08 Apr 2021
08 Apr 2021
GNSS-based water vapor estimation and validation during the MOSAiC expedition
- 1GFZ German Research Centre for Geosciences, Telegrafenberg, Potsdam, Germany
- 2DLR-SO Institute for Solar-Terrestrial Physics, Neustrelitz, Germany
- 3Technische Universität Berlin, Chair GNSS Remote Sensing, Navigation, and Positioning, Berlin, Germany
- 4Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
- 5Technische Universität Berlin, Chair Satellite Geodesy, Berlin, Germany
- 1GFZ German Research Centre for Geosciences, Telegrafenberg, Potsdam, Germany
- 2DLR-SO Institute for Solar-Terrestrial Physics, Neustrelitz, Germany
- 3Technische Universität Berlin, Chair GNSS Remote Sensing, Navigation, and Positioning, Berlin, Germany
- 4Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
- 5Technische Universität Berlin, Chair Satellite Geodesy, Berlin, Germany
Abstract. Within the transpolar drifting expedition MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate), GNSS was used among other techniques to monitor variations in atmospheric water vapor. Based on 15 months of continuously tracked GNSS data including GPS, GLONASS, and Galileo, epoch-wise coordinates and hourly zenith total delays (ZTD) were determined using a kinematic precise point positioning (PPP) approach. The derived ZTD values agree to 1.1 ± 0.2 mm (RMS of the differences 10.2 mm) with the numerical weather data of ECMWF’s latest reanalysis, ERA5, computed for the derived ship’s locations. This level of agreement is also confirmed by comparing the on-board estimates with ZTDs derived for terrestrial GNSS stations in Bremerhaven and Ny Ålesund and for the radio telescopes observing Very Long Baseline Interferometry in Ny Ålesund. Preliminary estimates of integrated water vapor derived from frequently launched radiosondes are used to assess the GNSS-derived integrated water vapor estimates. The overall difference of 0.08 ± 0.04 kg m−2 (RMS of the differences 1.47 kg m−2) demonstrates a good agreement between GNSS and radiosonde data. Finally, the water vapor variations associated with two warm air intrusion events in April 2020 are assessed.
Benjamin Männel et al.
Status: open (until 03 Jun 2021)
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RC1: 'Comment on amt-2021-79', Anonymous Referee #1, 16 Apr 2021
reply
Review of GNSS-based water vapor estimation and validation during the
MOSAiC expedition, by Mannel et al, amt-2021-79
This is an article on our present day ability to derive good quality
ZTDs from GNSS data obtained on a moving platform, in this case a slow
moving ship.It is interesting, wellwritten, easy to read, and deserves publication.
In principle the manuscript can be published as is, but
I have a few suggestions for improvements for the authors to consider.
1 The term "crows nest" will to most readers mean something else.
Consider to just remove the first entry and say "top of ship" or "top
of mast" in the second.2 Consider to include statistics on ZTDs from NYA2 versus ERA, possibly
also from NE Greenland if you have easy access to GNSS data from there.
Those from NYA2 you have already.3 The processing is done after the expedition. Include a few sentences
whether the quality of the GNSS ZTDs would be different was it done
in near real-time, which is important for the potential use of ZTDs from
ships in NWP.4 Ground based ZTDs are (to my knowledge) not assimilated in ERA, which
strengthen then use of ERA as an independent data source. You
could mention that.Then two comments that are more ment for eventual future work.
Presumably a research wessel will carry a high quality pressure sensor. It can be
expected to provide better quality ZHD than ERA on average. On top, with
respect to daily variability it will be effected by the earthly and
atmospheric tides as the ZHD proper, while those effects are not well
represented in an NWP model such as ERA. The
barometer could be used for the derivation of ZHD, to
derive ZWD from the GNSS ZTD.The on-board barometer could also provide an
a priori for the ZHD in the GNSS data processing, when deriving GNSS ZWD to
obtain the GNSS ZTD. As humidity levels are very very low in part of the
year in the Arctic, a dominant part of the ZWD estimated in the GNSS data
processing is in reality due to variability of the pressure (and
hence ZHD). Using a better apriori for ZHD would reduce the problem
that the mapping functions for ZHD and ZWD are not identical in the GNSS data processing.
Benjamin Männel et al.
Benjamin Männel et al.
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