Water-vapor-weighted mean temperature,
Water vapor is an important trace gas and one of the most variable components in the troposphere. The transport, concentration and phase transition of water vapor are directly involved in the atmospheric radiation and hydrological cycle. It plays a key role in many climate changes and weather processes (Adler et al., 2016; Mahoney et al., 2016; Song et al., 2016). However, water vapor has high spatial–temporal variability, and its content is often small within the atmosphere. It is a challenge to measure water vapor content accurately and timely. For decades, several methods have been studied, such as radiosondes and water vapor radiometers, sun photometers and GPS (Campmany et al., 2010; Ciesielski et al., 2010; Liu et al., 2013; Perez-Ramirez et al., 2014; Li et al., 2016). Compared with the traditional water vapor observations, ground-based GPS water vapor measurement has the advantages of high accuracy, high spatial–temporal resolution, all-weather availability and low-cost (Haase et al., 2003; Pacione and Vespe, 2008; Lee et al., 2010; Means, 2013; Lu et al., 2015). Ground-based GPS water vapor products, mainly including precipitable water vapor (PWV), are widely used in many fields such as real-time vapor monitoring, weather and climate research, and numerical weather prediction (NWP) (Van Baelen and Penide, 2009; Karabatic et al., 2011; Rohm et al., 2014; Adams et al., 2017).
GPS observations require some kind of meteorological element to estimate
PWV. Zenith hydrostatic delay (ZHD) can be calculated using surface
pressure
Main differences between
There are the three main approaches that are used to estimate The integration of vertical temperature and humidity profiles is
believed to be the most accurate method. The profile data can be extracted
from radio soundings or NWP data sets (Wang et al., 2016).
However, some inconveniences have to be endured. It usually takes a
considerable amount of time to acquire the NWP data, which are normally
released in a large volume every 6 h. This limits the use of NWP data
in the near-real-time GPS–PWV retrieval. Radiosonde data are another profile
data source, but it has low spatial and temporal resolution. At most of the
radiosonde sites, sounding balloons are cast daily at 00:00 and 12:00 UTC.
Furthermore, a large number of GPS stations are not located close
enough to the radio sounding sites. Therefore, such methods are appropriate
for climate research or the study of long-term PWV trends, but do not meet
the real-time requirements. Several global empirical models of Many studies indicated that the
According to Rohm et al. (2014), GPS–ZTD can be estimated
very precisely by real-time GPS data processing. This means that
The objective of this study is mainly to (1) develop global gridded
As the definition of
We employed radiosonde data from the Integrated Global Radiosonde Archive
(IGRA,
Profile data are usually provided by NWP products at certain vertical
levels. The ERA-Interim product from ECMWF provides data on a regular
512 longitude by 256 latitude N128 Gaussian grid after the grid
transformation performed by the NCAR Data Support Section (DSS). On each grid node of
ERA-Interim, temperature, relative humidity and geopotential at 37 isobaric
levels from 1000 to 1 hPa can be obtained. By dividing the geopotential by
constant gravitational acceleration value (
In theory, the computation of Eq. (5) should be integrated through the
entire atmospheric column, and the geopotential height should be converted
to the geometric height. However, water vapor is solely concentrated in the
troposphere, and most of it is specifically located within the first
3 km a.s.l. (above sea-level). Moreover, in the two selected data sets, the
geopotential heights of top pressure levels are approximately
30–40 km. Geopotential height is very close to geometric
height in such height ranges. According to our computation, the relative
difference between them is only between 0.1 % and 0.9 %. In
fact, the height difference
At each reanalysis grid node, the computation of Eq. (5) always starts
from the surface height to the top pressure level. The pressure levels below
surface height were rejected.
Many studies have indicated the close relationship between
Correlation coefficients between
We first carried out a linear regression analysis on 4 years of
Denary logarithm of the standard deviation of
Since the
A linear formula
The time variation in the
Distributions of the
Our new regression model found similar values for the coefficients
To further assess the precision of the
The model estimations of
Taking
Detailed statistics of the distributions of the bias and RMSE using
different models are shown in Fig. 7 and Table 2. At over 97.37 % of
the radiosonde stations, the biases of
Statistics of
The distributions of (1) the biases and (2) the
RMSEs of
Number of radiosonde sites at which the five global applied
To identify the superior First, Brown–Forsythe tests (Brown and Forsythe, 1974) of the equality
of variances were carried out at each site for estimating the ANOVA is a technique used to analyze the differences among group means
(Hogg and Ledolter, 1987). It evaluates the null hypothesis that the samples all
have the same mean against the alternative that the means are not the same.
If the null hypothesis is rejected at a 5 % significance level, the
After multiple tests and comparisons, the best model at each radiosonde
station may be identified. However, at some sites no superior model can be
confirmed. All the models are believed to have equivalent performance.
Finally, we counted the number of sites at which each
In Fig. 8 the
On the other hand, even
Variation in the uncertainty in
GPS–PWV has different error sources with different properties. It is complicated to evaluate the GPS–PWV uncertainty here due to the lack of collaborated additional independent techniques that monitor water vapor at the GPS site.
Comprehensive research on the uncertainty in GPS–PWV has been carried out
by Ning et al. (2016). The uncertainties in ZTD, ZHD and conversion
factor
Ning et al. (2016) assumed the
The uncertainty in
Different typical values for
Contributions of different terms to the total uncertainty in GPS–PWV with the typical values shown in Table 4.
The No significant difference exists between Fig. 10a and b.
Because of the small value of With the typical values in Table 4 (values a and b), a reduction of
As Fig. 10c shows, the uncertainty associated with The uncertainty associated with
Theoretical analyses of
At another representative station, the IGRA station no. 50557
(49.17
Statistics on the relative errors of different PWV retrievals.
It is worth mentioning that the uncertainty in ZHD may be underestimated in
some situations. There are two reasons for this. Firstly, the calculation of
ZHD assumes that the water vapor does not contribute to the mass of the
atmosphere. The ZHD error introduced by this assumption is often negligible.
But in some very wet regions, the mass of water vapor could produce
significant errors in the ZHD calculation. Secondly, and more importantly,
the error of
To study the impact of
Relative RMSEs of PWV
The
RMSEs of the PWV
PWV differences of the PWV
Among our selected 74 IGS sites, there are only 11 sites located within 5 km
to a nearby IGRA radiosonde station. At these common stations, we generated
PWV from the radiosonde data (PWV
We developed two global gridded
More precise
According to our experiments, we are confident that the time-varying global
gridded
Radiosonde data: ERA-Interim project: GPS-ZTD product:
The supplement related to this article is available online at:
PJ, SY and YW conceived and designed the experiments; PJ, YL performed the experiments and analyzed the results; and DC and YL processed the data. All authors contributed to the writing of the paper.
The authors declare that they have no conflict of interest.
This study is supported by the National Natural Science Foundation of China (no. 41604028), the Anhui Provincial Natural Science Foundation (no. 1708085QD83), and the Doctoral Research Start-up Funds Projects of Anhui University (no. J01001966). The authors thank the European Centre for Medium-Range Weather Forecasts for providing the ERA-Interim data set. We also thank the National Centers for Environmental Information for the IGRA data sets and International GNSS Service for the GNSS troposphere products. Edited by: Roeland Van Malderen Reviewed by: David Adams and four anonymous referees