Erebus GPS timeseries
We use NASA's Jet Propulsion Laboratory's (JPL) GipsyX software in PPP mode with ambiguity resolution applied to 24 hour segments of data to generate daily position solutions. We use JPL's orbit and clock products and International GNSS Service (IGS) antenna phase center models. Where...
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U.S. Antarctic Program (USAP) Data Center
2021
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Online Access: | https://dx.doi.org/10.15784/601471 https://www.usap-dc.org/view/dataset/601471 |
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ftdatacite:10.15784/601471 2023-05-15T13:30:27+02:00 Erebus GPS timeseries Grapenthin, Ronni 2021 https://dx.doi.org/10.15784/601471 https://www.usap-dc.org/view/dataset/601471 en eng U.S. Antarctic Program (USAP) Data Center Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY GPS Cryosphere Antarctica dataset Dataset 2021 ftdatacite https://doi.org/10.15784/601471 2021-11-05T12:55:41Z We use NASA's Jet Propulsion Laboratory's (JPL) GipsyX software in PPP mode with ambiguity resolution applied to 24 hour segments of data to generate daily position solutions. We use JPL's orbit and clock products and International GNSS Service (IGS) antenna phase center models. Where available, we use JPL's second order ionospheric corrections, otherwise we fall back on those provided by the IGS. To correct tropospheric delays, we use the GPT2 model as implemented in GipsyX. Ocean tidal loading corrections utilize the TPXO7.2 and ATLAS model, a combination of hydrodynamic model and altimetry data, with respect to Earth's Center of Mass implemented in SPOTL. We obtain position solutions for each station day in a fiducial-free reference frame, which we then transform into the 2014 International Reference Frame using JPL's transformation coefficients and generate timeseries of position change relative to the first epoch, given in the *.series files which are ASCII files with the following columns: decimal year displacement east (m) displacement north (m) displacement up (m) sigma east (m) sigma north (m) sigma up (m) east-north covariance east-up covariance north-up covariance Year (YYYY) Month (MM) Day (DD) Hour (hh) Minute (mm) Second (ss) Solution path We generate position time series relative to stable Antarctic plate by removing the plate velocities modeled by Argus et al (2010). These are provided in the *.npy files that be readily read into python scripts: pos_ts = np.load('test.npy').flatten()[0] pos_ts['itrf'] provides the ITRF data as above pos_ts['plate'] provides the data with Antarctic plate motion removed. Dataset Antarc* Antarctic Antarctica DataCite Metadata Store (German National Library of Science and Technology) Antarctic |
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Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
GPS Cryosphere Antarctica |
spellingShingle |
GPS Cryosphere Antarctica Grapenthin, Ronni Erebus GPS timeseries |
topic_facet |
GPS Cryosphere Antarctica |
description |
We use NASA's Jet Propulsion Laboratory's (JPL) GipsyX software in PPP mode with ambiguity resolution applied to 24 hour segments of data to generate daily position solutions. We use JPL's orbit and clock products and International GNSS Service (IGS) antenna phase center models. Where available, we use JPL's second order ionospheric corrections, otherwise we fall back on those provided by the IGS. To correct tropospheric delays, we use the GPT2 model as implemented in GipsyX. Ocean tidal loading corrections utilize the TPXO7.2 and ATLAS model, a combination of hydrodynamic model and altimetry data, with respect to Earth's Center of Mass implemented in SPOTL. We obtain position solutions for each station day in a fiducial-free reference frame, which we then transform into the 2014 International Reference Frame using JPL's transformation coefficients and generate timeseries of position change relative to the first epoch, given in the *.series files which are ASCII files with the following columns: decimal year displacement east (m) displacement north (m) displacement up (m) sigma east (m) sigma north (m) sigma up (m) east-north covariance east-up covariance north-up covariance Year (YYYY) Month (MM) Day (DD) Hour (hh) Minute (mm) Second (ss) Solution path We generate position time series relative to stable Antarctic plate by removing the plate velocities modeled by Argus et al (2010). These are provided in the *.npy files that be readily read into python scripts: pos_ts = np.load('test.npy').flatten()[0] pos_ts['itrf'] provides the ITRF data as above pos_ts['plate'] provides the data with Antarctic plate motion removed. |
format |
Dataset |
author |
Grapenthin, Ronni |
author_facet |
Grapenthin, Ronni |
author_sort |
Grapenthin, Ronni |
title |
Erebus GPS timeseries |
title_short |
Erebus GPS timeseries |
title_full |
Erebus GPS timeseries |
title_fullStr |
Erebus GPS timeseries |
title_full_unstemmed |
Erebus GPS timeseries |
title_sort |
erebus gps timeseries |
publisher |
U.S. Antarctic Program (USAP) Data Center |
publishDate |
2021 |
url |
https://dx.doi.org/10.15784/601471 https://www.usap-dc.org/view/dataset/601471 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic Antarctica |
genre_facet |
Antarc* Antarctic Antarctica |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.15784/601471 |
_version_ |
1766009014618947584 |