A comprehensive analysis of multi-scale field aligned currents: Characteristics, controlling parameters, and relationships

We explore the characteristics, controlling parameters, and relationships of multi-scale field aligned currents (FACs) using a rigorous, comprehensive, and cross-platform analysis. Our unique approach combines FAC data from the Swarm satellites and the Advanced Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) to create a database of small-scale (∼10-150 km, 250 km) FACs. We examine these data for the repeatable behavior of FACs across scales (i.e., the characteristics), the dependence on the interplanetary magnetic field (IMF) orientation, and the degree to which each scale ‘departs’ from nominal large-scale specification. We retrieve new information by utilizing magnetic latitude and local time dependence, correlation analyses, and quantification of the departure of smaller from larger scales. We find that: 1) FACs characteristics and dependence on controlling parameters do not map between scales in a straight forward manner; 2) relationships between FAC scales exhibit local time dependence; and 3) the dayside high-latitude region is characterized by remarkably distinct FAC behavior when analyzed at different scales, and the locations of distinction correspond to ‘anomalous’ ionosphere-thermosphere (IT) behavior. Comparing with nominal large-scale FACs, we find that differences are characterized by a horseshoe shape, maximizing across dayside local times, and that difference magnitudes increase when smaller scale observed FACs are considered. We suggest that both new physics and increased resolution of models are required to address the multi-scale complexities. We include a summary table of our findings to provide a quick reference for differences between multi-scale FACs

Global geomagnetic perturbation forecasting using deep learning

Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field, which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation. Since GIC data is proprietary, the time variability of the horizontal component of the magnetic field perturbation (dB/dt) is used as a proxy for GICs. In this work, we develop a fast, global dB/dt forecasting model, which forecasts 30 min into the future using only solar wind measurements as input. The model summarizes 2 hr of solar wind measurement using a Gated Recurrent Unit and generates forecasts of coefficients that are folded with a spherical harmonic basis to enable global forecasts. When deployed, our model produces results in under a second, and generates global forecasts for horizontal magnetic perturbation components at 1 min cadence. We evaluate our model across models in literature for two specific storms of 5 August 2011 and 17 March 2015, while having a self-consistent benchmark model set. Our model outperforms, or has consistent performance with state-of-the-practice high time cadence local and low time cadence global models, while also outperforming/having comparable performance with the benchmark models. Such quick inferences at high temporal cadence and arbitrary spatial resolutions may ultimately enable accurate forewarning of dB/dt for any place on Earth, resulting in precautionary measures to be taken in an informed manner.</p

Challenges and Facilitating Factors in Sustaining Community-Based Participatory Research Partnerships: Lessons Learned from the Detroit, New York City and Seattle Urban Research Centers

In order to address the social, physical and economic determinants of urban health, researchers, public health practitioners, and community members have turned to more comprehensive and participatory approaches to research and interventions. One such approach, community-based participatory research (CBPR) in public health, has received considerable attention over the past decade, and numerous publications have described theoretical underpinnings, values, principles and practice. Issues related to the long-term sustainability of partnerships and activities have received limited attention. The purpose of this article is to examine the experiences and lessons learned from three Urban Research Centers (URCs) in Detroit, New York City, and Seattle, which were initially established in 1995 with core support from the Centers for Disease Control and Prevention (CDC). The experience of these Centers after core funding ceased in 2003 provides a case study to identify the challenges and facilitating factors for sustaining partnerships. We examine three broad dimensions of CBPR partnerships that we consider important for sustainability: (1) sustaining relationships and commitments among the partners involved; (2) sustaining the knowledge, capacity and values generated from the partnership; and (3) sustaining funding, staff, programs, policy changes and the partnership itself. We discuss the challenges faced by the URCs in sustaining these dimensions and the strategies used to overcome these challenges. Based on these experiences, we offer recommendations for: strategies that partnerships may find useful in sustaining their CBPR efforts; ways in which a Center mechanism can be useful for promoting sustainability; and considerations for funders of CBPR to increase sustainability