Geomagnetic storm forecasting from solar coronal holes (2211.16572v1)

Abstract: Coronal holes (CHs) are the source of high-speed streams (HSSs) in the solar wind, whose interaction with the slow solar wind creates corotating interaction regions (CIRs) in the heliosphere. Whenever the CIRs hit the Earth, they can cause geomagnetic storms. We develop a method to predict the strength of CIR/HSS-driven geomagnetic storms directly from solar observations using the CH areas and associated magnetic field polarity. First, we build a dataset comprising the properties of CHs on the Sun, the associated HSSs, CIRs, and orientation of the interplanetary magnetic field (IMF) at L1, and the strength of the associated geomagnetic storms by the geomagnetic indices Dst and Kp. Then, we predict the Dst and Kp indices using a Gaussian Process model, which accounts for the annual variation of the orientation of Earth's magnetic field axis. We demonstrate that the polarity of the IMF at L1 associated with CIRs is preserved in around 83% of cases when compared to the polarity of their CH sources. Testing our model over the period 2010-2020, we obtained a correlation coefficient between the predicted and observed Dst index of R = 0.63/0.73, and Kp index of R = 0.65/0.67, for HSSs having a polarity towards/away from the Sun. These findings demonstrate the possibility of predicting CIR/HSS-driven geomagnetic storms directly from solar observations and extending the forecasting lead time up to several days, which is relevant for enhancing space weather predictions.

• The paper demonstrates a novel method using Gaussian Process Regression to predict Dst and Kp indices from solar coronal hole properties.
• It employs advanced imaging segmentation and data association techniques to link CH areas with solar wind speeds and related geomagnetic measurements.
• The model achieves moderate correlations (R~0.63–0.73 for Dst and 0.65–0.67 for Kp) and extends storm forecast lead times by several days.

Geomagnetic Storm Forecasting from Solar Coronal Holes

Overview

This paper presents a method for forecasting geomagnetic storms driven by corotating interaction regions (CIRs) and high-speed streams (HSSs) originating from solar coronal holes (CHs). The approach uses Gaussian Process Regression (GPR) models to predict geomagnetic indices Dst and Kp from solar observations, leveraging the relationship between CH characteristics and geomagnetic activity.

Methodology

Data Collection and Event Detection

The methodology involves the construction of a comprehensive dataset by identifying and linking signatures across different sources. Specifically, CH properties, HSSs velocities, CIRs orientations, and geomagnetic indices (Dst and Kp) are collated over the 2010–2020 period. Event detection and association are performed through:

1. CH area identification using segmentation methods on AIA 193 Å images.
2. Detection of corresponding HSSs, IMF, and geomagnetic indices peaks.
3. Matching polarities between CHs and IMF to ensure valid event correlation.

Gaussian Process Regression

Two primary aspects are modeled using GPR:

1. Solar Wind Velocity Prediction: An RBF kernel is used to fit the relationship between CH areas and solar wind speed. Results show moderate correlation with velocity predictions saturating around 600 km/s beyond certain CH areas.
2. Geomagnetic Activity Modeling: To account for the effects of Bs, the Dst and Kp indices are separated by polarity and modeled against the day of the year using a periodic kernel. The analysis reveals periodic patterns aligning with annual Bs variations.

Results and Evaluation

The predictions achieved R values of 0.63/0.73 for the Dst index and 0.65/0.67 for the Kp index, depending on CH polarity. The forecasting model reliably extends the lead time for predicting geomagnetic storms directly from solar observations, offering several days of advance warning, which significantly enhances space weather predictions.

A key source of uncertainty in the model is identified in predicting solar wind speed based on CH area. Using actual in-situ measurements of solar wind speed reduces this uncertainty, improving correlation and error metrics markedly.

Implications and Future Work

The findings underscore the potential to enhance lead times in geomagnetic storm forecasting from days to hours. This advancement is critical for space weather applications, informing protections against cosmic influences on Earth-based technologies. The paper suggests that improvements in solar wind prediction models would further refine this methodology. Future research could pivot towards refining Gaussian Process models and exploring complementary data sources to increase prediction accuracy and reliability.

Conclusion

The paper establishes a robust framework for geomagnetic storm forecasting using remote solar observations, thereby providing a significant lead in space weather prediction capabilities. The use of GPR models to integrate CH characteristics and geomagnetic indices demonstrates a viable approach to extending warning times and improving predictive accuracy, emphasizing the utility of solar-based forecasting models. The insights gleaned from this work offer pathways for further enhancement and application in operational settings.