Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images

@article{Hu2022ProbabilisticPO,
  title={Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images},
  author={Anna Hu and Carl Shneider and Animesh Tiwari and Enrico Camporeale},
  journal={Space Weather},
  year={2022},
  volume={20}
}
We present a new model for the probability that the disturbance storm time (Dst) index exceeds −100 nT, with a lead time between 1 and 3 days. Dst provides essential information about the strength of the ring current around the Earth caused by the protons and electrons from the solar wind, and it is routinely used as a proxy for geomagnetic storms. The model is developed using an ensemble of Convolutional Neural Networks that are trained using Solar and Heliospheric Observatory (SoHO) images… 
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References

SHOWING 1-10 OF 42 REFERENCES

Solar Wind Prediction Using Deep Learning

Emanating from the base of the Sun's corona, the solar wind fills the interplanetary medium with a magnetized stream of charged particles whose interaction with the Earth's magnetosphere has space…

Multiple‐Hour‐Ahead Forecast of the Dst Index Using a Combination of Long Short‐Term Memory Neural Network and Gaussian Process

A method that combines a Long Short‐Term Memory (LSTM) recurrent neural network with a Gaussian process (GP) model to provide up to 6‐hr‐ahead probabilistic forecasts of the Dst geomagnetic index is presented.

Application of the Deep Convolutional Neural Network to the Forecast of Solar Flare Occurrence Using Full-disk Solar Magnetograms

The results indicate a sufficient possibility that deep learning methods can improve the capability of the solar flare forecast as well as similar types of forecast problems.

Improvements in short‐term forecasting of geomagnetic activity

We have improved our space weather forecasting algorithms to now predict Dst and AE in addition to Kp for up to 6 h of forecast times. These predictions can be accessed in real time at…

A Gray‐Box Model for a Probabilistic Estimate of Regional Ground Magnetic Perturbations: Enhancing the NOAA Operational Geospace Model With Machine Learning

A novel algorithm is presented that predicts the probability that the time derivative of the horizontal component of the ground magnetic field dB/dt exceeds a specified threshold at a given location, and it is shown that the ML‐enhanced algorithm consistently improves all the metrics considered.

Predicting Solar Flares Using a Novel Deep Convolutional Neural Network

The experimental results indicate that the proposed novel convolutional neural network model is a highly effective method for flare forecasting, with quite excellent prediction performance.

Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks

Performance comparison of the two ANN models shows that both are able to well forecast SYM‐H index 1 h in advance, with an accuracy of more than 95% in terms of the coefficient of determination R2 and the model based on LSTM is slightly more accurate than that based on CNN when including SYM-H index at previous steps among the inputs.

The Anemomilos prediction methodology for Dst

This paper describes new capabilities for operational geomagnetic Disturbance storm time (Dst) index forecasts. We present a data‐driven, deterministic algorithm called Anemomilos for forecasting Dst…

Prediction of the Dst Index with Bagging Ensemble-learning Algorithm

The Bagging ensemble-learning algorithm is used, which combines three algorithms—the artificial neural network, support vector regression, and long short-term memory network—to predict the Dst index 1–6 hr in advance, and shows that the Bagging algorithm brings better stability to the model.

Dynamic Time Warping as a New Evaluation for Dst Forecast With Machine Learning

A new method is proposed to measure whether two time series are shifted in time with respect to each other, such as the persistence model output versus the observation, based on Dynamical Time Warping, which shows promising results in confirming the visual observations of the neural network's output.