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1. Applications of Statistical Methods and Machine Learning in the Space Sciences

   Poduval, B.; Pitman, K. M.; and the SOC Team (January 2022, Journal of geophysical research)

   Applications and methods of artificial intelligence (AI) are becoming powerful tools for scientific investigations in the space sciences, particularly in the analysis and interpretation of the plethora of spacecraft and ground-based observations of the near-Earth and faraway space. Applications of Statistical Methods and Machine Learning in the Space Sciences was a virtual conference held during 17-21 May 2021 to bring together experts in AI and in the various subfields of space sciences to further explore the utility of AI, machine learning, and statistical analysis techniques while sharing the current status of applications in these fields. The conference concluded by emphasizing the scope of AI techniques available to the space science community for addressing outstanding problems with great success as revealed in the number of research works presented. 

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2. GST Data-processing Workflow: Image Registration and Alignment

   https://doi.org/10.3847/1538-4365/ac91c9  

   Yang, Xu; Cao, Wenda; Yurchyshyn, Vasyl (October 2022, The Astrophysical Journal Supplement Series)

   Abstract Multiple solar instrument observation campaigns are increasingly popular among the solar physics and space science communities. Scientists organize high-resolution ground-based telescopes and spacecraft to study the evolution of the complex solar atmosphere and the origin of space weather. Image registration and coalignment between different instruments are vital for accurate data product comparison. We developed a Python language package for registration of ground-based high-resolution imaging data acquired by the Goode Solar Telescope (GST) to space-based full-disk continuum intensity data provided by the Solar Dynamics Observatory (SDO) with the scale-invariant feature transform method. The package also includes tools to align data sets obtained in different wavelengths and at different times utilizing the optical flow method. We present the image registration and coalignment workflow. The aliment accuracy of each alignment method is tested with the aid of radiative magnetohydrodynamics simulation data. We update the pointing information in GST data fits headers and generate GST and SDO imaging data products as science-ready four-dimensional (x,y,λ,t) data cubes. 

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3. Temperatures in the Upper Mesosphere and Lower Thermosphere from O2 Atmospheric Band Emission Observed by ICON/MIGHTI

   https://doi.org/10.1007/s11214-022-00935-x  

   Stevens, M. H.; Englert, C. R.; Harlander, J. M.; Marr, K. D.; Harding, B. J.; Triplett, C. C.; Mlynczak, M. G.; Yuan, T.; Evans, J. S.; Mende, S. B.; et al (December 2022, Space Science Reviews)

   Abstract The Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) was launched aboard NASA’s Ionospheric Connection (ICON) Explorer satellite in October 2019 to measure winds and temperatures on the limb in the upper mesosphere and lower thermosphere (MLT). Temperatures are observed using the molecular oxygen atmospheric band near 763 nm from 90–127 km altitude in the daytime and 90–108 km in the nighttime. Here we describe the measurement approach and methodology of the temperature retrieval, including unique on-orbit operations that allow for a better understanding of the instrument response. The MIGHTI measurement approach for temperatures is distinguished by concurrent observations from two different sensors, allowing for two self-consistent temperature products. We compare the MIGHTI temperatures against existing MLT space-borne and ground-based observations. The MIGHTI temperatures are within 7 K of these observations on average from 90–95 km throughout the day and night. In the daytime on average from 99–105 km, MIGHTI temperatures are higher than coincident observations by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on NASA’s TIMED satellite by 18 K. Because the difference between the MIGHTI and SABER observations is predominantly a constant bias at a given altitude, conclusions of scientific analyses that are based on temperature variations are largely unaffected. 

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4. Incorporation of Satellite Precipitation Uncertainty in a Landslide Hazard Nowcasting System

   https://doi.org/10.1175/JHM-D-19-0295.1  

   Hartke, Samantha H.; Wright, Daniel B.; Kirschbaum, Dalia B.; Stanley, Thomas A.; Li, Zhe (August 2020, Journal of Hydrometeorology)

   null (Ed.)

   Abstract Many existing models that predict landslide hazards utilize ground-based sources of precipitation data. In locations where ground-based precipitation observations are limited (i.e., a vast majority of the globe), or for landslide hazard models that assess regional or global domains, satellite multisensor precipitation products offer a promising near-real-time alternative to ground-based data. NASA’s global Landslide Hazard Assessment for Situational Awareness (LHASA) model uses the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) product to issue hazard “nowcasts” in near–real time for areas that are currently at risk for landsliding. Satellite-based precipitation estimates, however, can contain considerable systematic bias and random error, especially over mountainous terrain and during extreme rainfall events. This study combines a precipitation error modeling framework with a probabilistic adaptation of LHASA. Compared with the routine version of LHASA, this probabilistic version correctly predicts more of the observed landslides in the study region with fewer false alarms by high hazard nowcasts. This study demonstrates that improvements in landslide hazard prediction can be achieved regardless of whether the IMERG error model is trained using abundant ground-based precipitation observations or using far fewer and more scattered observations, suggesting that the approach is viable in data-limited regions. Results emphasize the importance of accounting for both random error and systematic satellite precipitation bias. The approach provides an example of how environmental prediction models can incorporate satellite precipitation uncertainty. Other applications such as flood and drought monitoring and forecasting could likely benefit from consideration of precipitation uncertainty. 

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5. Observations of Particle Loss due to Injection‐Associated Electromagnetic Ion Cyclotron Waves

   https://doi.org/10.1029/2020JA028503  

   Kim, Hyomin; Schiller, Quintin; Engebretson, Mark J.; Noh, Sungjun; Kuzichev, Ilya; Lanzerotti, Louis J.; Gerrard, Andrew J.; Kim, Khan‐Hyuk; Lessard, Marc R.; Spence, Harlan E.; et al (February 2021, Journal of Geophysical Research: Space Physics)

   Abstract We report on observations of electromagnetic ion cyclotron (EMIC) waves and their interactions with injected ring current particles and high energy radiation belt electrons. The magnetic field experiment aboard the twin Van Allen Probes spacecraft measured EMIC waves nearL = 5.5–6. Particle data from the spacecraft show that the waves were associated with particle injections. The wave activity was also observed by a ground‐based magnetometer near the spacecraft geomagnetic footprint over a more extensive temporal range. Phase space density profiles, calculated from directional differential electron flux data from Van Allen Probes, show that there was a significant energy‐dependent relativistic electron dropout over a limitedL‐shell range during and after the EMIC wave activity. In addition, the NOAA spacecraft observed relativistic electron precipitation associated with the EMIC waves near the footprint of the Van Allen Probes spacecraft. The observations suggest EMIC wave‐induced relativistic electron loss in the radiation belt. 

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