As humanity becomes ever more dependent on technology, nations are investing more resources into space weather forecasting to predict hazardous solar storms, which can knock out power grids and disable satellites. Yet the performance of space weather forecasting lags well behind that of conventional weather forecasting.
To close this gap, space weather forecasting should expand its frontiers with more satellite monitoring stations and should borrow and adapt techniques from weather and climate research, wrote Morley in a recent analysis.
Compared with atmospheric weather models, space weather models have fewer observational data to drive them, which limits the predictions they can make to short lead times. Whereas weather forecasters can track storms using satellite imagery of the entire planet, space weather forecasters must rely on just a few satellites to monitor the solar wind and possible solar storms.
For instance, the National Oceanic and Atmospheric Administration’s Deep Space Climate Observatory (DSCOVR) satellite sits at the location in space called L1, where the gravitational pulls of Earth and the Sun cancel out. At this point, which is roughly 1.5 million kilometers from Earth, or barely 1% of the way to the Sun, detectors can provide warnings with only short lead times: about 30 minutes before a storm hits Earth in most cases or as little as 17 minutes in advance of extremely fast solar storms.
To provide longer lead times, satellites could be stationed farther upwind by using a solar sail to propel the spacecraft closer to the Sun. NASA’s Sunjammer mission was designed to demonstrate this concept but never flew. Satellites could also be positioned at other gravitational null spots—like the L5 point, off to one side of the Sun—to provide more spatial coverage of the Sun’s magnetic field.
There are also computational tools that space weather forecasters could borrow from weather forecasters to extend lead times. Statistical downsampling, for example, is often used in climate models to artificially generate details from sparse data sets based on previously observed correlations.
But to make use of such empirical tools effectively, space weather models must also be able to quantify the uncertainty they introduce, according to the author. One possible strategy for this is ensemble forecasting—running many simulations to better understand the range of possible outcomes, as commonly seen in “spaghetti plots” of forecasted hurricane tracks. Precisely tracking the accuracy of space weather forecasts is crucial if they are to one day be as reliable and useful as weather forecasts, according to the author. (Space Weather, https://doi.org/10.1029/2018SW002108, 2020)
—Mark Zastrow, Science Writer