============== | README.txt | ============== Project Information =================== Date: 26 April, 2020 Dataset Title: Data Pertaining to Initial Simulations Using the Conductance Model for Extreme Events (CMEE) Dataset Creators: A. Mukhopadhyay, D. T. Welling, M. W. Liemohn, A. J. Ridley, S. Chakrabarty, B. J. Anderson Dataset Contact: Agnit Mukhopadhyay agnitm@umich.edu Primary Funding: NX17AB87G (NASA), Grant 1663770 (NSF), 80NSSC17K0015 & 0NSSC18K1120 (NASA Earth and Space Science Fellowship, NESSF) Key Points: =========== - An updated auroral conductance module is built for global models, using nonlinear regression & empirical adjustments to span extreme events. - Expanded dataset raises the ceiling of conductance values, impacting the ionospheric potential dB/dt & dB predictions during extreme events. - Application of the expanded model with empirical adjustments refines the conductance pattern, and improves dB/dt predictions significantly. Research Abstract: ================== Ionospheric conductance is a crucial factor in accurately estimating the closure of magnetospheric currents in the ionosphere. Despite its importance in predictive investigations of the magnetosphere - ionosphere coupling, the estimation of ionospheric conductance in the auroral region is precarious in most global first-principles based models. This impreciseness in estimating this auroral conductance impedes both our understanding of the magnetosphere-ionosphere system during extreme space weather events, and predictive capabilities of ground-based magnetic perturbations during extreme driving which generate geomagnetically induced currents. In this article, we address this concern, with the development of an advanced Conductance Model for Extreme Events (CMEE) that estimates the auroral conductance from field aligned current values. CMEE has been developed using nonlinear regression over a year's worth of one-minute resolution output from assimilative maps, specifically including times of extreme driving of the solar wind-magnetosphere-ionosphere system. The model also includes provisions to enhance the conductance in the aurora using additional adjustments to refine the auroral oval. CMEE has been incorporated within the Ridley Ionosphere Model (RIM) of the Space Weather Modeling Framework (SWMF) for usage in space weather simulations. This paper compares performance of CMEE against the existing conductance model in RIM, through a validation process for six space weather events. The performance analysis indicate overall improvement in the ionospheric feedback to the magnetosphere. Specifically, the model is able to improve the prediction of ionospheric currents which impact the simulated dB/dt and dB, resulting in substantial improvements in dB/dt predictive skill. Methodology: ============ The data are simulation setup and results from space weather simulations conducted using the Space Weather Modeling Framework (SWMF) driven with the Conductance Model for Extreme Events (CMEE). The data also include observations from various in-situ and ground-based measurements of the global state of the near-Earth space environment. Files contained here: ===================== The files and folders are distinctly divided into three categories - 1) Simulations, 2) Observations, and 3) Combined Files. Each category contain 100+ files, and have their own README files to assist users to traverse through the information. Description of the individual datasets are described in the following: 1. SIMULATION FILES - The simulation files contain run setups and final results from the ionospheric (IE) and magnetospheric (GM) domain of the SWMF. 48 distinct simulations were conducted through this study. These simulations are divided into 8 simulation sets and 6 events. The 8 simulation sets are alphabetically ordered as SET A, B, C, D, E, F, G and H. These sets have different configurations in their ionospheric conductance specifications and/or domain setup. The individual studies have been saved in the zipped directories (ending with suffix '.tar') that start with 'SET_' going from A to H. Difference in each setup has been recorded and elucidated further in the README_Simulation.txt file. Each simulation set is further divided into 6 events, pertaining to the 6 space weather events specified by the CCMC/SWPC GEM Challenge of 2011 published in Table 1 of Pulkkinen, A., et al. (2013), doi:10.1002/swe.20056. Results contained in these files are standard SWMF output and have been post-processed using standard tools available in IDL and Python. For more information, please refer to the SWMF User Manual available for public use at the Center for Space Environment Modeling (CSEM; csem.engin.umich.edu). For more instructions about working with the simulation sets, please refer to the README_Simulation.txt file. For additional information or discrepancies, please contact the author. 2. OBSERVATION FILES - The observation files are stored in the zipped folder named 'Observational_data.tar'. The directory contains data from various sources of in-situ and ground-based measurements like AMIE, SuperDARN, AMPERE and ground magnetometers. These data are stored in different formats. Most commonly used formats are .txt, .csv, .save, .pkl and .cdf format. Most non-binary files have their data format listed in the header section. For binary files, the data has been stored as dictionaries for convenience when opening the files for post-processing. For more information about accessing these files, please refer to the README file within the Observational_data.tar directory. 3. COMBINED FILES - These files are post-processed files that have combined the simulation data with observation data. Most of the data are in .pkl or .txt format. This data can be directly used to plot results for comparison and analysis. For more information about accessing these files, please refer to the README file within the Observational_data.tar directory. 4. MISCELLANEOUS FILES - This directory contains the FORTRAN90 and data files used to run CMEE within the SWMF. It contains the two .F90 files which can be included in IE/Ridley_serial/src/ directory within the SWMF's working directory and be used to run the updated model. These changes are presently available already within the SWMF. For more details about using these options, please refer to the SWMF User Manual or contact the author at agnitm@umich.edu HOW TO READ THE SIMULATION OUTPUT: ================================== While standard tools to read SWMF output are already publicly available using IDL and Python (see SpacePy/PyBats - https://spacepy.github.io/pybats.html), the post-processed simulation outputs in the Modeled Dataset, Observational Dataset and Combined Dataset could be read using any numerical language. The data is stored in ASCII format in the .txt files and in binary format in .pkl files. The authors recommend using Python for the easy visualization of the .pkl binary files. The files are stored in dictionaries with clear instructions about the various quantities and data products available. The information contained in these files will soon be available in the publication that follows this work (see Mukhopadhyay et al., 2020 in the Related Publications section). Related Publications: ======================= Mukhopadhyay, A., et al. (2020). Conductance Model for Extreme Events : Impact of Auroral Conductance on Space Weather Forecasts. Forthcoming. For information about the Space Weather Modeling Framework, please refer to the following publications and weblinks: 1. Center for Space Environment Modeling (CSEM) - http://csem.engin.umich.edu/ 2. Toth et al. (2005). Space Weather Modeling Framework: A new tool for the space science community. J. Geophys. Res., 110, A12226, doi:10.1029/2005JA011126. 3. Toth et al. (2012). Adaptive numerical algorithms in space weather modeling, Journal of Computational Physics, Volume 231, Issue 3, https://doi.org/10.1016/j.jcp.2011.02.006. For information about the Ridley Legacy Model (RLM), please refer to the following publication: 1. Ridley, A. J., Gombosi, T. I., and DeZeeuw, D. L.: Ionospheric control of the magnetosphere: conductance, Ann. Geophys., 22, 567–584, https://doi.org/10.5194/angeo-22-567-2004, 2004. For information about the Conductance Model for Extreme Events (CMEE) and Oval Adjustments, please refer to the following publication: 1. Mukhopadhyay, A., et al. (2020). Conductance Model for Extreme Events : Impact of Auroral Conductance on Space Weather Forecasts. Forthcoming. Use and Access: =============== This data set is made available under an Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0).