# Retrieving Precipitable Water Vapor Data Using GPS Zenith Delays and Global Reanalysis Data in China

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## Abstract

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## 1. Introduction

_{m}. The Saastamoinen model [16] has been widely used to estimate ZHD because of its high accuracy of approximately 1–2 mm, and the surface barometric pressure P

_{s}is one of the essential parameters in this model. T

_{m}is often calculated from its linear relationship with the near-surface air temperature T

_{s}. Therefore, to derive GPS PWV with high accuracy, site-specific surface pressure P

_{s}and T

_{m}are also essential, which are, respectively, used to calculate ZHD and $\Pi $ in combination with the GPS data. However, collecting such meteorological parameters requires collocated meteorological sensors, which are often not present at many geodetic GPS stations. To employ those sites without collocated meteorological sensors in GPS meteorology, different methods were studied in various areas. For example, Jade [17] firstly interpolated the 2.5° × 2.5° National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data to obtain site-specific meteorological parameters for estimating GPS PWV. An interpolation of the NCEP pressure level data and NCEP surface data was explored. The experiments, conducted over a four-year period in India, indicated that the GPS PWV data derived using the interpolated surface pressure and weighted mean temperature from NCEP data were in strong agreement with those from real meteorological observations. However, bias between GPS PWV and horizontally interpolated NCEP PWV results apparently increased with highly undulated terrain. Vey [18] derived GPS PWV data that were obtained by using 141 global GPS stations over a 10-year period from 1994 to 2004, in which T

_{m}data were obtained from the European Center for Medium-Range Weather Forecasts (ECMWF) and P

_{s}data were interpolated from neighboring World Meteorological Organization (WMO) stations. In Karabatic’s study [19], P

_{s}and T

_{s}data of the nearest meteorological station were extrapolated to GPS stations in Austria, and they found that a PWV accuracy better than ±1 mm could be achieved when the distance between the two sites was within 20 km. In Poland, Bosy obtained specific GPS site P

_{s}and T

_{s}data by interpolating nearby meteorological data to the GPS site locations [20]. However, some systematic bias in the interpolation results appeared at some stations, which was attributed to unknown deficiencies in their interpolation procedure. The North American Regional Reanalysis (NARR) dataset for the year 2009 was used to estimate GPS PWV in California and Nevada by Means and Cayan [21]. The surface pressure and temperature were determined by the elevation of a specific station and the geopotential heights of the standard pressure levels of neighboring grids, and simple two-dimensional interpolation of the reanalysis gridpoint surface temperatures was performed. Means [22] also employed the same technique to obtain GPS PWV of over 500 sites from 2003 to 2009. Then, using these GPS PWV data, he studied the temporal and spatial extent of the North American monsoon in California and Nevada. Luo [12] presented a height-dependent linear ZHD correction model using freely accessible meteorological measurement data near GPS sites, and the mean ZHD bias was approximately 5 mm.

## 2. Methodology

#### 2.1. GPS PWV Estimation

_{0}represents surface pressure in hPa and

_{m}(see [28]) is:

_{v}is the specific gas constant for water vapor, ${k}_{2}^{\prime}$ = (17 ± 10) K·mbar

^{−1}, and ${k}_{3}$ = (3.776 ± 0.014) × 105 K

^{2}·mbar

^{−1}. T

_{m}is defined as:

_{m}can be estimated using the surface temperature measurement T

_{s}:

#### 2.2. Interpolation of NCEP FNL Global Analysis Data

_{s}and T

_{s}from the NWP data can be an effective complement for ground-based GPS meteorology as a solution for the absence of collocated meteorological sensors. In this study, we used the NCEP FNL Operational Global Analysis historical data to perform these interpolations. The NCEP FNL Operational Global Analysis data are produced from the same data assimilation and forecast system as the NCEP Global Forecast System (GFS) data. The difference between these two datasets is that approximately 10% more observations are assimilated into the initial condition for FNL than that for GFS. However, because it takes some time to wait for more observational data to be collected, the FNL analysis data are delayed by approximately 60–90 min compared with those from GFS analysis. These data can be downloaded freely from the website http://rda.ucar.edu/datasets/ds083.2/.

_{lower}and h

_{upper}represent the upper and lower level geopotential height and p

_{lower}and p

_{upper}are the air pressure of the lower and upper level, respectively. Pressure ${p}_{z}^{i}$ at a GPS site’s height z is estimated by the following equation:

^{i}is the pressure of height h

^{i}and h

^{i}is the height of one of the chosen mandatory levels or that of the surface, depending on which is the nearest level to the GPS station’s elevation.

_{s}is the station’s surface pressure and w

^{i}represents the interpolation coefficients of the vertical interpolated pressure ${p}_{z}^{i}$ at point i. w

^{i}is determined by the following formula (see [17]):

## 3. Interpolation Results

#### 3.1. Comparison between Interpolated and Observed P_{s}

_{0}, we can obtain the following formula:

_{0}is linear. From Equation (14), it can be inferred that the surface pressure error of 2.8 hPa produces an approximately 6.7 mm error in ZHD, and the equivalent error will be transferred to ZWD through the computation of Equation (3). This equates to a 1-mm error in PWV. The comparison between interpolated and measured surface pressure is based on the above analysis.

_{s}with their corresponding measured values from two datasets. In our study, we make these comparisons using the full-year data of 2012 from both datasets. BIAS and RMSE are selected as the statistics, and their mathematical expressions are as follows:

_{s}at all of the selected CMONOC and ISD sites. Table 1 gives the number of ISD and CMONOC sites at which absolute BIAS or RMSE were within the given value domain. This clearly indicates that both statistical values were below 1 hPa, that is, there was less than 0.5-mm error in the GPS PWV derivation, at most sites. Only at a few sites was the absolute BIAS or RMSE more than 2.8 hPa, which equates to more than a 1 mm GPS PWV error, as previously derived. Furthermore, it can be seen from Figure 4, which illustrates the BIAS and RMSE of all of the selected stations together with their locations on the map, that stations with a larger BIAS or RMSE are mainly distributed in southwest China or the northern part of Xinjiang Province. These sites had a common property in that they are all located in regions of higher relief. Taking the JIULONG station in Sichuan Province (ISD number: 564620) as an example, we found that it had a great difference in height with the four neighboring grids of the NCEP FNL model. The height of this station was 2994 m, whereas the lowest neighboring node’s height was 3703 m, so the altitude height differences were higher than 651 m, reflecting undulating terrain. Its interpolated and measured P

_{s}time series is illustrated in Figure 5. There is an obvious system bias between the two series. The reason for such a system deviation may be that the four pressure values used for horizontal interpolation were all one-side vertical extrapolated instead of interpolated because the JIULONG station was lower than all of the neighboring nodes, and the accuracy would decrease substantially if the vertical extrapolation distances were too long. The other stations that exhibit large interpolation errors also faced the same problem, including the XJWQ and QHME stations in CMONOC. However, more than 97.5% of stations with GPS PWV derivation errors of interpolated P

_{s}were below 1 mm. This demonstrates the effectiveness of our interpolation method in areas without significant relief.

_{s}did not decrease dramatically according to our experiment. Taking the Haikui typhoon, which landed in Ningbo City, Zhejiang Province on 8 August 2012, as an example, we compared the measured and interpolated P

_{s}of the ZJZS station, which is the nearest CMONOC station to the typhoon landing position. As Figure 6 shows, the differences between the two P

_{s}time series remain very small during the typhoon period from 5 to 11 August 2012. Their mean difference was −0.89 hPa, with a standard derivation of 0.37 hPa, and the largest difference was only −1.75 hPa. We also use the P

_{s}series generated from the GPT model (see [36]) for comparison purposes; its difference from real observations could be as high as 20.51 hPa, which obviously cannot be accepted in GPS PWV retrieval.

#### 3.2. Comparison between Interpolated and Observed T_{s}

_{s}. We also take the first derivative of Equation (5) with respect to the weighted mean temperature T

_{m}:

_{m}. Π increases from 0.1363 to 0.1699 with the increase of T

_{m}from 240 K to 300 K, while the first derivative of Π with respect to T

_{m}only decreases from 5.6198 × 10

^{−4}K

^{−1}to 5.5907 × 10

^{−4}K

^{−1}. The growth rate of Π is 20%, while the decline rate of Π’s derivative is merely 0.52%, which suggests that the derivative of Π with respect to T

_{m}is not sensitive to changes in T

_{m}and remains at a low value. When T

_{m}equals 290 K and Π is 0.1643, even if T

_{m}changed more than 5 K, the value of Π varied by only approximately 2.8 × 10

^{−3}, which represents a nearly 1.8% relative difference. The largest mean PWV value of approximately 50~60 mm occurs in Southern China in July, so such a relative difference is acceptable. This demonstrates that the impact of T

_{m}error on GPS PWV derivation is substantially lower than that of surface pressure error because Π is less sensitive to T

_{m}error.

_{s}, we chose 5.0 K as the threshold value of absolute BIAS and RMSE between the interpolated and measured values based on the above analysis. During these comparisons, a few gross differences between the interpolated and observed temperatures were found. These errors were attributed to the obvious measurement errors. Using the CMONOC station QHBM as an example, abnormal measured temperature values of −167 °C or 145 °C occurred for unknown reasons. Therefore, it is necessary to perform a quality check before performing statistical work. We marked differences with absolute values larger than 20 °C as false and excluded them from the final statistical analysis; 0.003% of the data from ISD and 0.035% of the data from CMONOC were deleted. The corrected statistical results are given in Table 2 and illustrated in Figure 8. Although the accuracy of the GPS-PWV derivation is not directly determined by T

_{s}, as it is also affected by the precision of the T

_{m}–T

_{s}conversion formula, the quality of the interpolated T

_{s}can also be a useful index to indicate the effectiveness of our interpolation scheme. The interpolation results for surface temperature at most stations showed the satisfactory accuracy of near-ground air temperatures for GPS-PWV derivation. However, sites with larger interpolation errors, which were mainly distributed in Southwestern and Northwestern China, also showed large surface temperature interpolation errors. The landforms of these regions included steep undulations, which likely led to poor outcomes. Overall, the accuracy of the interpolated T

_{s}is slightly lower than that of the interpolated P

_{s}, but their influences on the accuracy of the GPS-PWV derivation are of the same order because GPS-PWV is not as sensitive to T

_{s}as to P

_{s}.

## 4. Comparison of PWV Results

#### 4.1. Results of Different T_{m}

_{m}, using three different approaches:

- (1)
- At all of the RS stations, we directly integrated the radiosonde data assuming that the balloon ascended along a vertical path. The following approximate formula is used:$${T}_{m}=\frac{{\displaystyle \sum \frac{({z}_{2}-{z}_{1})e}{T}}}{{\displaystyle \sum \frac{({z}_{2}-{z}_{1})e}{{T}^{2}}}}$$
_{s}×RH, RH represents the relative humidity, and saturation vapor pressure is generated using ITS-90 equations proposed by [37]. We denoted T_{m}by this method as T_{m_RS}. - (2)
- At selected CMONOC stations in Table 3, T
_{m}was estimated from T_{s}using the T_{m}–T_{s}linear equation of Equation (7). From Wang’s research [38], coefficients a and b are determined by the climatic region of every site. T_{s}was measured by the meteorological sensors collocated to the CMONOC GNSS sites. Then, T_{m_OW}could be obtained. - (3)
- The same method as in Equation (2) was employed, except that T
_{s}was obtained from the interpolation schemes described in Section 2. To differentiate their results, we denoted the results from this method as T_{m_IW}.

_{m}results are shown in Table 4. In the comparisons, radio sounding was considered to be the most precise measuring method, so the results of the other two approaches were both compared with T

_{m_RS}. At each site, the RMSE of both T

_{m_OW}s and T

_{m_IW}to T

_{m_RS}were always below 5 K, and their values were very close at the same site. This indicates that the accuracies of T

_{m_OW}and T

_{m_IW}are equally acceptable.

_{m_OW}will not change under the above conditions, we can use T

_{m_OW}to evaluate T

_{m_IW}. We selected 10 CMONOC GNSS stations that met such conditions for further comparisons. The RMSE between the T

_{m_IW}and T

_{m_OW}of these stations are given in Table 5. The values of the RMSE were still lower than 5 K at most of the selected sites, except SCPZ. This is attributed to, as is previously mentioned, the fact that the SCPZ site had a large height difference to its neighboring NWP grid nodes. Taking this factor out of consideration, we are confident that this dataset exhibits accuracy in terms of the T

_{m_IW}that is acceptable, even if there was no precise meteorological data assimilated into the NCEP FNL dataset.

#### 4.2. Results of Different PWV

- (1)
- At the 22 CMONOC GNSS stations, we first employed GAMIT software to process GPS data under ITRF2008. The cut-off elevation angle was 10°, and ZTD was estimated with the GMF mapping function at each station. Because of the long distances between our selected stations, absolute ZTD and atmospheric delay horizontal gradient values, at intervals of one hour and two hours respectively, could be estimated directly without introducing any other GPS sites into this network. Then, we calculated GPS PWV, as described in Section 2.1. Real surface pressure measurements of P
_{s}and T_{m}were computed from real near-ground air temperature observations T_{s}. We denoted the PWV results as GPS_PWV_{obs}. - (2)
- This scheme is similar to method (1), expect that P
_{s}and T_{s}were generated from the interpolation of the NCEP FNL dataset, as proposed in Section 2.2. These GPS PWV results are referred to as GPS_PWV_{NCEP}. - (3)
- PWV can be integrated from the vertical profile of several meteorological parameters using the following formula:$$PWV={\displaystyle {\int}_{0}^{p\mathrm{s}}\frac{q}{{\rho}_{w}g}}dp,$$

_{s}represents surface pressure. Equation (18) can be approximated by:

- (4)
- PWV can also be integrated from NCEP FNL data using the same integral formula as Equation (19) and is referred to as NCEP_PWV.

_{obs}and GPS_PWV

_{NCEP}results. The only difference between them is the data source of P

_{s}and T

_{s}; therefore, such a comparison can directly indicate the feasibility of remedying the lack of P

_{s}and T

_{s}measurements in the NCEP FNL data interpolation. There were no meteorological data available at the XZNQ station, so it was excluded from the comparison. Figure 10 illustrates a scatter plot and several statistical and number density plots of differences between GPS_PWV

_{obs}and GPS_PWV

_{NCEP}. Simple linear regression shows that the two results are highly correlated, with a correlation coefficient of 0.9998 and a regression equation of y = 0.9983x − 0.1755, which is very close to y = x. All of the stations have BIAS within ±0.4 mm, and almost all of their absolute values are smaller than 0.2 mm; all of the RMSE values are smaller than 0.63 mm, with most below 0.4 mm. Deviations have a very low value in all of the PWV value fields. These comparisons clearly demonstrate that GPS_PWV

_{obs}and GPS_PWV

_{NCEP}are very similar. Therefore, the accuracy of deriving GPS PWV using P

_{s}and T

_{s}interpolated from NCEP reanalysis data is reliable compared with GPS PWV derived from real surface meteorological data.

_{NCEP}with integrated PWV, including RS_PWV and NCEP_PWV. GPS_PWV

_{obs}was not included in this comparison because its results are very close to those of GPS_PWV

_{NCEP}. Figure 11 shows statistical and number density plots of differences between the three PWV result sets. High correlations exist between GPS_PWV

_{NCEP}and integrated PWV, as demonstrated by the unary linear regressions, with correlation coefficients higher than 0.97. Overall, NCEP_PWV is more strongly correlated to GPS_PWV than RS_PWV. It is common that GPS_PWV

_{NCEP}is more consistent with NCEP_PWV than with RS_PWV in our study. At most stations, the BIAS values between GPS_PWV and NCEP_PWV are within ±2 mm and the RMSEs are approximately 2~3 mm, whereas the BIAS values between GPS_PWV and RS_PWV are within ±3 mm and the RMSEs are approximately 2~4 mm. The number density plot of the differences between the three PWV results indicates that the PWVs were concentrated below 5 mm in our study, and the negative deviation between GPS_PWV and the other two PWVs was also mainly distributed within this PWV range. As the blue line indicates, GPS_PWV shows a dry bias compared with RS. However, this is probably because of the position offsets between the GPS and RS stations. The differences of their horizontal positions or height can also produce some PWV differences.

_{NCEP}and NCEP_PWV to increase dramatically. Therefore, it is essential to perform quality control for GPS PWV retrieval in future work [39].

_{NCEP}only requires the interpolation of two levels of NCEP data, so changes in the reanalysis data exert less influence on it. For stations located in regions without intensive radio sounding observations, such as the LHAS and XZNQ stations, the differences between GPS_PWV

_{NCEP}and RS_PWV are smaller than those between GPS_PWV

_{NCEP}and NCEP_PWV.

_{NCEP}of the SHAO site, RS_PWV of the 58362 radiosonde site and SP_PWV of the Taihu AERONET site. Figure 13 is a scatter plot of the three PWV datasets. Common observation times between GPS_PWV and SP_PWV occurred more frequently than between RS_PWV and SP_PWV. Statistics are given in Table 6. Clearly, GPS_PWV

_{NCEP}is highly consistent with SP_PWV, with a bias of only 0.3464 mm and RMSE of 3.3439 mm, and the correlation coefficient was higher than 0.98. Their agreement was even better than that of RS_PWV and SP_PWV.

_{NCEP}at the SHAO station from UTC time 00:00 28 July to 24:00 9 August in 2012. The time interval of GPS_PWV

_{NCEP}was 6 h, whereas that of RS_PWV was 12 h. The SHAO site is located in Shanghai city. During this period, Shanghai suffered from two severe typhoons, Damrey and Haikui, in rapid succession. However, the two PWV time series still agreed very well, with a bias of −1.05 mm and RMSE of 2.83 mm. It can be seen that the PWV value fluctuated very dramatically in a short time period. Two rapid continuous PWV increase processes, that is, from 33 mm to 72 mm between 01.08.2012 06:00 and 02.08.2012 06:00 and from 47 mm to 73 mm between 06.08.2012 18:00 and 08.08.2012 12:00, were both reflected in the GPS_PWV

_{NCEP}and RS_PWV time series.

## 5. Conclusions and Outlook

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**Figure 4.**(

**a**) BIAS and (

**b**) RMSE between interpolated and measured surface pressure of stations from ISD and CMONOC.

**Figure 6.**ZJZS station’s surface pressure time series respectively from observation, interpolation, and the GPT model. The period is 5 to 11 August 2012, during which the Haikui typhoon landed in Ningbo City, Zhejiang Province, China.

**Figure 8.**(

**a**) BIAS and (

**b**) RMSE between interpolated and measured near-ground air temperature of stations from ISD and CMONOC.

**Figure 9.**Distribution of radiosonde and GNSS stations that were selected to compare their PWV results.

**Figure 10.**Scatter plot (

**top left**), BIAS, and RMSE at each GPS station (

**top right**); and number density plot of differences (

**bottom**) between GPS_PWV

_{obs}and GPS_PWV

_{NCEP}.

**Figure 11.**BIAS and RMSE at each GPS station (

**top**); and number density plot of differences (

**bottom**) between GPS_PWV

_{NCEP}and RS_PWV or NCEP_PWV.

**Figure 12.**Time series of difference between GPS_PWV

_{NCEP}and NCEP_PWV (

**blue**line) and ratio between observation numbers of L1 and L2 (

**red**line).

**Figure 14.**Time series of GPS_PWV

_{NCEP}from 00:00 on 28 July to 24:00 on 8 August 2012 at SHAO station.

**Table 1.**Number of sites at which absolute BIAS or RMSE of interpolated surface pressure were within a given special value domain.

Value Domain | Absolute BIAS as Statistics | RMSE as Statistics | ||
---|---|---|---|---|

ISD | CMONOC | ISD | CMONOC | |

≤1 hPa | 356 | 200 | 318 | 158 |

1 ~ 2.8 hPa | 17 | 36 | 55 | 76 |

2.8 ~ 5 hPa | 2 | 3 | 2 | 5 |

≥5 hPa | 1 | 1 | 1 | 1 |

**Table 2.**Number of sites at which absolute BIAS or RMSE of interpolated surface air temperature were within a given special value domain.

Value Domain | Absolute BIAS as Statistics | RMSE as Statistics | ||
---|---|---|---|---|

ISD | CMONOC | ISD | CMONOC | |

≤5.0 K | 373 | 232 | 365 | 226 |

>5.0 K | 3 | 8 | 11 | 14 |

GNSS Station | lat (°) | lon (°) | Height (m) | RS Station | lat (°) | lon (°) | Height (m) |
---|---|---|---|---|---|---|---|

HLAR | 49.27 | 119.74 | 627.89 | 50527 | 49.22 | 119.75 | 611.00 |

HRBN | 45.70 | 126.62 | 197.44 | 50953 | 45.68 | 126.62 | 143.00 |

JLYJ | 42.87 | 129.50 | 283.79 | 54292 | 42.88 | 129.47 | 178.00 |

LNSY | 41.83 | 123.58 | 69.02 | 54342 | 41.82 | 123.55 | 43.00 |

SDQD | 36.08 | 120.30 | 12.78 | 54857 | 36.07 | 120.33 | 77.00 |

GDST | 23.42 | 116.60 | 30.93 | 59316 | 23.35 | 116.68 | 3.00 |

HIHK | 19.99 | 110.25 | 54.55 | 59758 | 20.03 | 110.35 | 24.00 |

YNMZ | 23.36 | 103.40 | 1274.50 | 56985 | 23.38 | 103.38 | 1302.00 |

KMIN | 25.03 | 102.80 | 1985.37 | 56778 | 25.02 | 102.68 | 1892.00 |

SCGZ | 31.61 | 100.02 | 3352.28 | 56146 | 31.63 | 99.98 | 3394.00 |

GSPL | 35.55 | 106.59 | 1408.69 | 53915 | 35.55 | 106.67 | 1348.00 |

AHAQ | 30.62 | 116.99 | 57.75 | 58424 | 30.52 | 117.03 | 20.00 |

HBES | 30.28 | 109.49 | 472.38 | 57447 | 30.27 | 109.48 | 458.00 |

NMEL | 43.63 | 111.94 | 945.34 | 53068 | 43.65 | 112.00 | 966.00 |

NMEJ | 41.96 | 101.06 | 888.42 | 52267 | 41.98 | 101.07 | 941.00 |

LHAS | 29.66 | 91.10 | 3623.82 | 55591 | 29.70 | 91.13 | 3650.00 |

XZNQ | 31.49 | 92.11 | 4572.55 | 55299 | 31.48 | 92.05 | 4508.00 |

XJYN | 43.97 | 81.53 | 732.88 | 51431 | 43.95 | 81.33 | 664.00 |

XJKC | 41.73 | 82.98 | 1028.08 | 51644 | 41.72 | 82.95 | 1100.00 |

XJAL | 47.86 | 88.13 | 874.10 | 51076 | 47.73 | 88.08 | 737.00 |

GZGY | 26.47 | 106.67 | 1093.13 | 57816 | 26.48 | 106.65 | 1222.00 |

SHAO | 31.10 | 121.20 | 22.02 | 58362 | 31.40 | 121.47 | 4.00 |

RS Station Number | RMSE (K) | |
---|---|---|

T_{m_OW}–T_{m_RS} | T_{m_IW}–T_{m_RS} | |

50527 | 3.70 | 3.64 |

50953 | 4.74 | 4.61 |

54292 | 4.20 | 4.33 |

54342 | 3.63 | 3.69 |

54857 | 3.75 | 3.51 |

59316 | 2.25 | 2.10 |

59758 | 2.10 | 2.29 |

56985 | 2.74 | 3.07 |

56778 | 3.10 | 2.94 |

56146 | 2.58 | 2.28 |

53915 | 3.77 | 3.39 |

58424 | 3.33 | 3.24 |

57447 | 3.37 | 2.96 |

53068 | 4.86 | 4.27 |

52267 | 4.83 | 4.08 |

55591 | 3.41 | 2.58 |

55299 | 4.13 | 4.27 |

51431 | 4.01 | 3.25 |

51644 | 3.77 | 2.78 |

51076 | 3.89 | 3.68 |

57816 | 2.15 | 1.99 |

58362 | 3.48 | 3.66 |

**Table 5.**RMSE between T

_{m_IW}and T

_{m_OW}at CMONOC GNSS stations without nearby meteorological station.

Site Name | RMSE (K) |
---|---|

XZRT (Tibet) | 3.29 |

XZGE (Tibet) | 1.44 |

XZBG (Tibet) | 1.76 |

XZGZ (Tibet) | 1.98 |

WUSH (Xingjiang) | 1.95 |

XJKE (Xingjiang) | 1.39 |

XJDS (Xingjiang) | 2.32 |

NMWL (Inner Mongolia) | 2.63 |

QHWQ (Qinghai) | 2.67 |

SCPZ (Sichuan) | 5.69 |

BIAS(mm) | RMSE(mm) | Correlation | |
---|---|---|---|

GPS_PWV_{NCEP} vs. SP_PWV | 0.3464 | 3.3439 | 0.9870 |

RS_PWV vs. SP_PWV | −1.7908 | 3.8969 | 0.9763 |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Jiang, P.; Ye, S.; Chen, D.; Liu, Y.; Xia, P. Retrieving Precipitable Water Vapor Data Using GPS Zenith Delays and Global Reanalysis Data in China. *Remote Sens.* **2016**, *8*, 389.
https://doi.org/10.3390/rs8050389

**AMA Style**

Jiang P, Ye S, Chen D, Liu Y, Xia P. Retrieving Precipitable Water Vapor Data Using GPS Zenith Delays and Global Reanalysis Data in China. *Remote Sensing*. 2016; 8(5):389.
https://doi.org/10.3390/rs8050389

**Chicago/Turabian Style**

Jiang, Peng, Shirong Ye, Dezhong Chen, Yanyan Liu, and Pengfei Xia. 2016. "Retrieving Precipitable Water Vapor Data Using GPS Zenith Delays and Global Reanalysis Data in China" *Remote Sensing* 8, no. 5: 389.
https://doi.org/10.3390/rs8050389