Abstract Solar flares have been linked to some of the most significant space weather hazards at Earth. These hazards, including radio blackouts and energetic particle events, can start just minutes after the flare onset. Therefore, it is of great importance to identify and predict flare events. In this paper we introduce the Detection and EUV Flare Tracking (DEFT) tool, which allows us to identify flare signatures and their precursors using high spatial and temporal resolution extreme-ultraviolet (EUV) solar observations. The unique advantage of DEFT is its ability to identify small but significant EUV intensity changes that may lead to solar eruptions. Furthermore, the tool can identify the location of the disturbances and distinguish events occurring at the same time in multiple locations. The algorithm analyzes high temporal cadence observations obtained from the Solar Ultraviolet Imager instrument aboard the GOES-R satellite. In a study of 61 flares of various magnitudes observed in 2017, the “main” EUV flare signatures (those closest in time to the X-ray start time) were identified on average 6 minutes early. The “precursor” EUV signatures (second-closest EUV signatures to the X-ray start time) appeared on average 14 minutes early. Our next goal is to develop an operational version of DEFT and to simulate and test its real-time use. A fully operational DEFT has the potential to significantly improve space weather forecast times.
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Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast
Abstract
A novel method for real-time solar generation forecast using weather data, while exploiting both spatial and temporal structural dependencies is proposed. The network observed over time is projected to a lower-dimensional representation where a variety of weather measurements are used to train a structured regression model while weather forecast is used at the inference stage. Experiments were conducted at 288 locations in the San Antonio, TX area on obtained from the National Solar Radiation Database. The model predicts solar irradiance with a good accuracy (R2 0.91 for the summer, 0.85 for the winter, and 0.89 for the global model). The best accuracy was obtained by the Random Forest Regressor. Multiple experiments were conducted to characterize influence of missing data and different time horizons providing evidence that the new algorithm is robust for data missing not only completely at random but also when the mechanism is spatial, and temporal.
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- Award ID(s):
- 1636772
- NSF-PAR ID:
- 10168026
- Date Published:
- Journal Name:
- Proceedings of the Hawaii International Conference on System Sciences
- ISSN:
- 0073-1129
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.24251/HICSS.2020.363
