In the CRAMPON project (Cryospheric Monitoring and Prediction Online) we are developing an operational modeling tool to nowcast and predict mass balance and runoff of Swiss glaciers (see uploaded preview figure). As field data for calibration are sparse, we intend to assimilate near real-time remote sensing observations of snow cover on glaciers into the tool’s workflow. This enables frequent cross calibration and adjustment of model performance to in situ conditions.
In the CRAMPON project (Cryospheric Monitoring and Prediction Online) we are developing an operational modeling tool to nowcast and predict mass balance and runoff of Swiss glaciers (see uploaded preview figure). As field data for calibration are sparse, we intend to assimilate near real-time remote sensing observations of snow cover on glaciers into the tool’s workflow. This enables frequent cross calibration and adjustment of model performance to in situ conditions.
The purpose of this Master’s Thesis is to set up a framework that (a) automatically retrieves and prepares Sentinel-2 images as they become available over Switzerland, (b) maps the snow cover on all glaciers using a simplified multi-spectral classification developed at the University of Zurich by Dr. P. Rastner, and (c) statistically analyzes the resulting data (e.g. snow line elevations) with a focus on spatio-temporal variability. Special attention shall be given to determine possible uncertainties, validating the results, and implementing the processing line in an operational workflow.
The workflow shall be coded in the Open Source language Python. Ideally, the candidate has experiences in using optical remote sensing data and/or programming. Strong ambitions to establish the required skills are a must.
The purpose of this Master’s Thesis is to set up a framework that (a) automatically retrieves and prepares Sentinel-2 images as they become available over Switzerland, (b) maps the snow cover on all glaciers using a simplified multi-spectral classification developed at the University of Zurich by Dr. P. Rastner, and (c) statistically analyzes the resulting data (e.g. snow line elevations) with a focus on spatio-temporal variability. Special attention shall be given to determine possible uncertainties, validating the results, and implementing the processing line in an operational workflow. The workflow shall be coded in the Open Source language Python. Ideally, the candidate has experiences in using optical remote sensing data and/or programming. Strong ambitions to establish the required skills are a must.
Johannes Landmann, PhD candidate
Hönggerbergring 26, HIA D 54.2
landmann@vaw.baug.ethz.ch
phone: +41 44 633 38 75
Johannes Landmann, PhD candidate Hönggerbergring 26, HIA D 54.2 landmann@vaw.baug.ethz.ch phone: +41 44 633 38 75