Our project will deliver a machine-learning based model to specify and forecast near-earth solar wind (SW) conditions based on spacecraft measurements around L1. Almost all magnetosphere-ionosphere…

This $197,286.03 grant contract was awarded by the Shared Services Center, a civilian federal agency, to the University of Alabama in Huntsville (UAH) on December 15, 2020. The contract will deliver a machine-learning based model to improve the specification and forecasting of near-Earth solar wind conditions, which are critical inputs for space weather research and operations. The project leverages two decades of solar wind observation data to develop a more accurate model compared to the existing OMNIWEB industry standard. This effort aligns with the agency's goals to advance space weather modeling and prediction capabilities. The University of Alabama in Huntsville, a public research university, has a history of supporting federal missions through research, engineering, and technology development services, particularly for NASA and the Department of Defense. This grant contract does not utilize any set-aside designations.

Our project will deliver a machine-learning based model to specify and forecast near-earth solar wind (SW) conditions based on spacecraft measurements around L1. Almost all magnetosphere-ionosphere research and space weather forecasts rely on near- earth SW conditions that are obtained by propagating the L1 measurements to the earth s BOW shock. The industry-standard and widely used omniweb model is almost 15 years old and relies on a number of assumptions including IMF fronts being planer and SW properties staying the same along the front and over time. Although these assumptions allow one to deduce near-earth SW values, they are widely known to be often incorrect. Omniweb does not provide SW uncertainty either. We propose to overcome the limitations by developing a machine-learning based model using the state-of-art algorithm of artificial recurrent neural network and the long history of multi-point SW measurements. By providing this model to the community, we will improve the outputs of all models that use SW values as inputs. This project is based on an effective and novel use of two-decades of SW observations, and the outcome will benefit a wide range of space-weather-related research and operations. IT sits squarely within the program goal improvements of operational capabilities and advancements in related fundamental research and to improve numerical models and/or data utilization techniques that could advance specification and/or forecasting capabilities and that could also lead to improved science understanding. Particularly, IT will significantly promote the understanding of the sun and its interactions with earth and the solar system to advance space weather modeling and prediction capabilities applicable to space weather forecasting , and support the transition of space weather models and technology from research to operations and from operations to research.