The near-Earth space is far from a friendly working environment for satellite systems, particularly when disrupted by extreme space weather events and unexpected anthropogenic activities. Preparing for these threats, ranging from high doses of radiation-belt electrons and solar energetic particles to close encounters with space debris, demands reliable tools that can timely issue environment warnings for routine space operations. Over past decades, a list of space environment models has been developed following the traditional physics-based modeling approach, while the most recent trend of leveraging artificial intelligence (AI) technology has quickly generated new capabilities in near-Earth space weather predictions. In this new age, redefined new roles are also assigned to in-situ long-term satellite measurements, which not only can be distilled for advancing physics understandings but also serve as critical inputs needed for high-fidelity space weather predictions when combined with machine-learning techniques appropriately. Moreover, results generated from those new models augment spatiotemporally localized in-situ measurements, and thus provide opportunities for new science discoveries.
The goal of this Research Topic is to summarize backbone space data sets that can be used for driving operational predictions, cutting edge efforts in developing new space environment models—machine-learning based or not—for the inner magnetosphere region, as well as studies applying the models for learning new knowledge. Topics of interest may include, but are not limited to, the following list:
1. Reviews on the current and future status of long-term space weather datasets from NOAA POES/MetOp and GOES, LANL GEO and GPS satellites, etc.
2. Reviews on existing near-Earth space environment operational models
3. R&D models for radiation-belt electron nowcasting and forecasting
4. R&D models for substorm injected particle nowcasting and forecasting
5. R&D models for penetrating solar energetic protons
6. Retrospective studies using global distributions enabled by the above models
7. Concepts of future space weather operational missions
8. How to integrate AI effectively with traditional models.
The near-Earth space is far from a friendly working environment for satellite systems, particularly when disrupted by extreme space weather events and unexpected anthropogenic activities. Preparing for these threats, ranging from high doses of radiation-belt electrons and solar energetic particles to close encounters with space debris, demands reliable tools that can timely issue environment warnings for routine space operations. Over past decades, a list of space environment models has been developed following the traditional physics-based modeling approach, while the most recent trend of leveraging artificial intelligence (AI) technology has quickly generated new capabilities in near-Earth space weather predictions. In this new age, redefined new roles are also assigned to in-situ long-term satellite measurements, which not only can be distilled for advancing physics understandings but also serve as critical inputs needed for high-fidelity space weather predictions when combined with machine-learning techniques appropriately. Moreover, results generated from those new models augment spatiotemporally localized in-situ measurements, and thus provide opportunities for new science discoveries.
The goal of this Research Topic is to summarize backbone space data sets that can be used for driving operational predictions, cutting edge efforts in developing new space environment models—machine-learning based or not—for the inner magnetosphere region, as well as studies applying the models for learning new knowledge. Topics of interest may include, but are not limited to, the following list:
1. Reviews on the current and future status of long-term space weather datasets from NOAA POES/MetOp and GOES, LANL GEO and GPS satellites, etc.
2. Reviews on existing near-Earth space environment operational models
3. R&D models for radiation-belt electron nowcasting and forecasting
4. R&D models for substorm injected particle nowcasting and forecasting
5. R&D models for penetrating solar energetic protons
6. Retrospective studies using global distributions enabled by the above models
7. Concepts of future space weather operational missions
8. How to integrate AI effectively with traditional models.