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Global validation of data-assimilative electron ring current nowcast for space weather applications.

ABSTRACT: The hazardous plasma environment surrounding Earth poses risks to satellites due to internal charging and surface charging effects. Accurate predictions of these risks are crucial for minimizing damage and preparing for system failures of satellites. To forecast the plasma environment, it is essential to know the current state of the system, as the accuracy of the forecast depends on the accuracy of the initial condition of the forecast. In this study, we use data assimilation techniques to combine observational data and model predictions, and present the first global validation of a data-assimilative electron ring current nowcast during a geomagnetic storm. By assimilating measurements from one satellite and validating the results against another satellite in a different magnetic local time sector, we assess the global response and effectiveness of the data assimilation technique for space weather applications. Using this method, we found that the simulation accuracy can be drastically improved at times when observations are available while eliminating almost all of the bias previously present in the model. These findings contribute to the construction of improved operational models in estimating surface charging risks and providing realistic 'source' populations for radiation belt simulations.

SUBMITTER: Haas B

PROVIDER: S-EPMC10822866 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Global validation of data-assimilative electron ring current nowcast for space weather applications.

Haas Bernhard B Shprits Yuri Y YY Wutzig Michael M Szabó-Roberts Mátyás M García Peñaranda Marina M Castillo Tibocha Angelica M AM Himmelsbach Julia J Wang Dedong D Miyoshi Yoshizumi Y Kasahara Satoshi S Keika Kunihiro K Yokota Shoichiro S Shinohara Iku I Hori Tomo T

Scientific reports 20240128 1

The hazardous plasma environment surrounding Earth poses risks to satellites due to internal charging and surface charging effects. Accurate predictions of these risks are crucial for minimizing damage and preparing for system failures of satellites. To forecast the plasma environment, it is essential to know the current state of the system, as the accuracy of the forecast depends on the accuracy of the initial condition of the forecast. In this study, we use data assimilation techniques to comb ...[more]

PMID: 38282034

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Project description:BackgroundSpace technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations' Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health.ObjectiveThe aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges.MethodsWe used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs' Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified.ResultsA total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use.ConclusionsAlthough numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.

Project description:Machine learning (ML) has been applied to space weather problems with increasing frequency in recent years, driven by an influx of in-situ measurements and a desire to improve modeling and forecasting capabilities throughout the field. Space weather originates from solar perturbations and is comprised of the resulting complex variations they cause within the numerous systems between the Sun and Earth. These systems are often tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system highly impacted by space weather is the thermosphere, the neutral region of Earth's upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and computation of probability of collision between two space objects in low Earth orbit (LEO) for decision making in space operations. Even with (assumed) perfect forecast of model drivers, our incomplete knowledge of the system results in often inaccurate thermospheric neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of confidence in predictions. In this work, we propose two techniques to develop nonlinear ML regression models to predict thermospheric density while providing robust and reliable uncertainty estimates: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function. We show the performance capabilities for models trained on both local and global datasets. We show that the NLPD loss provides similar results for both techniques but the direct probability distribution prediction method has a much lower computational cost. For the global model regressed on the Space Environment Technologies High Accuracy Satellite Drag Model (HASDM) density database, we achieve errors of approximately 11% on independent test data with well-calibrated uncertainty estimates. Using an in-situ CHAllenging Minisatellite Payload (CHAMP) density dataset, models developed using both techniques provide test error on the order of 13%. The CHAMP models-on validation and test data-are within 2% of perfect calibration for the twenty prediction intervals tested. We show that this model can also be used to obtain global density predictions with uncertainties at a given epoch.

Dataset Information

Global validation of data-assimilative electron ring current nowcast for space weather applications.

Publications

Global validation of data-assimilative electron ring current nowcast for space weather applications.

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