Bibliography

Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress)

We advance the modeling capability of electron particle precipitation from the magnetosphere to the ionosphere through a new database and use of machine learning (ML) tools to gain utility from those data. We have compiled, curated, analyzed, and made available a new and more capable database of particle precipitation data that includes 51 satellite years of Defense Meteorological Satellite Program (DMSP) observations temporally aligned with solar wind and geomagnetic activity data. The new total electron energy flux particle precipitation nowcast model, a neural network called PrecipNet, takes advantage of increased expressive power afforded by ML approaches to appropriately utilize diverse information from the solar wind and geomagnetic activity and, importantly, their time histories. With a more capable representation of the organizing parameters and the target electron energy flux observations, PrecipNet achieves a \textgreater50\% reduction in errors from a current state-of-the-art model oval variation, assessment, tracking, intensity, and online nowcasting (OVATION Prime), better captures the dynamic changes of the auroral flux, and provides evidence that it can capably reconstruct mesoscale phenomena. We create and apply a new framework for space weather model evaluation that culminates previous guidance from across the solar-terrestrial research community. The research approach and results are representative of the “new frontier” of space weather research at the intersection of traditional and data science-driven discovery and provides a foundation for future efforts.

McGranaghan, Ryan; Ziegler, Jack; Bloch, Téo; Hatch, Spencer; Camporeale, Enrico; Lynch, Kristina; Owens, Mathew; Gjerloev, Jesper; Zhang, Binzheng; Skone, Susan;

Published by: Space Weather      Published on:

YEAR: 2021     DOI: 10.1029/2020SW002684

space weather; magnetosphere-ionosphere coupling; data science; evaluation; machine learning; particle precipitation

Geomagnetic storm-induced plasma density enhancements in the southern polar ionospheric region: A comparative study using St. Patrick s Day storms of 2013 and 2015

order to examine if the variations in the TEC were caused by thermospheric composition changes in the southern high-latitude regions, we present O/N 2 maps obtained from the GUVI

Shreedevi, PR; Choudhary, RK; Thampi, Smitha; Yadav, Sneha; Pant, TK; Yu, Yiqun; McGranaghan, Ryan; Thomas, Evan; Bhardwaj, Anil; Sinha, AK;

Published by: Space Weather      Published on:

YEAR: 2020     DOI: 10.1029/2019SW002383

Space Weather Modeling Capabilities Assessment: Auroral Precipitation and High-Latitude Ionospheric Electrodynamics

As part of its International Capabilities Assessment effort, the Community Coordinated Modeling Center initiated several working teams, one of which is focused on the validation of models and methods for determining auroral electrodynamic parameters, including particle precipitation, conductivities, electric fields, neutral density and winds, currents, Joule heating, auroral boundaries, and ion outflow. Auroral electrodynamic properties are needed as input to space weather models, to test and validate the accuracy of physical models, and to provide needed information for space weather customers and researchers. The working team developed a process for validating auroral electrodynamic quantities that begins with the selection of a set of events, followed by construction of ground truth databases using all available data and assimilative data analysis techniques. Using optimized, predefined metrics, the ground truth data for selected events can be used to assess model performance and improvement over time. The availability of global observations and sophisticated data assimilation techniques provides the means to create accurate ground truth databases routinely and accurately.

Robinson, Robert; Zhang, Yongliang; Garcia-Sage, Katherine; Fang, Xiaohua; Verkhoglyadova, Olga; Ngwira, Chigomezyo; Bingham, Suzy; Kosar, Burcu; Zheng, Yihua; Kaeppler, Stephen; Liemohn, Michael; Weygand, James; Crowley, Geoffrey; Merkin, Viacheslav; McGranaghan, Ryan; Mannucci, Anthony;

Published by: Space Weather      Published on: 01/2019

YEAR: 2019     DOI: 10.1029/2018SW002127

Observational aspects of the IT energy budget at the multi-scales

Verkhoglyadova, OP; Meng, X; Mannucci, AJ; McGranaghan, R;

Published by:       Published on:

YEAR: 2018     DOI: