Jordanova, Vania Koleva

DOE OSTI2024

Overview, Progress and Next Steps for Our Understanding of the Near-Earth Space Radiation and Plasma Environment: Science and Applications

The Near-Earth Space Radiation and Plasma Environment falls within the realm of G3 Cluster (G3 refers to ‘Near-Earth Radiation and Plasma Environment’ of the ‘Coupled Geospace System’) under the COSPAR (Committee On Space Research) /International Space Weather Action Teams (ISWAT) Initiative. The diverse and dynamic particle populations from this region pose challenges from both science and space weather-impact perspectives. The G3 cluster has intimate connections with solar, heliosphere clusters, and the other Geospace ones (G1, G2) through a chain of physical processes. This paper reviews recent scientific advances in understanding this complex space environment, identifies gaps in research and space weather applications, and maps out our recommendations on priorities for the next 5-10 years.

Zheng, Yihua↗

DOE OSTI2024

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1- and 5-min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM-H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1- and 5-min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hr in advance) in a large storm (SYM-H = -393 nT) using 5-min resolution data. When predicting the SYM-H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

79 ASTRONOMY AND ASTROPHYSICS↗