Now showing items 1-20 of 32

    • A Combined Neural Network‐ and Physics‐Based Approach for Modeling Plasmasphere Dynamics 

      Zhelavskaya, I. S.ORCIDiD; Aseev, N. A.ORCIDiD; Shprits, Y. Y.ORCIDiD (Journal of Geophysical Research: Space Physics, 2021-03-16)
      In recent years, feedforward neural networks (NNs) have been successfully applied to reconstruct global plasmasphere dynamics in the equatorial plane. These neural network‐based models capture the large‐scale dynamics of ...
    • A Data-Driven Framework to Characterize State-Level Water Use in the United States 

      Wongso, E.; Nateghi, R.ORCIDiD; Zaitchik, B.ORCIDiD; Quiring, S.ORCIDiD; Kumar, R.ORCIDiD (Water Resources Research, 2020)
      Access to credible estimates of water use is critical for making optimal operational decisions and investment plans to ensure reliable and affordable provisioning of water. Furthermore, identifying the key predictors of ...
    • Attribution of Observed Recent Decrease in Low Clouds Over the Northeastern Pacific to Cloud‐Controlling Factors 

      Andersen, HendrikORCIDiD; Cermak, JanORCIDiD; Zipfel, LukasORCIDiD; Myers, Timothy A.ORCIDiD (Geophysical Research Letters, 2022-01-28)
      Marine low clouds cool the Earth's climate, with their coverage (LCC) being controlled by their environment. Here, an observed significant decrease of LCC in the northeastern Pacific over the past two decades is linked ...
    • Automatic bad‐pixel mask maker for X‐ray pixel detectors with application to serial crystallography 

      Sadri, Alireza; Hadian-Jazi, Marjan; Yefanov, Oleksandr; Galchenkova, Marina; Kirkwood, Henry; Mills, Grant; Sikorski, Marcin; Letrun, Romain; de Wijn, Raphael; Vakili, Mohammad; Oberthuer, Dominik; Komadina, Dana; Brehm, Wolfgang; Mancuso, Adrian P.; Carnis, Jerome; Gelisio, Luca; Chapman, Henry N. (Journal of Applied Crystallography, 2022-11-21)
      X‐ray crystallography has witnessed a massive development over the past decade, driven by large increases in the intensity and brightness of X‐ray sources and enabled by employing high‐frame‐rate X‐ray detectors. The ...
    • Chemical Geodynamics Insights From a Machine Learning Approach 

      Stracke, A.ORCIDiD; Willig, M.; Genske, F.ORCIDiD; Béguelin, P.; Todd, E. (Geochemistry, Geophysics, Geosystems, 2022-10-19)
      The radiogenic isotope heterogeneity of oceanic basalts is often assessed using 2D isotope ratio diagrams. But because the underlying data are at least six dimensional (87Sr/86Sr, 143Nd/144Nd, 176Hf/177Hf, and 208,207,20 ...
    • Data reduction for X‐ray serial crystallography using machine learning 

      Rahmani, Vahid; Nawaz, Shah; Pennicard, David; Setty, Shabarish Pala Ramakantha; Graafsma, Heinz (Journal of Applied Crystallography, 2023-01-24)
      Serial crystallography experiments produce massive amounts of experimental data. Yet in spite of these large‐scale data sets, only a small percentage of the data are useful for downstream analysis. Thus, it is essential ...
    • Data‐Driven Medium‐Range Weather Prediction With a Resnet Pretrained on Climate Simulations: A New Model for WeatherBench 

      Rasp, StephanORCIDiD; Thuerey, NilsORCIDiD (Journal of Advances in Modeling Earth Systems, 2021-02-16)
      Numerical weather prediction has traditionally been based on the models that discretize the dynamical and physical equations of the atmosphere. Recently, however, the rise of deep learning has created increased interest ...
    • Deciphering the Whisper of Volcanoes: Monitoring Velocity Changes at Kamchatka's Klyuchevskoy Group With Fluctuating Noise Fields 

      Makus, PeterORCIDiD; Sens‐Schönfelder, ChristophORCIDiD; Illien, LucORCIDiD; Walter, Thomas R.ORCIDiD; Yates, AlexanderORCIDiD; Tilmann, FrederikORCIDiD (Journal of Geophysical Research: Solid Earth, 2023-03-30)
      Volcanic inflation and deflation often precede eruptions and can lead to seismic velocity changes (dv/v $dv/v$) in the subsurface. Recently, interferometry on the coda of ambient noise‐cross‐correlation functions yielded ...
    • Deep Emulators for Differentiation, Forecasting, and Parametrization in Earth Science Simulators 

      Nonnenmacher, MarcelORCIDiD; Greenberg, David S.ORCIDiD (Journal of Advances in Modeling Earth Systems, 2021-06-28)
      To understand and predict large, complex, and chaotic systems, Earth scientists build simulators from physical laws. Simulators generalize better to new scenarios, require fewer tunable parameters, and are more interpretable ...
    • Deep Learning Based Cloud Cover Parameterization for ICON 

      Grundner, ArthurORCIDiD; Beucler, TomORCIDiD; Gentine, PierreORCIDiD; Iglesias‐Suarez, FernandoORCIDiD; Giorgetta, Marco A.ORCIDiD; Eyring, VeronikaORCIDiD (Journal of Advances in Modeling Earth Systems, 2022-12-14)
      A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm‐resolving model (SRM) simulations. The ICOsahedral ...
    • Fast fitting of reflectivity data of growing thin films using neural networks 

      Greco, Alessandro; Starostin, Vladimir; Karapanagiotis, Christos; Hinderhofer, Alexander; Gerlach, Alexander; Pithan, Linus; Liehr, Sascha; Schreiber, Frank; Kowarik, Stefan (Journal of Applied Crystallography, 2019-11-08)
      X‐ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study shows how a simple artificial neural network model can be used to ...
    • Improving Atmospheric Angular Momentum Forecasts by Machine Learning 

      Dill, R.ORCIDiD; Saynisch‐Wagner, J.; Irrgang, C.ORCIDiD; Thomas, M. (Earth and Space Science, 2021-12-20)
      Earth angular momentum forecasts are naturally accompanied by forecast errors that typically grow with increasing forecast length. In contrast to this behavior, we have detected large quasi‐periodic deviations between ...
    • Interpretable Machine Learning Reveals Potential to Overcome Reactive Flood Adaptation in the Continental US 

      Veigel, NadjaORCIDiD; Kreibich, HeidiORCIDiD; Cominola, AndreaORCIDiD (Earth's Future, 2023-09-22)
      Abstract

      Floods cause average annual losses of more than US$30 billion in the US and are estimated ...

    • Machine learning based identification of dominant controls on runoff dynamics 

      Oppel, HenningORCIDiD; Schumann, Andreas H. (Hydrological Processes, 2020-03-16)
      Hydrological models used for flood prediction in ungauged catchments are commonly fitted to regionally transferred data. The key issue of this procedure is to identify hydrologically similar catchments. Therefore, the ...
    • Machine learning for scattering data: strategies, perspectives and applications to surface scattering 

      Hinderhofer, Alexander; Greco, Alessandro; Starostin, Vladimir; Munteanu, Valentin; Pithan, Linus; Gerlach, Alexander; Schreiber, Frank (Journal of Applied Crystallography, 2023-01-24)
      Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X‐ray and neutron scattering techniques, with an ...
    • Machine Learning‐Based Prediction of Spatiotemporal Uncertainties in Global Wind Velocity Reanalyses 

      Irrgang, ChristopherORCIDiD; Saynisch‐Wagner, Jan; Thomas, Maik (Journal of Advances in Modeling Earth Systems, 2020-05-13)
      The characterization of uncertainties in geophysical quantities is an important task with widespread applications for time series prediction, numerical modeling, and data assimilation. In this context, machine learning is ...
    • Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning 

      Stirnberg, RolandORCIDiD; Cermak, JanORCIDiD; Fuchs, JuliaORCIDiD; Andersen, HendrikORCIDiD (Journal of Geophysical Research: Atmospheres, 2020)
      The quantification of factors leading to harmfully high levels of particulate matter (PM) remains challenging. This study presents a novel approach using a statistical model that is trained to predict hourly concentrations ...
    • Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model 

      Smirnov, A. G.ORCIDiD; Berrendorf, M.ORCIDiD; Shprits, Y. Y.ORCIDiD; Kronberg, E. A.ORCIDiD; Allison, H. J.ORCIDiD; Aseev, N. A.ORCIDiD; Zhelavskaya, I. S.ORCIDiD; Morley, S. K.ORCIDiD; Reeves, G. D.ORCIDiD; Carver, M. R.ORCIDiD; Effenberger, F.ORCIDiD (Space Weather, 2020-10-30)
      The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic ...
    • Modeling Viscosity of Volcanic Melts With Artificial Neural Networks 

      Langhammer, D.ORCIDiD; Di Genova, D.; Steinle‐Neumann, G.ORCIDiD (Geochemistry, Geophysics, Geosystems, 2022-12-01)
      Viscosity is of great importance in governing the dynamics of volcanoes, including their eruptive style. The viscosity of a volcanic melt is dominated by temperature and chemical composition, both oxides and water content. ...
    • Multi‐objective downscaling of precipitation time series by genetic programming 

      Zerenner, TanjaORCIDiD; Venema, VictorORCIDiD; Friederichs, PetraORCIDiD; Simmer, ClemensORCIDiD (International Journal of Climatology, 2021-06-10)
      We use symbolic regression to estimate daily precipitation amounts at six stations in the Alpine region from a global reanalysis. Symbolic regression only prescribes the set of mathematical expressions allowed in the ...