Review of the Research Status and Progress of Ground-Based GNSS

Meteorology

Qiuying Guo

, Xuxiang Wu and Wengang Sang

College of Surveying and Geo-informatics, Shandong Jianzhu University, Fengming Road, Jinan 250101,P.R. China.

Email: qyguo@sdjzu.edu.cn

Keywords

: GNSS meteorology, atmospheric water vapour, tomography, data assimilation

Abstract:

The distribution of water vapor in troposphere is very uneven and has a strong sense of temporal and spatial

variation. The weather forecast requires accurate temporal and spatial information of the atmospheric

humidity field. The atmospheric water vapor information collected by traditional meteorological

observation methods has lower temporal-spatial resolution and higher observation cost. Using GNSS to

obtain atmospheric water vapor information has great potential of high quality, high temporal and spatial

resolution, low cost, all weather conditions and real-time monitoring. GNSS meteorology has an important

application value for atmospheric monitoring, extreme weather forecast and regional climate research. This

paper summarizes the state and progress of several respects of ground-based GNSS meteorology including

GNSS atmospheric vapor tomography, GNSS-derived data water vapor assimilation and GNSS

meteorological applications. The strategies and methods for GNSS tomography are summarized. And the

development and application prospects of the Multi-GNSS (GPS/Beidou/Glonass/Galileo) meteorology are

also discussed.

1 INTRODUCTION

The content of water vapor in the atmosphere is a

key parameter affecting the accuracy of weather

forecast. It is very important for the regional weather

forecast, especially for the accurate nowcasting. It is

also an important indicator for studying the long-

term climate change. Therefore dynamic monitoring

of atmospheric vapor content and real-time

constructing vertical distribution of the water vapor

field will play an important role in improving the

accuracy of weather forecast and modeling

atmosphere. The atmospheric water vapor

information collected by traditional meteorological

observation methods (radiosonde, radiometer) has

lower temporal-spatial resolution and higher

observation cost. The lack of four dimensional (4D)

distribution information of water vapor affects the

precision of the initial humidity field and the

accuracy of the numerical weather forecast.

Therefore it is a research focus in the field of

meteorology currently to find effective methods for

real-time acquisition of high precision atmospheric

vapor content and construct a real-time monitoring

system.

The propagation delay of GNSS satellite signal

through the atmosphere is related to the content of

water vapor in the troposphere. So the zenith

tropospheric delay (ZTD) can be inversed using

GNSS observations of the ground station. The ZTD

is devided into hydrostatic or dry component (ZHD)

and wet component (ZWD). The ZHD can be

calculated accurately according to Saastamoinen

model using the precise surface pressure data. Then

the ZWD can be obtained by subtracting ZHD form

ZTD. Finally, the ZWD can be converted to the

precipitable water vapor (PWV) by a ratio value Π,

which is related to the weighted mean temperature

(Bevis et al., 1992). So the PWV of GNSS signal

transmission route can thereby be calculated. And

the PWV is very important for weather forecasting

and extreme weather events monitoring.

Furthermore the 4D water vapor distribution can be

obtained by tomography when the density of the

GNSS stations available on the ground is sufficient.

GNSS water vapor tomography has the potential to

provide PWV fields with high temporal and spatial

Guo, Q., Wu, X. and Sang, W.

Review of the Research Status and Progress of Ground-Based GNSS Meteorology.

In Proceedings of the International Workshop on Environment and Geoscience (IWEG 2018), pages 199-206

ISBN: 978-989-758-342-1

199

resolution. And the PWV fields can make the 4D

temporal and spatial changes of water vapor

understood in more detail and more favorable for

monitoring and early warning storm disasters.

Therefore GNSS has become a very effective tool

for the study of meteorology currently

(Flores et al.,

2000;

Manning et al., 2014; Wang et al., 2016).

In recent years, using GNSS for meteorological

detection has become an important component of

World Meteorological Organization (WMO) new

global upper air observing system in the 21 century.

The application superiority of atmospheric sounding,

weather change monitoring and numerical weather

forecast using GNSS observation data makes GNSS

meteorology become a completely new and highly

potential field.

2 RESEARCH STATUS OF

GROUND-BASED GNSS

METEOROLOGY

Since 1990s, people have begun to use satellite

navigation theory and technology sensing the Earth's

atmosphere. A new method to measure the content

of atmospheric water vapor using satellite

navigation signal started from the inverse problem

of noise processing for signal atmospheric delay of

satellite navigation, which provides new technical

support for better monitoring unfavourable weather

and climate change. Ground-based GPS

meteorology was early proposed by Bevis M. et al.

(1992) to sense atmospheric water vapor and support

weather forecast (Bevis et al., 1992). The GNSS

reference station network has been established in

many countries in the world in recent years. There

are global International GNSS Service (IGS) and

national/regional tracking station network on the

space scale. IGS is the most widely distributed and

the largest GNSS reference network in the world.

The number of global IGS tracking stations has

exceeded 500 by the end of January 2017. Among

them about 200 stations are multi-system GNSS

continuous operation reference station (MGEX).

Also some satellite ground-based augmentation

systems have been built in the world in recent years,

such as American StarFire with about 100 stations

(Jiang, 2017). Also, HxGN SmartNet claims to be

the world’s largest Continuously Operating

Reference Stations (CORS) network with more than

4,000 reference stations covering the majority of

developed countries, which continues to provide

trusted GNSS data worldwide

(https://hxgnsmartnet.com/). In addition, many

countries and private organizations have built their

own CORS networks. According to incomplete

survey, there are more than 4000, 5000, 1300 CORS

stations in America, China and Japan respectively.

Europe and Australia all have more than 1000

CORS stations. All these CORS stations provide

very favorable conditions for GNSS ZWD/PWV

estimation. However, the distribution of these CORS

stations is uneven at present and many governmental

agencies and private organizations still do not make

GNSS CORS data available to the public. So there is

a need for policies on data sharing and collaboration

among the different organizations that operate

GNSS stations.

As the coverage area of the ground GNSS station

network is greatly increased, the algorithm for

obtaining the tropospheric zenith wet delay (ZWD)

has also been developed and improved. So the

accurate water vapor information can be obtained

with high temporal and spatial resolution. GNSS

tomography is such a technique to reconstruct

detailed information of water vapor over the

interested area using the slant wet delay (SWD)

observations. Currently, GNSS tomography

atmospheric water vapor information is one of the

research focus in GNSS meteorology.

To make a forecast we need to know the current

state of the atmosphere conditions. The accuracy of

the water vapor field in the lower atmosphere is

particularly important for the forecast of extreme

weather (such as storms). The emergence and

evolution of many extreme weather conditions are

very rapid. The sampling rate of traditional water

vapor observation methods is too low so that the

information of water vapor change in extreme

weather can not be captured in time. While GNSS

tropospheric products and tomographic data can

provide a reliable source of data with high spatial

and temporal resolution. Research shows that

assimilating GNSS ZTD/ZWD/PWV can effectively

improve the initial atmospheric humidity field and

have a positive effect on strong precipitation

forecast (Boniface et al., 2009;

Zeng et al., 2014).

The assimilation of the GNSS observations is a

relatively new and very promising approach to

improve the short-term forecasts. Therefore

assimilation of GNSS tropospheric products and

tomographic data to improve the extreme weather

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forecast has become another research hotspot in the

field of GNSS meteorology.

In the following sections, we introduced the

research status of ground-based GNSS meteorology

from the following aspects: GNSS water vapor

tomography, GNSS-derived water vapor data

assimilation and meteorological applications of

GNSS-derived water vapor.

2.1 GNSS Water Vapor Tomography

A lot of research and test work has been done on

GNSS tomography 3D water vapor distribution in

the past 20 years, especially in Europe and America.

Bevis M. et al. (1992) presented early in 1992 that

dense GPS networks could be used to sense the

vertical distribution of water vapor. Since Flores A.

et al. (2000) first proved the feasibility of the 4D

tropospheric tomography technology using GPS

slant wet delays by experiment, many researchers in

the geodesy and meteorology fields have carried out

the related research. Many experiments have proved

that the water vapor field obtained by GNSS has

good consistency with the traditional meteorological

observation methods and also proved the

effectiveness of the study of atmospheric state by

GNSS tomography (Flores et al., 2000;Bastin et al.,

2005;Song et al., 2006). The research contents on

GNSS tomography mainly involve voxel division of

tomographic area, tomography algorithm,

optimization of tomography parameters, applications

of GNSS tomography in the field of meteorology

and advantages of multi-constellation GNSS

tomography.

Bastin S. et al. (2005) proved the 3D water vapor

field obtained by GPS tomography using numerical

simulation for the first time and studied the

interaction between the regional sea breeze and the

topography using data sets provided by GPS

tomography for the description of the water vapor

variability. Song S. et al. (2006) obtained 3D

structure of water vapor information over Shanghai

area by GPS tomography technique using GPS slant

water vapor retrieved from Shanghai GPS network

and improved numerical forecasted wet field

obviously. Wang W. et al. (2011) carried out GPS

water vapor tomography experiments using three

algebraic reconstruction techniques on Shanghai

GPS network and discussed the range of relaxation

factor and the initial value of iteration for the

reconstruction algorithm. He L. et al. (2015)

analyzed eight algebraic reconstruction algorithms

and discussed various problems of GPS vapor

tomography with respect to constraint condition,

initial value, optimal relaxation factor and iteration

termination condition.

However, due to many influencing factors (such

as satellite constellation, geometric distribution of

GNSS stations, voxel division), the coefficient

matrix of the tomographic equation is often sparse

and severely deficient, which causes GNSS

tomography can not be solved directly. So there are

still some problems to be solved on ground-based

GNSS tomography water vapor distribution, such as

solutions to the ill-posed tomography equations,

reasonable density of stations, optimization of voxel

division and the optimal settings of tomography

parameters. In addition, the quality of the water

vapor field obtained by GNSS tomography is related

to many factors, such as the priori value of water

vapor field, the number of slant path tropospheric

delay observation, weighting scheme of

observations, spatial resolution of a tomographic

region and the parameter settings of tomography

algorithm (e.g. the stop criteria of iterative

reconstruction algorithms) (Wang and Wang,

2011a;Bender et al., 2011;Wang and Wang,

2011b;He et al., 2015;Yu et al., 2016;Xia and Ye,

2017;Yao and Zhao, 2017;Chen and Liu.,

2014;Möller, 2017). Bender M. et al. (2011) found

by studying Germany ground-based GNSS station

network that the spatial coverage of the atmosphere

by slant paths can change very fast as the GNSS

satellite constellation varies and a uniform quality of

the reconstructed fields can therefore not be

expected. Möller G. (2017) studied the mathematics

formulation of ill-conditioned, inverse problems on

GNSS tomography equations. And the research

showed that the GNSS tomography solutions are not

only sensitive to the observation error and the

change of observation geometry but also sensitive to

the solution scheme and the parameter settings,

which caused by the ill-conditioned GNSS

tomography equations.

Review of the Research Status and Progress of Ground-Based GNSS Meteorology

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Table 1: Summary of strategies and methods for GNSS tomography.

Research

content

Research strategies and methods Scheme or characteristic

Optimization of

voxel division of

tomography

Vertical

stratification

Even Equidistant spacing

Unconsistent with actual vertical

distribution of water vapor

Uneven

Uneven spacing

Better reflect vertical variation of

water vapor in troposphere

Exponential spacing

Horizontal

resolution

Inhomogeneous, Commonly

(10-50km)×(10-50km)

According to the density of the

ground GNSS stations

Solutions to

tomography

equations

Non-iterative

reconstruction

algorithm (NIRA)

Truncated singular value

decomposition (TSVD)

Directly inverse;

Need to determine optimal threshold

for singular values and regularisation

parameters

Tikhonov regularization (TR)

Iterative

reconstruction

algorithm

(IRA)

Algebraic reconstruction technique

Avoiding inversion problem;

High stability and reliability;

Need to determine relaxation factor

and stop criteria

Multiplicative algebraic

reconstruction technique (MART)

Combined

reconstruction

algorithm

NIRA+IRA

Solutions obtained by TSVD or TR

are used as initial value of IRA, which

can provide high quality initial value

for IRA.

Classical

constrained

solution

orizontal

constraints

Horizontal smoothing

Directly inverse;

Adding constraints; Inappropriate

constraints may debase the accuracy

of results.

Gauss weighted function

Vertical

constraints

Decrement based on

exponential function

Radiosonde observation

Most of the aforementioned research mainly aim

to improve the precision and reliability of GNSS

tomography solutions. Table 1 gives the summary of

different approaches. GNSS tomography has the

potential of providing 4D water vapor field with

near real-time and high temporal-spatial resolution,

which can be used for numerical weather forecast,

extreme weather event monitoring and climatology

research. Despite with more than 10 years of

development, GNSS tomography atmospheric water

vapor technology still faces many challenges. There

are many factors affecting GNSS tomography

results. For example, the slant path delay can

provide local changes related to atmospheric

information and is considered to be a promising

value of meteorological observation, but the

precision of slant path water vapor in the region at

low altitude is still low (Wang et al., 2016;

Möller,

2017). So the accurate estimation of the slant path

water vapor needs further study. The weighted mean

temperature T

is an important parameter for

calculating the atmospheric water vapor. A detailed

study of precise determination of T

needs further

development. Moreover, there exists the problem of

precision instability of GNSS tomography results.

With the significant progress of BeiDou and

Galileo systems, as well as updating Glonass, the

integrated multi-GNSS may improve water vapor

tomography (Bender et al., 2010;Wang et al.,

2014;Zhao et al., 2018;Dong and Jin, 2018). Bender

M. et al (2010) estimated the impact of GPS, Galileo

and GLONASS data on the GNSS tomography by

simulation which showed that the spatial coverage

of the atmosphere with slant paths is highly

improved by combining observations from two or

three satellite systems. But observations in the lower

part of the atmosphere, e. g, below 3 km, are still

rather sparse. Zhao Q. et al (2018) used multi-GNSS

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(GPS, GLONASS and BDS) data to validate the

tomographic results derived from various multi-

GNSS combined strategies and compared with

radiosonde data. Tomography experiments showed

that multi-GNSS observations can increase the

accuracy of 3D wet refractivity reconstruction but

not as well as was expected when using currently

available techniques. Dong Z. et al (2018) obtained

3D water vapor tomography results using multi-

GNSS data from Wuhan CORS and the reliability of

tomographic water vapor density reconstructed by

combining multi-GNSS is significantly enhanced

when compared to the GPS-only system.

Above mentioned study shows that the number

of satellite rays used has been almost doubled or

tripled when multi-GNSS observations were used in

the tomographic model, but the percentage of empty

voxels did not decreased as much as was expected.

The possible reason was that the spatial resolution of

the tomographic model was not improved as was

expected, which can be addressed by increasing the

spatial density of ground-based GNSS stations. In

summary, GNSS water vapor tomography

technology is still in the process of research. In fact,

the water vapor in the atmosphere of 4D variation

characteristics are very complex. And there is a lack

of analysis of the fine 4D structure of the

atmosphere on the medium and small scale in

operational applications. Therefore more research is

needed to improve the accuracy and reliability of

GNSS tomography solution.

2.2 GNSS-Derived Water Vapor Data

Assimilation

The data assimilation technique is a vital part of

numerical weather forecasting. Europe and the

United States have began research in the related

fields of data assimilation of GNSS tropospheric

products since 2000 (Bennitt and Jupp, 2012).

During the 2000-2001 years, some European

Research Group successfully implemented and

confirmed the concept of ground-based GNSS

meteorology. In the following European Union

project (E-GVAP, 2005-2017, http://egvap.dmi.dk),

the fifteen European countries participated in the

campaign of the European GPS meteorology and

began to be used in actual production. Currently,

near real-time tropospheric products provided

through E-GVAP have been used in the assimilation

of many numerical weather prediction (NWP)

models. Now GNSS receiver network with real-time

retrieval of PWV has been running in Europe and

the United States and successfully applied to

weather forecast, such as American GPS/MET and

European E-GVAP.

The study of GNSS meteorology for the last 20

years showed that assimilation of GNSS precipitable

water can effectively improve the quality of the

initial atmospheric humidity and has a positive

effect on improving the performance of strong

nowcasting precipitation forecast (Bennitt and Jupp,

2012;Mahfoufet al., 2015;Lindskog et al.,

2017;Zhong et al., 2017;Guerova et al., 2016). The

research contents on GNSS data assimilation mainly

involve improvement of the initial conditions for

NWP using GNSS-PWVs, assimilation algorithm,

assimilation impacts of ZTD/ZWD/SWD/PWV and

wet refractivity data obtained by GNSS tomography

using various schemes. In the current study, a 3-

dimensional variational data assimilation (3D-Var)

scheme was mainly used for data assimilation. The

European Centre for Medium-Range Weather

Forecasts (ECMWF) has pioneered work on

assimilation methods such as 4D-Var (Zhong et al.,

2017) And many questions on the ground-based

GNSS data assimilation need further study, such as

the methods to effectively assimilate the PWVs into

the NWP model, estimation of observation error,

adjustment of the initial field, determination of the

background error, selection of assimilation

algorithm and analysis of the impact of the GNSS

data assimilation on NWP models. There are still

many unresolved problems related to the GNSS data

assimilation. Therefore there's a lot of space for

research on how to effectively assimilate GNSS

tropospheric products and tomography data into the

operational system of the numerical weather forecast.

The research on GNSS meteorology in recent

years was mainly on retrieving high temporal and

spatial PWV and analyzing relationship between

water vapor and precipitation events (Guerova et al.,

2016;Lu et al., 2016;Yu et al., 2017;Zheng et al.,

2018). While the work on data assimilation

algorithm of GNSS tropospheric products is

relatively few. Also little has been done in the past

to use GNSS reprocessed troposphere products for

data assimilation in climate models. This field of

research has however seen starting some recent

initiatives. The European Reanalysis project, in

which the U.K. Met Office take part, will be used to

promote the data assimilation of ZTDs/PWVs in

climate re-analysis. Recently, the European Union

Commission carried out a new research project

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203

“GNSS4SWEC” running from 2013 to 2017. The

research fields of the project include GNSS

advanced processing techniques, GNSS for severe

weather monitoring and GNSS for climate

monitoring (http://gnss4swec.knmi.nl).

It has become a new hotspot of GNSS

meteorology research to assimilate GNSS

tomography results improving the initial field of the

numerical weather forecast model and the quality of

nowcasting. The new generation of high resolution

weather prediction model requires high resolution

input data and observation data. With the

improvement of the resolution of weather forecast

model, it becomes more and more important to

initialize of the mesoscale atmospheric phenomena

using high spatial and temporal resolution

observations. Therefore GNSS data assimilation is a

very promising approach to improve the quality of

the short-term weather forecast, especially for

extreme weather events, such as heavy rainfall.

2.3 Meteorological Applications of

Gnss-Derived Water Vapor

Meteorological applications of GNSS-derived water

vapor mainly involves the following aspects:

disaster monitoring, weather forecasting and climate

monitoring. High precise and high temporal-spatial

resolution PWV data is the important information

for disaster monitoring (such as torrential rain,

thunderstorm, typhoon, dense fog). A lot of research

has been done on the nowcasting of disastrous

weather using GPS-PWV (Poli et al., 2008;Manning

et al., 2012;Yao et al., 2017;Liang et al., 2015;Choy

et al., 2013). Poli P. et al. (2008) discuss the effect

of GNSS-derived data on NWP by using European

ground-based GNSS-ZTD data introduced into the

Météo-France global forecasting system. They

reported that the benefits of including such data

were most apparent in improved predictions of

temperature and wind, and especially, in superior

quantitative precipitation forecasts over France.

Boniface K. et al. (2009) evaluated the impacts of

assimilating GPS data on the precipitation forecast

on Mediterranean heavy rainfall forecasting.

Manning T. et al. (2012 ) presented a case study

based on the analysis of an extreme convective super

cellstorm in the Victorian region during March 2010

using GPS tomography and CORS network in

Australia. The study concluded that GPS

tomographic wet refractivity profiles showed an

excessive increase as a response to supercell

thunderstorm formation.

A number of experimental analysis on GNSS-

PWV data for nowcasting of disastrous weather

shows that the ground-based GNSS-PWV has the

same accuracy as radiosonde and radiometer and

high temporal-spatial GNSS-PWV data plays a

significant role in monitoring severe weather.

Assimilating PWV data can improve the initial

humidity field of NWP mode and improve the

accuracy of the numerical weather forecast.

Moreover, GNSS is not only used to sense the

precipitation but also to detect the wind and clouds.

Climate is defined as the average weather

conditions at a place usually over a period of years

as exhibited by temperature, air pressure, humidity,

precipitation, winds, sunshine and clouds. So GNSS

is also a promising climate monitoring tool capable

of providing accurate, long-term, and consistent data

for climate studies. The applications of GNSS-

derived water vapor in climate monitoring need

further studies.

3 DEVELOPMENT AND

APPLICATION PROSPECTS

At present, GNSS is mainly used for tropospheric

water vapor monitoring and weather forecast.

Actually, GNSS meteorology also have better

prospects for development and application. With the

ground-based GNSS observing networks

continuously densified, GNSS will be an important

technical means for monitoring the total and vertical

distribution of atmospheric water vapor along with

upper wind measurement and climate change

monitoring. GNSS will play a more important role

in medium and small scale weather analysis,

numerical weather forecast, disastrous weather

service and global climate change monitoring. In

addition, GNSS data supporting meteorological

applications mainly use GPS single constellation

system and observations of GPS ground reference

station currently. With the development and

integration of multi-constellation GNSS

(GPS/Beidou/Glonass/Galileo) system, the number

of GNSS observations will be greatly increased.

Moreover, with the rapid growth of the number of

GNSS reference stations, the rapid development of

mobile surveying system integrated with

GNSS/inertial navigation carried by vehicle, ship-

borne and unmanned aerial vehicles, the available

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GNSS observations will further increase, which will

also promote the development of GNSS

meteorology. Fostered by these developments,

advanced processing strategies are necessary to

exploit the full potential of future GNSS systems for

describing the physical state of the low atmosphere.

Other potential applications like estimating cloud-

base height should also be explored. So there has

great research potential in the fields of ground-based

GNSS meteorology.

ACKNOWLEDGMENTS

This work is supported by the Shandong Provincial

Natural Science Foundation, China

(ZR2017MD029), the Technology Project Plan of

Ministry of Housing and Urban-Rural Development

of the People’s Republic of China (2014-K8-070)

and Shandong Provincial Department of Housing

and Urban Rural Construction Technology Project

Plan (2017-R1-004). The authors greatly appreciate

the editor and the anonymous reviewers on this

manuscript.

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