We organized a one-day workshop on ‘How to give a good talk’.
The workshop covered various topics from Stage fright over Body Language to Humor in talks. The participants benefited a lot from the intensive feedback they received from their colleagues and the trainer.
Thank you again, Nae, for the great workshop!
Transport of snow by the wind can have high impact on local glacier mass changes as it leads to non-uniform amounts of snow on the ground. In order to simulate and better understand this process we introduce a new modeling framework that is included into the widely used atmospheric ‘Weather Research and Forecasting (WRF)’ model. Test simulations and sensitivity experiments show the physical consistency of the model. Complex interactions between different processes like snow erosion, drifting snow sublimation and the wind field show the necessity of coupling the snow and atmospheric models.
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023MS004007
As a continuation of the expedition to the Aletsch Glacier in winter 2024, a group of researchers conducted a second expedition in May as part of the M3OCCA program. The group aimed to gather GPR CMP data at three different locations of the accumulation area of the Aletsch Glacier. Snow pits were dug near CMP locations to obtain a density-depth profile at the upper few meters of snow to get the density profile between the visits in March and May 2024. The GPR CMP method provides vital information regarding the Electromagnetic (EM) wave velocity-depth within the firn body of the glacier. The density of the firn body is a function of the EM wave velocity; the obtained density-depth profile aids in the detection and estimation of annual firn layers to study the firn densification rate. This information assists in estimating the mean glacier mass balance by considering the firn density rather than assuming a constant density value for the entire glacier.
This expedition was part of the M3OCCA doctoral program project 2.3 (Improved Glacier volume to mass conversion), and the efforts of Dr. Christoph Mayer and Dr. Astrid Lambrecht from BAdW Munich, Akash Patil (M3OCCA PhD at BAdW Munich), and Manuel Saigger (M3OCCA PhD at the Institute of Geography FAU Erlangen) are much appreciated.
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At the end of February, a large field campaign with various measurement instruments from different research groups took place at the Aletsch Glacier. The group consisted of scientists and technicians from FAU Erlangen, different DLR institutes (HR, OS, DFD), Technical University Munich, Bavarian Academy of Sciences, the institute for snow and avalanche research (SLF), Ulm University and ETH Zürich. The campaign involved in-situ density and permittivity measurements, surface- and UAV-based ground penetrating radar (GPR) measurements, airborne acquisitions for tomography and SAR applications, bistatic radar measurements with the KAPRI system, and first tests with an optical localization system. The observed test sites were distributed over the glacier, reaching from the Jungfraufirn to the Mönchsjochplateau and further to the Ewigschneefeld. The surface-based GPR platform (top picture) developed in subproject 1.1 by our PhD student Lena Krabbe was tested in rough environmental conditions for the application of subsurface imaging of glacier stratification.
Within subproject 2.3, GPR was used to collect data illustrating the spatial distribution of the firn body. GPR transects across different parts of the accumulation area of the Aletsch Glacier were obtained by our PhD student Akash Patil, along with direct measurements using glaciological methods like snow pits and firn cores at some locations near the GPR transects. Isotope samples were also taken from the snow pit and firn cores to determine possible annual layers and their corresponding depths. This helps in understanding the regional variability of density distribution and glacier-climate interaction on a regional scale to determine and validate density assumptions that aid in estimating the mean glacier mass balance.
Many thanks to Dr. Thorsten Seehaus and Dr. Alexander Gross from the Institute of Geography FAU Erlangen, Michael Stelzig from LHFT, FAU Erlangen, and M3OCCA PhDs Patricia Schlenk (DLR, Munich) and Felix Pfluger (TUM, Munich) for their assistance during this expedition.
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Supraglacial lakes in Greenland are highly dynamic hydrological features in which glacial meltwater cumulates, allowing for the loss and transport of freshwater from a glacial surface to the ocean or a nearby waterbody. Standard supraglacial lake monitoring techniques, specifically image segmentation, rely heavily on a series of region-dependent thresholds, limiting the adaptability of the algorithm to different illumination and surface variations, while being susceptible to the inclusion of false positives such as shadows. In this study, a supraglacial lake segmentation algorithm is developed for Sentinel-2 images based on a deep learning architecture (U-Net) to evaluate the suitability of artificial intelligence techniques in this domain. Additionally, a deep learning-based cloud segmentation tool developed specifically for polar regions is implemented in the processing chain to remove cloudy imagery from the analysis. Using this technique, a time series of supraglacial lake development is created for the 2016 to 2022 melt seasons over Nioghalvfjerdsbræ (79°N Glacier) and Zachariæ Isstrøm in Northeast Greenland, an area that covers 26,302 km2 and represents roughly 10% of the Northeast Greenland Ice Stream. The total lake area was found to have a strong interannual variability, with the largest peak lake area of 380 km2 in 2019 and the smallest peak lake area of 67 km2 in 2018. These results were then compared against an algorithm based on a thresholding technique to evaluate the agreement of the methodologies. The deep learning-based time series shows a similar trend to that produced by a previously published thresholding technique, while being smoother and more encompassing of meltwater in higher-melt periods. Additionally, while not completely eliminating them, the deep learning model significantly reduces the inclusion of shadows as false positives. Overall, the use of deep learning on multispectral images for the purpose of supraglacial lake segmentation proves to be advantageous.
Explaining my research to the Federal Minister of Education and Research Bettina Stark-Watzinger at the Digital Summit 2023 was a great privilege! The Minister impressed me with her eagerness to understand the connections and implications of my research. My research area is green AI, where I am currently focusing on AI-based automation of glacier monitoring.With this automation, it will soon be possible to study the dynamics of glacier calving fronts in the Arctic over the years and during different seasons. This knowledge will help us to better understand the effects of climate change on glaciers. In addition, we will be able to calibrate our glacier and climate models with the extracted front positions and thus further improve them.
by Nora Gourmelon
Copyright photos: AI Grid/Franziska Peters
The next fieldwork season is coming! Several M3OCCA members will already join a large field campaign at Aletsch-Glacier in February/March 2024. Thus, a self-organized workshop on basic glacier safety was carried out in Erlangen. After repeating some theoretical background, the focus was set on hands-on exercises. Everyone got the chance to get first knowledge or to re-fresh the knowledge in topics like crevasse rescue, self-rescue, repelling…
Special Thanks to Manuel Saigger, who led the workshop.
The Antarctic Peninsula Ice Sheet (APIS) has become a significant contributor to rising sea levels, and accurately estimating ice discharge from its outlet glaciers is essential for assessing the mass balance of the region. This study calculates ice discharge from APIS outlet glaciers north of 70°S using five commonly used ice-thickness reconstructions, employing a consistent surface velocity field and flux gates. Results indicate a total volumetric ice discharge ranging from 45 to 141 km3 per year for 2015–2017, with a mean of 87 ± 44 km3 per year. The substantial differences in results highlight the large uncertainty in current ice-discharge estimates, emphasizing the challenge of accurately modeling the ice-thickness distribution in this complex and data-scarce region.
Existing mass budget estimates for the northern Antarctic Peninsula (>70° S) are affected by considerable limitations. We carried out the first region-wide analysis of geodetic mass balances throughout this region (coverage of 96.4 %) for the period 2013–2017 based on repeat pass bi-static TanDEM-X acquisitions. A total mass budget of −24.1±2.8 Gt/a is revealed. Imbalanced high ice discharge, particularly at former ice shelf tributaries, is the main driver of overall ice loss.
https://tc.copernicus.org/articles/17/4629/2023/tc-17-4629-2023.html
Knowledge about permafrost distribution is critical for the assessment of rock mass stability. By installing rock surface temperature loggers we aim to create a model to explore the distribution of permafrost in the Ötztal Alps. Solar incoming radiation and air temperature are the main drivers of permafrost evolution. Therefore we picked diverse locations in height, aspect and slope for installing the loggers.
The field work took place on a wonderful sunny day on 6th of September 2023. We are happy that everything worked out as planned and we returned home from the mountains in a safe way with good new stories on our shoulders.
