Applying Malawi Continuously Operating Reference Stations in GNSS Meteorology

Aug 2023 | No Comment

In this paper, the ZTD estimation approach and the evaluation of results from the GPS measurements are presented

Robert Galatiya Suya

Department of Land Surveying, School of the Built Environment, Malawi University of Business and Applied Sciences, Private Bag 303, Chichiri, Blantyre 3, Malawi

Charles Chisha Kapachika

Department of Land Surveying, School of the Built Environment, Malawi University of Business and Applied Sciences, Private Bag 303, Chichiri, Blantyre 3, Malawi

Mphatso Oscar Soko

Department of Land Surveying, School of the Built Environment, Malawi University of Business and Applied Sciences, Private Bag 303, Chichiri, Blantyre 3, Malawi

John Bosco Ogwang

Institute of Survey and Land Management, Po Box 89, Entebbe, Kampala, Uganda

Harvey Chilembwe

Department of Geography and Earth Sciences Faculty of Science, University of Malawi, P.O BOX 280, Zomba, Malawi

Francis Gitau

School of Mines and Engineering, Taita Taveta University, P.O Box 635- 80300 Voi, Kenya


Global Navigation Satellite System (GNSS) signals in the L-band are affected by the non-dispersive neutral atmosphere. Regardless of their center frequency, the L-band code and phase observations are affected by the same measure of delay. GNSS receivers play a significant role in quantifying the zenith tropospheric delay (ZTD) from satellite signals. Malawi has a Continuously Operating Reference Stations (CORS) network which was established to support research in geophysical geodesy and geodynamics. However, the quality of the observations tracked by the CORS has never been tested in terms of its meteorological application. In this paper, the ZTD estimation approach and the evaluation of results from the Global Positioning System (GPS) measurements are presented. The optimal approach of precise point positioning (PPP) was used to estimate ZTD from one-week datasets which were collected from six CORS monuments distributed in the northern and southern regions of Malawi. In addition, the zenith wet delay (ZWD) and zenith hydrostatic delay (ZHD) were also estimated to determine their respective contributions to the total delay in all the stations. Alongside the meteorological parameters, the positioning repeatabilities were also established for all stations. Results indicate that the averaged ZTD, ZWD, and ZHD can reach as high as 247mm, 47 mm, and 199 mm, respectively. The minimum ZTD, ZWD, and ZHD for the stations can drop to as low as 220 mm, 24 mm, and 181 mm, respectively. This indicates that the ZHD contributes to more than 90% of the total delay at the stations. For the positioning performance, there was no obvious disparity in the latitude (less than 0.5 cm), longitude (less than 1 cm), and ellipsoidal height repeatabilities (less than 1.5 cm). Thus, the results clearly demonstrate that the Malawi CORS network may be used for GNSS-based meteorological applications using the available geodetic receivers. However, for highprecision meteorological applications, Malawi may consider densifying the available network with geodetic-grade receivers.


Global Navigation Satellite System (GNSS), which comprises Global Positioning System (GPS, for the USA); Global’naya Navigatsionnaya Sputnikova Sistema (GLONASS or Russian Global Navigation Satellite System); BeiDou Navigation Satellite System (BDS, for China), and Galileo (for Europe), has revolutionized positioning, navigation, and timing (PNT) services. Other than PNT applications, GNSS has also extended its roles in meteorology (Gutman and Benjamin, 2001; Shoji, 2009; Kiyani et al., 2020) and weather studies (Gutman et al., 2004; Rahimi, Mohd Shafri and Norman, 2018).

In GNSS meteorology, satellite observations can be tracked with low-cost receivers such as smartphones with a single frequency (Pesyna et al., 2014; Krietemeyer et al., 2018) and a multi-frequency (Paziewski, 2020; Uradziński and Bakuła, 2020) tracking capability. These gadgets act as an alternative to employing reference stations, such as the International GNSS Service (IGS) or the Multi-GNSS Experiment (MGEX) stations, established by agencies, institutions, or countries. In their geographical positions, the stations may be installed alone or co-located with other space geodetic techniques such as Very Long Baseline Interferometry (VLBI); Satellite Laser Ranging (SLR); Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS); water vapour radiometers (WVR) or tide gauge stations. In both single and multi-purpose CORS networks, ZTDs can be estimated from the tracked GNSS (Bevis et al., 1992; Alshawaf et al., 2017), VLBI (Heinkelmann et al., 2007; Balidakis et al., 2018); SLR (Pollet et al., 2014); DORIS (Teke et al., 2013), and WVR (Bock et al., 2010) observations.

The estimated ZTD is a sum of the components including the zenith wet delay (ZWD) and the zenith hydrostatic delay (ZHD). The ZWD is directly related to ground pressure within the spatial region, whereas the ZHD is linked to the amount of water vapour in the atmosphere. The troposphere, being the first layer of the atmosphere, is influenced by the total refractivity, which is a function of temperature, pressure, and water vapour partial pressure (Essen and Froome, 1951). Furthermore, the quantity of precipitable water can be determined from the available water vapour in the atmosphere which tends to be proportional to ZWD (Hurter and Maier, 2013).

The measurement and monitoring of physical variables such as pressure, temperature, and humidity using GNSS signals is of profound significance to regional and short-term weather forecasting (Awange, 2011). On the other hand, the existing CORS networks need to accommodate a considerable density of stations to achieve ZTD of improved spatial resolution (Zhao et al., 2018). ZTDs have commonly been estimated from phase observables (Bevis et al., 1992) or combined code and phase (Ahmed et al., 2016; Zhao et al., 2018). As a consequence, ZTD estimates are derived from double differences (DD) or precise point positioning (PPP) techniques (Zumberge et al., 1997). The resultant ZTDs derived from PPP are consistent with the global reference system implied by the fixed global GNSS ephemerides. On the other hand, ZTDs estimated from the DD technique are biased by a datum offset depending on the baseline lengths in the CORS network.

Malawi has a local CORS network purposefully established to support geophysical and geodynamics studies (Shillington et al., 2016). The studies rely on a limited number of CORS monuments, the majority (91%) of which are geographically located in the northern part of the country. While the Malawi CORS network offers such research benefits, the possibility of using the existing CORS monuments in GNSS meteorology has been overlooked. In GNSS meteorology, PPP is one of the optimal techniques requiring only a single geodetic receiver to estimate meteorological parameters. Taking advantage of such a versatile approach, ZTD, ZHD, and ZWD are estimated for the CORS network in Malawi for ten days in this paper. This is achieved not only to demonstrate the feasibility of using Malawi’s CORS network in the estimation of meteorological parameters, but also to establish the overall positioning repeatability performance for the individual stations.

Estimation of meteorological parameters

The PPP Technique

As the GNSS signal propagates through the atmosphere, it is delayed by the ionosphere. Typically, GNSS dual frequency phase and code observables are combined to eliminate first-order ionospheric propagation delays. Hence, the ionosphere-free (IF) combinations of the dual-frequency GPS phase and code observations between satellite and receiver can be formulated as in Leick et al.(2015):

Characteristics of the Malawi CORS network

Decommissioned CORS network

Malawi briefly recorded GPS single-frequency observations from CORS monuments between March and May in 1997 in five different geographical locations. In all the five stations, GPS observations were logged using a TRIMBLE 4000SSI geodetic receiver equipped with a TRM29659.00 antenna. Operated by the East Africa 1997 campaign, the average data recording periods for the receivers are summarized from initial to the final day of year (DOY) in Table 1. Since the initial campaign, another CORS station has been installed in Malawi in 2008 by the Department of Land Surveys and funded by Hartebeesthoek Radio Astronomy Observatory (HartRAO) Space Geodesy Programme of South Africa. The CORS was installed at the top of the Home Affairs and Department of Human Resources building (Capitol Hill) in Lilongwe City (AFREF, 2008). This station (not included in Table 1) was specifically installed as part of African Reference Frame (AFREF) to support satellite positioning by GPS in Malawi. In this paper, the CORS stations that were constructed in 1997 have been termed “decommissioned CORS” (refer to Figure 1).

Operational CORS network

Recently, a new CORS network has been initiated by the Malawi Rifting GPS Network (MRGN) and the Africa Array GPS Network (AAGN) in Malawi. Currently, Malawi has six operational CORS monuments. Four of the CORS (Livingstonia, Vwaza Marsh, Karonga, and Chitipa) are operated by MRGN, and the other two (Mzuzu and Zomba) are operated by AAGN. The station characteristics for the operational CORS network in Malawi are summarized in Table 2. Figure 1 depicts the distribution of the CORS network in Malawi.

Experimental datasets and processing

The GNSS datasets for the stations used in this paper (Table 2) were obtained from the University NAVSTAR Consortium (UNAVCO) at https://www. UNAVCO is sponsored by the National Science Foundation (NSF) and the National Aeronautics and Space Administration (NASA) to provide free services to support research worldwide. In order to test the Malawi CORS network on GNSS meteorology, ten days of GNSS observations spanning from DOY 001 to DOY 010 in 2016 were downloaded from UNAVCO. Due to data unavailability in the first constructed CORS monuments, only the geodetic stations in Table 2 were considered.

All the necessary PPP corrections according to Kouba (2009) were applied. The processing parameters are summarized in Table 3. The ZTD was estimated using Equation [2.10] whereas the ZHD was modeled using [2.11]. The water vapour refractivity is responsible for most of the wet delay and it was estimated using [2.12]. The estimated quantities of ZTD, ZHD, and ZWD for the selected days were compared. Finally, the standard deviation was used to express the positioning repeatability of the CORS monuments.Results and discussion The estimated ZTD Using GPS datasets for ten days (DOY 001-010) of the year 2016, the ZTDs were estimated for the Malawi CORS network. Figure 2 depicts the ZTD-PPP derived time series for CTPM, KARO, LIVA, MZUZ, VWZM, and ZOMB CORS monuments.

Results and discussion

The estimated ZTD

Using GPS datasets for ten days (DOY 001-010) of the year 2016, the ZTDs were estimated for the Malawi CORS network. Figure 2 depicts the ZTD-PPP derived time series for CTPM, KARO, LIVA, MZUZ, VWZM, and ZOMB CORS monuments.

As can be seen from Figure 2, the estimated ZTDs for CTPM, LIVA, MZUZ, VWZM and ZOMB are consistent and within the same range of approximately 212 cm to 232 cm. However, KARO has the maximum estimated ZTD, reaching up to 250 cm on average. For the selected 10 days, the highest total delay for KARO is attributed to the large contribution of the ZHD (Table 4). The highest ZTD for 10 days simply indicates the high refractivity of dry gases in the troposphere for the KARO station. This is, on the other hand, caused by an increase in average atmospheric pressure, as demonstrated in Figure 2, reaching up to 950 mbars.

The estimated ZWD

The ZWD was derived from the difference between ZTD and ZHD and the associated time series for the estimated ZWD are shown in Figure 3 and the computed numerical values are presented in Table 4. From Table 4, it can be demonstrated that ZHD contributes to almost 90% of the total delay. This is evident from the numerical values between ZWD and ZHD. To better distinguish between the variations in the estimated meteorological parameters, the mean ZTD, ZWD and ZHD is illustrated in Figure 3. What is apparent is that ZOMB has the minimum ZWD of about 34 cm. This can also be verified from Table 4 and the least wet delay may be attributed to a higher influence of water vapour refractivity in the troposphere (Yuan et al., 2019). This can be explained better by comparing water vapour refractivity with the average atmospheric pressure of about 903 mbar for the selected days (Figure 4). On the other hand, LIVA CORS has the least atmospheric pressure, namely only about 864 mbars (Figure 5)

Positioning performance

In situations where the visible number of satellites is small, the overall positioning performance declines. For the determination of the ZTD described above, knowledge about the tracked satellite vehicles (SVs) at each CORS is thus necessary. Hence, for the selected days in this study, the visible SVs are illustrated in Figure 5. As can be noticed from Figure 6, at least ten GPS satellites were observed on all the selected days. As indicated in Suya (2019)convergence time and Positional Dilution of Precision (PDOP, this number of tracked satellites is more than enough for the estimation of parameters by PPP.

To assess the effect of the estimated ZTD on positioning performance, the positional stability of the CORS stations during the sampled period was examined. This was performed by computing the standard deviations between the estimated coordinates and the a priori coordinates. The estimated coordinates and their associated standard deviations that express the CORS 3D positioning repeatability for the six stations are presented in Table 5. The geodetic coordinates in Table 5 are referenced to the local geodetic datum of the World Geodetic System 1984 (WGS – 84).


Malawi CORS are commonly used for geophysical and geodynamics studies. This paper attempted to estimate the meteorological parameters from the operational CORS network using the PPP technique. Ten days of GNSS datasets from DOY 001 to 010 in 2016 were used to estimate the ZTD, ZWD, and ZHD, including coordinate repeatabilities for the six CORS monuments. Results indicate that the mean ZTD, ZWD, and ZHD can reach as high as 247cm, 47 cm, and 199 cm at the Karonga CORS monument, respectively. This was attributed to the high atmospheric pressure of about 903 mbars for the experimented days. On the other hand, the minimum ZTD, ZWD and ZHD for the stations can drop to as low as 220 mm, 24 mm, and 181 mm at the Livingstonia CORS monument, respectively. The reduced values were attributed to low pressure at the Livingstonia CORS monument. The study also indicates that the ZHD contributes to more than 90% of the total delay in the stations. In the case of positioning performance, there was no obvious disparity in the latitude (less than 0.5 cm), longitude (less than 1 cm), and ellipsoidal height repeatabilities (less than 1.5 cm). Therefore, the results clearly demonstrate that the Malawi CORS network may be used for GNSS-based meteorological applications using the available geodetic receivers. This study used datasets for a few days to fully quantify the meteorological parameters. Therefore, a similar study may be conducted with datasets spanning the whole period of a year or more. Furthermore, for high-precision meteorological applications, Malawi may consider densifying the available network with geodetic-grade receivers for the robust estimation of meteorological parameters.


The authors gratefully acknowledge UNAVCO for the CORS GNSS observation datasets used in this project. The satellite clocks, orbits, and all other PPP datasets were retrieved from the IGS (


Data processing was done in an in-house software developed by the first author.


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The paper originally published in South African Journal of Geomatics, Vol. 11. No. 2, August 2022 is republished with authors’ permission. Copyright is with the authors.

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