Publications

Welcome to our blog. Browse and filter through all our posts using the options below.

55 to 60 of 67

07FEB 2023
Publications
A Tutorial on the Sequential Sampling Impulse Radar Concept and Selected Applications
The concept of the sequential sampling is well known and still widely used in industrial automation radar applications due to its hardware simplicity, low cost and low power consumption. However, there is only a limited amount of publications that describe this concept and its variants in detail. This tutorial introduces the typical sequential sampling impulse […]
Read more about A Tutorial on the Sequential Sampling Impulse Radar Concept and Selected Applications
14NOV 2022
Event
Annual Retreat at Josefstal in November 2022
The first annual Retreat within the IDP M3OCCA took place at “Studienzentrum Josefstal” in early November 2022. During the workshop, all Ph.D. students provided an overview of their individual PhD projects with subsequent discussions. Additionally, further steps within the IDP M3OOCA program were discussed and defined.
Read more about Annual Retreat at Josefstal in November 2022
25JUN 2022
Event
Kick-Off workshop in Erlangen June 2022
Right to the beginning of the IDP M3OCCA, we organized a “kick-off” workshop at FAU Erlangen in June 2022. The workshop was the first gathering of all IDP M3OCCA Ph.D. students, including affiliated Ph.D. students. Many subproject supervisors were also around, so the workshop provided the fundament for a fruitful exchange and collaboration between all […]
Read more about Kick-Off workshop in Erlangen June 2022
11APR 2022
SP3
SP3.1: Targetting snow drift and refreezing in glacier mass budgets with machine learning
Glacier surface mass balance (SMB) modelling has traditionally focused on improving the determination of surface energy balance conditions. However, water routing in the firn including refreezing, as well as the complex patterns of surface accumulation caused by snow drift, are poorly, or not at all, represented in most SMB models. Studies in the literature have often noted the uncertainties due to neglecting such mechanisms. This sub-project aims to improve the model representation of (i) the internal water budget and (ii) the snow drift contribution to the SMB using deep learning.Glacier surface mass balance (SMB) modelling has traditionally focused on improving the determination of surface energy balance conditions. However, water routing in the firn including refreezing, as well as the complex patterns of surface accumulation caused by snow drift, are poorly, or not at all, represented in most SMB models. Studies in the literature have often noted the uncertainties due to neglecting such mechanisms. This sub-project aims to improve the model representation of (i) the internal water budget and (ii) the snow drift contribution to the SMB using deep learning.
Read more about SP3.1: Targetting snow drift and refreezing in glacier mass budgets with machine learning
11APR 2022
SP2
SP2.1: Glacier outlines from optical and SAR imagery by deep learning
Glacier extent is an ECV and various international attempts exist to harmonize a global glacier inventory data set with outlines if possible from multiple data sources (GLIMS, IASC Randolph Glacier Inventory, RGI). However, to date no real operational large-scale repeat mapping capabilities exist. Glacier outlines are required at different time intervals e.g. with matching observational intervals for specific mass balance computations out of remote sensing measurements (e.g. for comparison with in-situ observations or on regional scale) or to validate ice dynamic modelling.Glacier extent is an ECV and various international attempts exist to harmonize a global glacier inventory data set with outlines if possible from multiple data sources (GLIMS, IASC Randolph Glacier Inventory, RGI). However, to date no real operational large-scale repeat mapping capabilities exist. Glacier outlines are required at different time intervals e.g. with matching observational intervals for specific mass balance computations out of remote sensing measurements (e.g. for comparison with in-situ observations or on regional scale) or to validate ice dynamic modelling.
Read more about SP2.1: Glacier outlines from optical and SAR imagery by deep learning
11APR 2022
SP3
SP3.2: Reconciling machine learning and glacier system modelling
Within this sub-project, we envisage to combine model-driven simulations of a coupled glacier system with data-driven machine learning techniques at specific benchmark sites in the Alps (e.g., Hintereisferner, Vernagtferner). A well-established ice-flow model will first be employed to produce observation-informed training and test data, which will then inform machine learning components that can replace such classical methods. The ultimate goal is to reduces computational efforts and prepare such hybrid techniques for long timescales and/or regional scales.Within this sub-project, we envisage to combine model-driven simulations of a coupled glacier system with data-driven machine learning techniques at specific benchmark sites in the Alps (e.g., Hintereisferner, Vernagtferner). A well-established ice-flow model will first be employed to produce observation-informed training and test data, which will then inform machine learning components that can replace such classical methods. The ultimate goal is to reduces computational efforts and prepare such hybrid techniques for long timescales and/or regional scales.
Read more about SP3.2: Reconciling machine learning and glacier system modelling