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  • PANGAEA  (374)
  • MDPI Publishing  (9)
  • Blackwell Publishing Ltd
  • 2015-2019  (383)
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  • 1
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    PANGAEA
    In:  RMIT University, Melbourne | Supplement to: Wang, Xiaoming; Zhang, Kefei; Wu, Suqin; Fan, Shijie; Cheng, Yingyan (2016): Water vapor-weighted mean temperature and its impact on the determination of precipitable water vapor and its linear trend. Journal of Geophysical Research: Atmospheres, 121(2), 833-852, https://doi.org/10.1002/2015JD024181
    Publication Date: 2024-07-19
    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.
    Type: dataset publication series
    Format: application/zip, 372 datasets
    Location Call Number Limitation Availability
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  • 2
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    PANGAEA
    In:  Supplement to: Wallace, L M; Araki, Eiichiro; Saffer, Demian M; Wang, Xiaoming; Roesner, Alexander; Kopf, Achim J; Nakanishi, A; Power, William L; Kobayashi, R; Kinoshita, Chihiro; Toczko, Sean; Kimura, Toshinori; Machida, Shiki; Carr, Stephanie A (2016): Near-field observations of an offshore Mw 6.0 earthquake from an integrated seafloor and subseafloor monitoring network at the Nankai Trough, southwest Japan. Journal of Geophysical Research: Solid Earth, 121(11), 8338-8351, https://doi.org/10.1002/2016JB013417
    Publication Date: 2024-07-19
    Description: An Mw 6.0 earthquake struck ~50 km offshore the Kii Peninsula of southwest Honshu, Japan on 1 April 2016. This earthquake occurred directly beneath a cabled offshore monitoring network at the Nankai Trough subduction zone and within 25-35 km of two borehole observatories installed as part of the International Ocean Discovery Program's NanTroSEIZE project. The earthquake's location close to the seafloor and subseafloor network offers a unique opportunity to evaluate dense seafloor geodetic and seismological data in the near field of a moderate-sized offshore earthquake. We use the offshore seismic network to locate the main shock and aftershocks, seafloor pressure sensors, and borehole observatory data to determine the detailed distribution of seafloor and subseafloor deformation, and seafloor pressure observations to model the resulting tsunami. Contractional strain estimated from formation pore pressure records in the borehole observatories (equivalent to 0.37 to 0.15 µstrain) provides a key to narrowing the possible range of fault plane solutions. Together, these data show that the rupture occurred on a landward dipping thrust fault at 9-10 km below the seafloor, most likely on the plate interface. Pore pressure changes recorded in one of the observatories also provide evidence for significant afterslip for at least a few days following the main shock. The earthquake and its aftershocks are located within the coseismic slip region of the 1944 Tonankai earthquake (Mw ~8.0), and immediately downdip of swarms of very low frequency earthquakes in this region, illustrating the complex distribution of megathrust slip behavior at a dominantly locked seismogenic zone.
    Keywords: 332-C0010A; Absolute pressure gauges (APG); Chikyu; DATE/TIME; DRILL; Drilling/drill rig; Exp332; In-situ pressure; Integrated Ocean Drilling Program / International Ocean Discovery Program; IODP; NanTroSEIZE Stage 2: Riserless Observatory; Temperature, technical
    Type: dataset
    Format: text/tab-separated-values, 15668 data points
    Location Call Number Limitation Availability
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  • 3
    Publication Date: 2024-07-19
    Keywords: Analysis of precipitable water vapor from GPS measurements; DATE/TIME; Day of the year; HOFN; Island; Precipitable water vapour; Weather station/meteorological observation; WST
    Type: dataset
    Format: text/tab-separated-values, 42488 data points
    Location Call Number Limitation Availability
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  • 4
    Publication Date: 2024-07-19
    Keywords: Analysis of precipitable water vapor from GPS measurements; Canada; DATE/TIME; Day of the year; HOLB; Precipitable water vapour; Weather station/meteorological observation; WST
    Type: dataset
    Format: text/tab-separated-values, 53868 data points
    Location Call Number Limitation Availability
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  • 5
    Publication Date: 2024-07-19
    Keywords: Analysis of precipitable water vapor from GPS measurements; DATE/TIME; Day of the year; HRM1; Precipitable water vapour; United Kingdom; Weather station/meteorological observation; WST
    Type: dataset
    Format: text/tab-separated-values, 33812 data points
    Location Call Number Limitation Availability
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  • 6
    Publication Date: 2024-07-19
    Keywords: Analysis of precipitable water vapor from GPS measurements; DATE/TIME; Day of the year; HYDE; India; Precipitable water vapour; Weather station/meteorological observation; WST
    Type: dataset
    Format: text/tab-separated-values, 28992 data points
    Location Call Number Limitation Availability
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  • 7
    Publication Date: 2024-07-19
    Keywords: Analysis of precipitable water vapor from GPS measurements; DATE/TIME; Day of the year; HRAO; Precipitable water vapour; South Africa; Weather station/meteorological observation; WST
    Type: dataset
    Format: text/tab-separated-values, 44648 data points
    Location Call Number Limitation Availability
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  • 8
    Publication Date: 2024-07-19
    Keywords: Analysis of precipitable water vapor from GPS measurements; DATE/TIME; Day of the year; IDDR; Precipitable water vapour; United States of America; Weather station/meteorological observation; WST
    Type: dataset
    Format: text/tab-separated-values, 15550 data points
    Location Call Number Limitation Availability
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  • 9
    Publication Date: 2024-07-19
    Keywords: Analysis of precipitable water vapor from GPS measurements; Brazil; DATE/TIME; Day of the year; ILHA; Precipitable water vapour; Weather station/meteorological observation; WST
    Type: dataset
    Format: text/tab-separated-values, 10550 data points
    Location Call Number Limitation Availability
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  • 10
    Publication Date: 2024-07-19
    Keywords: Analysis of precipitable water vapor from GPS measurements; DATE/TIME; Day of the year; INEG; Mexico; Precipitable water vapour; Weather station/meteorological observation; WST
    Type: dataset
    Format: text/tab-separated-values, 20802 data points
    Location Call Number Limitation Availability
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