BOKU-Met seminar, Tuesdays 11:00 – 12:00
21.10.25 Philipp Maier BOKU-Met
In this talk, I will present the first paper of my doctoral thesis, which explores the capabilities of state-of-the-art climate projections for impact studies in the energy infrastructure sector. As even regionalized climate projections often lack the fine spatial resolution necessary to accurately represent valleys in mountainous regions like Western Austria, downscaling, bias adjustment, and machine learning techniques are essential for producing reliable results. Still, the challenge remains that certain localized phenomena like foehn are not resolved by the climate models due to the smoothing of complex orography. In this paper, we show how machine learning can be utilized to link synoptic weather patterns to foehn occurrences in the Rhine and Inn valleys. Since foehn directly influences wind power potential in mountainous regions, we investigate how well climate projections can reproduce the conditions linked foehn in a warming climate. Weights for individual climate projections are derived by analysing the performance of EURO-CORDEX models in their ability to produce foehn-enabling conditions in the historical period. Our results reveal a systematic negative bias in annual foehn occurrence for two GCMs and present a weighted trend analysis of foehn occurrence, showing projected changes in annual foehn occurrence as well as in seasonality.
https://bokuvienna.zoom.us/j/95795467199
21.10.25 Adrian Loy BOKU-Met
Biological aerosols have wide-reaching impacts on Earth’s climate, ecosystems, air quality, and
consequently, human health, yet their detection and classification remain challenging. Previous
studies have successfully derived aerosol size and chemical composition from aerosols' intensive
optical properties and inferred aerosol type based on their absorption and scattering Ångström
Exponents (SAE and AAE).
Characteristic regions within the Ångström matrix (AAE vs. SAE scatter plot) have been found
for Mineral dust, black carbon (BC), and brown carbon (BrC) aerosols; however, a similar well-
defined region for bioaerosols has not been established. This thesis investigates if an optical
footprint for bioaerosols can be identified within the Ångström matrix using measurements
conducted at the high-alpine Sonnblick Observatory, and assesses the feasibility of machine
learning classifiers, trained on the aerosol intensive optical properties AAE, SAE, Single
Scattering Albedo (SSA), and backscatter ratio (BSR), for their automatic classification.
The results revealed a consistent bioaerosol optical signature within the Ångström matrix,
characterized by SAE values between 1.2 and 2.1 and AAE values between 1.17 to 1.60. The
footprint was statistically distinct from mineral dust, biomass burning, and volcanic
aerosol events.
The XGBoost machine learning classifier achieved an F1 score of 0.89. Mineral dust and
biomass burning events were correctly classified in most instances, resulting in close to perfect
recall and high precision scores. Bioaerosol events were correctly identified in the majority of
cases, with a high recall of 93.62% but drastically lower precision of 80.37%, which can be
attributed to many “non-event” datapoints during the summer being classified as bioaerosols, since the bioaerosol signature was persistent throughout the summer.
https://bokuvienna.zoom.us/j/95795467199
28.10.25 Zihui Teng Aarhus University
Atmospheric aerosols influence climate radiative forcing through both aerosol–radiation interactions (ARI) and aerosol–cloud interactions (ACI). However, large uncertainties remain in quantifying these processes, making measurements of aerosol properties essential for reducing such uncertainties.
As part of my Ph.D. research, I conducted two measurement campaigns in coastal environments: one in Aarhus, Denmark, a Scandinavian urban coastal city, and the other at the remote Arctic site, Villum Research Station. In urban coastal city, aerosols originate from a complex mixture of anthropogenic and biogenic sources and are affected by both marine and continental air masses. Characterizing their properties is crucial for understanding not only their role in climate forcing but also their potential health impacts on local populations. We investigated in-situ aerosol light scattering, absorption, and size distributions, and combined these measurements with FLEXPART footprint analyses to link observed properties with chemical composition and source contributions.
In the Arctic, where warming occurs at roughly three times the global average, increased land exposure enhances emissions from surface dust and biogenic activity. Understanding Arctic aerosols is critical for improving estimates of their regional climate effects. We conducted in-situ measurements of aerosol light scattering, absorption, and size distributions, and integrated these with FLEXPART footprint analysis, lidar observations, and chemical composition data to identify aerosol types and sources in the region.
https://bokuvienna.zoom.us/j/95795467199
04.11.25 Alexander Dzwonek BOKU-Met
The approval of actinic keratosis or squamous cell carcinoma as occupational diseases depends on the received annual occupational ultraviolet radiation (UVR) exposure. The estimation of the annual occupational UVR exposure bases on daily personal UVR exposure measurements in conjunction with ambient UVR measurements for the determination of the “Exposure Ratio to Ambient” (ERTA). The ERTA can be used to transfer personal UVR exposure measurements to other dates/locations and can be applied for estimations of annual occupational UVR exposure.
To derive meaningful data from temporally arbitrary personal UVR exposure measurements is challenging. Beside the requirements on measuring equipment in a harsh environment and mounting (intensive movements), the duration of measurements has to be considered. Working hours of outdoor workers are rather variable and flexible compared to others. Nevertheless, companies respectively working contracts specify the work-time model which includes the normal working hours (start and end time) and these should be met in general. These are also the official base for all kind of considerations like approving an occupational disease for a worker. Personal UV exposure measurements campaigns cannot cover all cases of real occurring work time models. Therefore, it is often necessary to recalculate measured UVR exposure to the normal working hours (standard workday).
While ERTA is suitable for recalculations, it cannot always be determined directly. In practice, mismatches between the measuring periods of personal and ambient UVR exposure measurements can occur. In some cases, ambient UVR exposure measurements may not have been conducted at all and must be substituted with, for example, total daily values.
In this work, we will present a method - together with its validation - that enables to recalculate UVR exposure measurements made over any period during the day to any declared standard workday with high degree of accuracy, even if ERTA cannot be determined directly. Further, we will care on the estimation of annual personal UV exposure. The annual exposure depends on several factors like the number of working days, days off (leave days, public holiday etc.) and working hours. We will run some realistic scenarios for Vienna, Austria, starting with temporally arbitrary personal UVR exposure measurements during different working hours within the day. After recalculation to a declared standard working day and the determination of the ERTA, the annual exposure will be calculated for different work time models and number of days off.
https://bokuvienna.zoom.us/j/95795467199
18.11.25 Carina Karner TU Vienna
Phase transitions, such as the crystallization of a supercooled liquid, involve complex collective rearrangements that also occur in natural systems—for instance, during the freezing of cloud droplets or the formation of ice in aerosols. In all these cases, the full system information lies in a high-dimensional space, and understanding the emergence of order requires dimensionality reduction and pattern recognition. Traditionally, this has relied on physically motivated descriptors, such as symmetry functions or order parameters, to identify structural transitions. Yet such predefined measures can overlook subtle or unexpected features, especially in complex or out-of-equilibrium systems. Machine learning, particularly methods based on data compression such as autoencoders, offers a new route to discover hidden structures directly from raw data. In this work, we explore whether the signatures of phase transitions can be captured without relying on symmetry-based inputs, using particle-resolved data as a model case—illustrating how information-theoretic approaches to pattern recognition may generalize across physical and environmental systems.
https://bokuvienna.zoom.us/j/95795467199
