Forecast improvements from solar wind data assimilation
Lead Research Organisation:
University of Reading
Department Name: Meteorology
Abstract
Technological infrastructures, such as power grids and telecommunications networks, are vulnerable to space weather. For space-weather forecasting, increased lead time requires accurate representation of the solar wind conditions in near-Earth space. At present, solar wind forecast models are "free running" without an observational constraints beyond the initial conditions. Data assimilation (DA) is the process of merging model and observational data to ensure an optimal estimate for reality. Numerical Weather Prediction has made huge strides in accurate forecast lead time through the expansion of the observational network and the application of DA. The first solar wind DA experiments have used simple 2-dimensional models to reconstruct solar wind speed. This approach shows great promise for improved forecasting, though a number of outstanding issues remain. A method for incorporating the 3-dimensional structure has been proposed, though has yet to be implemented.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| NE/S007261/1 | 01/10/2019 | 30/09/2027 | |||
| 2439627 | Studentship | NE/S007261/1 | 01/10/2020 | 31/03/2024 | Harriet Turner |
| Description | This award has so far led to two published manuscripts based around solar wind forecasting. The solar wind is a constant stream of charged particles that flows off the Sun and fills the solar system and it can have a number of impacts on Earth. The first paper focussed on diagnosing some of the errors associated with a certain type of solar wind forecasting with the aim to inform a solar wind data assimilation scheme. Data assimilation (DA) combines model output and observations to form an optimum estimation of reality and has led to large advances in terrestrial weather forecasting. It has been underused in solar wind forecasting, but the Burger Radius Variation Data Assimilation (BRaVDA) scheme has been developed at the University of Reading. The second published paper was verifying the BRaVDA scheme for solar wind forecasting, finding that assimilation of observations from multiple sources and removing transient structures improves forecast accuracy. |
| Exploitation Route | This work should be continued through a post-doctoral research position to further investigate the uses of solar wind data assimilation. It has been shown to provide forecast improvements, but requires further investigation and development so that it can be implemented into operational systems. Operational forecasting that occurs at institutions such as the Met Office could benefit from the use of solar wind DA. |
| Sectors | Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Electronics,Energy,Transport |