Description
Earth's radiation belts SW services rely heavily on in-situ measurements, whether for services derived directly from measurements or through data assimilation or AI tools. Unfortunately, data are never perfect. They may suffer from contamination, saturation, outliers ... and do not offer a global coverage of the radiation belts. In this context and to move forward the following challenges will be discussed:
* Can measurement uncertainties be quantified and integrated into space weather products?
* How can we rely on a uniform and clean dataset—free from contamination, saturation, outliers, and properly cross-calibrated?
* While measurements are dense along certain orbits, other regions remain poorly sampled. What constitutes an ideal measurement set (in terms of location and time resolution) to efficiently complement physical and AI models?
Participants are encouraged to contribute (1-2 slides) with new insights addressing these open questions to help advance space weather services dedicated to radiation belts.
(Convenors: Sebastien Bourdarie (ONERA/ERS), Ingmar Sandberg (SPARC))
Topic N°1: Can measurement uncertainties be quantified and integrated into space weather products?
Topic N°1: Can measurement uncertainties be quantified and integrated into space weather products?
Topic N°2: How can we rely on a uniform and clean dataset-free from contamination, saturation, outliers, and properly cross-calibrated?
Topic N°2: How can we rely on a uniform and clean dataset-free from contamination, saturation, outliers, and properly cross-calibrated?
Topic N°2: How can we rely on a uniform and clean dataset-free from contamination, saturation, outliers, and properly cross-calibrated?
Topic N°3: While measurements are dense along certain orbits, other regions remain poorly sampled. What constitutes an ideal measurement set (in terms of location and time resolution) to efficiently complement physical and AI models?
Topic N°3: While measurements are dense along certain orbits, other regions remain poorly sampled. What constitutes an ideal measurement set (in terms of location and time resolution) to efficiently complement physical and AI models?
