DASP Blog: Weather forecasts are incredibly powerful - will space weather ever catch up?
In this DASP blog, Jose Arnal (University of Toronto) walks through the highlights of their paper on data assimilation techniques for space weather forecasting. If you have any questions, reach out to Jose at jose.arnal@mail.utoronto.ca.
An expansive network of observations around the globe largely drives the predictive power and success of atmospheric weather prediction. Every day, millions of measurements are combined with computer model predictions—using data assimilation—to produce the robust and accurate forecasts we’ve become accustomed to. However, the situation is quite different when it comes to space weather predictions.
Many critical infrastructures, both in space and on the ground, are vulnerable to the effects of extreme space weather events. With society's growing reliance on technologies that are sensitive to such disruptions, there is a need for reliable and accurate space weather forecasts. However, efforts to assimilate observational data into space weather models are still in their early stages. While we are far from the comprehensive effort involved in terrestrial weather prediction, which benefits from millions of observations, we can make significant progress by developing data assimilation algorithms tailored to the unique challenges of the Sun-Earth system while maximising the use of existing observational capabilities.
In our recent paper, we explore key questions in the design of data assimilation systems for magnetohydrodynamics (MHD)–the physical laws that govern the large-scale dynamics of space plasmas. Primarily, we seek to quantitatively compare the relative merits of two families of data assimilation systems: variational and sequential approaches . The former assimilates all observational data at once by solving a large optimization problem, while the latter ingests data one time-step at a time through statistical filtering. Figure 1 illustrates the effectiveness of these strategies in a 1D MHD simulation. A background simulation without data assimilation is compared to simulations that incorporate data. The data-assimilated predictions show significantly improved accuracy, highlighting the advantages of this approach. In addition to quantitative comparisons, we also demonstrate how to respect the solenoidal property of the magnetic field within the data assimilation analysis and provide a detailed account of the discrete magnetohydrodynamics adjoint equations required for the variational approach.
By advancing these techniques, we aim to bring space weather forecasting closer to the level of precision we expect from atmospheric forecasts, something that will be increasingly vital as our reliance on vulnerable technologies continues to grow.
Figure 1: Relative error in plasma density for a one-dimensional plasma expansion flow. The 𝑡 and 𝑥 axes denote the time and space coordinates, respectively. The left pane corresponds to a simulation with erroneous initial conditions. The middle pane shows data assimilation results with a sequential approach, while the right pane uses a variational approach. In both data assimilated cases, synthetic data is ingested from 𝑡=0 to 𝑡=5.
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