| Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning R Stirnberg, J Cermak, S Kotthaus, M Haeffelin, H Andersen, J Fuchs, ... Atmospheric Chemistry and Physics 21 (5), 3919-3948, 2021 | 54 | 2021 |
| An analysis of factors influencing the relationship between satellite-derived AOD and ground-level PM10 R Stirnberg, J Cermak, H Andersen Remote Sensing 10 (9), 1353, 2018 | 29 | 2018 |
| Mapping and understanding patterns of air quality using satellite data and machine learning R Stirnberg, J Cermak, J Fuchs, H Andersen Journal of Geophysical Research: Atmospheres 125 (4), e2019JD031380, 2020 | 25 | 2020 |
| Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning. R Stirnberg, J Cermak, S Kotthaus, M Haeffelin, H Andersen, J Fuchs, ... Atmospheric Chemistry & Physics Discussions, 2020 | 12 | 2020 |
| A new satellite-based retrieval of low-cloud liquid-water path using machine learning and meteosat seviri data M Kim, J Cermak, H Andersen, J Fuchs, R Stirnberg Remote Sensing 12 (21), 3475, 2020 | 9 | 2020 |
| Glacier changes in the Susitna Basin, Alaska, USA,(1951–2015) using GIS and remote sensing methods R Wastlhuber, R Hock, C Kienholz, M Braun Remote Sensing 9 (5), 478, 2017 | 5 | 2017 |
| Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning H Andersen, J Cermak, R Stirnberg, J Fuchs, M Kim, E Pauli Tellus B: Chemical and Physical Meteorology 73 (1), 1-14, 2021 | 4 | 2021 |
| Variability of air pollution (PM1) analyzed using explainable Machine Learning R Stirnberg, J Cermak, S Kotthaus, M Haeffelin, H Andersen, M Kim Proceedings of the 9th International Workshop on Climate Informatics: CI …, 2019 | 1 | 2019 |
| Detection and attribution of reduced satellite-observed AOD to COVID-19 with machine learning H Andersen, J Cermak, J Fuchs, R Stirnberg, M Kim, E Pauli AGU Fall Meeting Abstracts 2020, A180-0016, 2020 | | 2020 |
| Intercomparisons of liquid water path based on SEVIRI images and gradient boosting regression trees with in-situ observations and satellite-derived products M Kim, J Cermak, H Andersen, J Fuchs, R Stirnberg EGU General Assembly Conference Abstracts, 18806, 2020 | | 2020 |
| Environmental Influences on Patterns of Atmospheric Particulate Matter: a QuantitativeStudy Using Ground-and Satellite-Based Observations R Stirnberg | | 2020 |
| Understanding driving factors of ground PM10 concentrations using satellite AOD and a machine learning approach. R Stirnberg, J Cermak, J Fuchs, H Andersen Geophysical Research Abstracts 21, 2019 | | 2019 |
| Conditions for deriving air quality information from satellite data R Stirnberg, J Cermak EGU General Assembly Conference Abstracts, 13917, 2018 | | 2018 |
| VARIABILITY OF AIR POLLUTION (PM1) EXPLAINED USING A MACHINE LEARNING APPROACH R Stirnberg, J Cermak, S Kotthaus, M Haeffelin, J Fuchs, H Andersen, ... | | |
