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Applicability of methods for estimating GPS precipitable water in the Qinghai-Tibet Plateau
Journal of Atmospheric and Solar-Terrestrial Physics 89 (2012) 76–82
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Applicability of methods for estimating GPS precipitable water in the Qinghai-Tibet Plateau Guirong Xu n, Chunguang Cui, Rong Wan, Anwei Lai, Xia Wan, Zhikang Fu, Guangliu Feng Institute of Heavy Rain, China Meteorological Administration, Wuhan 430074, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 February 2012 Received in revised form 1 August 2012 Accepted 9 August 2012 Available online 28 August 2012
Atmospheric weighted mean temperature (Tm) and zenith hydrostatic delay (ZHD) are key parameters in determining the accuracy of GPS precipitable water (PW) estimations. Using radiosonde profiles with a high vertical resolution of 10–30 m, we calculated Tm, ZHD and PW and investigated the applicability of the modeled Tm and ZHD, including the GPS PW obtained with them, in the Qinghai-Tibet Plateau (QTP). It is found that (1) the bias and RMS error of the Bevis Tm model in the QTP are less than 5% and 2%, respectively, with a diurnal variation that is small in the morning and large otherwise. (2) The ZHD models have an RMS error within 1% in the QTP, which is smaller than that in the plain of Central China, which is within 1.5%. The ZHD bias of the Saastamoinen model is less than 0.5% in the QTP, smaller than those of the Hopfield and Black models. The diurnal variations of the modeled ZHD bias and RMS error are complex in the QTP, varying with the ZHD model and station. (3) The relative RMS error of GPS PW increases with the station altitude, with a range of 12–66% in the QTP and within 8% in the plain of Central China. The diurnal variations of the GPS PW bias and RMS error in the QTP are similar to those of the modeled ZHD. (4) The accuracy of GPS PW is mainly affected by the ZHD model, and a good Tm can improve the accuracy of GPS PW in the QTP. & 2012 Elsevier Ltd. All rights reserved.
Keywords: Applicability GPS Precipitable water Qinghai-Tibet Plateau
1. Introduction As the largest and highest plateau in the world, the QinghaiTibet Plateau (QTP) plays an important role in the weather and climate over China and surrounding areas (Xu and Chen, 2006). The QTP serves as ‘‘the world’s water tower’’. The land–ocean– atmosphere interaction around the QTP has a significant impact on the global natural and climate environment (Xu et al., 2008a). A data assimilation experiment showed that information regarding the moisture, temperature and pressure in the QTP area is of great importance and is useful in the forecasting of precipitation in its downstream areas (Peng et al., 2009). However, few observations are available in the QTP because of the complex terrain and terrible weather conditions. Recently, Bevis et al. (1992, 1994) proposed an approach to estimate the precipitable water (PW) from ground-based GPS observation based on the linear relationship between the zenith wet delay (ZWD) and PW. Since then, GPS technology has been broadly applied in atmospheric monitoring and numerical weather forecasting (Businger et al., 1996; Ware et al., 2000; Dick et al., 2001; Baker et al., 2001; MacDonald and Xie (2002); Song et al., 2006). GPS observations n Correspondence to: Institute of Heavy Rain, China Meteorological Administration, No. 3 Donghudong Road, Wuhan 430074, Hubei, China. Tel.: þ86 27 67847954; fax: þ 86 27 87806597. E-mail addresses: [email protected], [email protected] (G. Xu).
1364-6826/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jastp.2012.08.008
can be obtained automatically with high temporal resolution regardless of weather conditions, which offers the capability of GPS in monitoring atmospheric water vapor especially in the QTP. Xu et al. (2008b, 2011) studied the atmospheric water vapor collected in the QTP and its surrounding area using the GPS water vapor observation network of the Japan International Co-operation Agency (JICA). The atmospheric weighted mean temperature (Tm) and ZWD are two important factors in determining the accuracy of GPS PW in the approach to deriving the GPS PW given by Bevis et al. (1992, 1994). The Tm depends on local factors such as location and season. If assuming Tm to be a constant value for all areas and seasons, it can vary by as much as 15%, as expressed by Tm ¼260720 K. Bevis et al. (1992) investigated the correlation between Tm and surface temperature Ts (in Kelvin) by analyzing 8718 radiosonde profiles during a two-year interval from sites in the United States with a latitude range of 271–651 and a height range of 0–1.6 km. They found a linear relationship, Tm ¼70.2þ0.72Ts, with a 4.7 K root-mean-square (RMS) error corresponding to a relative error of less than 2%. Because it is difficult to model the ZWD, it is usually obtained by subtracting the modeled zenith hydrostatic delay (ZHD) from the zenith total delay (ZTD). Obviously, the ZWD will tend to accumulate the ZHD modeling error, and the ZHD model will therefore have an effect on the accuracy of GPS PW (Davis et al., 1985; Elgered et al., 1991; Bevis et al., 1992, 1994). Liu et al. (2000) used radiosonde data in Hong Kong to evaluate the accuracy of the popular ZHD models and found
G. Xu et al. / Journal of Atmospheric and Solar-Terrestrial Physics 89 (2012) 76–82
an RMS error of approximately 17 mm among them. Assessing the accuracy of GPS PW in the middle Yangtze River Valley, Xu et al. (2008, 2009) showed that the popular ZHD models can result in an error of approximately 4–10 mm, and that using the local Tm and modified ZHD model can significantly reduce the bias between the GPS PW and the radiosonde profiles. However, due to the lack of observations, few studies have been performed on the accuracy of GPS PW based on Bevis Tm and the popular ZHD models in areas with an altitude above 1600 m, especially in the QTP (Ross and Rosenfeld, 1997; Yang et al., 2002; Jade et al., 2005; Liang et al., 2006; Musa et al., 2011). This lack of research inspired us to perform an investigation into the applicability of the methods for estimating GPS PW in the QTP.
2. Data and methodology Three GPS stations in the QTP and two GPS stations in the plain of Central China were used in this study. As shown in Fig. 1 and Table 1, the five GPS stations have close latitudes but distinct longitudes. The GPS data and radiosonde profiles in the QTP are obtained from the GPS water vapor observation network of JICA (Xu et al., 2008b), and those in the plain are from the Hubei GPS water vapor observation network and heavy rain database managed by the Institute of Heavy Rain (IHR) of the China Meteorological Administration (CMA). The GPS data temporal resolution is 1 h. All radiosonde profiles have a vertical resolution of 10–30 m with a temporal resolution of 3 or 6 h. In deriving PW from ground-based GPS observation, the ZTD can be calculated with GAMIT software developed by the Massachusetts Institute of Technology (MIT). The ZHD can then be
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calculated with one of the following models: ( ZHDS ¼ 0:2277 FðjPs,HÞ , Fðj,HÞ ¼ 10:0026cosð2jÞ0:00028H (
ZHDH ¼ 1:552ðhHÞ TPss h ¼ 40:082 þ0:14898ðT s 273:16Þ
ZHDB ¼ 0:2343ðT s 4:12Þ
Ps Ts
,
ð1Þ
ð2Þ
ð3Þ
in the above equations, the unit of ZHD is cm, and the subscripts S, H and B represent the Saastamoinen (Saastamoinen, 1973; Elgered et al., 1991), Hopfield (Hopfield, 1971) and Black (Black, 1978) models, respectively. Ps is the surface pressure in hPa. Ts is the surface temperature in K. j and H are the latitude and height (in km) of the station, respectively. The ZWD is calculated by subtracting the ZHD from the ZTD. Then, the PW can be derived from the ZWD through the following equation given by Bevis et al. (1994): ( PW ¼ PZWD 6 ð4Þ P ¼ r R ½ðk10=T Þ þ k0 , w v
m
3
2
where rw denotes the density of liquid water, Rv is the specific gas constant for water vapor, and Tm represents the weighted mean temperature of the atmosphere. Tm is defined as (Davis et al., 1985) R ðe=TÞdz , ð5Þ Tm ¼ R ðe=T 2 Þdz where e is the water vapor pressure, and T is the absolute temperature. Tm has a linear relationship with the surface temperature Ts. Here, we calculate Tm through the regression of Bevis et al. (1992) T m ¼ 70:2 þ 0:72T s
ð6Þ
In Eq. (4), 0 k2
¼ k2 mk1 ,
ð7Þ
where m is Mw/Md, the ratio of the molar masses of water vapor to dry air. The physical constants k1, k2, and k3 are from the widely used equation for atmospheric refractivity N: N ¼ k1
Pd e e þ k2 þ k3 2 , T T T
ð8Þ
where Pd is the partial pressure for dry air. Eq. (8) can be rewritten as (Davis et al., 1985) 0
N ¼ k1 Rd r þ k2
Fig. 1. Distribution of GPS stations. The dots indicate the station locations, and the shaded area represents an altitude above 2000 m.
Table 1 Coordinate information of GPS stations and time span of the data. Station name Longitude/1E Latitude/1N Altitude/m Data time span NAQU GAIZ LITA BFES BFYG
92.1 84.1 100.3 109.5 111.3
31.5 32.3 30.0 30.3 30.7
4478 4394 3925 457 134
February–March 2008 February–July 2008 February–July 2008 June–July 2010 June–July 2010
e e þ k3 2 : T T
ð9Þ
The first part of Eq. (9) is termed the dry or hydrostatic delay, Nd ¼k1Rdr, where Rd is the specific gas constant for dry air, and r is the total mass density. The remaining two parts are termed the wet delay, N w ¼ k20 ðe=TÞ þ k3 ðe=T 2 Þ. The ZHD and ZWD can be calculated by the numerical integration of Nd and Nw in the zenith direction, respectively. In this study, we adopt the values of k1, k2, and k3 in Bevis et al. (1994): k1 ¼77.60 K hPa 1, k2 ¼70.4 K hPa 1, k3 ¼3.739 105 K2 hPa 1. Using the radiosonde profiles of e, T and humidity, we can calculate Tm and ZWD according to Eqs. (5) and (9), respectively, and PW can also be obtained by the following equation: Z Ps 1 qdp ð10Þ PW ¼ rw g 0 where q is the specific humidity and g is the gravity acceleration. Fig. 2 presents the variations of ZWD, PW and Tm versus
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G. Xu et al. / Journal of Atmospheric and Solar-Terrestrial Physics 89 (2012) 76–82
Fig. 2. Variations of radiosonde ZWD, PW and Tm versus integrated height at (a) NAQU and (b) BFYG.
Table 2 Variables and their processing strategies. Variable
Processing strategy
Tm_B ZHDS ZHDH ZHDB PWS PWH PWB PWS þ Tm_obs PWH þ Tm_obs PWB þ Tm_obs PWZWD_obs þ Tm
Tm calculated with the Bevis Tm model in Eq. (6) ZHD calculated with the Saastamoinen model ZHD calculated with the Hopfield model ZHD calculated with the Black model GPS PW calculated with the Saastamoinen model and Bevis Tm model GPS PW calculated with the Hopfield model and Bevis Tm model GPS PW calculated with the Black model and Bevis Tm model GPS PW calculated with the Saastamoinen model and radiosonde Tm GPS PW calculated with the Hopfield model and radiosonde Tm GPS PW calculated with the Black model and radiosonde Tm PW calculated with radiosonde ZWD and the Bevis Tm model
integrated height derived from radiosonde profiles at NAQU and BFYG. It is found that the radiosonde ZWD, PW and Tm are almost constant with an integrated height above 10 km. The differences among ZWD, PW and Tm at a height of 10 km and 30 km over NAQU are 4.4%, 4.0% and 0.4%, respectively, and those over BFYG are 0.5%, 0.4% and 0.1%, respectively. Hence, it is reasonable to take the radiosonde ZWD, PW and Tm with an integrated height above 10 km as credible observations. The ZHD is then calculated by subtracting the radiosonde ZWD from GPS ZTD as a reliable observation. With the above observations, the applicability of Bevis Tm, the ZHD model and GPS PW in the QTP are assessed. Table 2 lists the variables and their processing strategies, as used in this study. The bias and RMS error of the variables in Table 2 are calculated by comparison with the observed Tm, ZHD and PW.
3. Applicability of GPS PW in the QTP 3.1. Tm model The bias and RMS error of the Bevis Tm model are calculated by comparison with the radiosonde Tm. As shown in Table 3, the correlation coefficient between Tm_B and the radiosonde Tm is greater than 0.61 at a 99% confidence level, except that at NAQU, the correlation coefficient is 0.15 with a confidence level below 80%. Both the bias and RMS error of Tm_B are small in the plain and large in the QTP. In the plain, the bias is approximately 1 K with a relative error of less than 0.5%, and the RMS error is approximately 2 K with a relative error of less than 1%. However, in the QTP, the relative bias ranges from 1.6% to 4.4% and that of the RMS error is 1.3%–2.0%. Bevis et al. (1992) used 8718 radiosonde
G. Xu et al. / Journal of Atmospheric and Solar-Terrestrial Physics 89 (2012) 76–82
profiles during a two-year interval from sites in the United States with a latitude range of 271–651 and a height range of 0–1.6 km to yield Eq. (6), with an RMS deviation less than 2%. Our results indicate that the Bevis Tm model is applicable in both the plain of Central China and the QTP, with a more satisfactory performance in the plain. Fig. 3 presents the diurnal variations of the Tm bias and RMS error. It is observed that the Tm bias and RMS error have a similar diurnal variation. As a whole, the Tm bias and RMS error are small in the morning and large the rest of the time in the QTP; in the plain, they are small in the daytime and large at nighttime.
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three ZHD models are close and decrease with the station altitude, which varies from 1.2% in the plain to 0.5% in the QTP. As shown in Fig. 4, the ZHD bias of the Saastamoinen and Black models are small in the daytime and large at nighttime in the plain, while that of the Hopfield model is the opposite. However, the diurnal variation of the ZHD bias is complex in the QTP, which varies with the ZHD model and station. The diurnal variation of the ZHD RMS error is also complex. For a given station, the three ZHD models have a similar diurnal variation in the RMS error, but the diurnal variation pattern varies with the station. 3.3. GPS PW
3.2. ZHD model The bias and RMS error of the three ZHD models are calculated and shown in Table 4. Compared with the ZHD observations (calculated by subtracting the radiosonde ZWD from the GPS ZTD), the correlation coefficient between the ZHD model values and observations is less than 0.41, except that at NAQU, the value is greater than 0.70. Among the three ZHD models, the relative bias of the Saastamoinen model is the smallest with a value within 0.5% in both the plain and the QTP, and that of the Black model is within 1.5%. The ZHD of the Hopfield model is smaller than the ZHD observation, and the absolute relative bias between them increases with the station altitude, which varies from 0.5% in the plain to 11.9% in the QTP. The relative RMS errors of the Table 3 Bias and RMS error of Tm_B compared with the radiosonde Tm. Station
Corr. coef.
Bias/K
Rela. bias/%
RMS/K
Rela. RMS/%
NAQU GAIZ LITA BFES BFYG
0.1506 0.8156 0.6795 0.6199 0.6218
11.0 5.8 4.2 0.5 1.1
4.4 2.2 1.6 0.2 0.4
5.1 3.5 4.8 2.0 2.2
2.0 1.3 1.8 0.7 0.8
Adopting the Bevis Tm and popular ZHD models, GPS PW is calculated and compared with the radiosonde PW. As shown in Table 5, GPS PW has a good positive correlation with the radiosonde Table 4 Bias and RMS errors of the three ZHD models compared with the observed ZHD. ZHD model
Station
Corr. coef.
ZHDS
NAQU GAIZ LITA BFES BFYG
0.7443 0.3519 0.4003 0.1778 0.1854
0.4 0.2 0.4 0.7 0.6
ZHDH
NAQU GAIZ LITA BFES BFYG
0.7097 0.2728 0.0890 0.1969 0.1903
ZHDB
NAQU GAIZ LITA BFES BFYG
0.7543 0.3496 0.3771 0.1888 0.1895
Fig. 3. Diurnal variation of Tm bias (solid line) and RMS error (dashed line).
Bias/cm
Rela. bias/%
RMS/cm
Rela. RMS/%
0.3 0.2 0.3 -0.3 -0.3
0.7 1.0 0.8 2.1 2.7
0.5 0.8 0.6 1.0 1.2
16.0 14.7 13.7 3.0 1.2
11.9 10.8 9.6 1.4 0.5
0.7 1.1 1.0 2.1 2.7
0.6 0.8 0.7 1.0 1.2
1.0 1.8 2.0 2.2 2.5
0.8 1.3 1.4 1.0 1.1
0.7 1.0 0.8 2.1 2.7
0.5 0.8 0.6 1.0 1.2
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G. Xu et al. / Journal of Atmospheric and Solar-Terrestrial Physics 89 (2012) 76–82
Fig. 4. Diurnal variations of the ZHD model bias (solid line) and RMS error (dashed line).
PW. The correlation coefficient between them is greater than 0.91 with a 99% confidence level, except that at NAQU, the correlation coefficient has a smaller value of 0.6. Compared with the radiosonde PW, PWS is large in the plain and small in the QTP except at NAQU; PWH is large in both the plain and the QTP; and PWB is the opposite. Among PWS, PWH and PWB, the relative bias of PWS is the smallest and that of PWH is the largest; both the absolute relative bias of PWH and PWB increase with the station altitude. Moreover, all of the absolute relative biases of PWS, PWH and PWB are larger in the QTP than in the plain. The RMS errors of PWS, PWH and PWB are less than 2.0 mm in the QTP and 5.0 mm in the plain. However, all of the relative RMS errors of PWS, PWH and PWB increase with the station altitude, which is below 8% in the plain and above 10% in the QTP. According to the results of Yang et al. (2002) and Liang et al. (2006), the GPS PW values obtained at Lasa (with an altitude of 3659 m) and NAQU have an RMS error of 1.5 mm. In addition, the GPS PW values obtained over the Malaysian Peninsula have an RMS error varying from 3.4 mm to 4.3 mm (Musa et al., 2011). Our results indicate that the GPS PW bias in the plain of Central China and the QTP agree with the above results. However, it is still necessary to perform a comparison with the relative RMS error of GPS PW to arrive at a valid conclusion. The diurnal variations of the GPS PW bias and RMS error obtained with the Bevis Tm and ZHD models are presented in Fig. 5. It is observed that the GPS PW bias obtained with the Saastamoinen and Hopfield models are large in the daytime and small at nighttime in the plain, while that of the Black model is
the opposite. However, the diurnal variation of the GPS PW bias is complex in the QTP, varying with the ZHD model and station. The diurnal variation of the GPS PW RMS error is also complex. For a given station, the diurnal variations of the GPS PW RMS errors obtained with the three ZHD models are similar, but the diurnal variation pattern varies with the station. In addition, it is interesting that the diurnal variation patterns of the GPS PW bias and RMS error are similar to those of the ZHD models (see Figs. 4 and 5), which may imply that the ZHD model has an effect on the diurnal variations of the GPS PW bias and RMS error. 3.4. Effects of Tm and ZHD on GPS PW To assess the effects of the modeled Tm and ZHD on GPS PW, we compute GPS PW by replacing the Tm and ZWD in Eq. (4) with the radiosonde Tm and radiosonde ZWD, respectively. Compared with the previous results obtained with the Bevis Tm and popular ZHD models (see Table 5), replacing Bevis Tm with the radiosonde Tm makes no obvious difference in the bias and RMS error of GPS PW(see Table 6). However, as shown in Table 7, replacing the ZWD obtained by subtracting the modeled ZHD from the GPS ZTD with the radiosonde ZWD can significantly reduce the bias and RMS error of GPS PW. The relative bias of GPS PW is reduced to less than 1% in the plain and 4% in the QTP, and the relative RMS error of GPS PW also decreases to within 1% in the plain and 2% in the QTP. The results in Tables 3 and 5–7 imply that the bias and RMS error of GPS PW are mainly affected by the ZHD model, and a
G. Xu et al. / Journal of Atmospheric and Solar-Terrestrial Physics 89 (2012) 76–82
good Tm can improve the accuracy of GPS PW in the QTP. Ross and Rosenfeld (1997) concluded that a site-specific model would be superior to the geographically and globally invariant regression relationship used for Tm, and our results provide evidence for their conclusion.
Table 5 Bias and RMS error of GPS PW compared with radiosonde PW. GPS PW
Station
Corr. coef.
Bias/mm
PWS
NAQU GAIZ LITA BFES BFYG
0.6099 0.9314 0.9755 0.9175 0.9156
0.7 0.2 0.4 1.5 1.2
PWH
NAQU GAIZ LITA BFES BFYG
0.6066 0.9284 0.9742 0.9183 0.9160
PWB
NAQU GAIZ LITA BFES BFYG
0.6222 0.9292 0.9749 0.9185 0.9161
Rela. bias/%
RMS/mm
Rela. RMS/%
41.2 3.4 4.0 2.7 2.0
1.1 1.6 1.3 3.6 4.4
66.3 22.7 11.6 6.6 7.8
24.1 22.8 21.4 5.1 2.2
1478.4 318.8 195.1 9.4 3.8
1.1 1.6 1.3 3.5 4.4
65.9 22.8 11.8 6.6 7.8
1.5 2.6 3.0 3.3 3.9
90.6 36.6 27.1 6.1 6.8
1.1 1.6 1.3 3.5 4.4
64.8 22.7 11.7 6.5 7.8
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4. Conclusions Tm and ZHD are important for the accuracy of GPS PW. In this paper, we calculated the modeled Tm and ZHD as well as the GPS PW in the QTP, and we compared them with those obtained from radiosonde profiles with a high vertical resolution of 10–30 m. The results can be summarized as follows: 1. The Bevis Tm model has a larger bias and RMS error in the QTP than in the plain of Central China. The Tm bias varies from 1.6% to 4.4%, with an RMS error of less than 2.0%; they are small in the morning and large at other times in the QTP, and in the plain, they are small in the daytime and large at nighttime. 2. The RMS error of the ZHD model in the QTP is smaller than that in the plain of Central China, being within 1.0% and 1.5%, respectively. Among the ZHD models of Saastamoinen, Hopfield and Black, the ZHD bias of the Saastamoinen model is the smallest in the QTP, with an absolute value of less than 0.5%. The diurnal variations of the ZHD bias and RMS error are similar in the plain, but they are complex in the QTP, varying with the ZHD model and station. 3. The relative RMS error of GPS PW increases with the station altitude, which is below 8% in the plain of Central China and ranges from 12% to 66% in the QTP. The bias of the GPS PW with the adoption of the Saastamoinen model is the smallest in the QTP, and that for the Hopfield model is the largest. The diurnal variations of the GPS PW bias and RMS error are
Fig. 5. Diurnal variations of GPS PW bias (solid line) and RMS error (dashed line) obtained with Bevis Tm and the three ZHD models.
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G. Xu et al. / Journal of Atmospheric and Solar-Terrestrial Physics 89 (2012) 76–82
Table 6 Bias and RMS error of GPS PW obtained with the three ZHD models and radiosonde Tm. GPS PW
Station
Corr. coef.
Bias/mm
PWS þ Tm_obs
NAQU GAIZ LITA BFES BFYG
0.6251 0.9329 0.9758 0.9211 0.9181
0.6 0.4 0.6 1.6 1.4
PWH þ Tm_obs
NAQU GAIZ LITA BFES BFYG
0.6719 0.9335 0.9695 0.9220 0.9184
PWB þ Tm_obs
NAQU GAIZ LITA BFES BFYG
0.6272 0.9299 0.9750 0.9217 0.9183
Rela. bias/%
RMS/mm
Rela. RMS/%
35.8 5.1 5.3 2.9 2.4
1.0 1.6 1.3 3.5 4.4
63.5 22.2 11.5 6.4 7.7
23.1 22.2 21.0 5.2 2.4
1414.0 310.2 190.9 9.6 4.2
1.1 1.6 1.4 3.5 4.4
69.0 22.3 12.8 6.4 7.7
1.5 2.7 3.1 3.2 3.6
90.6 37.6 28.0 5.9 6.5
1.0 1.6 1.3 3.5 4.4
62.3 22.4 11.7 6.4 7.7
Table 7 Bias and RMS error of PW obtained with the radiosonde ZWD and the Bevis Tm model. PW
Station Corr. coef. Bias/mm Rela. bias/% RMS/mm Rela. RMS/%
PWZWD_obs þ Tm NAQU GAIZ LITA BFES BFYG
0.9997 0.9999 0.9997 0.9992 0.9994
0.06 0.14 0.18 0.25 0.21
3.7 2.0 1.6 0.5 0.4
0.03 0.09 0.15 0.36 0.39
1.9 1.3 1.4 0.7 0.7
similar to those of the ZHD, being similar in the plain but varying with the ZHD model and station in the QTP. 4. In both the plain of Central China and the QTP, the bias and RMS error of GPS PW are mainly affected by the ZHD model, and their diurnal variations are also related to the ZHD model. Moreover, a local Tm can improve the accuracy of GPS PW. Acknowledgments This work was supported by the social commonweal research program of the Ministry of Science and Technology of China (Grant no. GYHY201006009) and the National Natural Science Foundation of China (Grant no. 41175016). We are grateful to the State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, for providing the GPS observations and radiosonde profiles in the Qinghai-Tibet Plateau from the JICA database. Special thanks are given to Dr. Xinan Yue at the University Corporation for Atmospheric Research (UCAR) for his effort in improving this paper greatly. References Baker, H.C., Dodson, A.H., Penna, N.T., Higgins, M., Offiler, D., 2001. Ground-based GPS water vapor estimation: Potential for meteorological forecasting. Journal of Atmospheric and Solar-Terrestrial Physics 63, 1305–1314.
Bevis, M., Businger, S., Herring, T.A., Rocken, C., Anthes, R.A., Ware, R.H., 1992. GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system. Journal of Geophysical Research 97, 15787–15801. Bevis, M., Businger, S., Chiswell, S., Herring, T.A., Anthes, R.A., Rocken, C., Ware, R.H., 1994. GPS meteorology: Mapping zenith wet delays onto precipitable water. Journal of Applied Meteorology 33, 379–386. Black, H.D., 1978. An easily implemented algorithm for the tropospheric range correction. Journal of Geophysical Research 83, 1825–1828. Businger, S., Chiswell, S., Bevis, M., Duan, J., Anthes, R.A., Rocken, C., Ware, R.H., Exner, M., Vanhove, T., Solheim, F.S., 1996. The promise of GPS in atmospheric monitoring. Bulletin of the American Meteorological Society 77, 5–18. Davis, J.L., Herring, T.A., Shapiro, I.I., Rogers, A.E., Elgered, G., 1985. Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length. Radio Science 20, 1593–1607. Dick, G., Gendt, G., Reigber, C., 2001. First experience with near real-time water vapor estimation in a German GPS network. Journal of Atmospheric and Solar-Terrestrial Physics 63, 1295–1304. Elgered, G., Davis, J.L., Herring, T.A., Shapiro, I.I., 1991. Geodesy by radio interferometry: Water vapor radiometry for estimation of the wet delay. Journal of Geophysical Research 96, 6541–6555. Hopfield, H.S., 1971. Tropospheric effect on electromagnetically measured range: Prediction from surface weather data. Radio Science 6, 357–367. Jade, S., Vijayan, M.S.M., Gaur, V.K., Prabhu, T.P., Sahu, S.C., 2005. Estimates of precipitable water vapour from GPS data over the Indian subcontinent. Journal of Atmospheric and Solar-Terrestrial Physics 67, 623–635. Liang, H., Liu, J., Zhang, J., Bi, Y., Wang, K., 2006. Research on retrieval of the amount of atmospheric water vapor over Qinghai-Xizang Plateau. Plateau Meteorology (in Chinese) 25, 1055–1063. Liu, Y., IZ, H B, Chen, Y., 2000. Precise determination of dry zenith delay for GPS meteorology applications. Acta Geodaetica et Cartographica Sinica 29, 172–180. MacDonald, A.E., Xie, Y., 2002. Diagnosis of three-dimensional water vapor using a GPS network. Monthly Weather Review 130, 386–397. Musa, T.A., Amir, S., Othman, R., Ses, S., Omar, K., Abdullah, K., Lim, S., Rizos, C., 2011. GPS meteorology in a low-latitude region: Remote sensing of atmospheric water vapor over the Malaysian Peninsula. Journal of Atmospheric and Solar-Terrestrial Physics 73, 2410–2422. Peng, S., Xu, X., Shi, X., Wang, D., Zhu, Y., Pu, J., 2009. The early-warning effects of assimilation of the observations over the large-scale slope of the ‘‘World Roof’’ on its downstream weather forecasting. Chinese Science Bulletin 54, 706–710. Ross, R.J., Rosenfeld, S., 1997. Estimating mean weighted temperature of the atmosphere for Global Positioning System applications. Journal of Geophysical Research 102, 21719–21730. Saastamoinen, J., 1973. Contributions to the theory of atmospheric refraction. Bulletin Geodesique 107, 13–34. Song, S., Zhu, W., Ding, J., Peng, J., 2006. 3D water-vapor tomography with Shanghai GPS network to improve forecasted moisture field. Chinese Science Bulletin 51, 607–614. Ware, R.H., Fulker, D.W., Stein, S.A., Anderson, D.N., Avery, S.K., Clark, R.D., Droegemeier, K.K., Kuettner, J.P., Minster, J.B., Sorooshian, S., 2000. SuomiNet: A real-time national GPS network for atmospheric research and education. Monthly Weather Review 81, 677–694. Xu, G., Chen, B., Wan, R., Li, W., Feng, G., 2008. Error analysis on precipitable water derived from ground-based GPS with various methods. Torrential Rain and Disasters (in Chinese) 27, 346–350. Xu, G., Cui, C., Li, W., Zhang, W., Feng, G., 2011. Variation of GPS precipitable water over the Qinghai-Tibet Plateau: Possible teleconnection triggering rainfall over the Yangtze river valley. Terrestrial, Atmospheric and Oceanic Science 22, 195–202. Xu, G., Wan, R., Li, W., Chen, B., Feng, G., 2009. Improvement on the method for estimating precipitable water from ground-based GPS. Torrential Rain and Disasters (in Chinese) 28, 203–209. Xu, X., Chen, L., 2006. Advances of the study on Tibetan Plateau experiment of atmospheric sciences. Journal of Applied Meteorological Science (in Chinese) 17, 756–772. Xu, X., Lu, C., Shi, X., Gao, S., 2008a. World water tower: An atmospheric perspective. Geophysical Research Letters 35, L20815. Xu, X., Zhang, R., Koike, T., Lu, C., Shi, X., Zhang, S., Bian, L., Cheng, X., Li, P., Ding, G., 2008b. A new integrated observational system over the Tibetan Plateau. Bulletin of the American Meteorological Society 89, 1492–1496. Yang, G., Liu, J., Mao, J., 2002. Analysis on water vapor obtained with ground-based GPS over Tibetan area. Meteorological Science and Technology (in Chinese) 30, 266–272.
Contents lists available at SciVerse ScienceDirect
Journal of Atmospheric and Solar-Terrestrial Physics journal homepage: www.elsevier.com/locate/jastp
Applicability of methods for estimating GPS precipitable water in the Qinghai-Tibet Plateau Guirong Xu n, Chunguang Cui, Rong Wan, Anwei Lai, Xia Wan, Zhikang Fu, Guangliu Feng Institute of Heavy Rain, China Meteorological Administration, Wuhan 430074, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 February 2012 Received in revised form 1 August 2012 Accepted 9 August 2012 Available online 28 August 2012
Atmospheric weighted mean temperature (Tm) and zenith hydrostatic delay (ZHD) are key parameters in determining the accuracy of GPS precipitable water (PW) estimations. Using radiosonde profiles with a high vertical resolution of 10–30 m, we calculated Tm, ZHD and PW and investigated the applicability of the modeled Tm and ZHD, including the GPS PW obtained with them, in the Qinghai-Tibet Plateau (QTP). It is found that (1) the bias and RMS error of the Bevis Tm model in the QTP are less than 5% and 2%, respectively, with a diurnal variation that is small in the morning and large otherwise. (2) The ZHD models have an RMS error within 1% in the QTP, which is smaller than that in the plain of Central China, which is within 1.5%. The ZHD bias of the Saastamoinen model is less than 0.5% in the QTP, smaller than those of the Hopfield and Black models. The diurnal variations of the modeled ZHD bias and RMS error are complex in the QTP, varying with the ZHD model and station. (3) The relative RMS error of GPS PW increases with the station altitude, with a range of 12–66% in the QTP and within 8% in the plain of Central China. The diurnal variations of the GPS PW bias and RMS error in the QTP are similar to those of the modeled ZHD. (4) The accuracy of GPS PW is mainly affected by the ZHD model, and a good Tm can improve the accuracy of GPS PW in the QTP. & 2012 Elsevier Ltd. All rights reserved.
Keywords: Applicability GPS Precipitable water Qinghai-Tibet Plateau
1. Introduction As the largest and highest plateau in the world, the QinghaiTibet Plateau (QTP) plays an important role in the weather and climate over China and surrounding areas (Xu and Chen, 2006). The QTP serves as ‘‘the world’s water tower’’. The land–ocean– atmosphere interaction around the QTP has a significant impact on the global natural and climate environment (Xu et al., 2008a). A data assimilation experiment showed that information regarding the moisture, temperature and pressure in the QTP area is of great importance and is useful in the forecasting of precipitation in its downstream areas (Peng et al., 2009). However, few observations are available in the QTP because of the complex terrain and terrible weather conditions. Recently, Bevis et al. (1992, 1994) proposed an approach to estimate the precipitable water (PW) from ground-based GPS observation based on the linear relationship between the zenith wet delay (ZWD) and PW. Since then, GPS technology has been broadly applied in atmospheric monitoring and numerical weather forecasting (Businger et al., 1996; Ware et al., 2000; Dick et al., 2001; Baker et al., 2001; MacDonald and Xie (2002); Song et al., 2006). GPS observations n Correspondence to: Institute of Heavy Rain, China Meteorological Administration, No. 3 Donghudong Road, Wuhan 430074, Hubei, China. Tel.: þ86 27 67847954; fax: þ 86 27 87806597. E-mail addresses: [email protected], [email protected] (G. Xu).
1364-6826/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jastp.2012.08.008
can be obtained automatically with high temporal resolution regardless of weather conditions, which offers the capability of GPS in monitoring atmospheric water vapor especially in the QTP. Xu et al. (2008b, 2011) studied the atmospheric water vapor collected in the QTP and its surrounding area using the GPS water vapor observation network of the Japan International Co-operation Agency (JICA). The atmospheric weighted mean temperature (Tm) and ZWD are two important factors in determining the accuracy of GPS PW in the approach to deriving the GPS PW given by Bevis et al. (1992, 1994). The Tm depends on local factors such as location and season. If assuming Tm to be a constant value for all areas and seasons, it can vary by as much as 15%, as expressed by Tm ¼260720 K. Bevis et al. (1992) investigated the correlation between Tm and surface temperature Ts (in Kelvin) by analyzing 8718 radiosonde profiles during a two-year interval from sites in the United States with a latitude range of 271–651 and a height range of 0–1.6 km. They found a linear relationship, Tm ¼70.2þ0.72Ts, with a 4.7 K root-mean-square (RMS) error corresponding to a relative error of less than 2%. Because it is difficult to model the ZWD, it is usually obtained by subtracting the modeled zenith hydrostatic delay (ZHD) from the zenith total delay (ZTD). Obviously, the ZWD will tend to accumulate the ZHD modeling error, and the ZHD model will therefore have an effect on the accuracy of GPS PW (Davis et al., 1985; Elgered et al., 1991; Bevis et al., 1992, 1994). Liu et al. (2000) used radiosonde data in Hong Kong to evaluate the accuracy of the popular ZHD models and found
G. Xu et al. / Journal of Atmospheric and Solar-Terrestrial Physics 89 (2012) 76–82
an RMS error of approximately 17 mm among them. Assessing the accuracy of GPS PW in the middle Yangtze River Valley, Xu et al. (2008, 2009) showed that the popular ZHD models can result in an error of approximately 4–10 mm, and that using the local Tm and modified ZHD model can significantly reduce the bias between the GPS PW and the radiosonde profiles. However, due to the lack of observations, few studies have been performed on the accuracy of GPS PW based on Bevis Tm and the popular ZHD models in areas with an altitude above 1600 m, especially in the QTP (Ross and Rosenfeld, 1997; Yang et al., 2002; Jade et al., 2005; Liang et al., 2006; Musa et al., 2011). This lack of research inspired us to perform an investigation into the applicability of the methods for estimating GPS PW in the QTP.
2. Data and methodology Three GPS stations in the QTP and two GPS stations in the plain of Central China were used in this study. As shown in Fig. 1 and Table 1, the five GPS stations have close latitudes but distinct longitudes. The GPS data and radiosonde profiles in the QTP are obtained from the GPS water vapor observation network of JICA (Xu et al., 2008b), and those in the plain are from the Hubei GPS water vapor observation network and heavy rain database managed by the Institute of Heavy Rain (IHR) of the China Meteorological Administration (CMA). The GPS data temporal resolution is 1 h. All radiosonde profiles have a vertical resolution of 10–30 m with a temporal resolution of 3 or 6 h. In deriving PW from ground-based GPS observation, the ZTD can be calculated with GAMIT software developed by the Massachusetts Institute of Technology (MIT). The ZHD can then be
77
calculated with one of the following models: ( ZHDS ¼ 0:2277 FðjPs,HÞ , Fðj,HÞ ¼ 10:0026cosð2jÞ0:00028H (
ZHDH ¼ 1:552ðhHÞ TPss h ¼ 40:082 þ0:14898ðT s 273:16Þ
ZHDB ¼ 0:2343ðT s 4:12Þ
Ps Ts
,
ð1Þ
ð2Þ
ð3Þ
in the above equations, the unit of ZHD is cm, and the subscripts S, H and B represent the Saastamoinen (Saastamoinen, 1973; Elgered et al., 1991), Hopfield (Hopfield, 1971) and Black (Black, 1978) models, respectively. Ps is the surface pressure in hPa. Ts is the surface temperature in K. j and H are the latitude and height (in km) of the station, respectively. The ZWD is calculated by subtracting the ZHD from the ZTD. Then, the PW can be derived from the ZWD through the following equation given by Bevis et al. (1994): ( PW ¼ PZWD 6 ð4Þ P ¼ r R ½ðk10=T Þ þ k0 , w v
m
3
2
where rw denotes the density of liquid water, Rv is the specific gas constant for water vapor, and Tm represents the weighted mean temperature of the atmosphere. Tm is defined as (Davis et al., 1985) R ðe=TÞdz , ð5Þ Tm ¼ R ðe=T 2 Þdz where e is the water vapor pressure, and T is the absolute temperature. Tm has a linear relationship with the surface temperature Ts. Here, we calculate Tm through the regression of Bevis et al. (1992) T m ¼ 70:2 þ 0:72T s
ð6Þ
In Eq. (4), 0 k2
¼ k2 mk1 ,
ð7Þ
where m is Mw/Md, the ratio of the molar masses of water vapor to dry air. The physical constants k1, k2, and k3 are from the widely used equation for atmospheric refractivity N: N ¼ k1
Pd e e þ k2 þ k3 2 , T T T
ð8Þ
where Pd is the partial pressure for dry air. Eq. (8) can be rewritten as (Davis et al., 1985) 0
N ¼ k1 Rd r þ k2
Fig. 1. Distribution of GPS stations. The dots indicate the station locations, and the shaded area represents an altitude above 2000 m.
Table 1 Coordinate information of GPS stations and time span of the data. Station name Longitude/1E Latitude/1N Altitude/m Data time span NAQU GAIZ LITA BFES BFYG
92.1 84.1 100.3 109.5 111.3
31.5 32.3 30.0 30.3 30.7
4478 4394 3925 457 134
February–March 2008 February–July 2008 February–July 2008 June–July 2010 June–July 2010
e e þ k3 2 : T T
ð9Þ
The first part of Eq. (9) is termed the dry or hydrostatic delay, Nd ¼k1Rdr, where Rd is the specific gas constant for dry air, and r is the total mass density. The remaining two parts are termed the wet delay, N w ¼ k20 ðe=TÞ þ k3 ðe=T 2 Þ. The ZHD and ZWD can be calculated by the numerical integration of Nd and Nw in the zenith direction, respectively. In this study, we adopt the values of k1, k2, and k3 in Bevis et al. (1994): k1 ¼77.60 K hPa 1, k2 ¼70.4 K hPa 1, k3 ¼3.739 105 K2 hPa 1. Using the radiosonde profiles of e, T and humidity, we can calculate Tm and ZWD according to Eqs. (5) and (9), respectively, and PW can also be obtained by the following equation: Z Ps 1 qdp ð10Þ PW ¼ rw g 0 where q is the specific humidity and g is the gravity acceleration. Fig. 2 presents the variations of ZWD, PW and Tm versus
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G. Xu et al. / Journal of Atmospheric and Solar-Terrestrial Physics 89 (2012) 76–82
Fig. 2. Variations of radiosonde ZWD, PW and Tm versus integrated height at (a) NAQU and (b) BFYG.
Table 2 Variables and their processing strategies. Variable
Processing strategy
Tm_B ZHDS ZHDH ZHDB PWS PWH PWB PWS þ Tm_obs PWH þ Tm_obs PWB þ Tm_obs PWZWD_obs þ Tm
Tm calculated with the Bevis Tm model in Eq. (6) ZHD calculated with the Saastamoinen model ZHD calculated with the Hopfield model ZHD calculated with the Black model GPS PW calculated with the Saastamoinen model and Bevis Tm model GPS PW calculated with the Hopfield model and Bevis Tm model GPS PW calculated with the Black model and Bevis Tm model GPS PW calculated with the Saastamoinen model and radiosonde Tm GPS PW calculated with the Hopfield model and radiosonde Tm GPS PW calculated with the Black model and radiosonde Tm PW calculated with radiosonde ZWD and the Bevis Tm model
integrated height derived from radiosonde profiles at NAQU and BFYG. It is found that the radiosonde ZWD, PW and Tm are almost constant with an integrated height above 10 km. The differences among ZWD, PW and Tm at a height of 10 km and 30 km over NAQU are 4.4%, 4.0% and 0.4%, respectively, and those over BFYG are 0.5%, 0.4% and 0.1%, respectively. Hence, it is reasonable to take the radiosonde ZWD, PW and Tm with an integrated height above 10 km as credible observations. The ZHD is then calculated by subtracting the radiosonde ZWD from GPS ZTD as a reliable observation. With the above observations, the applicability of Bevis Tm, the ZHD model and GPS PW in the QTP are assessed. Table 2 lists the variables and their processing strategies, as used in this study. The bias and RMS error of the variables in Table 2 are calculated by comparison with the observed Tm, ZHD and PW.
3. Applicability of GPS PW in the QTP 3.1. Tm model The bias and RMS error of the Bevis Tm model are calculated by comparison with the radiosonde Tm. As shown in Table 3, the correlation coefficient between Tm_B and the radiosonde Tm is greater than 0.61 at a 99% confidence level, except that at NAQU, the correlation coefficient is 0.15 with a confidence level below 80%. Both the bias and RMS error of Tm_B are small in the plain and large in the QTP. In the plain, the bias is approximately 1 K with a relative error of less than 0.5%, and the RMS error is approximately 2 K with a relative error of less than 1%. However, in the QTP, the relative bias ranges from 1.6% to 4.4% and that of the RMS error is 1.3%–2.0%. Bevis et al. (1992) used 8718 radiosonde
G. Xu et al. / Journal of Atmospheric and Solar-Terrestrial Physics 89 (2012) 76–82
profiles during a two-year interval from sites in the United States with a latitude range of 271–651 and a height range of 0–1.6 km to yield Eq. (6), with an RMS deviation less than 2%. Our results indicate that the Bevis Tm model is applicable in both the plain of Central China and the QTP, with a more satisfactory performance in the plain. Fig. 3 presents the diurnal variations of the Tm bias and RMS error. It is observed that the Tm bias and RMS error have a similar diurnal variation. As a whole, the Tm bias and RMS error are small in the morning and large the rest of the time in the QTP; in the plain, they are small in the daytime and large at nighttime.
79
three ZHD models are close and decrease with the station altitude, which varies from 1.2% in the plain to 0.5% in the QTP. As shown in Fig. 4, the ZHD bias of the Saastamoinen and Black models are small in the daytime and large at nighttime in the plain, while that of the Hopfield model is the opposite. However, the diurnal variation of the ZHD bias is complex in the QTP, which varies with the ZHD model and station. The diurnal variation of the ZHD RMS error is also complex. For a given station, the three ZHD models have a similar diurnal variation in the RMS error, but the diurnal variation pattern varies with the station. 3.3. GPS PW
3.2. ZHD model The bias and RMS error of the three ZHD models are calculated and shown in Table 4. Compared with the ZHD observations (calculated by subtracting the radiosonde ZWD from the GPS ZTD), the correlation coefficient between the ZHD model values and observations is less than 0.41, except that at NAQU, the value is greater than 0.70. Among the three ZHD models, the relative bias of the Saastamoinen model is the smallest with a value within 0.5% in both the plain and the QTP, and that of the Black model is within 1.5%. The ZHD of the Hopfield model is smaller than the ZHD observation, and the absolute relative bias between them increases with the station altitude, which varies from 0.5% in the plain to 11.9% in the QTP. The relative RMS errors of the Table 3 Bias and RMS error of Tm_B compared with the radiosonde Tm. Station
Corr. coef.
Bias/K
Rela. bias/%
RMS/K
Rela. RMS/%
NAQU GAIZ LITA BFES BFYG
0.1506 0.8156 0.6795 0.6199 0.6218
11.0 5.8 4.2 0.5 1.1
4.4 2.2 1.6 0.2 0.4
5.1 3.5 4.8 2.0 2.2
2.0 1.3 1.8 0.7 0.8
Adopting the Bevis Tm and popular ZHD models, GPS PW is calculated and compared with the radiosonde PW. As shown in Table 5, GPS PW has a good positive correlation with the radiosonde Table 4 Bias and RMS errors of the three ZHD models compared with the observed ZHD. ZHD model
Station
Corr. coef.
ZHDS
NAQU GAIZ LITA BFES BFYG
0.7443 0.3519 0.4003 0.1778 0.1854
0.4 0.2 0.4 0.7 0.6
ZHDH
NAQU GAIZ LITA BFES BFYG
0.7097 0.2728 0.0890 0.1969 0.1903
ZHDB
NAQU GAIZ LITA BFES BFYG
0.7543 0.3496 0.3771 0.1888 0.1895
Fig. 3. Diurnal variation of Tm bias (solid line) and RMS error (dashed line).
Bias/cm
Rela. bias/%
RMS/cm
Rela. RMS/%
0.3 0.2 0.3 -0.3 -0.3
0.7 1.0 0.8 2.1 2.7
0.5 0.8 0.6 1.0 1.2
16.0 14.7 13.7 3.0 1.2
11.9 10.8 9.6 1.4 0.5
0.7 1.1 1.0 2.1 2.7
0.6 0.8 0.7 1.0 1.2
1.0 1.8 2.0 2.2 2.5
0.8 1.3 1.4 1.0 1.1
0.7 1.0 0.8 2.1 2.7
0.5 0.8 0.6 1.0 1.2
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G. Xu et al. / Journal of Atmospheric and Solar-Terrestrial Physics 89 (2012) 76–82
Fig. 4. Diurnal variations of the ZHD model bias (solid line) and RMS error (dashed line).
PW. The correlation coefficient between them is greater than 0.91 with a 99% confidence level, except that at NAQU, the correlation coefficient has a smaller value of 0.6. Compared with the radiosonde PW, PWS is large in the plain and small in the QTP except at NAQU; PWH is large in both the plain and the QTP; and PWB is the opposite. Among PWS, PWH and PWB, the relative bias of PWS is the smallest and that of PWH is the largest; both the absolute relative bias of PWH and PWB increase with the station altitude. Moreover, all of the absolute relative biases of PWS, PWH and PWB are larger in the QTP than in the plain. The RMS errors of PWS, PWH and PWB are less than 2.0 mm in the QTP and 5.0 mm in the plain. However, all of the relative RMS errors of PWS, PWH and PWB increase with the station altitude, which is below 8% in the plain and above 10% in the QTP. According to the results of Yang et al. (2002) and Liang et al. (2006), the GPS PW values obtained at Lasa (with an altitude of 3659 m) and NAQU have an RMS error of 1.5 mm. In addition, the GPS PW values obtained over the Malaysian Peninsula have an RMS error varying from 3.4 mm to 4.3 mm (Musa et al., 2011). Our results indicate that the GPS PW bias in the plain of Central China and the QTP agree with the above results. However, it is still necessary to perform a comparison with the relative RMS error of GPS PW to arrive at a valid conclusion. The diurnal variations of the GPS PW bias and RMS error obtained with the Bevis Tm and ZHD models are presented in Fig. 5. It is observed that the GPS PW bias obtained with the Saastamoinen and Hopfield models are large in the daytime and small at nighttime in the plain, while that of the Black model is
the opposite. However, the diurnal variation of the GPS PW bias is complex in the QTP, varying with the ZHD model and station. The diurnal variation of the GPS PW RMS error is also complex. For a given station, the diurnal variations of the GPS PW RMS errors obtained with the three ZHD models are similar, but the diurnal variation pattern varies with the station. In addition, it is interesting that the diurnal variation patterns of the GPS PW bias and RMS error are similar to those of the ZHD models (see Figs. 4 and 5), which may imply that the ZHD model has an effect on the diurnal variations of the GPS PW bias and RMS error. 3.4. Effects of Tm and ZHD on GPS PW To assess the effects of the modeled Tm and ZHD on GPS PW, we compute GPS PW by replacing the Tm and ZWD in Eq. (4) with the radiosonde Tm and radiosonde ZWD, respectively. Compared with the previous results obtained with the Bevis Tm and popular ZHD models (see Table 5), replacing Bevis Tm with the radiosonde Tm makes no obvious difference in the bias and RMS error of GPS PW(see Table 6). However, as shown in Table 7, replacing the ZWD obtained by subtracting the modeled ZHD from the GPS ZTD with the radiosonde ZWD can significantly reduce the bias and RMS error of GPS PW. The relative bias of GPS PW is reduced to less than 1% in the plain and 4% in the QTP, and the relative RMS error of GPS PW also decreases to within 1% in the plain and 2% in the QTP. The results in Tables 3 and 5–7 imply that the bias and RMS error of GPS PW are mainly affected by the ZHD model, and a
G. Xu et al. / Journal of Atmospheric and Solar-Terrestrial Physics 89 (2012) 76–82
good Tm can improve the accuracy of GPS PW in the QTP. Ross and Rosenfeld (1997) concluded that a site-specific model would be superior to the geographically and globally invariant regression relationship used for Tm, and our results provide evidence for their conclusion.
Table 5 Bias and RMS error of GPS PW compared with radiosonde PW. GPS PW
Station
Corr. coef.
Bias/mm
PWS
NAQU GAIZ LITA BFES BFYG
0.6099 0.9314 0.9755 0.9175 0.9156
0.7 0.2 0.4 1.5 1.2
PWH
NAQU GAIZ LITA BFES BFYG
0.6066 0.9284 0.9742 0.9183 0.9160
PWB
NAQU GAIZ LITA BFES BFYG
0.6222 0.9292 0.9749 0.9185 0.9161
Rela. bias/%
RMS/mm
Rela. RMS/%
41.2 3.4 4.0 2.7 2.0
1.1 1.6 1.3 3.6 4.4
66.3 22.7 11.6 6.6 7.8
24.1 22.8 21.4 5.1 2.2
1478.4 318.8 195.1 9.4 3.8
1.1 1.6 1.3 3.5 4.4
65.9 22.8 11.8 6.6 7.8
1.5 2.6 3.0 3.3 3.9
90.6 36.6 27.1 6.1 6.8
1.1 1.6 1.3 3.5 4.4
64.8 22.7 11.7 6.5 7.8
81
4. Conclusions Tm and ZHD are important for the accuracy of GPS PW. In this paper, we calculated the modeled Tm and ZHD as well as the GPS PW in the QTP, and we compared them with those obtained from radiosonde profiles with a high vertical resolution of 10–30 m. The results can be summarized as follows: 1. The Bevis Tm model has a larger bias and RMS error in the QTP than in the plain of Central China. The Tm bias varies from 1.6% to 4.4%, with an RMS error of less than 2.0%; they are small in the morning and large at other times in the QTP, and in the plain, they are small in the daytime and large at nighttime. 2. The RMS error of the ZHD model in the QTP is smaller than that in the plain of Central China, being within 1.0% and 1.5%, respectively. Among the ZHD models of Saastamoinen, Hopfield and Black, the ZHD bias of the Saastamoinen model is the smallest in the QTP, with an absolute value of less than 0.5%. The diurnal variations of the ZHD bias and RMS error are similar in the plain, but they are complex in the QTP, varying with the ZHD model and station. 3. The relative RMS error of GPS PW increases with the station altitude, which is below 8% in the plain of Central China and ranges from 12% to 66% in the QTP. The bias of the GPS PW with the adoption of the Saastamoinen model is the smallest in the QTP, and that for the Hopfield model is the largest. The diurnal variations of the GPS PW bias and RMS error are
Fig. 5. Diurnal variations of GPS PW bias (solid line) and RMS error (dashed line) obtained with Bevis Tm and the three ZHD models.
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Table 6 Bias and RMS error of GPS PW obtained with the three ZHD models and radiosonde Tm. GPS PW
Station
Corr. coef.
Bias/mm
PWS þ Tm_obs
NAQU GAIZ LITA BFES BFYG
0.6251 0.9329 0.9758 0.9211 0.9181
0.6 0.4 0.6 1.6 1.4
PWH þ Tm_obs
NAQU GAIZ LITA BFES BFYG
0.6719 0.9335 0.9695 0.9220 0.9184
PWB þ Tm_obs
NAQU GAIZ LITA BFES BFYG
0.6272 0.9299 0.9750 0.9217 0.9183
Rela. bias/%
RMS/mm
Rela. RMS/%
35.8 5.1 5.3 2.9 2.4
1.0 1.6 1.3 3.5 4.4
63.5 22.2 11.5 6.4 7.7
23.1 22.2 21.0 5.2 2.4
1414.0 310.2 190.9 9.6 4.2
1.1 1.6 1.4 3.5 4.4
69.0 22.3 12.8 6.4 7.7
1.5 2.7 3.1 3.2 3.6
90.6 37.6 28.0 5.9 6.5
1.0 1.6 1.3 3.5 4.4
62.3 22.4 11.7 6.4 7.7
Table 7 Bias and RMS error of PW obtained with the radiosonde ZWD and the Bevis Tm model. PW
Station Corr. coef. Bias/mm Rela. bias/% RMS/mm Rela. RMS/%
PWZWD_obs þ Tm NAQU GAIZ LITA BFES BFYG
0.9997 0.9999 0.9997 0.9992 0.9994
0.06 0.14 0.18 0.25 0.21
3.7 2.0 1.6 0.5 0.4
0.03 0.09 0.15 0.36 0.39
1.9 1.3 1.4 0.7 0.7
similar to those of the ZHD, being similar in the plain but varying with the ZHD model and station in the QTP. 4. In both the plain of Central China and the QTP, the bias and RMS error of GPS PW are mainly affected by the ZHD model, and their diurnal variations are also related to the ZHD model. Moreover, a local Tm can improve the accuracy of GPS PW. Acknowledgments This work was supported by the social commonweal research program of the Ministry of Science and Technology of China (Grant no. GYHY201006009) and the National Natural Science Foundation of China (Grant no. 41175016). We are grateful to the State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, for providing the GPS observations and radiosonde profiles in the Qinghai-Tibet Plateau from the JICA database. Special thanks are given to Dr. Xinan Yue at the University Corporation for Atmospheric Research (UCAR) for his effort in improving this paper greatly. References Baker, H.C., Dodson, A.H., Penna, N.T., Higgins, M., Offiler, D., 2001. Ground-based GPS water vapor estimation: Potential for meteorological forecasting. Journal of Atmospheric and Solar-Terrestrial Physics 63, 1305–1314.
Bevis, M., Businger, S., Herring, T.A., Rocken, C., Anthes, R.A., Ware, R.H., 1992. GPS meteorology: Remote sensing of atmospheric water vapor using the global positioning system. Journal of Geophysical Research 97, 15787–15801. Bevis, M., Businger, S., Chiswell, S., Herring, T.A., Anthes, R.A., Rocken, C., Ware, R.H., 1994. GPS meteorology: Mapping zenith wet delays onto precipitable water. Journal of Applied Meteorology 33, 379–386. Black, H.D., 1978. An easily implemented algorithm for the tropospheric range correction. Journal of Geophysical Research 83, 1825–1828. Businger, S., Chiswell, S., Bevis, M., Duan, J., Anthes, R.A., Rocken, C., Ware, R.H., Exner, M., Vanhove, T., Solheim, F.S., 1996. The promise of GPS in atmospheric monitoring. Bulletin of the American Meteorological Society 77, 5–18. Davis, J.L., Herring, T.A., Shapiro, I.I., Rogers, A.E., Elgered, G., 1985. Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length. Radio Science 20, 1593–1607. Dick, G., Gendt, G., Reigber, C., 2001. First experience with near real-time water vapor estimation in a German GPS network. Journal of Atmospheric and Solar-Terrestrial Physics 63, 1295–1304. Elgered, G., Davis, J.L., Herring, T.A., Shapiro, I.I., 1991. Geodesy by radio interferometry: Water vapor radiometry for estimation of the wet delay. Journal of Geophysical Research 96, 6541–6555. Hopfield, H.S., 1971. Tropospheric effect on electromagnetically measured range: Prediction from surface weather data. Radio Science 6, 357–367. Jade, S., Vijayan, M.S.M., Gaur, V.K., Prabhu, T.P., Sahu, S.C., 2005. Estimates of precipitable water vapour from GPS data over the Indian subcontinent. Journal of Atmospheric and Solar-Terrestrial Physics 67, 623–635. Liang, H., Liu, J., Zhang, J., Bi, Y., Wang, K., 2006. Research on retrieval of the amount of atmospheric water vapor over Qinghai-Xizang Plateau. Plateau Meteorology (in Chinese) 25, 1055–1063. Liu, Y., IZ, H B, Chen, Y., 2000. Precise determination of dry zenith delay for GPS meteorology applications. Acta Geodaetica et Cartographica Sinica 29, 172–180. MacDonald, A.E., Xie, Y., 2002. Diagnosis of three-dimensional water vapor using a GPS network. Monthly Weather Review 130, 386–397. Musa, T.A., Amir, S., Othman, R., Ses, S., Omar, K., Abdullah, K., Lim, S., Rizos, C., 2011. GPS meteorology in a low-latitude region: Remote sensing of atmospheric water vapor over the Malaysian Peninsula. Journal of Atmospheric and Solar-Terrestrial Physics 73, 2410–2422. Peng, S., Xu, X., Shi, X., Wang, D., Zhu, Y., Pu, J., 2009. The early-warning effects of assimilation of the observations over the large-scale slope of the ‘‘World Roof’’ on its downstream weather forecasting. Chinese Science Bulletin 54, 706–710. Ross, R.J., Rosenfeld, S., 1997. Estimating mean weighted temperature of the atmosphere for Global Positioning System applications. Journal of Geophysical Research 102, 21719–21730. Saastamoinen, J., 1973. Contributions to the theory of atmospheric refraction. Bulletin Geodesique 107, 13–34. Song, S., Zhu, W., Ding, J., Peng, J., 2006. 3D water-vapor tomography with Shanghai GPS network to improve forecasted moisture field. Chinese Science Bulletin 51, 607–614. Ware, R.H., Fulker, D.W., Stein, S.A., Anderson, D.N., Avery, S.K., Clark, R.D., Droegemeier, K.K., Kuettner, J.P., Minster, J.B., Sorooshian, S., 2000. SuomiNet: A real-time national GPS network for atmospheric research and education. Monthly Weather Review 81, 677–694. Xu, G., Chen, B., Wan, R., Li, W., Feng, G., 2008. Error analysis on precipitable water derived from ground-based GPS with various methods. Torrential Rain and Disasters (in Chinese) 27, 346–350. Xu, G., Cui, C., Li, W., Zhang, W., Feng, G., 2011. Variation of GPS precipitable water over the Qinghai-Tibet Plateau: Possible teleconnection triggering rainfall over the Yangtze river valley. Terrestrial, Atmospheric and Oceanic Science 22, 195–202. Xu, G., Wan, R., Li, W., Chen, B., Feng, G., 2009. Improvement on the method for estimating precipitable water from ground-based GPS. Torrential Rain and Disasters (in Chinese) 28, 203–209. Xu, X., Chen, L., 2006. Advances of the study on Tibetan Plateau experiment of atmospheric sciences. Journal of Applied Meteorological Science (in Chinese) 17, 756–772. Xu, X., Lu, C., Shi, X., Gao, S., 2008a. World water tower: An atmospheric perspective. Geophysical Research Letters 35, L20815. Xu, X., Zhang, R., Koike, T., Lu, C., Shi, X., Zhang, S., Bian, L., Cheng, X., Li, P., Ding, G., 2008b. A new integrated observational system over the Tibetan Plateau. Bulletin of the American Meteorological Society 89, 1492–1496. Yang, G., Liu, J., Mao, J., 2002. Analysis on water vapor obtained with ground-based GPS over Tibetan area. Meteorological Science and Technology (in Chinese) 30, 266–272.