Accuracy of orbits for GPS atmospheric water vapour estimation

Dodson, Alan; Baker, H.C.

doi:10.1016/s0079-1946(97)00253-x

Cited by 12 publications

(4 citation statements)

References 4 publications

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“…This standard deviation is very close to the results of fixing IGS final orbits, which indicates that our orbit reweighting strategy efficiently accounts for errors in predicted orbits. In addition, the precision of GPS‐derived ZTD from IGS final orbits in postprocessed mode, based on intersoftware comparisons, is on the order of 7 mm [ Dodson and Baker , 1998; Haase et al , 2001], which may be a better indication of the absolute accuracy of the GPS technique. The agreement of the near real‐time ZTD is within this level of precision.…”

Section: Resultsmentioning

confidence: 99%

Sensitivity of zenith total delay accuracy to GPS orbit errors and implications for near‐real‐time GPS meteorology

Calais

Haase

2002

J. Geophys. Res.

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[1] Global Positioning System (GPS) measurements have been demonstrated to provide precipitable water vapor (PWV) estimates with a level of accuracy that is comparable to that of radiosondes and microwave radiometers. GPS measurements therefore have the potential to become a significant source of data for operational weather forecasting, provided that PWV (or the intermediate zenith total delay (ZTD)) can be made available in near real-time with a minimum accuracy degradation. Despite the recent decrease in the latency and increase in accuracy provided by the International GPS Service (IGS) ultrarapid predicted GPS orbit products, we show that the accuracy of these orbits continues to be a limiting factor for the accuracy of near real-time GPS-derived atmospheric estimates. In this work, a coefficient matrix is derived from the normal equations of the least squares adjustment model for the GPS observables that maps the orbital parameter errors into ZTD errors. This is used to analyze the sensitivity of GPS derived tropospheric errors to an extensive set of parameters, including their time dependence, in a computationally efficient manner. We show that ZTD errors are dominated by biases in the orbital semimajor axis, with minor contributions from the inclination and argument of perigee, and that this error increases significantly after the fourth to fifth hour of the prediction window. We implemented a GPS data processing strategy based on an iterative estimation of the three most critical orbital parameters (semimajor axis, inclination and argument of perigee) together with the ZTD parameters. We tested this strategy in a 3500 Â 3500 km network of 15 GPS sites in western Europe providing hourly data files. We show that the standard deviation improvement compared to a strategy based only on the orbit quality index provided with the predicted orbit products is on the order of 20%. The analysis of one month of data in near-real-time shows a bias lower than 1 mm ZTD and a standard deviation lower than 6 mm ZTD compared to using the most precise IGS final orbits. We also show that this strategy is robust and capable of dealing with very large orbit errors appropriately. We demonstrate that the same quality is achievable with a 1500 Â 1500 km network which has positive implications for decentralized processing strategies. The near real-time processing methodology described here meets the current timeliness requirements of operational meteorology (30 mn to 2 hours, depending on the application), while ensuring a level of accuracy similar to that provided in postprocessed mode with precise final IGS orbits (1 mm ZTD bias, 6 mm ZTD RMS). The method we propose can also be considered as an ''on-the-fly'' orbit quality control for near real-time GPS applications.

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Section: Resultsmentioning

confidence: 99%

Sensitivity of zenith total delay accuracy to GPS orbit errors and implications for near‐real‐time GPS meteorology

Calais

Haase

2002

J. Geophys. Res.

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“…Note that the somewhat large standard deviations quoted are the orthogonal standard deviations and so include the contribution from the radiosonde measurements, suggesting that the actual scatter in the GPS estimates can be expected to be rather smaller that the quoted numbers. Investigations into the accuracy of PWV estimates derived using predicted orbits (e.g., Dodson and Baker 1998;Kruse et al 1999) suggest that as long as the prediction lead is only on the order of hours, the accuracy of the PWV estimates is not badly degraded. Because the median estimate compared best with the radiosondes and the median is a robust operator, the time series of median estimates is taken as the reference time series with which to examine in detail the results of the slidingwindow process.…”

Section: Applicationsmentioning

confidence: 99%

GPS Meteorology: Sliding-Window Analysis*

Foster

Bevis

Businger

2005

Journal of Atmospheric and Oceanic Technology

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The sliding-window technique uses a moving time window to select GPS data for processing. This makes it possible to routinely incorporate the most recently collected data and generate estimates for atmospheric delay or precipitable water in (near) real time. As a consequence of the technique several estimates may be generated for each time epoch, and these multiple estimates can be used to explore and analyze the characteristics of the atmospheric estimates and the effect of the processing model and parameters. Examples of some of the analyses that can be undertaken are presented. Insights into the phenomenology of the atmospheric estimates provided by sliding-window analysis permit the fine-tuning of the GPS processing as well as the possibility of both improving the accuracy of the near-real-time estimates themselves and constraining the errors associated with them. The overlapping data windows and the multiple estimates that characterize the sliding-window method can lead to ambiguity in the meaning of many terms and expressions commonly used in GPS meteorology. In order to prevent confusion in discussions of sliding-window processing, a nomenclature is proposed that formalizes the meaning of the primary terms and defines the geometric and physical relationships between them.

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“…PWV is the amount of water vapor represented by the height of the column of the liquid water and IWV is the amount of water vapor at a specific direction (Bevis et al, 1992). The GPS IWV has an accuracy of 2 1 2 kg m − − (Dodson andBaker, 1998, Rocken et al, 1997). According to the exiting results, the GPS PWV has an accuracy which is comparable to the PWV which is estimated from the radiosonde and Water Vapor Radiometer (WVR) profiles (Bevis et al, 1992, Emardson et al, 1998, Rocken et al, 1993, Tregoning et al, 1998.…”

Section: Introductionmentioning

confidence: 99%