Space radiation is one of the main concerns for human space flights. The prediction of the radiation dose for the actual spacecraft geometry is very important for the planning of long-duration missions. We present a numerical method for the fast calculation of the radiation dose rate during a space flight. We demonstrate its application for dose calculations during the first and the second sessions of the MATROSHKA-R space experiment with a spherical tissue-equivalent phantom. The main advantage of the method is the short simulation time, so it can be applied for urgent radiation dose calculations for low-Earth orbit space missions. The method uses depth-dose curve and shield-and-composition distribution functions to calculate a radiation dose at the point of interest. The spacecraft geometry is processed into a shield-and-composition distribution function using a ray-tracing method. Depth-dose curves are calculated using the GEANT4 Monte-Carlo code (version 10.00.P02) for a double-layer aluminum-water shielding. Aluminum-water shielding is a good approximation of the real geometry, as water is a good equivalent for biological tissues, and aluminum is the major material of spacecraft bodies.
The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic energies have been developed, the behavior of medium energy (120-600 keV) electrons, especially in the MEO region, remains poorly quantified. At these energies, electrons are driven by both convective and diffusive transport, and their prediction usually requires sophisticated 4D modeling codes. In this paper, we present an alternative approach using the Light Gradient Boosting (LightGBM) machine learning algorithm. The Medium Energy electRon fLux In Earth's outer radiatioN belt (MERLIN) model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained on >15 years of the GPS electron flux data and tested on more than 1.5 years of measurements. Tenfold cross validation yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes o f flux. Evaluation on the test set shows high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide space weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment, and surface charging analysis.
Storm Time Depletions of Multi-MeV Radiation Belt Electrons Observed at Different Pitch Angles
(2019)
During geomagnetic storms, the rapid depletion of the high-energy (several MeV) outer radiation belt electrons is the result of loss to the interplanetary medium through the magnetopause, outward radial diffusion, and loss to the atmosphere due to wave-particle interactions. We have performed a statistical study of 110 storms using pitch angle resolved electron flux measurements from the Van Allen Probes mission and found that inside of the radiation belt (L* = 3 - 5) the number of storms that result in depletion of electrons with equatorial pitch angle alpha(eq) = 30 degrees is higher than number of storms that result in depletion of electrons with equatorial pitch angle alpha(eq) = 75 degrees. We conclude that this result is consistent with electron scattering by whistler and electromagnetic ion cyclotron waves. At the outer edge of the radiation belt (L* >= 5.2) the number of storms that result in depletion is also large (similar to 40-50%), emphasizing the significance of the magnetopause shadowing effect and outward radial transport.
Solar wind observations show that geomagnetic storms are mainly driven by interplanetary coronal mass ejections (ICMEs) and corotating or stream interaction regions (C/SIRs). We present a binary classifier that assigns one of these drivers to 7,546 storms between 1930 and 2015 using ground‐based geomagnetic field observations only. The input data consists of the long‐term stable Hourly Magnetospheric Currents index alongside the corresponding midlatitude geomagnetic observatory time series. This data set provides comprehensive information on the global storm time magnetic disturbance field, particularly its spatial variability, over eight solar cycles. For the first time, we use this information statistically with regard to an automated storm driver identification. Our supervised classification model significantly outperforms unskilled baseline models (78% accuracy with 26[19]% misidentified interplanetary coronal mass ejections [corotating or stream interaction regions]) and delivers plausible driver occurrences with regard to storm intensity and solar cycle phase. Our results can readily be used to advance related studies fundamental to space weather research, for example, studies connecting galactic cosmic ray modulation and geomagnetic disturbances. They are fully reproducible by means of the underlying open‐source software (Pick, 2019, http://doi.org/10.5880/GFZ.2.3.2019.003)
Solar wind observations show that geomagnetic storms are mainly driven by interplanetary coronal mass ejections (ICMEs) and corotating or stream interaction regions (C/SIRs). We present a binary classifier that assigns one of these drivers to 7,546 storms between 1930 and 2015 using ground‐based geomagnetic field observations only. The input data consists of the long‐term stable Hourly Magnetospheric Currents index alongside the corresponding midlatitude geomagnetic observatory time series. This data set provides comprehensive information on the global storm time magnetic disturbance field, particularly its spatial variability, over eight solar cycles. For the first time, we use this information statistically with regard to an automated storm driver identification. Our supervised classification model significantly outperforms unskilled baseline models (78% accuracy with 26[19]% misidentified interplanetary coronal mass ejections [corotating or stream interaction regions]) and delivers plausible driver occurrences with regard to storm intensity and solar cycle phase. Our results can readily be used to advance related studies fundamental to space weather research, for example, studies connecting galactic cosmic ray modulation and geomagnetic disturbances. They are fully reproducible by means of the underlying open‐source software (Pick, 2019, http://doi.org/10.5880/GFZ.2.3.2019.003)