SWASTi: A physics-based modelling toolkit for space weather

Building and Using Coupled Models for the Space Weather System: Lessons Learned

Space Weather Model Moves Into Prime Time

Space weather forecasting is still in its infancy, but there have been enormous advances in the past 15 years. First-principles and physics-based models are now assimilating data, providing forecasts and “nowcasts” of space weather conditions surrounding Earth. Space weather products for end users are growing in sophistication and utility and are often accompanied by useful visual displays (Simpson, 2004). Several sources of space weather forecasts exist today, including research products and operational forecasts from civilian and Department of Defense sources. As the space weather community works toward a comprehensive “Sun-to-mud” forecasting capability, it is natural to expect challenging initial steps, similar to what occurred in terrestrial forecasting more than 50 years ago (Siscoe, 2006). This should not deter the implementation of first-principles-based forecasting for the upper atmosphere, magnetosphere, and ground-induced currents, augmented by data-driven methods where appropriate. Consider, for example, forecasting upper atmosphere disruptions following detection of a coronal mass ejection (CME) near the Sun. The community should seize the moment to establish an ambitious program of forecasting that takes advantage of the lead time available (usually 1-3 days) after a CME has been first detected. Forecasting geomagnetic storms due to high-speed streams is likely less challenging, with potentially longer lead times. The progress made so far has come from transitioning research models into operational environments. As the community works to expand capabilities, it is time to ask whether “transition to operations” is the appropriate paradigm for advancing the field. Once a research model has been transitioned, what are the next steps? Successful implementation of a space weather forecasting capability requires a vibrant research community focused on improving forecasts. Such a community will, of course, help transition models (Tobiska, 2009; Araujo-Pradere, 2009), but it will also continue to develop and improve those models that have already been transitioned. New scientific insights and tools will be developed in the process. In terrestrial weather, the research divisions of the operational centers produce “reanalyses” that are variants of the operational models run in postprocessing to produce long-term consistent estimates of the atmospheric state. These reanalyses have found widespread use in scientific investigations of the atmosphere, demonstrating that activities related to operations can benefit pure research. The operational models all began as research codes, but their use in applied research brings new benefits. Positive feedback from operations to research is not surprising since models are often used to gain scientific insights. Now is a critical time to exercise vision and plan for an era when space weather forecasting undergoes continuous refinement and improvement, providing both operational benefits and new scientific advances. The implementation of a more comprehensive forecasting capability should begin immediately, with the idea of improving forecasts as new scientific knowledge is gained and as operational results are assessed by a broad community. Improving forecasts is an ongoing challenge, requiring enhanced physical understanding as represented in the models, data-driven techniques where needed, and effective observational systems. Advancements in forecasting require both the dedicated involvement of the scientific community and the existence of an applied research community. To achieve this, a stable but evolving forecasting infrastructure, accessible to the broader scientific community, is required. Such an infrastructure can serve as a focal point for model development and expanded observational systems. The space science community should give serious thought to how we will transition from an era of “first implementation” to an era of continuous improvement. So much has been accomplished. Let us start preparing today for the next great frontier in space weather research. The author wishes to acknowledge Attila Komjathy for a careful reading of this opinion. This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Anthony J. Mannucci is supervisor of the Ionospheric and Atmospheric Remote Sensing Group at Jet Propulsion Laboratory, California Institute of Technology, Pasadena, Calif., USA. E-mail: tony.mannucci@jpl.nasa.gov.

The US‐UK Space Weather Workshop

In this chapter, we review the status of the art of the three-dimensional (3D) time-dependent magnetohydrodynamic (MHD) models of solar wind. We first highlight the influence of adverse space weather conditions by presenting the Halloween Sun-Earth events and their associated effects in the terrestrial space and point out the importance of 3D physics-based MHD models in space weather forecast. Then we summarize three well-known frameworks, including the architectures, the characteristics of the numerical schemes, and the applications in modeling the background solar wind and solar disturbances’ propagation in interplanetary space. The three architectural MHD model frameworks are CORona-HELiosphere (CORHEL), Space Weather Modeling Framework (SWMF) and Space Weather Integrated Model (SWIM). These models are developed respectively by the Center for Integrated Space Weather Modeling (CISM), the Center for Space Environment Modeling (CSEM), and the Solar-Interplanetary-GeoMAgnetic (SIGMA) Weather Group of the State Key Laboratory for Space Weather, National Space Science Center, Chinese Academy of Sciences. Next we give a brief description of hybrid empirical-MHD models for solar-interplanetary modeling, each of which is comprised of a theoretic, empirical or kinematic solar coronal model and a 3D MHD heliospheric model. Then concise summarizations are presented for other miscellaneous models for solar-interplanetary modeling. For completeness, 3D MHD models in studying the interaction between the solar wind and the magnetosphere and the ionosphere are also presented in this chapter. Finally, brief concluding remarks are given in the last section.

In order to better safeguard society and infrastructure from space weather hazards, improved forecasting capabilities are required. To maximise the efficiency of mitigation strategies, forecasting products must not only be accurate, but also timely and tailored to end-user needs. For understanding and predicting the behaviour of the near-Earth space environment in changing solar wind conditions, physics-based modelling is extremely powerful, though often comes at considerable computational expense. The Bergen-Imperial Global Geospace (BIGG) project is an ongoing collaborative effort to provide new space weather forecasting capabilities to the ESA space weather service network via the use of two 3D magnetohydrodynamic (MHD) magnetosphere models, GorgonOps and the Space Weather Modelling Framework (SWMF). Solar wind observations as measured in situ at L1 will be continuously and automatically ingested as simulation inputs, with minimal human intervention. Both models have been optimised such that they are able to run in faster than real time, using only modest computational resources, delivering bespoke forecasting products to the end-user community via a web portal and API in a timely fashion. This multi-model approach will provide forecast diversity and redundancy to ensure continuous and reliable service provision to Europe and beyond.

Convective Ionospheric Storms: A Major Space Weather Problem

<p>Coronal Mass Ejections (CMEs) are the primary source of strong space weather disturbances at Earth and other locations in the heliosphere. Understanding the physical processes involved in their formation at the Sun, propagation in the heliosphere, and impact on planetary bodies is therefore critical to improve current space weather predictions throughout the heliosphere. The capability of CMEs to drive strong space weather disturbances at Earth and other planetary and spacecraft locations primarily depends on their dynamic pressure, internal magnetic field strength, and magnetic field orientation at the impact location. In addition, phenomena such as the interaction with the solar wind and other solar transients along the way, or the pre-conditioning of interplanetary space due to the passage of previous CMEs, can significantly modify the properties of individual CMEs and alter their ultimate space weather impact. Investigating and modeling such phenomena via advanced physics-based heliospheric models is therefore crucial to improve the space weather prediction capabilities in relation to both single and complex CME events. </p><p>In this talk, we present our progress in developing novel methods to model CMEs in the inner heliosphere using the EUHFORIA MHD model in combination with remote-sensing solar observations. We discuss the various observational techniques that can be used to constrain the initial CME parameters for EUHFORIA simulations. We present current efforts in developing more realistic magnetised CME models aimed at describing their internal magnetic structure in a more realistic fashion. We show how the combination of these two approaches allows the investigation of CME propagation and evolution throughout the heliosphere to a higher level of detail, and results in significantly improved predictions of CME impact at Earth and other locations in the heliosphere. Finally, we discuss current limitations and future improvements in the context of studying space weather events throughout the heliosphere.</p>

Adaptive numerical algorithms in space weather modeling

<p>Plasmasphere is a torus of cold plasma surrounding the Earth and is a very dynamic region. Its dynamics is driven by space weather. Having an accurate model of the plasmasphere is very important for wave-particle interactions and radiation belt modeling. In recent years, feedforward neural networks (NNs) have been successfully applied to reconstruct the global plasmasphere dynamics in the equatorial plane [<em>Bortnik et al</em>., 2016, <em>Zhelavskaya et al</em>., 2017, <em>Chu et al</em>., 2017]. These neural network-based models have been able to capture the large-scale dynamics of the plasmasphere, such as plume formation and the erosion of the plasmasphere on the night side. However, NNs have one limitation. When data is abundant, NNs perform really well. In contrast, when the coverage is limited or non-existent, as during geomagnetic storms, NNs do not perform well. The reason is that since these data are underrepresented in the training set, NNs cannot learn from the limited number of examples. This limitation can be overcome by employing physics-based modeling during such intervals. Physics-based models perform stably during high geomagnetic activity time periods if initialized and configured correctly. In this work, we show the combined approach to model the global plasmasphere dynamics that utilizes advantages of both neural network- and physics-based modeling and produces accurate global plasma density reconstruction during extreme events. We present examples of the global plasma density reconstruction for a number of extreme geomagnetic storms that occured in the past including the Halloween storm in 2003. We validate the global density reconstructions by comparing them to the IMAGE EUV images of the He+ particles distribution in the Earth’s plasmasphere for the same time periods.</p>

http://www.agu.org/journals/sw/swa/news/article/?id=2011SW000669 The National Weather Service's (NWS) Space Weather Prediction Center (SWPC) is transitioning the first large-scale, physics-based space weather prediction model into operations on the NWS National Centers for Environmental Prediction (NCEP) supercomputing system (see also C. Schultz, Space weather model moves into prime time, Space Weather, 9, S03005, doi:10.1029/2011SW000669, 2011). The model is intended to provide 1- to 4-day advance warning of geomagnetic storms from quasi-recurrent solar wind structures and Earth-directed coronal mass ejections (CMEs). A team has been put together at SWPC to bring an advanced numerical model—developed with broad participation of the research community—into operational status. The modeling system consists of two main parts: (1) a semiempirical near-Sun module (Wang-Sheeley-Arge (WSA)) that approximates the outflow at the base of the solar wind; and (2) a sophisticated three-dimensional magnetohydrodynamic numerical model (Enlil) that simulates the resulting flow evolution out to Earth. The former module is driven by observations of the solar surface magnetic field accumulated over a solar rotation and composited into a synoptic map; this input is used to drive a parameterized model of the near-Sun expansion of the solar corona, which provides input for the interplanetary module to compute the quasi-steady (ambient) solar wind outflow. Finally, when an Earth-directed CME is detected in coronagraph images from NASA spacecraft, these images are used to characterize the basic properties of the CME, including speed, direction, and size. This input “cone” representation is injected into the preexisting ambient flow, and the subsequent transient evolution forms the basis for the prediction of the CME's arrival time at Earth, its intensity, and its duration (Figure 1). The system will be delivered to NCEP in fall 2011, to undergo a year of trial operation. During that time, potential improvements to the modeling system will be evaluated, and early assessments of its performance will be undertaken. This transition draws upon contributions from many agencies and institutions, including the Center for Integrated Space Weather Modeling, Community Coordinated Modeling Center, Naval Research Laboratory, National Center for Atmospheric Research, Laboratory for Atmospheric and Space Physics, Office of Naval Research, Air Force Weather Agency (AFWA), and the Air Force Research Laboratory (AFRL). The long-term success of the system will hinge on training (collective experience), the quality of inputs (better interpretation, new observations), customer interactions, and the establishment of an effective operations-to-research (and the reverse) chain, which is critical to the continued improvement of the system. We thank C. Nick Arge for his essential contribution in the development and implementation of the WSA model. Annette Parsons is the AFWA project administrator at SWPC. Douglas Biesecker is the project verification and validation lead at SWPC. Dusan Odstrcil is the Enlil originator and a researcher at George Mason University, Fairfax, Va. George Millward is the project technical lead at SWPC. Steve Hill is the development and transition section lead at SWPC. Vic Pizzo is project scientist at NOAA SWPC, Boulder, Colo.; E-mail: vic.pizzo@noaa.gov

Towards a scientific understanding of the risk from extreme space weather

Atmospheric mass density estimates derived from accelerometers onboard satellites such as CHAllenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) are crucial in gaining insight into open science questions about the dynamic coupling between space weather events and the upper atmosphere. Recent advances in physics‐based satellite drag coefficient modeling allow derivation of new density data sets. This paper uses physics‐based satellite drag coefficient models for CHAMP and GRACE to derive new estimates for the neutral atmospheric density. Results show an average difference of 14–18% for CHAMP and 10–24% for GRACE between the new and existing data sets depending on the space weather conditions (i.e., solar and geomagnetic activity levels). The newly derived densities are also compared with existing models, and results are presented. These densities are expected to be useful to the wider scientific community for validating the development of physics‐based models and helping to answer open scientific questions regarding our understanding of upper atmosphere dynamics such as the sensitivity of temporal and global density variations to solar and geomagnetic forcing.

Geomagnetic activity is often described using summary indices to summarize the likelihood of space weather impacts, as well as when parameterizing space weather models. The geomagnetic indexKpin particular, is widely used for these purposes. Current state-of-the-art forecast models provide deterministicKppredictions using a variety of methods – including empirically-derived functions, physics-based models, and neural networks – but do not provide uncertainty estimates associated with the forecast. This paper provides a sample methodology to generate a 3-hour-aheadKpprediction with uncertainty bounds and from this provide a probabilistic geomagnetic storm forecast. Specifically, we have used a two-layered architecture to separately predict storm (Kp ≥ 5−) and non-storm cases. As solar wind-driven models are limited in their ability to predict the onset of transient-driven activity we also introduce a model variant using solar X-ray flux to assess whether simple models including proxies for solar activity can improve the predictions of geomagnetic storm activity with lead times longer than the L1-to-Earth propagation time. By comparing the performance of these models we show that including operationally-available information about solar irradiance enhances the ability of predictive models to capture the onset of geomagnetic storms and that this can be achieved while also enabling probabilistic forecasts.

Solar wind streams, acting as background, govern the propagation of space weather drivers in the heliosphere, which induce geomagnetic storm activities. Therefore, predictions of the solar wind parameters are the core of space weather forecasts. This work presents an indigenous three-dimensional (3D) Solar Wind model (SWASTi-SW). This numerical framework for forecasting the ambient solar wind is based on a well-established scheme that uses a semi-empirical coronal model and a physics-based inner heliospheric model. This study demonstrates a more generalized version of Wang-Sheeley-Arge (WSA) relation, which provides a speed profile input to the heliospheric domain. Line-of-sight observations of GONG and HMI magnetograms are used as inputs for the coronal model, which in turn, provides the solar wind plasma properties at 0.1 AU. These results are then used as an initial boundary condition for the magnetohydrodynamics (MHD) model of the inner heliosphere to compute the solar wind properties up to 2.1 AU. Along with the validation run for multiple Carrington rotations, the effect of variation of specific heat ratio and study of stream interaction region (SIR) is also presented. This work showcases the multi-directional features of SIRs and provides synthetic measurements for potential observations from the Solar Wind Ion Spectrometer (SWIS) subsystem of Aditya Solar wind Particle EXperiment (ASPEX) payload on-board ISRO's upcoming solar mission Aditya-L1.