Great strides have been made in weather forecasting since the earliest days of meteorology. Today, the weather on Earth is often predicted using ensemble forecasting, a method that brings together multiple different numerical models with a range of initial conditions. In a new study, Schunk et al. applied methods typically associated with earthly meteorology to space weather forecasting, creating a Multimodel Ensemble Prediction System (MEPS) for the ionosphere-thermosphere-electrodynamics (ITE) system. Space weather can affect a variety of important civilian and military systems, like radar, surveillance, power grids, and navigation systems, and assimilating as many data sources as possible can help scientists predict how it will affect Earth’s ionosphere and upper atmosphere. To that end, the new MEPS presented in this study is composed of seven physics-based data assimilation models covering the ionosphere and thermosphere.
The authors reconstructed a selected storm event using only four of those models that spanned the middle- and low-latitude ionosphere, the region of the Earth’s upper atmosphere where charged particles, such as electrons and ions, are present and can interfere with radio waves. Although those four models have some analytical techniques in common, they are based on different physical background models and different overall methods of analysis. The researchers ran the models with various combinations of data collection methods to determine how extensively input type affected the model output. The team used slant total electron content (TEC) measurements—a count of the number of electrons between a radio transmitter and receiver—taken by 530 ground receivers linked with orbiting GPS satellites, as well as radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) satellite and bottomside profiles from 80 ionosondes.
The four data assimilation models each provide a three-dimensional distribution of the plasma that makes up Earth’s ionosphere and information about how those distributions develop over time. Two of the models also provide distributions for the drivers of change in the ionosphere, like winds and equatorial electric fields. This study confirmed the value of using many different models for space weather forecasting: Each of the four models produced varying results from the same data. The results indicate that in space weather, as in terrestrial weather, the best way to produce accurate and precise forecasts is by incorporating as many different models and data sources as possible. Future research will focus on the three remaining data assimilation models in Schunk et al.’s proposed MEPS as well as on how to improve all of the individual models. (Radio Science, doi:10.1002/2015RS005888, 2016)
—Leah Crane, Freelance Writer