![]() The point made is that in space weather, data availability is lower and sensitivity to boundary conditions is higher than it is for terrestrial weather. This figure contains two axes: availability of data and sensitivity to boundary conditions. The central concept is contained in their Figure 1. 2015a), Siscoe and Solomon’s analysis is relevant to this effort. Since such “medium-term” forecasts are the focus of an effort within the NASA Living With a Star (LWS) Partnership for Collaborative Space Weather Modeling ( Mannucci et al. They refer specifically to forecasts with lead times of one day or more, by analogy with terrestrial forecasts. ![]() Data assimilation is a prerequisite for accurate forecasts in the real world, as has been amply demonstrated in the meteorological realm.Ī pioneering paper by Siscoe & Solomon (2006) identifies unique aspects of space weather forecasting that distinguish the data assimilation methods in space weather from those in terrestrial weather. Data assimilation refers to setting boundary conditions or initial states based on actual measurements. The numerical outputs of atmospheric forecast models are sensitive to a significant degree to “boundary conditions” (or equivalently, “initial states”) that are needed to integrate the dynamical equations. This reliance comes about because forecasts are based on “first-principles” or “physics-based” general circulation models of the atmosphere, which computationally integrate systems of differential equations. It is important to note that all types of weather forecast, space or terrestrial, rely on some form of data assimilation. The unique aspects of space weather need to be considered for determining the relevant science that leads to predictive capability. Geospace science for improved forecasts 2.1 The role of data assimilation We conclude with a summary and suggestions for future research directions. Then we discuss three scientific focus areas where progress will lead to improved prediction, that represent scientific forefront areas. The rest of the paper is organized as follows: in the next section, we discuss how science and forecasting are related. This paper addresses a subset of scientific topics that, if better understood, will likely lead to improved forecasts of Earth’s upper atmosphere – the thermosphere and ionosphere. Addressing scientific challenges to better understand the thermosphere-ionosphere system has both scientific and practical benefits. It is reasonable to expect that as scientific knowledge increases, prediction accuracy will generally increase also. The possibility that scientific knowledge enables prediction of natural phenomena is a widely held opinion, certainly considered valid in the meteorological realm ( Kalnay 2002). A central tenet of space weather is that scientific understanding can be achieved “sufficient for prediction” of space weather related phenomena ( OFCM 2010). “Space weather” has become a prominent scientific paradigm. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The possibility of establishing a “positive feedback loop” where improved scientific knowledge leads to improved forecasts is described (Siscoe 2006, Space Weather, 4, S01003 Mannucci 2012, Space Weather, 10, S07003). We describe other research presented at the workshop that appears in the topical issue. Three areas are discussed in some detail in this paper: (1) the role of lower atmosphere forcing in the response of the T-I to geomagnetic disturbances (2) the significant deposition of energy at polar latitudes during geomagnetic disturbances and (3) recent developments in understanding the propagation of coronal mass ejections through the heliosphere and prospects for forecasting the north-south component of the interplanetary magnetic field (IMF) using observations at the Lagrangian L 5 point. The purpose of the workshop, and this topical issue that arose from the workshop, was to discuss research frontiers that will lead to improved space weather forecasts. ![]() The workshop focus was “Scientific Challenges in Thermosphere-Ionosphere Forecasting” to emphasize that forecasting presumes a sufficiently advanced state of scientific knowledge, yet one that is still evolving. * Corresponding author: in forecasting space weather in the thermosphere and ionosphere (T-I) led to a community workshop held at NASA’s Jet Propulsion Laboratory in October, 2014. University of Texas at Arlington, Arlington, TX Johns Hopkins University Applied Physics Laboratory, Laurel, MDĪir Force Research Laboratory, Albuquerque, NM Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA
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