bias of Vaisala RS80-A data atMacQuarie Island can be as much as 20% and is consistent over the 10-year period, but it is not reflected inRAs. It is likely due to the small size (~35 km long and ~5 km wide) of the MacQuarie Island, and thus it is
not well represented by the model grid box (> 100 km). The impact of the radiosonde bias on RAs depends on
the weight of radiosonde data in RAs, differences in spatial representativeness between a point station and agrid box, and the contrary effects of biases from difference datasets.
CONCLUSIONS
The atmospheric precipitable water from NCEP/NCAR, ERA-40 and JRA-25 is evaluated by comparing
with the PW data derived from the ground-based GPS measurements. The direct comparisons show that theNNR and JRA-25 have a dry bias at more than 70% of stations. The agreements between GPS and RAs in
PW diurnal cycle vary from station to station. In U.S.A., GPS and RAs show similar patterns in amplitude,
but NNR has the best agreement with GPS in phase. In Europe, NNR and JRA produces much weaker PW
diurnal cycle; ERA-40 has consistent diurnal variations with GPS in summer, but fails to predict the seasonaltransition of the diurnal phase. RAs have good agreement with GPS on sub-monthly variability and its diurnaland seasonal variations. RAs and GPS have very good agreements on PW inter-annual variability. The
radiosonde humidity biases are not always evident in RAs. This study demonstrates that the global, 2-hourly
GPS PW dataset is a valuable source for directly validating the reanalyses, helping homogenization ofradiosonde humidity data for the reanalyses, and possibly using as an input data for RAs in the future.
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ACKNOWLEDGEMENTS
This work was supported by a NOAA grant (No. NA06OAR4310117). We would like to thank Kevin
Trenberth and Aiguo Dai (both at NCAR) for useful comments. The National Center for AtmosphericResearch is sponsored by the U.S. National Science Foundation.

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Fig. 1 Histograms of annual mean PW
differences at all stations. The mean,minimum and maximum values, andthe percentage of stations with negative
PW differences are given in the legend.

Fig. 2. The left panel shows the scatter plots of annual mean
PW differences versus the surface altitude differences.
The right panel is the histogram of annual mean PW
differences only for stations with the surface altitudedifferences less than 100 m.

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Fig. 3 Spatial distribution of the amplitude (mm, left panel)and phase (LST, right panel) of the diurnal harmonic of JJA
mean PW in U.S.A.

Fig. 4 Same as in Fig. 3 but for Europe.

Fig. 5 Comparisons of seasonal variations of PW fitted diurnal anomalies (in mm, color) in Europe. The contours
show the sub-monthly variability (mm).

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