Data

Long-term global GPS-derived precipitable water vapor data set

RMIT University, Australia
Professor Kefei Zhang (Associated with, Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://doi.pangaea.de/10.1594/PANGAEA.862525&rft.title=Long-term global GPS-derived precipitable water vapor data set&rft.identifier=a4799a0f909953bc1b1eb91a2ca33516&rft.publisher=RMIT University, Australia&rft.description=Water vapor-weighted mean temperature, Tm, is a vital parameter for retrieving precipitable water vapor (PWV) from the zenith wet delay (ZWD) of Global Navigation Satellite Systems (GNSS) signal propagation. In this study, the Tm at 368 GNSS stations for 2000-2012 were calculated using three methods: (1) temperature and humidity profiles from ERA-Interim, (2) the Bevis Tm-Ts relationship, and (3) the Global Pressure and Temperature 2 wet model. Tm derived from the first method was used as a reference to assess the errors of the other two methods. Comparisons show that the relative errors of the Tm derived from these two methods are in the range of 1-3% across more than 95% of all the stations. The PWVs were calculated using the aforementioned three types of Tm and the GNSS-derived ZWD at 107 stations. Again, the PWVs calculated using Tm from the first method were used as the reference of the other two PWVs. The root-mean-square errors of these two PWVs are both in the range of 0.1-0.7 mm. The second method is recommended in real-time applications, since its performance is slightly better than the third method. In addition, the linear trends of the PWV time series from the first method were also used as the reference to evaluate the trends from the other two methods. Results show that 13% and 23% of the PWV trends from the respective second and third methods have a relative error of larger than 10%. For climate change studies, the first method, if available, is always recommended.&rft.creator=Professor Kefei Zhang&rft.date=2018&rft.relation=http://dx.doi.org/10.1002/2015JD024181&rft_rights=All rights reserved&rft_rights=CC BY-NC: Attribution-Noncommercial 3.0 AU http://creativecommons.org/licenses/by-nc/3.0/au&rft_subject=Water vapor-weighted mean temperature &rft_subject=Precipitable water vapor&rft_subject=Climate change&rft_subject=GPT2w&rft_subject=ERA-Interim&rft_subject=Atmospheric Dynamics&rft_subject=EARTH SCIENCES&rft_subject=ATMOSPHERIC SCIENCES&rft.type=dataset&rft.language=English Access the data

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CC BY-NC: Attribution-Noncommercial 3.0 AU
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Water vapor-weighted mean temperature, Tm, is a vital parameter for retrieving precipitable water vapor (PWV) from the zenith wet delay (ZWD) of Global Navigation Satellite Systems (GNSS) signal propagation. In this study, the Tm at 368 GNSS stations for 2000-2012 were calculated using three methods: (1) temperature and humidity profiles from ERA-Interim, (2) the Bevis Tm-Ts relationship, and (3) the Global Pressure and Temperature 2 wet model. Tm derived from the first method was used as a reference to assess the errors of the other two methods. Comparisons show that the relative errors of the Tm derived from these two methods are in the range of 1-3% across more than 95% of all the stations. The PWVs were calculated using the aforementioned three types of Tm and the GNSS-derived ZWD at 107 stations. Again, the PWVs calculated using Tm from the first method were used as the reference of the other two PWVs. The root-mean-square errors of these two PWVs are both in the range of 0.1-0.7 mm. The second method is recommended in real-time applications, since its performance is slightly better than the third method. In addition, the linear trends of the PWV time series from the first method were also used as the reference to evaluate the trends from the other two methods. Results show that 13% and 23% of the PWV trends from the respective second and third methods have a relative error of larger than 10%. For climate change studies, the first method, if available, is always recommended.

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  • Local : a4799a0f909953bc1b1eb91a2ca33516