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    Range of the seasonal signals in the PW. The denotation of the clusters (Cl) is given in Table 1.

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    (a) Difference PW time series of the collocated GPS stations TROM and TRO1 in Tromsø, Norway (TROM − TRO1). The jump with an offset of 0.3 mm corresponds to an antenna change at the station TRO1 on 22 Dec 1998. (b) Daily PW values at TRO1 show a dominant seasonal signal.

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    Time series analysis of the PW values from the station Cape Ferguson, Australia. (a) Daily values (black) and 30-day median filtered data (gray), (b) interannual component, (c) spectra of the time series (black) and the applied low pass filter (gray), and (d) seasonal signal.

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    Seasonal signals of the PW in different regions; PW = mean annual PW of a region. Stations not presented here show a seasonal signal having a shape similar to the other stations in their neighborhood. The range in the seasonal signal of all stations is shown in Fig. 1.

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    Comparison of the daily PW estimates from NCEP and GPS. (a) GPS-derived PW time series for Metsahovi (METS), Finland; (b) correlogram; (c) difference PW time series (GPS − NCEP); and (d) amplitude spectra (black: GPS, gray: NCEP). (e)–(h) As in (a)–(d), but for Wuhan (WUHN), China.

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    (a) Correlation coefficients between the PW from NCEP and GPS. (b) Bias between the PW estimates from NCEP and GPS. Stations marked with a black dot exhibit a bias of <1.5 mm.

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    Dependency of the differences (NCEP − GPS) on the total water vapor content at the stations Wuhan (WUHN), China, and Magadan (MAD0), Siberia: (a) DOY and (b) PW.

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    Regional differences (NCEP − GPS) of the seasonal signals in the PW.

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    Comparison of the PW anomalies from GPS (black dotted line) and NCEP (gray solid line) for the stations Kootwijk (KOSG), Netherlands; Mawson (MAW1), Antarctica; Cocos Island (COCO), Indian Ocean; and Bahrain (BAHR), Persian Gulf.

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    Comparison of the monthly variability in the PW from GPS (black dotted line) and NCEP (gray solid line) for the stations (a) Ondrejov (GOPE), Czech Republic, and (b) Karratha (KARR), Australia. (c),(d) Differences in the PW variability (NCEP − GPS) For the same stations.

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    Regional differences (NCEP − GPS) in the seasonal signal of the PW variability.

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Validation of Precipitable Water Vapor within the NCEP/DOE Reanalysis Using Global GPS Observations from One Decade

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  • 1 Institut für Planetare Geodäsie, Technische Universität Dresden, Dresden, Germany
  • 2 Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum, Potsdam, Germany
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Abstract

In contrast to previous studies validating numerical weather prediction (NWP) models using observations from the global positioning system (GPS), this paper focuses on the validation of seasonal and interannual variations in the water vapor. The main advantage of the performed validation is the independence of the GPS water vapor estimates compared to studies using water vapor datasets from radiosondes or satellite microwave radiometers that are already assimilated into the NWP models. Tropospheric parameters from a GPS reanalysis carried out in a common project of the Technical Universities in Munich and Dresden were converted into precipitable water (PW) using surface pressure observations from the WMO and mean atmospheric temperature data from ECMWF. PW time series were generated for 141 globally distributed GPS sites covering the time period from the beginning of 1994 to the end of 2004. The GPS-derived PW time series were carefully examined for their homogeneity. The validation of the NWP model from NCEP shows that the differences between the modeled and observed PW values are time dependent. In addition to establishing a long-term mean, this study also validates the seasonal cycle and interannual variations in the PW. Over Europe and large parts of North America the seasonal cycle and the interannual variations in the PW from GPS and NCEP agree very well. The results reveal a submillimeter accuracy of the GPS-derived PW anomalies. In the regions mentioned above, NCEP provides a highly accurate database for studies of long-term changes in the atmospheric water vapor. However, in the Southern Hemisphere large differences in the seasonal signals and in the PW anomalies were found between GPS and NCEP. The seasonal signal of the PW is underestimated by NCEP in the tropics and in Antarctica by up to 40% and 25%, respectively. Climate change studies based on water vapor data from NCEP should consider the large uncertainties in the analysis when interpreting these data, especially in the tropics.

* Current affiliation: Institut für Erdmessung, Leibniz Universität, Hannover, Germany

+ Current affiliation: Institut für Astronomische und Physikalische Geodäsie, Technische Universität München, Munich, Germany

# Current affiliation: Institut für Geodäsie und Photogrammetrie, Eidgenössische Technische Hochschule, Zurich, Switzerland

Corresponding author address: Sibylle Vey, Institut für Erdmessung, Leibniz Universität Hannover, 30167 Hannover, Germany. Email: vey@ife.uni-hannover.de

Abstract

In contrast to previous studies validating numerical weather prediction (NWP) models using observations from the global positioning system (GPS), this paper focuses on the validation of seasonal and interannual variations in the water vapor. The main advantage of the performed validation is the independence of the GPS water vapor estimates compared to studies using water vapor datasets from radiosondes or satellite microwave radiometers that are already assimilated into the NWP models. Tropospheric parameters from a GPS reanalysis carried out in a common project of the Technical Universities in Munich and Dresden were converted into precipitable water (PW) using surface pressure observations from the WMO and mean atmospheric temperature data from ECMWF. PW time series were generated for 141 globally distributed GPS sites covering the time period from the beginning of 1994 to the end of 2004. The GPS-derived PW time series were carefully examined for their homogeneity. The validation of the NWP model from NCEP shows that the differences between the modeled and observed PW values are time dependent. In addition to establishing a long-term mean, this study also validates the seasonal cycle and interannual variations in the PW. Over Europe and large parts of North America the seasonal cycle and the interannual variations in the PW from GPS and NCEP agree very well. The results reveal a submillimeter accuracy of the GPS-derived PW anomalies. In the regions mentioned above, NCEP provides a highly accurate database for studies of long-term changes in the atmospheric water vapor. However, in the Southern Hemisphere large differences in the seasonal signals and in the PW anomalies were found between GPS and NCEP. The seasonal signal of the PW is underestimated by NCEP in the tropics and in Antarctica by up to 40% and 25%, respectively. Climate change studies based on water vapor data from NCEP should consider the large uncertainties in the analysis when interpreting these data, especially in the tropics.

* Current affiliation: Institut für Erdmessung, Leibniz Universität, Hannover, Germany

+ Current affiliation: Institut für Astronomische und Physikalische Geodäsie, Technische Universität München, Munich, Germany

# Current affiliation: Institut für Geodäsie und Photogrammetrie, Eidgenössische Technische Hochschule, Zurich, Switzerland

Corresponding author address: Sibylle Vey, Institut für Erdmessung, Leibniz Universität Hannover, 30167 Hannover, Germany. Email: vey@ife.uni-hannover.de

1. Introduction

Atmospheric water vapor significantly influences many processes of the earth’s weather and climate. Water vapor is one of the main variables controlling the greenhouse effect and it plays a crucial role in the global energy cycle. Accurate knowledge of the water vapor distribution in the atmosphere and its change with time is indispensable for the description and understanding of global climate processes. In contrast to other greenhouse gases such as carbon dioxide or methane, water vapor has a much higher temporal and spatial variability. Given the current state of the global upper-atmospheric observing system consisting primarily of radiosondes and satellites, water vapor is generally observed insufficiently compared to other meteorological parameters.

Traditionally, humidity was mainly measured by radiosondes. Today, the radiosonde network consists of more than 900 stations worldwide. However, the radiosonde observations are distributed very inhomogenously. Most of the stations are situated in industrial countries. There are nearly no radiosonde observations over the oceans and in the polar regions. Additionally, the interpretation of the radiosonde data is complicated because of the differences in the instruments used and the applied analysis strategies of the individual countries (Garand et al. 1992). The measurements of the relative humidity from distinct sensors can differ by up to 20%. The systematic error in the relative humidity mainly depends on the calibrations of the humidity sensor performed prior to the start of the radiosonde (Soden and Lanzante 1996). Globally, nearly 40 different types of radiosondes are in use.

Today, numerous remote sensing methods exist that are able to observe water vapor globally. However, each of these methods has its limitations. Since 1979, passive radiometers have been working in the infrared band, but their observations are of low accuracy and limited to clear sky conditions (Chaboureau et al. 1998; Divakarla et al. 2006). Humidity measurements from downward-looking microwave radiometers are independent of the cloud coverage and provide high accuracy over the oceans in nonprecipitating atmospheres (Sun 1993). However, over land and ice surfaces the accuracy of the water vapor estimates from these microwave radiometers is degraded because of assumptions that have to be made about surface emissivity (Miao et al. 2001; Wang and Manning 2003; Karbou et al. 2005; Deeter 2007).

Radio occultations, between low earth-orbiting satellites and satellites of the Global Positioning System (GPS), are independent of both weather and surface conditions. The sounding of upper-tropospheric humidity by GPS radio occultations is of high accuracy but the accuracy decreases in the lower troposphere (e.g., Marquardt et al. 2001; Wickert et al. 2002; Beyerle et al. 2002, 2004; Hajj et al. 2004; Liou et al. 2005; Larsen et al. 2005). However, the lowest 5-km layer of the troposphere includes about 95% of the total atmospheric water vapor. Ground-based GPS measurements have proven to estimate the content of integrated water vapor in an air column very accurately (e.g., Bevis et al. 1992, 1994; Rocken et al. 1995; Tregoning et al. 1998; Basili et al. 2001; Niell et al. 2001). Water vapor estimates from ground-based GPS observations do generally not provide information on the vertical distribution of the water vapor, but they are of high temporal resolution from a few minutes up to 2 h and they are independent of all weather conditions (Yuan et al. 1993; Rocken et al. 1997; Yunck et al. 2000).

Concerning water vapor data, numerical weather prediction (NWP) models were originally based on radiosonde observations. Today satellite observations from infrared and microwave radiometers are also operationally assimilated into the NWP models (Kanamitsu et al. 1991; Kistler et al. 2001; Andersson et al. 2007). Studies on weather and climate change in the last 20 to 40 yr are increasingly based on analyses of NWP models. For example, investigations of the general atmospheric circulation and the feedback mechanism between temperature and humidity or the estimation of energy and humidity transports between the atmosphere and the Earth surface were carried out on the basis of NWP model data (Allan et al. 2002; Bengtsson et al. 2004; Philipona et al. 2005).

The use of NWP models for climate studies requires an exact knowledge of the accuracy of the analyses results provided by these models. In particular, the parameters of heat and humidity transports are only to a small extent based on direct observations. In regions where they are needed most, humidity observations with sufficient accuracy and precision are not available. Hence, these parameters are less certain than other analysis output parameters such as temperature or air pressure for which many more observations are available (Kalnay et al. 1996; Bengtsson et al. 2004). Therefore, water vapor information generated by NWP models particularly needs to be validated to allow a reliable interpretation of these results.

One major advantage of the validation conducted in this study compared to previous NWP validations is that the GPS-derived water vapor represents an independent data source. Validations using, for example, the New Global Water Vapor (NVAP) dataset from Randel et al. (1996) are not independent because the NVAP dataset mainly consists of the same radiosonde and satellite radiometer observations that were already assimilated in the NWP model (Trenberth and Guillemot 1998). Possible systematic errors caused by the assimilated observations cannot be discovered from comparisons with the NVAP dataset. Former studies using GPS observations for validating the water vapor in NWP models were carried out with an objective of improving the short- and medium-range weather forecast in different countries. These investigations are mainly based on regional weather models and are restricted to some days or months. Additionally, most of the investigations are concentrated on Europe, North America, and Japan (e.g., Cucurull et al. 2000; Köpken 2001; Johnsen and Rockel 2001; Vedel et al. 2001; Gutman and Benjamin 2001; Okamura and Kimura 2003; Bock et al. 2005; Guerova et al. 2006). Smith et al. (2007) investigated the impact of data assimilation from GPS-derived water vapor on numerical weather forecasts over the United States using 6 yr of data. Validation of the water vapor in global weather models performed with GPS data were conducted for the operational analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Canadian Meteorological Center (Hagemann et al. 2003; Deblonde et al. 2005). Both studies are based on data of 4 to 7 months only. They mainly concentrate on investigating the bias between the model and the GPS-derived water vapor and the scatter of the water vapor differences. The following analysis covers the complete time period from the beginning of 1994 up to the end of 2004. The main focus is the validation of the seasonal signals and interannual variations in the water vapor of the NWP model from the National Centers for Environmental Prediction (NCEP). The variability of the water vapor values from the model output will also be intercompared and validated with observations.

2. Estimation of precipitable water from GPS

The atmospheric constituents (including the water vapor) along the signal’s path from the GPS satellite to the receiver cause a propagation delay of the GPS signal. The delay is estimated as an additional parameter—the so-called zenith total delay (ZTD)—within the GPS data processing. This study is based on tropospheric parameters estimated in a common GPS reprocessing project of the Technical Universities in Munich and Dresden (Steigenberger et al. 2006). The reprocessing of a global GPS network covers the period from the beginning of 1994 up to the end of 2004 and was carried out with a modified version of the Bernese GPS software, version 5.0 (Dach et al. 2007).

The most important modifications in the reprocessing compared to the processing strategies of the analysis centers of the International Global Navigation Satellite System (GNSS) Service (IGS) are (i) the application of the isobaric hydrostatic mapping function (IMF) based on data of the NWP model of the ECMWF (Niell 2000; Vey et al. 2006) and (ii) the consideration of higher-order ionospheric effects (Fritsche et al. 2005). Some further processing-related aspects are that absolute antenna phase center variations for GPS receiver and satellites were used (Schmid et al. 2005), the cutoff elevation angle was set to 3°, and an elevation-dependent weighting was applied to the observations. The parameters for the zenith wet delay were estimated using continuous piecewise-linear interpolation with a 2-h parameter spacing. The ZTD parameter results from the sum of the estimated zenith wet delay and the a priori zenith hydrostatic delay. Tropospheric gradients in the north–south and east–west directions were estimated in 24-h intervals. For further details on the strategy of the GPS reprocessing, see Steigenberger et al. (2006).

Precipitable water (PW)—defined as the total water vapor contained in an air column from the earth’s surface up to the top of the atmosphere—was derived from the estimated ZTD parameters according to Bevis et al. (1994). In a first step, the zenith wet delay (ZWD) was calculated by subtracting the zenith hydrostatic delay (ZHD) from the ZTD values:
i1520-0442-23-7-1675-e21
The ZHD strongly depends on the air pressure Ps at the GPS antenna height and can be modeled with sufficient accuracy by the equation from Saastamoinen (1972):
i1520-0442-23-7-1675-e22
with
i1520-0442-23-7-1675-e23
The term f (φ, h) considers the dependence of the gravity acceleration on the ellipsoidal latitude φ and the ellipsoidal height h of the GPS station. For the ZHD modeling daily pressure, observations from the World Meteorological Organization (WMO) were used. The reduction of the air pressure extracted from the WMO database to the GPS antenna height was conducted with the hypsometric equation (Stull 1995):
i1520-0442-23-7-1675-e24
with
  • PGPS air pressure at GPS antenna height (hPa),
  • PS air pressure at the height of the pressure sensor (hPa),
  • ΔH = HGPSHS height difference (m),
  • g gravity acceleration,
  • Rd = 287.053 J K−1 kg−1 gas constant of dry air,
  • T mean temperature of the layer between the GPS antenna and the meteorological sensor (K).
As shown by Hagemann et al. (2003) and Wang et al. (2007), pressure observations from stations of the WMO in the vicinity of GPS stations have an accuracy of better than 2 hPa. Because of the long record length of the pressure time series from the WMO, these observations are preferable to the short pressure records at the IGS stations. For the collocation of the GPS stations and the WMO pressure sensor, the following requirements were imposed in our analysis: the height difference and the horizontal distance between the GPS antenna and the pressure needed to be less than 400 m and 50 km, respectively. The WMO pressure values were available with a daily resolution. Therefore the 2-h ZTD estimates were combined into a daily ZTD.
In a second step, the ZWD was converted into the corresponding amount of precipitable water with the help of the conversion factor Π:
i1520-0442-23-7-1675-e25
where Π varies as a function of the mean temperature of the atmosphere Tm (Bevis et al. 1994):
i1520-0442-23-7-1675-e26
with
  • ρw = 1025 kg m−3 density of liquid water,
  • Rν = 461.51 J K−1 kg−1 specific gas constant of water vapor,
  • k2 and k3 atmospheric refraction constants, k2 = 22.1 ± 2.2 K hpa−1, k3 = 373900 ± 1200 K2 hpa−1.
The mean temperature of the atmosphere is very well represented by the global reanalysis from ECMWF and NCEP (Wang et al. 2005). The differences between the data from both reanalysis and global radiosonde observations are <2 K for most of the stations, where ECMWF represents Tm slightly better than NCEP (Wang et al. 2005). In contrast, the estimation of Tm from surface temperature observations using the vertical temperature lapse rate given in Bevis et al. (1992) underestimates Tm in the tropics and subtropics by up to 6 K and overestimates Tm in the middle and high latitudes by up to 5 K (Wang et al. 2005). For the transformation of the zenith wet delay into the corresponding amount of precipitable water, the data of the mean atmospheric temperature from ECMWF were used.

3. PW results

Precipitable water time series with a daily resolution were estimated for 141 stations. However, many PW time series cover the period from 1994 to 2004 only partly. For further analysis 62 stations covering a period of at least 5 yr with data gaps smaller than 3 months were selected as a compromise between the time series length and the number of available stations (Fig. 1).

a. Accuracy

The accuracy of the PW results strongly depends on the accuracy of the ZTD values. Here only the main error sources concerning the estimation of ZTD will be discussed. One important error source is the modeling of the location of the phase center of the satellite and receiver antennas relative to their geometrical reference point. These models consist of a constant term (phase center offsets, or PCOs) and elevation- and azimuth-dependent parts (phase center variations, or PCVs). Relative antenna phase center models can be computed using short baselines and a reference antenna (Mader 1999). Unfortunately, the models are relative to a reference antenna (a Dorne Margoin T model) that has its own elevation dependence. Absolute PCVs for receiver antennas estimated by robot calibrations are independent of a reference antenna (Menge et al. 1998). Additionally, they were used to estimate a consistent set of PCVs for satellite antennas (Schmid and Rothacher 2003). The change from relative to absolute PCVs causes 4–5-mm smaller zenith delays, reducing the bias between the ZTD estimates from GPS and Very Long Baseline Interferometry (VLBI) significantly (Schmid and Rothacher 2003; Zhu et al. 2003; Steigenberger et al. 2007). Absolute PCVs from field and from robot calibrations agree at the level of few millimeters (Görres et al. 2006). Radomes installed on top of the antennas delay the GPS signal and modify its directional dependence on the azimuth and the elevation angle of the incoming signal. Radomes cause systematic errors in the estimated ZTD parameters that can reach some millimeters depending on the shape of the radome (Johansson 1998; Schupler 2001). The effect of radomes was not considered in this study. Hence, the installation or change of radomes can cause significant jumps in the ZTD time series.

A further important error source in the estimation of the ZTD parameters is uncertainties in the mapping functions that model the elevation dependence of the tropospheric delay. The widely used Niell mapping function (NMF) is based on radiosonde data of the United States (Niell 1996). By assuming longitudinal homogeneity and symmetry between the Southern and the Northern Hemisphere, the NMF was extended to the entire globe. Seasonal variations are approximated by a sine function. A small error in the hydrostatic mapping function of 0.1% at an elevation angle of 5°—as used in the NMF—causes an error of 3 mm in the ZTD estimates (Niell 1996). Uncertainties in the mapping function can cause systematic as well as seasonal errors in the GPS-derived ZTD time series (Vey et al. 2006).

In the last years the modeling of the elevation-dependence of the tropospheric parameters could be improved by mapping functions based on data of numerical weather prediction models (Niell 2001; Boehm et al. 2006). The application of mapping functions based on NWP model data—such as the isobaric mapping function used in this study—reduces the elevation dependence of the estimated ZTD parameters by 20% compared to a solution based on the NMF (Vey et al. 2006). Additionally, the bias between the ZTD parameters from GPS and VLBI decreases when the IMF is applied instead of the NMF (Tesmer et al. 2006). Mapping functions based on NWP data are closer to reality than the NMF. Therefore, the errors in the ZTD estimation due to uncertainties in the IMF can be stated to be smaller than 3 mm (Vey et al. 2006). Site-specific effects like multipath signal masking or snow accumulation on top of a GPS antenna or radome can significantly influence the parameter estimation and cause errors in the ZTD estimates of up to 2 mm (Jaldehag et al. 1996; Park et al. 2004). Also, higher-order ionospheric effects have a visible impact on the GPS parameter estimation (Kedar et al. 2003). If higher-order ionospheric effects are neglected, errors in the ZTD values of up to 1 mm can be expected (Fritsche et al. 2005).

A comparison of the ZTD parameters from six GPS–GPS-collocated stations shows a standard deviation of 2.5 to 5.4 mm (Steigenberger et al. 2007). Compared to the IGS combined solution, the precision of the tropospheric delay estimates in our reprocessing could be improved by 20%. The improvement is related to the application of enhanced models in the reprocessing (Steigenberger et al. 2007). Additionally, a validation of the troposphere parameters from the GPS and a VLBI reprocessing using the same analysis strategy for both techniques was carried out at 27 collocated GPS–VLBI stations by Steigenberger et al. (2007). For most of the stations the bias in the troposphere parameters estimated from both techniques ranges between −5 and 9 mm and the rms of the differences ranges between 4 and 10 mm. This translates into a systematic error of 0.7 to 1.7 mm in the PW estimates.

The accuracy of the ZHD modeling is mainly dependent on errors in the air pressure measurements (Janes et al. 1991). Using Eq. (2.2), it can be written as
i1520-0442-23-7-1675-e31
An error σP in the air pressure of 2 hPa results in an error σZHD in the modeled hydrostatic delay of 4.6 mm. When reducing the WMO pressure observations into the GPS antenna height, 98% of the errors in the measured pressure propagate into the reduced pressure. An uncertainty in the height difference of 3 m causes an error of 0.4 hPa in the reduced pressure. Temperature errors have almost no impact on the error budget for small height differences. However, for large height differences a temperature error of 2 K causes an error in the reduced pressure of 0.8 hPa (see Appendix A). Uncertainties in the reduced pressure mainly cause offsets in the PW estimates, which can reach up to 0.8 mm.
Errors σΠ in the conversion factor resulting from the conversion of the ZWD into the PW depend primarily on uncertainties σTm in the mean atmospheric temperature (Bevis et al. 1992, 1994; Schüler 2001):
i1520-0442-23-7-1675-e32
The mean temperature of the atmosphere Tm from ECMWF, which was used in this study, has an accuracy of better than 2 K (Wang et al. 2005). In the polar regions, with a low water vapor content and typical ZWD values of 60 mm, the impact of an error in Tm on the PW estimation is almost zero. However, in the tropics, with ZWD values reaching up to 450 mm, the uncertainty in Tm affects the PW results by up to 0.5 mm. Errors in the PW estimates caused by Tm are related to the total amount of PW, which shows strong seasonal variations. Hence, the effect of errors in Tm is season dependent.
The main error sources for PW estimation, separated into systematic and seasonal effects, are summarized in Table 2. In conclusion, the accuracy of the PW estimates resulting from the error propagation of Eqs. (2.1) and (2.5) can be stated as
i1520-0442-23-7-1675-e33
Applying Eqs. (3.1) and (3.2), the accuracy of the PW results mainly depends on the accuracy of the ZTD estimates, the pressure, and the mean atmospheric temperature:
i1520-0442-23-7-1675-e34
Based on the accuracy of the ZTD parameters of 4 to 10 mm, and considering an error in the pressure of 2 hPa and in the temperature of 2 K, the rms of the PW estimates should be in the range of 1.1 to 1.9 mm. Comparisons of the GPS-derived precipitable water with PW estimates from satellite-based microwave radiometers in the dry climate over Antarctica reveal an accuracy of the PW values of 1 mm (Vey and Dietrich 2008). The accuracy obtained of the GPS PW here is consistent with the RMS errors of 1 to 3 mm from previous studies (e.g., Bengtsson 2003; Li et al. 2003; Liou et al. 2000; Vey et al. 2004; Hagemann et al. 2003; Gendt et al. 2004; Deblonde et al. 2005; Wang et al. 2007).

b. Identification of consistent time series

The homogeneity of the GPS-derived PW time series is a prerequisite for the validation of the seasonal and interannual changes in the modeled PW. The combined tropospheric parameter time series from the IGS solution, which are provided from 1997 until 2006, show inconsistencies of several millimeters due to changes in the reference frame used and the models and processing strategies applied (Steigenberger et al. 2007). Such processing-related inhomogeneities are not present in our reprocessed series because a consistent processing strategy was applied for the whole period of time.

However, changes in the hardware or in the data recording of the GPS stations can significantly affect the GPS parameter estimation. Gradinarsky et al. (2002) compared PW time series from GPS and radiosonde observations and found that the installation of a radome on top of a GPS antenna can cause significant jumps of 0.4 to 1.3 mm in the GPS-derived PW time series. Figure 2a shows a jump of 0.3 mm in the PW difference time series from the two collocated GPS stations TROM and TRO1 in Tromsø, Norway. This jump is caused by an antenna change of the station TRO1 on 22 December 1998. In the PW time series of station TRO1 this jump is not visible because it is covered by a strong seasonal signal of several centimeters (Fig. 2b).

To detect inhomogeneities in the PW time series, the differences of the PW estimates from neighboring GPS stations were systematically investigated. In addition to radome and antenna changes, changes in the number of observations due to modified elevation cutoff angles or signal masking were also found to cause inhomogeneities. Only one-third of the PW time series can be assumed to be homogeneous. For the PW time series with jumps, an offset correction was applied. In the following time series analysis, only homogeneous time series or time series with an offset correction of better than 0.3 mm were included. PW time series with a slowly and irregularly changing number of observations were excluded from the subsequent analysis. Further details on the analysis of the homogeneity of the PW time series can be found in Vey et al. (2009).

c. Time series analysis

Using the classical component model, the time series were decomposed into a long-term mean xt, interannual variations gt, a seasonal component st, and a residual part rt (Schlittgen and Streitberg 1999; Stier 2001):
i1520-0442-23-7-1675-e35
First we subtracted xt, the long-term mean, from the time series. To estimate the interannual variations gt we applied a low-pass filter with a Finite Impulse Response (FIR). The advantage of FIR filters is that they are characterized by small transition zones and low ripple (Kumar et al. 1992). The applied FIR filter has a cutoff frequency of 0.8 cycles yr−1, a transition zone of 0.2 cycles yr−1, and a ripple of 1%. All frequencies higher than 1.4 yr can pass almost completely and all frequencies below 1.1 yr are attenuated almost entirely (Fig. 3c). The filtered time series represent the interannual variations gt including anomalies and trends. Exemplarily, the filtered time series of the station Townsville (TOW2), Australia, is shown in Fig. 3b. After subtraction of the long-term mean and the anomalies from the time series, the seasonal component st was estimated by data stacking (Fig. 3d). To estimate the residual component rt, the seasonal signal was also removed from the time series.

d. Cluster analysis

To conclude how well NCEP represents the water vapor in different areas, the stations were clustered regionally. Gaffen et al. (1992) defined five humidity regimes from 56 globally distributed radiosonde stations. Using these humidity regimes and geographical information, the a priori clusters were formed. In a second step a variance analysis of the seasonal precipitable water signal of a station and the mean seasonal precipitable water signal of the cluster were conducted, and all stations with a variance of more than 2 mm were removed from the cluster. Typical seasonal signals in the PW of different regions are shown in Fig. 4. In the Northern Hemisphere the seasonal signals in different areas exhibit very similar behavior with a clear minimum at the end of January and a maximum at the end of July following the annual temperature cycle. The seasonal signal in the PW of the stations in North Australia exhibits a maximum at the end of February and a minimum in mid-August, being influenced by the monsoon and seasonal temperature variations. The seasonal variations in the PW over Antarctica with a range of 3 to 4 mm are very small, which is related to the very low amount of water vapor in this area. In climate-sensitive regions such as in the tropics and in Asia, the generation of clusters turned out not to be reasonable because of a low observation density and high moisture variability (Figs. 4c,e). In these cases the results will be given separately for each station. An overview of the created clusters can be found in Fig. 1.

4. Validation of NCEP

In a common project, two analysis centers, NCEP and the National Center for Atmospheric Research (NCAR), reanalyzed global atmospheric observations for the period from 1948 to 2002 (Kalnay et al. 1996; Kistler et al. 2001). The reanalysis is based on the operational weather model implemented in 1995 at NCEP (Kanamitsu et al. 1991). The model has a temporal resolution of 6 h and a horizontal resolution of 2.5° × 2.5° and consists of 28 vertical layers. To prevent inhomogeneities, the model and the assimilation scheme were kept constant for the complete reanalysis period. However, changes in the type and number of assimilated observations can still influence the reanalysis results. For example, the assimilation of satellite observations since 1979 has a significant impact on the reanalysis results. A repetition of the reanalysis was carried out by the Department of Energy (DOE) in relation to the Atmospheric Model Intercomparison Project (AMIP-II) for the years from 1979 up to present (Kanamitsu et al. 2002). The NCEP/DOE AMIP-II Reanalysis (R-2) is based on an improved model physics and more consistent observations. The validation was carried out using the data from this NCEP Reanalysis (hereafter referred to simply as NCEP; data are available online at http://www.cdc.noaa.gov/data/gridded/data.ncep.reanalysis2.html).

While NCEP provides mean values for grid cells with a ground surface of more than 10 000 km2, the GPS measurement is only representative for a region with a diameter of about 100 km. The PW values of the four closest grid cells were interpolated linearly to the position of the GPS station. Different reference heights between the GPS station and the model topography were considered by a vertical interpolation assuming a linear temperature lapse rate of the standard atmosphere. The GPS-derived PW data have a daily resolution. Because we are interested in seasonal and interannual signals, the 6-h values from NCEP were averaged to daily values. Additionally, only those data points from the continuous NCEP time series were extracted for which corresponding GPS observations were available.

a. Correlation

The PW values from GPS and from NCEP are highly correlated as shown in Figs. 5a,d for the stations Metsahovi, Finland, and Wuhan, China. The correlation coefficient for most of the stations in the Northern Hemisphere is close to 1 (Fig. 6a). Lower correlation coefficients were found for the Southern Hemisphere, especially for the Antarctic Peninsula and South America. This can be attributed to the very low number of observations being assimilated in the model in these regions. The strong influence of the observations on the analysis results can be seen in Australia. A higher observation density over Australia yields a much better agreement between the PW values from NCEP and GPS in this area compared to the rest of the Southern Hemisphere.

b. Bias

The differences in the long-term mean of the PW estimates from GPS and NCEP are for 50% of the stations in the range of ±1.5 mm and for 80% of the stations in the range of ±3 mm (Fig. 6b). Larger biases are mainly related to large height differences between the NCEP topography and the GPS antenna height. In the case of the station Lhasa, Tibet, situated at an elevation of 3658 m, the reference height of the model is 4563 m. The large height difference causes a significant error in the height reduction of the PW data due to the assumption made about the temperature lapse rate. Therefore, all stations with an elevation difference of more than 400 m were excluded from the validation.1 Additionally, for stations at high elevation the pressure reduction from mean sea level to the GPS antenna height is significantly influenced by errors in the temperature lapse rate. The hypsometric equation used is based on the temperature lapse rate of 6.5 K km−1 of the standard atmosphere. Depending on the location and the season, the temperature lapse rate can vary between 3 and 8 K km−1. For a station at an elevation of 900 m such as Zimmerwald, Switzerland, an error of 3 K in the mean atmospheric temperature can cause a significant error in the reduced pressure of 1.2 hPa (see Appendix A). Stations with an elevation of more than 900 m were also excluded from the calculation of the cluster mean values.2

The bias between the PW values from GPS and NCEP of the remaining stations is, except for very few stations, in the range of ±3 mm (Table 3). The GPS-derived PW values have an accuracy of 1.1 to 1.9 mm (section 3a). Therefore, reasons for larger biases between the PW estimates from GPS and NCEP are related to the NCEP analysis. For large parts of North America NCEP shows, compared to the GPS PW, 2 to 3 mm smaller values over the last decade. This confirms the results of local case studies that found a wet bias of the NCEP analysis over North America (Gutman and Benjamin 2001). Comparisons with water vapor values from GPS radio occultations also reveal more humid results for the NCEP analysis (Kursinski and Hajj 2001). The humidity in the analysis strongly depends on the assimilated observations (Andersson et al. 2007). Over land surfaces, mainly observations from radiosondes and satellite radiometers working in the infrared band are assimilated.

Over Europe values from the NCEP analysis were found to be drier than the GPS results. The systematic differences in the biases between North America and Europe can be related to differences in the radiosonde observations between both regions. The use of different radiosonde types and the application of different algorithms for the calibration of the radiosondes and the conversion of the measured relative humidity to the assimilated dewpoint temperature can cause significant differences between the radiosonde observations from different countries (Soden and Lanzante 1996; Miloshevich et al. 2006). Also, different limits applied for the recording of the humidity at low temperature or at high humidity can cause systematic differences in the radiosonde records (Garand et al. 1992).

Over the oceans infrared and microwave radiometer observations are assimilated into the model. The Special Sensor Microwave Imager (SSM/I) reveals a systematically higher humidity compared to radiosonde measurements (Andersson et al. 2007). The positive bias in the NCEP PW for many stations in the coastal areas can be explained by the assimilation of SSM/I data. Inconsistencies between the analysis and local radiosonde data can occur if the assimilated local observations are not representative for the region. The high biases at the stations Noumea (NOUM), New Caledonia, and Townsville (TOW2), North Australia, might be caused by such inconsistencies as their neighboring stations show a much smaller bias. Other examples are the stations Hoefn and Reykjavik, Iceland, where the steep topography at the coast has a remarkable impact on the water vapor behavior at smaller scales. Such local effects in the frontier between ocean and land are insufficiently represented by NCEP because of the poor spatial resolution of 2.5° × 2.5° of the global model.

c. Seasonal signal

The time series of the difference of the PW estimates from GPS and NCEP are represented exemplarily for the stations Metsahovi (METS), Finland, and Wuhan (WUHN), China, in Figs. 5c,g. At Metsahovi the standard deviation of the differences is very small (a value of 1.2 mm). In the case of Wuhan the PW difference time series shows a clear seasonal signal, causing a much higher standard deviation of the differences compared to Metsahovi. At Wuhan the spectrum of the PW estimates shows a significantly smaller amplitude of the annual and semiannual cycle in the PW from NCEP than in the PW from GPS (Fig. 5h).

As can be seen in Fig. 7, the PW differences between GPS and NCEP increase with a higher amount of water vapor. In the case of Wuhan, the difference in the water vapor is anticorrelated to the absolute PW values; hence, the regression line has a negative slope. For the station Magadan (MAG0), Siberia, a positive correlation and regression slope indicates a larger seasonal signal in the NCEP PW time series compared to the seasonal signal in the GPS PW time series.

In order to develop a measure for the relative error of the seasonal signal, we defined the following relation
i1520-0442-23-7-1675-e41
between ΔŝN,G(t) = ŝNCEP(t) − ŝGPS(t), which is the difference in the seasonal signal ŝ(t) from NCEP and GPS and the mean seasonal signal ŝN,G(t) = (1/2)[ŝNCEP(t) + ŝGPS(t)].

The PW differences in the seasonal signals from GPS and NCEP represented in Fig. 8 show a similar behavior for the stations in every specific common cluster. In Europe the differences in PW are independent of the season (Fig. 8a). The standard deviation of the differences is smaller than 1.5 mm and the relative error of the seasonal signal (2%) is very low. The theoretically estimated precision of the GPS-derived PW values is consistent with the average differences. The results also testify to a high precision of the modeled PW over Europe. The water vapor values from GPS and NCEP also agree very well for the stations in the central and southeast regions of the United States as well as in South Australia. The relative errors in the seasonal signals of the water vapor in these regions are better than 5% (Table 3).

Large relative errors in the seasonal signals of about 25% occur in the tropics and in Antarctica. Because of the extremely low water vapor content in Antarctica, the absolute differences in the seasonal signal between GPS and NCEP in this area are smaller than 1 mm whereas these differences reach 3 to 4 mm in the tropics (Figs. 8g,h). For most of the regions the differences in the seasonal signals of the PW from NCEP and GPS are larger than the errors in the GPS-derived PW. Hence, the observed differences can be attributed to deficiencies in the water vapor modeling of NCEP. Further evidence for this interpretation is the strong dependence of the differences on the total water vapor content.

For most of the stations NCEP underestimates the amplitude of the seasonal signal in the precipitable water. This underestimation is pronounced in the tropics. For example, at the stations Cocos Island (COCO) in the Indian Ocean and BAHR in the Persian Gulf, the amplitude in the seasonal signal of PW is captured by NCEP with only 75% and 60%, respectively (Table 3). In the tropics, the definition of the atmospheric structure in NCEP is inferior compared to other regions (Trenberth and Guillemot 1998). Additionally, the number of radiosonde observations in the tropics is very limited and high cloud coverage is responsible for the low quality of the observations from satellite infrared radiometers. The low number of accurate observations available for the assimilation in the tropics causes the analysis results of the humidity to be of much lower quality than in other regions.

At the station Bermuda (BRMU) in the North American Basin the water vapor is underestimated by 4 mm in the summer months, corresponding to a relative error in the seasonal signal of 25% (Fig. 8b). In this case, the discrepancies are caused by small-scale effects related to the location of the station Bermuda, which is situated on an island. The stations on the west coast of North America show errors in the seasonal signal of the PW of 10%–20%. The topography of the Rocky Mountains complicates the correct reproduction of the water vapor by the model in this area. An overestimation of the amplitude of the seasonal signal in PW by NCEP was found for the stations in Siberia and northern Canada (Figs. 7 and 8c). The explanation for the differences at these stations is dry biases of radiosonde observations, which occur at low temperatures (Soden and Lanzante 1996; Soden et al. 2004). The assimilation of these observations causes a dry bias in the NCEP analysis data in the winter months. This conclusion corresponds to the results of a validation of the PW in ECMWF from Hagemann et al. (2003). They detected a dry bias in the ECMWF analysis in the high northern latitudes for the winter months. NCEP and ECMWF are mostly assimilating the same observations. The amplitude of the seasonal signal in the PW from NCEP was found to be overestimated by up to 7% in the high northern latitudes (Table 3).

d. Anomalies

PW anomalies were validated at the GPS stations containing time series with a length of at least 7 yr. As shown for the station KOSG in Fig. 9a, as an example, the interannual variations in the PW are well represented by NCEP for stations in Europe. In this region, the differences between the water vapor anomalies from GPS and NCEP are very small, having a standard deviation of 0.3 mm (Table 3). The anomalies from both methods also agree very well for the stations in Iceland, North America, and Antarctica. They confirm the high accuracy of the PW anomalies from GPS and show that NCEP correctly represents the interannual variations in the water vapor of the regions mentioned above.

However, the stations in southeastern North America show differences in the PW anomalies of up to 1 mm (Table 3). In some cases, such as for the station COCO in the Indian Ocean in Fig. 9c, the anomalies from GPS and NCEP can differ by almost 3 mm. In most cases the water vapor anomalies are underestimated by the model. An exception represents the PW anomaly at the station BAHR, which for 1998 is overestimated by the model (Fig. 9d). That year is characterized by a strong influence of El Niño (McPhaden 1999).

e. Variability

The standard deviation of the PW values of one month was defined as monthly variability of the water vapor. The example of the station at Pency (GOPE), Czech Republic, in Fig. 10a shows a mean variability in the PW of 4 mm over the last decade. Additionally, the monthly variability is marked by significant seasonal signals. The variability in the PW at GOPE varies by ±1 mm during one year, showing a higher variability in the summer months. For the PW variability the same time series analysis was applied as for the PW time series itself (section 3c).

The agreement of the PW variabilities between GPS and NCEP strongly depends on the region. NCEP very well represents the PW variability over Europe. As exemplarily shown for GOPE in Fig. 10c, the bias and the standard deviation of the differences in the variabilities from GPS and NCEP are both smaller than 0.1 mm. In Europe the error in the seasonal signal of the PW variability is smaller than 5%. Over large parts of North America and Iceland NCEP also well represents the seasonal signal in the PW variability (Table 4).

However, in the Southern Hemisphere, large differences occur between the variabilities from GPS and NCEP. The example of station Karratha (KARR) in North Australia in Figs. 10b,d shows a 1-mm smaller mean variability for the PW from NCEP than for that from GPS. Additionally, the difference in the variability from both methods reveals a seasonal signal. Significant differences in the seasonal signal of the variabilities with amplitudes of up to 1 mm were detected for all stations in North Australia (Fig. 11c). The magnitude of the differences in the PW variability strongly depends on the total water vapor content. This dependency indicates a weakness of the NCEP analysis. The accuracy of the PW variability from GPS is mainly independent of the amount of water vapor. From the results found over Europe the accuracy of the PW variability can be stated to be in the range of 0.1 mm. The large differences in the variabilities from GPS and NCEP over North Australia of more than 1 mm are interpreted as deficiencies in the NCEP analysis. For most of the stations NCEP underestimates the mean variability as well as the seasonal signal in the PW variability (Table 4). For the Antarctic stations represented in Fig. 11d the differences between the variabilities from GPS and NCEP are small. However, because of the low water vapor content in Antarctica the relative error in the seasonal signal of the PW variability is 35%.

5. Conclusions

Comparison of NCEP water vapor with independent GPS data over a time period of up to one decade shows that the differences between modeled and observed PW values in some locations are time dependent. Hence, the differences in the PW values from GPS and NCEP need to be analyzed at seasonal and interannual time scales. Biases estimated from previous studies covered only few months (Hagemann et al. 2003; Deblonde et al. 2005) and are mostly not valid for longer periods of time.

Over Europe and large parts of North America the seasonal cycle and the interannual variations in the PW from GPS and NCEP agree very well. The monthly variability in the PW is also very well represented by NCEP. In these regions NCEP depicts a highly accurate database for studies of long-term changes in the atmospheric water vapor. The results of the validation reveal a submillimeter accuracy of the GPS-derived PW anomalies. Hence, GPS observations are a valuable data source for climate studies, supposing the GPS processing strategy is kept unchanged and modifications in the tracking hardware or in the number of the recorded GPS observations are considered in the analysis.

In the Southern Hemisphere large differences in the seasonal signals and in the anomalies of the PW as well as in the PW variability were found between the data from GPS and from NCEP. In the tropics and in Antarctica the seasonal signal in the PW is strongly underestimated by NCEP by up to 40% and 25%, respectively. In these regions the number of observations being assimilated in the model is very limited and they are often of lower accuracy. Hence, the global observing system needs to be extended in the Southern Hemisphere and differences between PW values measured by satellites over the oceans and GPS need to be sorted out and resolved. Additionally, GPS measurements provide a data source of highly accurate PW estimates that could be assimilated into the model.

Climate change studies based on water vapor data from NCEP should consider the high uncertainties in the analysis when interpreting these model outputs, especially in the tropics. The validation carried out here exemplarily for NCEP can be extended to other numerical weather prediction or climate models.

Acknowledgments

We thank NCEP for providing model data of the precipitable water on its website. We gratefully acknowledge the constructive comments of the reviewers. The investigations presented here were partly supported by the German Research Foundation (DFG). Sincere thanks go to Johannes Böhm for making the data of the height of the 200-hPa surface and the mean atmospheric temperature of the ECMWF available to us. One of the authors (S. Vey) was supported by the Studienstiftung des deutschen Volkes, which is gratefully acknowledged.

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APPENDIX A

Pressure Reduction Errors

The errors in the pressure resulting from the reduction of the air pressure to the GPS antenna height can be deduced from Eq. (2.4):
i1520-0442-23-7-1675-ea1
As an example, for an air pressure Ps of 980 hPa at the meteorological sensor, a temperature T of 273 K and a height difference ΔH of 100 m the error in the reduced pressure σPGPS results in
i1520-0442-23-7-1675-ea2
It shows that about 98% of the error in the measured pressure propagates into the reduced pressure. Uncertainties in the height difference of 1 to 3 m cause significant errors of 0.1 to 0.4 hPa in the reduced pressure. Temperature errors have nearly no impact on the error budget for small height differences. However, for large height differences of, for example, 1000 m, Eq. (A.1) gives
i1520-0442-23-7-1675-ea3
In this case a temperature error of 3 K causes an error in the reduced pressure of 1.2 hPa.

APPENDIX B

Station List

Locations of the stations used in the analysis are given in Table B1.

Fig. 1.
Fig. 1.

Range of the seasonal signals in the PW. The denotation of the clusters (Cl) is given in Table 1.

Citation: Journal of Climate 23, 7; 10.1175/2009JCLI2787.1

Fig. 2.
Fig. 2.

(a) Difference PW time series of the collocated GPS stations TROM and TRO1 in Tromsø, Norway (TROM − TRO1). The jump with an offset of 0.3 mm corresponds to an antenna change at the station TRO1 on 22 Dec 1998. (b) Daily PW values at TRO1 show a dominant seasonal signal.

Citation: Journal of Climate 23, 7; 10.1175/2009JCLI2787.1

Fig. 3.
Fig. 3.

Time series analysis of the PW values from the station Cape Ferguson, Australia. (a) Daily values (black) and 30-day median filtered data (gray), (b) interannual component, (c) spectra of the time series (black) and the applied low pass filter (gray), and (d) seasonal signal.

Citation: Journal of Climate 23, 7; 10.1175/2009JCLI2787.1

Fig. 4.
Fig. 4.

Seasonal signals of the PW in different regions; PW = mean annual PW of a region. Stations not presented here show a seasonal signal having a shape similar to the other stations in their neighborhood. The range in the seasonal signal of all stations is shown in Fig. 1.

Citation: Journal of Climate 23, 7; 10.1175/2009JCLI2787.1

Fig. 5.
Fig. 5.

Comparison of the daily PW estimates from NCEP and GPS. (a) GPS-derived PW time series for Metsahovi (METS), Finland; (b) correlogram; (c) difference PW time series (GPS − NCEP); and (d) amplitude spectra (black: GPS, gray: NCEP). (e)–(h) As in (a)–(d), but for Wuhan (WUHN), China.

Citation: Journal of Climate 23, 7; 10.1175/2009JCLI2787.1

Fig. 6.
Fig. 6.

(a) Correlation coefficients between the PW from NCEP and GPS. (b) Bias between the PW estimates from NCEP and GPS. Stations marked with a black dot exhibit a bias of <1.5 mm.

Citation: Journal of Climate 23, 7; 10.1175/2009JCLI2787.1

Fig. 7.
Fig. 7.

Dependency of the differences (NCEP − GPS) on the total water vapor content at the stations Wuhan (WUHN), China, and Magadan (MAD0), Siberia: (a) DOY and (b) PW.

Citation: Journal of Climate 23, 7; 10.1175/2009JCLI2787.1

Fig. 8.
Fig. 8.

Regional differences (NCEP − GPS) of the seasonal signals in the PW.

Citation: Journal of Climate 23, 7; 10.1175/2009JCLI2787.1

Fig. 9.
Fig. 9.

Comparison of the PW anomalies from GPS (black dotted line) and NCEP (gray solid line) for the stations Kootwijk (KOSG), Netherlands; Mawson (MAW1), Antarctica; Cocos Island (COCO), Indian Ocean; and Bahrain (BAHR), Persian Gulf.

Citation: Journal of Climate 23, 7; 10.1175/2009JCLI2787.1

Fig. 10.
Fig. 10.

Comparison of the monthly variability in the PW from GPS (black dotted line) and NCEP (gray solid line) for the stations (a) Ondrejov (GOPE), Czech Republic, and (b) Karratha (KARR), Australia. (c),(d) Differences in the PW variability (NCEP − GPS) For the same stations.

Citation: Journal of Climate 23, 7; 10.1175/2009JCLI2787.1

Fig. 11.
Fig. 11.

Regional differences (NCEP − GPS) in the seasonal signal of the PW variability.

Citation: Journal of Climate 23, 7; 10.1175/2009JCLI2787.1

Table 1.

Cluster naming. Locations of the clusters are given in Fig. 1.

Table 1.
Table 2.

Main error sources affecting the estimation of GPS-derived precipitable water. For better comparability, all effects are converted to PW using a conversion factor Π of 0.17.

Table 2.
Table 3.

Validation statistics of the PW from NCEP. An overview of the cluster names and location is given in Fig. 1; N = number of stations used for the validation of the entire signal (first value) and the interannual component (second value), r = correlation coefficient, Bias = PW(NCEP) − PW(GPS), Std dev = standard deviation of the differences of the entire signal ΔPW and the interannual component ΔPWa, and σ(PWs) = relative error of the seasonal signal.

Table 3.
Table 4.

Validation statistics of the variability V in the PW from NCEP. An overview of the cluster names and location is given in Fig. 1; N = number of stations used for the validation of the entire signal (first value) and the interannual component (second value); Bias(V) = bias in the PW variability (NCEP − GPS); Std dev (ΔV) = standard deviation of the differences in the PW variability; σ(Vs) = relative error in the seasonal signal of the PW variability; Std dev (ΔVa) = standard deviation of the differences in the interannual component of the PW variability.

Table 4.

Table B1. Station list.

i1520-0442-23-7-1675-tb01
1

Height difference between GPS and NCEP >400 m for the stations AREQ, LHAS, USUD, WHIT.

2

Elevation of the GPS station >900 m for AREQ, BRAZ, LHAS, USUD, WHIT, PIE1, IISC, ZIMM.

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