| Name: | Vishal Upendran |
| Affiliation: | Bay Area Environmental Research Institute |
| Conference ID : | ASI2024_321 |
| Title : | Multiscale Geoeffectiveness Forecasting using SHEATH and DAGGER |
| Authors : | Vishal Upendran1,2, Raman Mukundan3, Michael Heyns4, Sahiti Yerramilli5, Banafsheh Ferdousi3, Panagiotis Tigas6, Angelos Vourlidas7, Evangelos Paouris7,8 |
| Authors Affiliation: | 1 Bay Area Environmental Research Institute, Moffett Field, CA, USA
2 Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA, USA
3 University of New Hampshire, Department of Physics and Astronomy
4 Imperial College London, Department of Physics
5 Carnegie Mellon University, School of Computer Science
6 University of Oxford, Oxford Applied and Theoretical Machine Learning Group
7 The Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Rd, Laurel, MD 20723, USA
8 George Mason University, 4400 University Dr, Fairfax, VA 22030, USA
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| Mode of Presentation: | Poster |
| Abstract Category : | Sun, Solar System, Exoplanets, and Astrobiology |
| Abstract : | To safeguard critical infrastructure against space weather hazards such as geomagnetically induced currents, we need to develop operational forecasting tools. These tools need to (i) be computationally fast and inexpensive, (ii) resolve signatures over a range of length and time scales, and (iii) be actionable - including forecast uncertainties and an appropriate lead-time to enable informed decision making. To address this need for a lightweight, multiscale, ground magnetic perturbation forecasting tool, the Deep leArninG Geomagnetic pErtuRbation (DAGGER) pipeline was created in 2020 during the Frontier Development Lab (FDL) research sprint. The core of the pipeline leverages spherical harmonic basis functions to forecast magnetic perturbations at both global and local scales. The FDL-X 2023 program has focused on elevating DAGGER's technical readiness and integrating it across other FDL forecasting and data product modules. We present two components of this workflow: SHEATH and DAGGER++. SHEATH is a solar wind forecaster which ingests full-disc SDOML v2 data to forecast solar wind and IMF properties at L1, while DAGGER++ is an upgrade on the original DAGGER model. SHEATH increases the forecast horizon of the entire pipeline to several days, while DAGGER++ provides high-fidelity forecasts by incorporating magnetosphere-ionosphere contextual data. The whole pipeline now quantifies uncertainty in forecasts, and enables DAGGER to facilitate real-time deployment and integration with Sun-side and Earth-side modules. This work has been enabled by FDL-X (fdlxhelio.org); a derivative of Frontier Development Lab (FDL.ai); as a public/private partnership between NASA, Trillium Technologies, and commercial AI partners Google Cloud and Nvidia.
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