Benefits of Research-to-Operations and Operations-to-Research Interplay

An interplay between research and operations benefits both, as reflected by the operational community’s use of real-time data from research spacecraft and by use of operational ground and space assets for science. There are many examples of the value of operational assets to the research community and of research assets to the operational community. Examples include research data from GOES, POES, DMSP, and COSMIC, and operational use of data from ACE, SOHO, SDO, and STEREO. The routine provision of space weather data from science missions is invaluable. Nevertheless, although NOAA’s operational mission data are generally available, other data sets, e.g., from DOD’s LANL and GPS spacecraft, are not receiving the necessary support or being made available to the scientific community. The operational use of models, and their validation, help to identify model limitations and contribute to future model improvements. Research models and theory are becoming more accessible to operators and are contributing to improved forecasts (e.g., the Community Coordinated Modeling Center). By making output from research and operational models widely available to the research community, broad-based model development and supporting validation and verification are facilitated. Conducting space weather operations in a closely coupled fashion with related research efforts benefits from an infusion of the latest knowledge and technology. Including research personnel in the review of requirements for operational sensors and data maximizes the data’s value to both operations and science. Such efforts advance the pace of model development and transition to operations by all participants, ranging from the academic researcher to the SWPC, AFWA, and NASA operators. The SWL at NASA GSFC had many successes in transitioning models to NASA operations. The first major transition of a large-scale physics-based numerical model to SWPC operations, the WSA-Enlil model (developed as part of the NSF Center for Integrated Space Weather Modeling), was achieved in 2011, building on 20 years of scientific research funded by multiple agencies.

Second Applications Recommendation [A2.0]—Work in a Multiagency Partnership to Achieve Continuity of Solar and Solar Wind Observations

Solar, interplanetary, and near-Earth observations of the space environment are the mainstays of the space weather enterprise. Space observations are used for (1) space environment situational awareness; (2) inputs to models that provide spatial and temporal predictions; (3) assimilation into models to improve model accuracy; (4) validation of model performance, both in operations and during model development; (5) building of a historical database for climatology and empirical models; (6) specific tailored products such as, for example, satellite anomaly resolution; and (7) research to improve understanding of the space environment and the physical processes involved in solar-terrestrial interactions.

Most of these observations are needed in real time to be useful for operations. They come from NOAA or DOD operational missions, from NASA’s science missions, or from science activities at other agencies. Examples include NOAA’s Geostationary Operational Environmental Satellites (GOES), the Air Force’s Solar Observing Optical Network (SOON), and H-alpha monitoring via NSO’s Global Oscillation Network Group (GONG), or NASA missions such as SDO, SOHO, ACE, and STEREO. They also come from other agency programs, such as ground-based magnetometers operated by the U.S. Geological Survey, or from international collaborations, such as the proposed collaboration between the United States and Taiwan to jointly fund and operate the COSMIC-2 spacecraft constellation. For some uses—such as resolving satellite anomalies, validating operational model performance, and providing data for long-term climate purposes—data are not needed in real time.

The space environment from the Sun to Earth and other planets is vast, in terms not only of the volume that has to be monitored but also the parameters that have to be measured and the spectral ranges that need

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