Impact of different NWM-derived mapping functions on VLBI and GPS analysis

Abstract

In recent years, numerical weather models have shown the potential to provide a good representation of the electrically neutral atmosphere. This fact has been exploited for the modeling of space geodetic observations. The Vienna Mapping Functions 1 (VMF1) are the NWM-based model recommended by the latest IERS Conventions. The VMF1 are being produced 6 hourly based on the European Centre for Medium-Range Weather Forecasts operational model. UNB-VMF1 provide meteorological parameters aiding neutral atmosphere modeling for VLBI and GNSS, based on the same concept but utilizing the Canadian Meteorological Centre model. This study presents comparisons between the VMF1 and the UNB-VMF1 in both delay and position domains, using global networks of VLBI and GPS stations. It is shown that the zenith delays agree better than 3.5 mm (hydrostatic) and 20 mm (wet) which implies an equivalent predicted height error of less than 2 mm. In the position domain and VLBI analysis, comparison of the weighted root-mean-square error (wrms) of the height component showed a maximum difference of 1.7 mm. For 48% of the stations, the use of VMF1 reduced the height wrms of the stations by 2.6% on average compared to a respective reduction of 1.7% for 41% of the stations employing the UNB-VMF1. For the subset of VLBI stations participating in a large number of sessions, neither mapping function outranked the other. GPS analysis using Precise Point Positioning had a sub-mm respective difference, while the wrms of the individual solutions had a maximum value of 12 mm for the 1-year-long analysis. A clear advantage of one NWM over the other was not shown, and the statistics proved that the two mapping functions yield equal results in geodetic analysis.

Impact of Tropospheric Mismodelling in GNSS Precise Point Positioning: A Simulation Study Utilizing Ray-Traced Tropospheric Delays from a High-Resolution NWM

“…An indication of the high quality of the NWMs in this respect is the agreement between them. For example, the two state of the art NWM based tropospheric delay models, VMF1 and UNB-VMF1, gave almost the same results in a geodetic analysis [14]. There were some differences for specific locations and times, but a clear advantage of one NWM over the other was not demonstrated in a statistical sense.…”

“…The most prominent example of an MF based on NWM data is the Vienna Mapping Functions 1 (VMF1) [10]. Recently, various other MFs based on NWM data have been successfully applied in practice [11][12][13][14]. This includes those MFs based on NWM data that are no longer based on the assumption of a spherically layered atmosphere, such as the Vienna Mapping Functions 3 (VMF3) [15] and variants thereof [16].…”

Evaluation of Zenith Tropospheric Delay Derived from Ray-Traced VMF3 Product over the West African Region Using GNSS Observations

“…is introduces an error in the measured range and degrades the positioning accuracy, particularly the vertical component [3]. e influence of the troposphere on GNSS signals ranges from about 2.0 to 2.6 m in the zenith direction to about 20 to 28 m near the horizon and at lower elevation angles [4].…”

Beyond Mapping Functions and Gradients

“…the delay along the bended ray from the observer to the emitter) to two "zenithal delays", named hydrostatic delay (very often improperly called "dry" delay) L h z and wet delay L w z , essentially caused by the water vapor. The mapping functions are usually written [5] in the form of continuous fractions, that were introduced by Marini [6,7] and normalized by Herring [8] in the form…”