| Can deep learning beat numerical weather prediction? MG Schultz, C Betancourt, B Gong, F Kleinert, M Langguth, LH Leufen, ... Philosophical Transactions of the Royal Society A 379 (2194), 20200097, 2021 | 311 | 2021 |
| IntelliO3-ts v1. 0: a neural network approach to predict near-surface ozone concentrations in Germany F Kleinert, LH Leufen, MG Schultz Geoscientific Model Development 14 (1), 1-25, 2021 | 35* | 2021 |
| Calculating the turbulent fluxes in the atmospheric surface layer with neural networks LH Leufen, G Schädler Geoscientific model development 12 (5), 2033-2047, 2019 | 9 | 2019 |
| Representing chemical history in ozone time-series predictions–a model experiment study building on the MLAir (v1. 5) deep learning framework F Kleinert, LH Leufen, A Lupascu, T Butler, MG Schultz Geoscientific Model Development 15 (23), 8913-8930, 2022 | 5 | 2022 |
| Exploring decomposition of temporal patterns to facilitate learning of neural networks for ground-level daily maximum 8-hour average ozone prediction LH Leufen, F Kleinert, MG Schultz Environmental Data Science 1, e10, 2022 | 5 | 2022 |
| MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series LH Leufen, F Kleinert, MG Schultz Geoscientific Model Development 14 (3), 1553–1574, 2021 | 5 | 2021 |
| TOAR Data Infrastructure S Schröder, MG Schultz, N Selke, J Sun, J Ahring, A Mozaffari, ... Version v1. 0, FZ-Juelich B2SHARE [data set] 10, 2021 | 3 | 2021 |
| O3ResNet: A deep learning based forecast system to predict local ground-level daily maximum 8-hour average ozone in rural and suburban environment LH Leufen, F Kleinert, MG Schultz Artificial Intelligence for the Earth Systems, 1-42, 2023 | 1 | 2023 |
| Representing chemical history for ozone time-series predictions-a method development study for deep learning models F Kleinert, LH Leufen, A Lupascu, T Butler, MG Schultz EGU21, 2021 | 1 | 2021 |
| Time Filter Assisted Deep Learning to Predict Air Pollution LH Leufen Universitäts-und Landesbibliothek Bonn, 2023 | | 2023 |
| Introduction to the AQ-WATCH multi-model air quality forecast system CWY Li, M Sofiev, R Timmermans, R Kranenburg, G Pfister, R Kumar, ... EGU23, 2023 | | 2023 |
| Forecasting near-surface ozone using temporally decomposed input variables and deep neural networks LH Leufen, F Kleinert, MG Schultz 103rd AMS Annual Meeting, 2023 | | 2023 |
| Geodata enrichment for air quality N Selke, A Mozaffari, LH Leufen, M Schultz, S Schröder Living Planet Symposium 2022, 2022 | | 2022 |
| Tropospheric Ozone Assessment Report (TOAR) Data Infrastructure S Schröder, M Schultz, M Romberg, J Sun, LH Leufen, A Mozaffari, E Epp WMO Data Conference, 2020 | | 2020 |
| DeepRain–Improved local-scale prediction of precipitation through deep learning. M Schultz, F Kleinert, L Leufen, J Ahring, S Theis, J Keller, G Pipa, ... Geophysical Research Abstracts 21, 2019 | | 2019 |
| Calculating the turbulent fluxes in the atmospheric surface layer using feedforward networks LH Leufen, G Schädler EGU General Assembly 2019, 2019 | | 2019 |
Martin SchultzGroup leader Earth System Data Exploration, Jülich Supercomputing Centre, Forschungszentrum JülichVerified email at fz-juelich.de
