Between the Rock and a Hard Place: The CCMC as a Transit Station Between Modelers and Forecasters
The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in
support of the generation of advanced space weather models. As one of its main functions, the CCMC provides
to researchers the use of space science models, even if they are not model owners themselves. The second
CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This
second activity involves model evaluations, model transitions to operations, and the development of draft
Space Weather forecasting tools. This presentation will focus on the latter element. Specifically, we will
discuss the process of transition research models, or information generated by research models, to Space
Weather Forecasting organizations. We will analyze successes as well as obstacles to further progress, and
we will suggest avenues for increased transitioning success.
Predicting the Space Environment: the CISM Experience and Perspective
The Center for Integrated Space Weather Modeling (CISM) is an NSF Science and Technology Center that is a consortium of eleven institutions headed by Boston University. CISM focuses its activities around developing a suite of coupled, physics-based simulation models that describe the space environment from the Sun to the Earth. These models are being used as scientific tools to improve our understanding of the space environment, as education tools with which to help train the next generation of space scientists and space weather professionals, and as tools with which to predict the conditions in space. In this presentation we will first discuss forecasting needs and how physics-based models can and can't help. We will then describe CISM's collaboration with the Space Weather Prediction Center and others towards transitioning physics-based models into operational use and report on recent validation studies of CISM models and their usefulness as prediction tools.
The Space Weather Modeling Framework
The Center for Space Environment Modeling (CSEM) at the University of Michigan
has developed the Space Weather Modeling Framework (SWMF) that integrates independently developed
models into a high performance simulation tool. The SWMF models a dozen physics domains spanning from
the solar corona and heliosphere to the magnetosphere, ionosphere and thermosphere of the Earth. The
SWMF can perform a realistic Sun-to-thermosphere simulation faster than real time on today's
supercomputers. We also use subsets of the SWMF components to model various space physics systems. I
will describe the current status of the SWMF and our plans for future development.
Coronal Modeling: Present Status and Challenges for the Future*
The solar corona strongly influences space weather at Earth, via eruptive phenomena such as coronal mass ejections, and through its structure, which leads to the formation of fast solar wind streams that trigger recurrent geomagnetic activity. MHD models that address both the dynamics and structure of the solar corona have advanced considerably in recent years, but many challenges remain if these models are to provide reliable space weather forecasting tools. In this talk we describe these challenges and the prospects for meeting them. *Research supported by NASA, NSF and AFOSR.
Numerical Simulation of Interplanetary Coronal Mass Ejections and STEREO Observations
Magnetospheric Space Weather Forecasting: Status and Challenges for the Upcoming Solar Cycle
As we approach the next solar maximum the predictive capabilities of magnetosphere models have increased significantly compared to the last maximum, due to model improvements, increased computational power, and more data sources. However, we also face new challenges as more and more human activities become susceptible to space weather effects such as trans-polar flights and GPS navigation and position controls. We first review the current status of coupled Geospace models and how new computational capabilities are making real-time forecasts much more feasible compared to just a few years ago. In the second part of this talk we outline the challenges that lie ahead to efficiently validate and transition the models into operations. We also discuss some ideas that may help streamlining the validation and transitioning process.
The Current Status and Challenges for Upper Atmosphere Models
The Earth's upper atmosphere and ionosphere display highly variable and turbulent densities, temperatures, and winds, and these features are manifestations of space weather. During geomagnetic storms, wind gusts in the upper atmosphere can become supersonic, localized density troughs and peaks form, localized temperature hot spots occur, cyclonic-like and tornado-like winds periodically form, and the radiation levels can dramatically increase. Unfortunately, these space weather features can have detrimental effects on human systems and operations. As society becomes more dependent on sophisticated technological systems, specification and forecasting of space weather disturbances become crucial to our economy, safety, and security. Consequently, a significant effort is being devoted to developing both data assimilation and coupled physics-based models of the space environment. The data assimilation models are useful for specification and the coupled physics-based models are needed for forecasting. Significant progress has been made in the development of physics-based, Kalman filter, data assimilation models for the global ionosphere. The state-of-the-art models can assimilate several data types, including in situ electron densities from satellites, bottomside electron densities from 100 ionosondes, line-of-sight Total Electron Content (TEC) measurements between 1000 ground stations and the GPS satellites, TEC via occultations between low-altitude and high-altitude satellites, and line-of-sight ultraviolet emission data. Likewise, significant progress has been made in developing coupled physics-based models of the space environment from the Sun to the Earth. However, the construction of these models is not always straightforward for several reasons: (1) Although there are a lot of data available via the world wide web, the data quality is generally not adequate for data assimilation models and data errors are not routinely provided; (2) There is a limited number of real-time data sources for specification and forecast models; (3) The application of a rigorous Kalman filter is not feasible and approximations are necessary; (4) The coupled physics-based models have uncertain parameters that need to be determined and/or have missing physics; and (5) Validation of data assimilation and coupled physics-based models requires massive independent data sets so that statistics can be done to determine the accuracy of the models. The status and challenges related to the development of data assimilation and coupled physics-based models will be discussed.