Mapping function induced bias in tropospheric delay estimation using GPS

Kleijer, F.

doi:10.1016/s1464-1895(01)00085-0

Cited by 10 publications

(7 citation statements)

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“…The difficulties of spatially and temporally correlated observation errors have generally been circumvented in data assimilation by applying thinning of data, or through observation processing algorithms that are assumed to remove the observation error correlations (Stewart et al, 2013). Methods have been developed to account for serially correlated errors (Järvinen et al, 1999), but there is certainly room for improvement regarding spatially correlated errors, although some general research within this area has been carried out (Lin et al, 2000;Liu and Rabier, 2002;Bormann and Bauer, 2010;Stewart et al, 2013). Some studies have focused on GNSS ZTD observations (Kleijer, 2001;Stoew, 2004;Eresmaa and Järvi-nen, 2005), but the handling of the correlated observation errors is still an active area of research.…”

Section: Introductionmentioning

confidence: 99%

“…Methods have been developed to account for serially correlated errors (Järvinen et al, 1999), but there is certainly room for improvement regarding spatially correlated errors, although some general research within this area has been carried out (Lin et al, 2000;Liu and Rabier, 2002;Bormann and Bauer, 2010;Stewart et al, 2013). Some studies have focused on GNSS ZTD observations (Kleijer, 2001;Stoew, 2004;Eresmaa and Järvi-nen, 2005), but the handling of the correlated observation errors is still an active area of research. GNSS ZTD observations processed by the NGAA centre have been used within the MetCoOp forecasting system, aiming at improving short-range forecasts of, in particular, moisture, clouds and precipitation.…”

Section: Introductionmentioning

confidence: 99%

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Data assimilation of GNSS zenith total delays from a Nordic processing centre

et al. 2017

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Abstract. Atmospheric moisture-related information estimated from Global Navigation Satellite System (GNSS) ground-based receiver stations by the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art kilometre-scale numerical weather prediction system. Different processing techniques have been implemented to derive the moisture-related GNSS information in the form of zenith total delays (ZTDs) and these are described and compared. In addition full-scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture-related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the ensuing forecast quality. The sensitivity of results to aspects of the data processing, station density, bias-correction and data assimilation have been investigated. Results show benefits to forecast quality when using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition, it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data assimilation of GNSS zenith total delays from a Nordic processing centre

et al. 2017

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“…The performance of the horizontal gradients is also limited, because they can only consider a linear asymmetry of the troposphere around the geodetic site [26]. Kleijer [27] analyzed that significant biases can be introduced in the estimated ZWD when the atmosphere is not symmetrical. However, the suggestion of using an accurate wet mapping function is still limited by the assumption of the homogeneous troposphere.…”

Section: Introductionmentioning

confidence: 99%

Precise Point Positioning on the Reliable Detection of Tropospheric Model Errors

Verhagen

2020

Sensors

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Precise point positioning (PPP) is one of the well-known applications of Global Navigation Satellite System (GNSS) and provides precise positioning solutions using accurate satellite orbit and clock products. The tropospheric delay due to the neutral atmosphere for microwave signals is one of the main sources of measurement error in PPP. As one component of this delay, the hydrostatic delay is usually compensated by using an empirical correction model. However, how to eliminate the effects of the wet delay during a weather event is a challenge because current troposphere models are not capable of considering the complex atmosphere around the receiver during situations such as typhoons, storms, heavy rainfall, et cetera. Thus, how positioning results can be improved if the residual wet delays are taken into account needs to be investigated . In this contribution, a real-time procedure of recursive detection, identification and adaptation (DIA) is applied to detect the model errors which have the same effects on both phase and code observables; e.g., the model error caused by the tropospheric delay. Once the model errors are identified, additional parameters are added to the functional model to account for the measurement residuals. This approach is evaluated with Global Positioning System (GPS) data during two rainfall events in Darwin, Australia, proving the usefulness of compensated residual slant wet delay for positioning results. Comparisons with the standard approach show that the precision of the up component is improved significantly during the periods of the weather events; for the two case studies, 72.46 % and 64.41 % improvements of root mean squared error (RMS) resulted, and the precision of the horizontal component obtained by the proposed approach is also improved more than 30 % compared to the standard approach. The results also show that the identified model errors are concentrated at the beginning of both heavy rainfall processes when the front causes significant spatial and temporal gradients of the integrated water vapor above the receiver.

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“…The difficulties of spatially and temporally correlated observation errors have generally been circumvented in data assimilation by applying thinning of data, or through observation processing algorithms that are assumed to remove the observation error correlations (Stewart et al, 2013). Methods have been developed to account for serially correlated errors (Järvinen et al, 1999), but there is certainly room for improvement regarding spatially correlated errors, although some general research within this area has been carried out (Lin et al, 2000;Liu and Rabier, 2002;Bormann and Bauer, 2010;Stewart et al, 2013). Some studies have focused on GNSS ZTD observations (Kleijer, 2001;Stoew, 2004;Eresmaa and Järvinen, 2005), but the handling of the correlated observation errors is still an active area of research.…”

Section: Introductionmentioning

confidence: 99%

Data assimilation of GNSS Zenith Total Delays from a Nordic processing centre

Lindskog¹,

Ridal²,

Þorsteinsson³

et al. 2017

Preprint

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<p><strong>Abstract.</strong> Atmospheric moisture-related information obtained from Global Navigation Satellite System (GNSS) observations from ground-based receiver stations of the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art km-scale numerical weather prediction system. Different processing techniques have been implemented to derive the the moisture-related GNSS information in the form of Zenith Total Delays (ZTD) and these are described and compared. In addition full scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the following forecast quality. The sensitivity of results to aspects of the data processing, observation density, bias-correction and data assimilation have been investigated. Results show a benefit on forecast quality of using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.</p>

show abstract

Mapping function induced bias in tropospheric delay estimation using GPS

Cited by 10 publications

References 3 publications

Data assimilation of GNSS zenith total delays from a Nordic processing centre

Data assimilation of GNSS zenith total delays from a Nordic processing centre

Precise Point Positioning on the Reliable Detection of Tropospheric Model Errors

Data assimilation of GNSS Zenith Total Delays from a Nordic processing centre

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