Browsing by Subject "machine learning"
Now showing items 1-20 of 22
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A Combined Neural Network‐ and Physics‐Based Approach for Modeling Plasmasphere Dynamics
; ; (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
;; ; ; (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
; ; ; (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 ... -
Chemical Geodynamics Insights From a Machine Learning Approach
; ; ; ; (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
(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
; (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
; ; ; ; ; (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
; (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 ... -
Fast fitting of reflectivity data of growing thin films using neural networks
(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
; ; ; (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 ... -
Machine learning based identification of dominant controls on runoff dynamics
; 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‐Based Prediction of Spatiotemporal Uncertainties in Global Wind Velocity Reanalyses
; 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
; ; ; (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
; ; ; ; ; ; ; ; ; ; (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 ... -
Multi‐objective downscaling of precipitation time series by genetic programming
; ; ; (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 ... -
Non‐Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models
; ; ; ; ; (Journal of Advances in Modeling Earth Systems, 2022-08-13)
Deep learning can accurately represent sub‐grid‐scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal ... -
Potential and Limitations of Machine Learning for Modeling Warm‐Rain Cloud Microphysical Processes
; (Journal of Advances in Modeling Earth Systems, 2020-11-30)
The use of machine learning based on neural networks for cloud microphysical parameterizations is investigated. As an example, we use the warm‐rain formation by collision‐coalescence, that is, the parameterization of ... -
Potential for Early Forecast of Moroccan Wheat Yields Based on Climatic Drivers
; ; ; (Geophysical Research Letters, 2020)
Wheat production plays an important role in Morocco. Current wheat forecast systems use weather and vegetation data during the crop growing phase, thus limiting the earliest possible release date to early spring. However, ... -
Predicting Geothermal Heat Flow in Antarctica With a Machine Learning Approach
; (Journal of Geophysical Research: Solid Earth, 2021-06-10)
We present a machine learning approach to statistically derive geothermal heat flow (GHF) for Antarctica. The adopted approach estimates GHF from multiple geophysical and geological data sets, assuming that GHF is substantially ... -
Predicting Imminence of Analog Megathrust Earthquakes With Machine Learning: Implications for Monitoring Subduction Zones
; ; ; ; ; (Geophysical Research Letters, 2020-03-31)
Subduction zones are monitored using space geodesy with increasing resolution, with the aim of better capturing the deformation accompanying the seismic cycle. Here, we investigate data characteristics that maximize the ...
