Journal cover Journal topic
The Cryosphere An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 4.713
IF4.713
IF 5-year value: 4.927
IF 5-year
4.927
CiteScore value: 8.0
CiteScore
8.0
SNIP value: 1.425
SNIP1.425
IPP value: 4.65
IPP4.65
SJR value: 2.353
SJR2.353
Scimago H <br class='widget-line-break'>index value: 71
Scimago H
index
71
h5-index value: 53
h5-index53
Preprints
https://doi.org/10.5194/tc-2020-281
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-281
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  04 Nov 2020

04 Nov 2020

Review status
This preprint is currently under review for the journal TC.

Assimilating near real-time mass balance observations into a model ensemble using a particle filter

Johannes M. Landmann1,2, Hans R. Künsch3, Matthias Huss1,2,4, Christophe Ogier1,2, Markus Kalisch3, and Daniel Farinotti1,2 Johannes M. Landmann et al.
  • 1Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
  • 2Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
  • 3Seminar for Statistics, ETH Zurich, Zurich, Switzerland
  • 4Department of Geosciences, University of Fribourg, Fribourg, Switzerland

Abstract. Glaciers fulfil important short-term functions like drinking water supply and they are important indicators of climate change. This is why the interest in near real-time mass balance nowcasting is high. Here, we address this interest and provide an evaluation of seven continuous observations of point mass balance based on on-line cameras transmitting images every 20 minutes on three Swiss glaciers during summer 2019. Like this, we read 352 near real-time daily point mass balances in total from the camera images, revealing melt rates of up to 0.12 meter water equivalent per day (m w.e. d−1) and the biggest total melt on the tongue of Findelgletscher with more than 5 m w.e. in 81 days. These observations are assimilated into an ensemble of three temperature index (TI) and one simplified energy balance mass balance models using an augmented particle filter with a custom resampling method. The state augmentation allows estimating model parameters over time. The custom resampling ensures that temporarily poorly performing models are kept in the ensemble instead of being removed during the resampling step of the particle filter. We analyse model performance over the observation period, and find that the model probability within the ensemble is highest on average with 58 % for an enhanced TI model, a simple TI model reaches about 19 %, while models incorporating additional energy fluxes have probabilities between 8 % and 15 %. When compared to reference forecasts produced with both mean model parameters and parameters tuned on single mass balance observations, the mass balances produced with the particle filter performs about equally well on the daily scale, but outperforms predictions of cumulative mass balance. The particle filter improves the performance scores of the reference forecasts by 91–97 % in these cases. A leave-one-out cross-validation on the individual glaciers shows that the particle filter is able to reproduce point observations at locations on the glacier where it was not calibrated, as the filtered mass balances do not deviate more than 8 % from the cumulative observations at the test locations. A comparison with glacier-wide annual mass balance by Glacier Monitoring Switzerland (GLAMOS) involving additional measurements distributed over the entire glacier, mostly show good agreement, but also deviations of up to 0.41 m w.e. for one instance.

Johannes M. Landmann et al.

Interactive discussion

Status: open (until 30 Dec 2020)
Status: open (until 30 Dec 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement

Johannes M. Landmann et al.

Video supplement

Time lapse video melt at Holfuy station RHO-1 Johannes Marian Landmann https://doi.org/10.5446/48820

Time lapse video melt at Holfuy station RHO-2 Johannes Marian Landmann https://doi.org/10.5446/48821

Time lapse video melt at Holfuy station RHO-3 Johannes Marian Landmann https://doi.org/10.5446/48822

Time lapse video melt at Holfuy station RHO-4 Johannes Marian Landmann https://doi.org/10.5446/48823

Time lapse video melt at Holfuy station FIN-1 Johannes Marian Landmann https://doi.org/10.5446/48824

Time lapse video melt at Holfuy station FIN-2 Johannes Marian Landmann https://doi.org/10.5446/48825

Time lapse video melt at Holfuy station PLM-1 Johannes Marian Landmann https://doi.org/10.5446/48826

Johannes M. Landmann et al.

Viewed

Total article views: 284 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
195 86 3 284 3 1
  • HTML: 195
  • PDF: 86
  • XML: 3
  • Total: 284
  • BibTeX: 3
  • EndNote: 1
Views and downloads (calculated since 04 Nov 2020)
Cumulative views and downloads (calculated since 04 Nov 2020)

Viewed (geographical distribution)

Total article views: 181 (including HTML, PDF, and XML) Thereof 178 with geography defined and 3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 27 Nov 2020
Publications Copernicus
Download
Short summary
In this study, we (1) acquire real-time information on point glacier mass balance with autonomous cameras and (2) assimilate these observations into a mass balance model ensemble driven by meteorological input. For doing so, we use a customized particle filter that we designed for the specific purposes of our study. We find melt rates of up to 0.12 m water equivalent per day, and show that our assimilation method has a higher performance than reference mass balance models.
In this study, we (1) acquire real-time information on point glacier mass balance with...
Citation