Impact of GPS Processing on the Estimation of Snow Water Equivalent Using Refracted GPS Signals

Steiner, Ladina; Meindl, Michael; Marty, Christoph; Geiger, Alain

doi:10.1109/tgrs.2019.2934016

Cited by 22 publications

(14 citation statements)

References 39 publications

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“…Field measurements carry uncertainties from a variety of sources (sampling tube, weight scale, sampling technique, etc.). Few studies discuss the accuracy of SWE observations comprehensively (e.g., Stuefer et al, 2013). Commonly, a relative uncertainty of ±10 % is applied (e.g., Schattan et al, 2017).…”

Section: Estimating the Uncertainty Of Automatically Derived Sd And Smentioning

confidence: 99%

Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

et al. 2019

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Abstract. Snow water equivalent (SWE) measurements of seasonal snowpack are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be inferred from neutron counts. We present the analyses of temporally continuous SWE measurements by a CRS on an alpine glacier in Switzerland (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which differed markedly in the amount and timing of snow accumulation. By combining SWE with snow depth measurements, we calculate the daily mean density of the snowpack. Compared to manual field observations from snow pits, the autonomous measurements overestimate SWE by +2 % ± 13 %. Snow depth and the bulk snow density deviate from the manual measurements by ±6 % and ±9 %, respectively. The CRS measured with high reliability over two winter seasons and is thus considered a promising method to observe SWE at remote alpine sites. We use the daily observations to classify winter season days into those dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, snowmelt) or snow densification. For each of these process-dominated days the prevailing meteorological conditions are distinct. The continuous SWE measurements were also used to define a scaling factor for precipitation amounts from nearby meteorological stations. With this analysis, we show that a best-possible constant scaling factor results in cumulative precipitation amounts that differ by a mean absolute error of less than 80 mm w.e. from snow accumulation at this site.

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Section: Estimating the Uncertainty Of Automatically Derived Sd And Smentioning

confidence: 99%

Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

et al. 2019

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“…However, the BER target is not the actual BER of C-Users because the interference between communication signal and positioning signal is not considered in the traditional method. The actual BER of C-Users should be calculated by (5) after having the solution which is calculated using (38). Like BER target , σ 2 target is not the actual ranging error of C-Users in the traditional method and it should be calculated by (4) after having the solution which is calculated using (39).…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Due to the shelter of wall, satellite signal will be obviously weakened after passing through the building [3]. Therefore, the well known Global Navigation Satellite Systems (GNSS) [4], such as the Global Positioning System (GPS) and the Beidou System (BDS) can only be used in open areas and the accuracy of traditional GNSS is greatly reduced indoors [5], [6]. The emerging Wi-Fi, Bluetooth or Wireless Sensor Networks (WSNs) based positioning techniques have good coverage and performance indoors only with dense…”

Section: Introductionmentioning

confidence: 99%

Joint Uplink Power Allocation Method in Wireless Communication and Positioning Integrated System

et al. 2021

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With the development of wireless communication, there are usually both communication and positioning requirements. In our previous work, we studied the power allocation algorithm for positioning user (P-User) based on Multi-Scale Non-Orthogonal Multiple Access (MS-NOMA) downlink signal where all communication users (C-Users) have the same power. In the uplink case, the power allocation scheme for both C-User and P-User needs to be considered jointly as all C-Users may have different power. In this paper, we analyze the Quality of Service (QoS) and ranging accuracy based on the MS-NOMA signal by deriving their simple expressions. Then, a two stage joint iterative power allocation algorithm (JIPAA) is proposed. In the first stage, considering the QoS of C-User requirement and total power limitation, the power of P-Users is allocated to obtain optimal ranging performance. In the second stage, the power of C-Users is allocated to minimize the power consumption. With the iterative process, the optimal power of each C-User and P-User is calculated. The numerical results show that the proposed JIPAA algorithm dramatically promotes ranging accuracy under the same energy consumption.INDEX TERMS Multi-scale non-orthogonal multiple access (MS-NOMA), power allocation, quality of service (QoS), ranging accuracy.

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“…A good overview of the location and the sensors is given in or Koch et al (2019). At this site, GNSS measurements are ongoing since several years (Henkel et al, 2018;Koch et al, 2014;Koch et al, 2019;Steiner et al, 2019b). The site of Laret is located on an open meadow at 1510 m a.s.l, and is wind sheltered resulting in a very uniform snow depth.…”

Section: Study Sites and Datamentioning

confidence: 99%

“…In a further step, Henkel et al (2018) exploited the GNSS carrier phase measurements for deriving SWE with a low-cost GNSS system for dry-snow conditions. A similar approach relying on carrier phase measurements allowed an hourly SWE estimation from the GNSS signal (Steiner et al, 2018;Steiner et al, 2019a;Steiner et al, 2019b). Koch et al (2019) generalized the techniques of Koch et al (2014), and Henkel et al (2018) for dry-and wet-snow conditions by combining GNSS carrier phases and signal strength, snow permittivity models and a simple snow densification model to simultaneously derive SWE, HS and LWC with only one GNSS sensor system.…”

Section: Introductionmentioning

confidence: 99%

GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient

Capelli

Koch

Henkel³

et al. 2021

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Abstract. Snow water equivalent (SWE) can be measured using low-cost Global Navigation Satellite System (GNSS) sensors with one antenna placed below the snowpack and another one serving as a reference above the snow. The underlying GNSS signal-based algorithm for SWE determination for dry- and wet-snow conditions processes the carrier phases and signal strengths and derives additionally liquid water content (LWC) and snow depth (HS). So far, the algorithm was tested intensively for high-alpine conditions with distinct seasonal accumulation and ablation phases. In general, snow occurrence, snow amount, snow density and LWC can vary considerably with climatic conditions and elevation. Regarding alpine regions, lower elevations mean generally earlier and faster melting, more rain-on-snow events and shallower snowpack. Therefore, we assessed the applicability of the GNSS-based SWE measurement at four stations along a steep elevation gradient (820, 1185, 1510 and 2540 m a.s.l.) in the eastern Swiss Alps during two winter seasons (2018–2020). Reference data of SWE, LWC and HS were collected manually and with additional automated sensors at all locations. The GNSS-derived SWE estimates agreed very well with manual reference measurements along the elevation gradient and the accuracy (RMSE = 34 mm, RMSRE = 11 %) was similar under wet- and dry-snow conditions, although significant differences in snow density and meteorological conditions existed between the locations. The GNSS-derived SWE was more accurate than measured with other automated SWE sensors. However, with the current version of the GNSS algorithm, the determination of daily changes of SWE was found to be less suitable compared to manual measurements or pluviometer recordings and needs further refinement. The values of the GNSS-derived LWC were robust and within the precision of the manual and radar measurements. The additionally derived HS correlated well with the validation data. We conclude that SWE can reliably be determined using low-cost GNSS-sensors under a broad range of climatic conditions and LWC and HS are valuable add-ons.

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Impact of GPS Processing on the Estimation of Snow Water Equivalent Using Refracted GPS Signals

Cited by 22 publications

References 39 publications

Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

Joint Uplink Power Allocation Method in Wireless Communication and Positioning Integrated System

GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient

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