ArticlePDF AvailableLiterature Review

Space Radiation and Plasma Effects on Satellites and Aviation: Quantities and Metrics for Tracking Performance of Space Weather Environment Models


Abstract and Figures

The Community Coordinated Modeling Center has been leading community‐wide space science and space weather model validation projects for many years. These efforts have been broadened and extended via the newly launched International Forum for Space Weather Modeling Capabilities Assessment ( Its objective is to track space weather models' progress and performance over time, a capability that is critically needed in space weather operations and different user communities in general. The Space Radiation and Plasma Effects Working Team of the aforementioned International Forum works on one of the many focused evaluation topics and deals with five different subtopics (‐all.php) and varieties of particle populations: Surface Charging from tens of eV to 50‐keV electrons and internal charging due to energetic electrons from hundreds keV to several MeVs. Single‐event effects from solar energetic particles and galactic cosmic rays (several MeV to TeV), total dose due to accumulation of doses from electrons (>100 keV) and protons (>1 MeV) in a broad energy range, and radiation effects from solar energetic particles and galactic cosmic rays at aviation altitudes. A unique aspect of the Space Radiation and Plasma Effects focus area is that it bridges the space environments, engineering, and user communities. The intent of the paper is to provide an overview of the current status and to suggest a guide for how to best validate space environment models for operational/engineering use, which includes selection of essential space environment and effect quantities and appropriate metrics.
Content may be subject to copyright.
Space Radiation and Plasma Effects on Satellites
and Aviation: Quantities and Metrics
for Tracking Performance of Space
Weather Environment Models
Yihua Zheng
, Natalia Yu Ganushkina
, Pier Jiggens
, Insoo Jun
, Matthias Meier
Joseph I. Minow
, T. Paul O'Brien
, Dave Pitchford
, Yuri Shprits
W. Kent Tobiska
, Michael A. Xapsos
, Timothy B. Guild
, Joseph E. Mazur
, and
Maria M. Kuznetsova
Space Weather Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA,
Finnish Meteorological Institute,
Helsinki, Finland,
Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI,
The Space Environment and Effects Section, European Space Research and Technology Centre, Noordwijk,
Mission Environments Group, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA,
Institute of Aerospace Medicine, German Aerospace Center, Köln, Germany,
NASA Langley Research Center,
Hampton, VA, USA,
Aerospace Corporation, Chantilly, VA, USA,
SES Engineering, Château de Betzdorf, Luxembourg,
Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany,
Department of Earth,
Planetary and Space Sciences, University of California, Los Angeles, CA, USA,
Space Environment Technologies, Los
Angeles, CA, USA,
Radiation Effects and Analysis Group, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Abstract The Community Coordinated Modeling Center has been leading communitywide space
science and space weather model validation projects for many years. These efforts have been broadened
and extended via the newly launched International Forum for Space Weather Modeling Capabilities
Assessment ( Its objective is to track space weather models' progress
and performance over time, a capability that is critically needed in space weather operations and different
user communities in general. The Space Radiation and Plasma Effects Working Team of the aforementioned
International Forum works on one of the many focused evaluation topics and deals with ve different
subtopics ( and varieties of particle
populations: Surface Charging from tens of eV to 50keV electrons and internal charging due to energetic
electrons from hundreds keV to several MeVs. Singleevent effects from solar energetic particles and galactic
cosmic rays (several MeV to TeV), total dose due to accumulation of doses from electrons (>100 keV) and
protons (>1 MeV) in a broad energy range, and radiation effects from solar energetic particles and galactic
cosmic rays at aviation altitudes. A unique aspect of the Space Radiation and Plasma Effects focus area is
that it bridges the space environments, engineering, and user communities. The intent of the paper is to
provide an overview of the current status and to suggest a guide for how to best validate space environment
models for operational/engineering use, which includes selection of essential space environment and effect
quantities and appropriate metrics.
Plain Language Summary In order to track space weather models' progress and performance
over time, userfocused metrics using proper physical quantities are critically needed. This paper
summarizes the working team's initial efforts of dening two types of interlinked physical quantities from
both science and engineering perspectives in the subject of space radiation and plasma effects on space assets.
1. Introduction
Space assets (including aircraft) are subject to an environment consisting of different particle populations
that often evolve dynamically over time and space, and potentially bringing about deleterious effects on
spacecraft electronics and/or life in space (e.g., Feynman & Gabriel, 2000). Figure 1 summarizes the main
space weather impacts and their environmental sources, from a space hardware perspective. The blue boxes
are used to show each impact with sources to its right and specic impacts under the blue line. Particles
across a broad energy range contribute to satellite impacts, which include cold, dense, and hot electrons
from a few eV to tens of keV that could lead to surface charging, energetic electrons that are above a few
©2019. The Authors.
This is an open access article under the
terms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.
Special Section:
Space Weather Capabilities
Key Points:
Providing an overview of the current
status and proposing a guide for how
to best validate space environment
models for operational use
Two types of physical quantities for
both science and engineering
purposes have been identied
Proper metrics are needed for
evaluating space environment
models for different application
Correspondence to:
Y. Zheng,
Zheng, Y., Ganushkina, N. Y., Jiggens,
P., Jun, I., Meier, M., Minow, J. I., et al.
(2019). Space radiation and plasma
effects on satellites and aviation:
Quantities and metrics for tracking
performance of space weather
environment models. Space Weather,
17, 13841403.
Received 31 JUL 2018
Accepted 14 JUL 2019
Accepted article online 29 JUL 2019
Published online 15 OCT 2019
hundred keV possibly leading to internal charging, solar energetic particles (SEPs), galactic cosmic rays
(GCRs), and trapped inner belt protons/ions that are sources for singleevent effects on spacecraft electronics
(e.g., O'Bryan et al., 2009) and avionics (e.g., Dyer & Truscott, 1999; Normand, 1996). In addition,
noncharged particles including UV radiation (photons), energetic neutrons, atomic oxygen, and neutral
atmosphere could pose various hazards. Energetic protons, electrons, heavy ions, and neutrons can lead to
total dose effects over time. Micrometeoroids and orbital debris are potential hazards for spacecraft as
well. Table 12.1 in Daly et al. (2007) also provides a concise summary of space weather effects due to space
GCR and SEPs can also have adverse effects on humans in space (e.g., Chancellor et al., 2014). From human
perspective, space radiation can have acute inight effects, longterm cancer risks, and risks to the central
nervous system and cardiovascular system.
The space environment and its associated effects span vast and complex domains and involve multiple dis-
ciplines such as space science, quantum physics, material science, biological and medical science (for human
effects), and computational physics. Here we mainly focus on space environment specication, but with
users' (types of users will be mentioned in section 3) needs in mind. Traditionally, space weather environ-
ment information (both models and observations) and engineering models of effects tend to exist in isolation
and reside in different communities. To break the impasse and bridge the gaps and to make space environ-
ment models (primarily developed by scientists) more useful to the engineering and user community, it is
imperative to have standardized and more userfocused physical parameters/metrics to measure their perfor-
mance over time, particularly the physical quantities that matter to engineers/users and that can be easily
understood/translated in terms of impact assessment and monitoring. This serves as a key motivational force
behind the International Forum for Space Weather Capabilities Assessment. It allows us to tackle problems
related to space weather effects from one particular and tangible angle.
In cooperation with the community, the Space Radiation and Plasma Effects Working Team has been
working together to select appropriate physical quantities/metrics that can be qualitatively translated into
effects. It deals with how particles (mainly charged particles) at different energies affect satellites and
Figure 1. Summary of space weather impacts on satellites and their environment sources.
Space Weather
airline passengers and hardware. The effects include surface charging, internal charging, singleevent effects
(SEEs), total dose, and radiation effects at aviation altitudes. Figure 2 shows the focus/subdomains (the types
of impacts and their sources) of the Space Radiation and Plasma Effects Working Team.
2. Space Radiation and Plasma Effects on Space Assets
2.1. Spacecraft Charging
Spacecraft charging (see NASA Handbooks, NASAHDBK4002A w/CHANGE 1, NASAHDBK4006)
remains a serious operational threat for the design and operation of space assets. It usually manifests as
surface charging and/or internal charging. When charge is built up either in the outside (surface) material
or in the material (internal), an electrostatic discharge (ESD) can occur when the electric eld exceeds the
breakdown strength of the material. If the discharge occurred at or near a sensitive component, these ESD
currents can cause compromised function and/or catastrophic destruction of sensitive electronics, solar array
failures, uncommanded change in system states (phantom commands), loss of synchronization in timing cir-
cuits, spurious mode switching, poweron resets, erroneous sensor signals, telemetry noise, and/or loss of
data. Other concerns with discharges are possible electromagnetic interference and material damage.
Electromagnetic interference can produce noise levels in receive bands that exceed the receiver sensitivity,
communications issues due to the excess noise, or phantom commands or signals. ESDs can damage
missioncritical materials, including thermal control coatings, reentry thermal protection systems, and opti-
cal materials such as dielectric coatings and mirror surfaces. The reattracted photoionized outgassing mate-
rials can be deposited as surface contaminants. Surface and internal charging can also compromise science
instrument and sensor functionality.
The distinction between surface charging and internal charging is that internal charging is caused by ener-
getic particles that can penetrate and deposit charge very close to a victim site (e.g., Garrett, 2016, and refer-
ences therein). Surface charging occurs on areas that can be seen and touched on the outside of a spacecraft.
Surface discharges occur on or near the outer surface of a spacecraft and discharges must be coupled to an
interior affected site rather than directly to the victim. Energy from surface arcs is attenuated by the coupling
factors necessary to get to victims (most often inside the spacecraft) and, therefore, is less of a threat to
Figure 2. Space radiation and plasma impacts and their sources.
Space Weather
electronics. External wiring and antenna feeds, of course, are susceptible to surface charging. Internal char-
ging, by contrast, may cause a discharge directly to a victim pin or wire with very little attenuation if caused
by electron deposition in circuit boards, wire insulation, or connector potting. It has been shown that differ-
ential charging followed by discharging is a major source of spacecraft anomalies (Koons et al., 2000).
Surface charging typically results from the buildup of charge on surfaces when assets are immersed in
uxes of charged particles. It also results from induced currents from asymmetric plasma ows or
planetary magnetic elds (e.g., Garrett, 2016). The interaction of a spacecraft and a planetary ionosphere
can generate a plasma wake (Ferguson, 1985; NASAHDBK4006, and references therein) that can distort
the potentials around the vehicle, as demonstrated by the International Space Station. Additionally,
electric elds caused by the movement of a conducting body across a planetary magnetic eld can induce
currents and result in charging in the structure. As indicated in Figure 2, ring current, aurora, and plasma
sheet particles can be potential space environmental sources for surface charging (e.g., Ganushkina et al.,
2017; MatéoVélez et al., 2018).
Internal charging refers to the accumulation of electric charge on interior, ungrounded metals, or in the
dielectrics inside a spacecraft by penetrating/energetic electrons. The resulting discharge is termed as
Internal ElectroStatic Discharge, which may be even more common than originally thought (Bodeau,
2005, 2010).
A few eV to 50keV electrons are considered source for surface charging (MatéoVélez et al., 2018) and elec-
trons greater than 100 keV (>100 keV) are responsible for internal charging (the main source is radiation belt
electrons). Sometimes it is difcult to differentiate between surface charging and internal charging as the
root cause of an anomaly. The transitional energy of surface charging and internal charging is usually con-
sidered to be ~50100 keV. Geosynchronous orbit (GEO) and its vicinity are believed to constitute one of the
most susceptible regions for surface charging as electron injections from substorms or substormlike transi-
ents elevate the ux level of the electrons that are in the energy range for surface charging. In addition, aur-
oral region and ring current are major sources as well. Charged particles for internal charging are mainly
from the radiation belts.
For more details on spacecraft charging, its history, studies/understanding, and its mitigation
techniques/practices, see the NASA Technical Handbooks regarding spacecraft charging (NASAHDBK
4002A w/CHANGE 1, NASAHDBK4006; Ferguson & Hillard, 2003; Ferguson et al, 2015; Garrett, 2016,
Frooninckx & Sojka, 1992; and references therein).
2.2. Space Radiation Effects on Spacecraft Electronics
Space radiation environment consists of SEPs, GCRs, energetic particles trapped in the South Atlantic
anomaly region, and energetic electrons in radiation belts. Radiation effects on electronics can be classied
into two classes: those caused by the total accumulated radiation dose over the life of a mission (gradual) and
those caused by singleevent effects (sudden/transient). In general, the basic effect of radiationmatter
interaction is to bring energy deposition into the target object. Depending on the particle species and energy,
and physical processes involved in the targeted material/structure, this energy deposition will result in a vari-
ety of effects.
2.2.1. SEEs
SEEs are a serious problem for electronics operated in space (e.g., Edmonds, Barnes, and Scheick, 2000a
JPL publication; O'Bryan et al., 2009; Xapsos et al., 2007), and they are becoming an issue for advanced
technologies in avionics (e. g., Dyer & Truscott, 1999; Dyer et al., 2018), and even at sea level. The charge
deposited by a single ionizing particle (producing a dense track of electronhole pairs in devices, circuits,
and components) can produce a wide range of effects, including singleevent upset (transient and
nondestructive, affecting mainly memories), multiple bit upset (nondestructive), singleevent transient
(nondestructive), singleevent functional interrupt (nondestructive), singleevent latchup (destructive,
affecting mainly complementary metaloxidesemiconductor structure), singleevent burnout (destructive;
affecting mainly power metaloxidesemiconductor eldeffect transistors), singleevent gate rupture
(potentially destructive, affecting mainly submicronic structure), and single hard error (another destructive
effect). In general, the sensitivity of a technology to SEE increases as the device dimension decreases and as
the circuit speed increases.
Space Weather
2.2.2. Total Dose Including Total Ionizing Dose and Displacement Damage Dose
When a charged particle (or a photon) travels through a material, it interacts with electrons in the
material and causes some of the atoms to become ionized, creating electronhole pairs. Such effects
accumulate in insulators (e.g., a gate oxide in a complementary metaloxidesemiconductor device). The
accumulated trapped charge is measured by the accumulated ionization, which in turn is measured by
the sum (over particles) of the energy lost by the particles to the material via interactions with the
electrons. Therefore, a useful measure is the total energy, per unit mass of material, transferred to the
material via ionization from all ionizing particles, which is called the total ionizing dose (TID; e.g.,
Cochran et al., 2009; Edmonds et al., 2000).
The TID, mostly due to electrons and protons, can result in device failure (or biological damage to
astronauts). In either case, TID can be measured in terms of the absorbed dose, which is a measure of the
energy absorbed by matter. Absorbed dose is quantied using either a unit called the rad (an acronym for
radiation absorbed dose; 1 rad = 100 ergs/g) or the SI unit which is the gray (Gy): 1 Gy = 100 rads = 1 J/kg
(J: joule, kg: kilogram).
The TID is calculated from the trapped protons and electrons, secondary Bremsstrahlung photons, and solar
energetic particles (the contribution from galactic cosmic ray ions is negligible in the presence of these other
sources). The dose prole curvethat indicates the dose received through a shield of varying thickness
(most often a hollow aluminum sphere) is usually used for evaluating the TID on a component.
Displacement damage (e.g., Edmonds et al., 2000; Jun et al., 2003) is the result of nuclear interactions,
typically scattering, which cause lattice defects. Displacement damage is due to cumulative longterm
nonionizing damage from protons, electrons, and neutrons. The collision between an incoming particle
and the nucleus of a lattice atom subsequently displaces the atom from its original lattice position.
The particles producing displacement damage include protons of all energies, electrons with energies above
150 keV, and neutrons (e.g., from onboard power sources). Shielding has some effect, but it depends on loca-
tion of the device (e.g., solar cells). Displacement damage is typically of lesser concern than singleevent
effects or TID, although protons cause displacement damage in solar cells (Messenger et al., 2001, 2004,
2014) and bipolar devices. Displacement damage degrades minority carrier lifetime; a typical effect would
be degradation of gain and leakage current in bipolar transistors.
The total energy loss per unit distance of travel is called the linear energy transfer, or LET. The LET is usually
normalized by dividing by the density of the medium; the most popular units are MeVcm
/mg. The reason
for this normalization is that it makes the LET for a given particle and energy similar in different materials.
LET depends also on the incident particle species and energy.
The interactions of radiation particles with materials and resulting effects on different types of devices and
electronic components are very complex (e.g., Cochran et al., 2009; O'Bryan et al., 2009). More details can
be found in various publications (e.g., Edmonds et al., 2000; Srour & McGarrity, 1988; Velazco et al., 2007).
2.3. Space Radiation Effects at Aviation Altitudes
The primary source of radiation hazards at aviation altitudes are from GCRs and SEPs.
GCRs have energies (10
eV/nucleon) much higher than SEPs (10
eV/nucleon). GCR ions are
typically GeV (gigaelectron volt) and above while SEP ions are in the energy range of tens MeV to hundreds
MeV. For some extreme SEP events, ions can be accelerated to GeVs and higher. In the nearEarth's
environment and (within the heliosphere in general), GCR ux (dose) is at continuous background levels
while SEP uxes/dose are highly dynamic and can vary several orders of magnitude (spikesin Figure 3)
on a short time scale. During large SEP events the intensity of >100MeV protons hitting the upper
atmosphere can be >1,000 times that of GCR protons. Other speculative radiation sources affecting aviation
(if present) might be precipitating energetic electrons from the radiation belt as discussed by Tobiska et al.
(2016) and Terrestrial Gamma ray Flashes. Further measurements and analyses need to be done to ascertain
their contribution to the atmospheric radiation environment. While ionizing radiation from GCRs and SEPs
pose health consequences/risks (such as longterm cancer risks and potential damage to the central nervous
system and cardiovascular systems) to airline passengers and crews (human in space in general), SEEs on
avionics from highenergy particles and lowenergy, thermalized neutrons (via their interactions with
Space Weather
nuclei inside avionic systems) are also concerns for aviation (e.g., Dyer & Truscott, 1999; Normand, 1996;
Tobiska et al., 2015). However, the working team's initial efforts in this area have been mainly on the
radiation effects in terms of dose or dose equivalent on passengers and aircrews. SEEs on avionics will be
part of future expertise and model expansion. Such impacts can be combined into SEEs on space
hardware/systems in general, although SEEs on avionics require accurate understanding and modeling of
the particle and atmosphere interactions, together with magnetic rigidity cutoff consideration.
3. Users
The working team has identied potential user groups. For surface charging, internal charging, singleevent
effects, and total dose, the users are more or less similar.
1. Satellite designers for both commercial and government
2. Satellite operators and anomaly analysts for both commercial and government
3. Scientists for both academia and government
4. Insurance companies
For radiation effects at aviation altitudes, the users are mainly air crews, passengers, regulators, airlines, and
scientists studying the relevant environment.
4. Physical Quantities and Metrics
4.1. Physical Quantities
Table 1 shows the physical parameters that have been selected from both engineering and science perspec-
tives, following team discussions. The science quantities are carefully chosen so that through a unied and
agreedupon engineering effect model (with a simplied geometry, default material, and so on), the impact
can be readily computed/assessed, though may be qualitative due to the complexities and lack of a thorough
Figure 3. LRO (Lunar Reconnaissance Orbiter)/CRaTER (Cosmic Ray Telescope for the Effects of Radiation) microdosi-
meter measurements from launch in June 2009 to December 2014. Doses due to SEPs appear as spikes while those from
GCRs is the slowly varying background (Mazur et al., 2015; Figure 1).
Table 1
The Physical Quantities Chosen for Validation From Both Science and Engineering Perspectives
Effect quantity Science predictands Time scale (Space Weather)
Surface charging >10keV electron ux >10 keV eux, Te, Ne Seconds
Internal charging >100 fA/cm
[100 mils] 1 MeV and >2 MeV eux 24hr averaged
Singleevent effects SEE rate [100 mils] >30 MeV p+ ux, >15 MeV·cm
LET ux 5 min, daily, weekly
Total dose Dose in Si [100 mils, 4 mils] 3050 MeV p+ ux, >1.5 MeV eux, 110 MeV p+ Daily, weekly
Atmospheric radiation Dose rate in aircraft (Dindex) Two spectral parameters (power law with rigidity) 5 min, hourly
Space Weather
understanding and testing associated with impact analysis. For example, NASCAP2K can be used for sur-
face charging, DICTAT/NUMIT for internal charging, SHIELDOSE2 for total dose, CRÈME 96 for single
event effects, and NAIRAS for radiation effects on aviation. More information (including references) about
the models mentioned here can be found in section 7. The effect quantities have been found to correlate
with each type of observed effects/anomalies (e.g., O'Brien, 2009; Thomsen et al., 2013; Wrenn & Smith,
1996; Wrenn et al., 2002; Edmonds, Barnes and Scheick, 2000a JPL publication; NASAHDBK4002A,
NASAHDBK4006). In addition, the time scale relevant to the effects is noted in the last column. For
example, the integral ux of the greater than 10keV electrons is correlated well with surface charging
anomalies. From science perspective, the same quantity plus electron temperature and density will be
examined for model and data comparison. In contrast, internal charging is an accumulative effect over
a certain time period, such as a 24,48, and 72hr interval or even longer. The 100 fA/cm
[100 mils]
(meaning 100 femtoampere/cm
behind 100 mils aluminum shielding; 1 mil = 0.001 in. = 25.4 μm) is a
threshold for internal charging problems and will be used as an engineering quantity for analyzing
internal charging effects. Energetic electron ux at 1 MeV or the integral ux at greater than 2 MeV have been
selected from the science perspective. A point to note is that 100 mils is the nominal aluminum shielding
thickness and 4 mils (~100 μm) is the nominal cover glass thickness for solar cells onboard GEO spacecraft
using chemical propulsion orbit raising. Nowadays, Electric Orbit Raising is increasingly used for GEO
missions (e.g., Horne & Pitchford, 2015) and the extended period spent in the inner radiation belt means that
thicker cover glasses150 to 200 μm (6 to 8 mils) are becoming common. And for spacecraft operating in low
Medium Earth Orbit (such as SES's O3b constellation), much thicker cover glasses are often used (typically
600800 μm).
For SEEs, the science quantities for consideration are the >30MeV proton ux or the >15MeVcm
LET ux (as discussed above, LET has the advantage in that for a given particle and energy, the LET value
is nearly the same in different materials. However, it is not a perfect quantity for space weather modelers as
they need to take another model to transport ux through shielding and then calculate LET. It may be
replaced by the >100MeV/nucleon heavy ion ux). SEE rate behind 100mils aluminum spherical shielding
is used as an engineering quantity. Temporal scales of interest are 5 min, daily, or weekly. It should be noted
that SEE rate is energy, composition/species dependent (heavy ions pose greater concerns, yet observation-
ally their measurements are not readily available), and device dependent. For total dose effects, the 3050
MeV proton ux, the greater than 1.5MeV electron ux, and the 110MeV proton ux are the science
quantities for evaluation of the environment models. The dose in silicon behind different levels of shielding
such as 100 mils, 4 mils is the quantity for assessing the impact. Since total dose is an accumulated longterm
effect, the time scales of interest are daily, weekly, yearly, or mission lifetime. For radiation effects at aviation
altitude, geomagnetic shielding, atmospheric shielding, and the inuence of the solar wind need to be
considered. Dose rate or dose equivalent rate (e.g., the rates of the ambient dose equivalent and the absorbed
dose in silicon are used in Meier et al. (2018)) in aircraft is used for assessing the impact. For effective
communication with users in the aviation community, the Dindex, which is directly based on dose rates
by solar energetic particles in the atmosphere, has been suggested (Meier & Matthiä et al., 2014, 2018) instead
of the Sscales ( that are based on particle uxes with
energies above 10 MeV outside the atmosphere. Although the Sscales have been used by National
Oceanic and Atmospheric Administration/Space Weather Prediction Center for classication of the effects
of solar radiation storms (SEP events) on different infrastructures, their use for the radiation assessment at
aviation altitudes is rather limited due to the fact that the threshold value 10 MeV (for the integral ux) is
far too low for causing signicant radiation exposure deep in the atmosphere at ight altitudes. The Dindex
has been used as an operational quantity to inform airlines in Germany since 2014. Furthermore, it is also
used by the U.S. Federal Aviation Administration operating Maps of Ionizing Radiation in the
Atmosphere, the latest upgrade of its Solar Radiation Alert System, providing nearrealtime (lag of 510
min) calculations of dose rates in the atmosphere during solar proton events through the U.S. National
Oceanic and Atmospheric Administration's Weather Wire Service, which is publicly accessible (Copeland,
2018; Copeland et al., 2018).
Understandably, there are great complexities involved in assessing engineering effects (such as different
material dependencies and geometry), compounded by our still insufcient knowledge/lack of understand-
ing of the space environment's interactions with spacecraft (e.g., Hands et al., 2018) and inadequate testing.
Space Weather
The situation is even worse for quantifying impacts on humans. However, the quantities that have been
carefully selected in Table 1 should be able to serve as the starting set for tracking performance of space
environment models over time. Much like the 500mb constant pressure charts used by meteorologists, here
we are trying to nd the key parameters that can provide a quick glimpse of potential engineering effects.
Identifying the right parameters/quantities for corresponding space weather hazards is the crucial rst step,
as pointed out by Feynman and Gabriel (2000).
One of the commonalities among the ve types of different effects is that energy spectrum is needed for an
accurate assessment of the corresponding effects. In terms of energy spectra, power law distributions are
quite common and important for our understanding of natural and manmade phenomena. For power
law distribution in energy, it is often difcult to measure the tail end of the distribution (at very high
energies; e.g., Clauset et al., 2009). Also, the particles and plasmas relevant in this paper cover a wide range
of energies and exist in different regions of space; besides the power law energy spectra, there are other
varieties such as double power law (Mewaldt, 2006), relativistic kappalike distribution (Xiao et al., 2008),
and bump on tail (Zhao et al., 2017). How different types of energy spectra affect the validation results is
beyond the scope of this paper.
4.2. Metrics
Different types of metrics will be used to evaluate model performance. We will explore both the traditional
and relatively new ones (details can be found below). In addition, through collaboration, we will also
leverage the terrestrial weather forecast verication and model evaluation tools, such as the National
Center for Atmospheric Research's Model Evaluation Tool (MET) for our extended model verication and
validation efforts. Generally speaking, the types of metrics chosen should reect, and be relevant to, the types
of applications. The ultimate goal is to identify the metrics that matter most, which is expected to be an evol-
ving and iterative process.
We will start with the common ones where they are relevant.
4.2.1. Traditional Metrics RootMeanSquare Difference
One of the most meaningful and widely used ways to evaluate model performance is to calculate rootmean
square difference between the model estimates and observations dened as
RMS ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
where x
and x
are the observed and modeled values, respectively. Rootmeansquare (RMS) difference
has the same unit as observed and modeled values, x
and x
. Perfect model predictions have RMS
differences of 0. Therefore, the closer the RMS error is to 0, the more accurate the model is. Prediction Efciency
Prediction efciency, one of the skill scores against the mean of observations, is also commonly used to
describe performance of models:
PE ¼1
xobs<xobs >ðÞ
where x
and x
are again the observed and modeled values and <x
> is the mean value of the observed
measurements. In this study, the mean value of the observations <x
> was considered as a reference model
instead of any empirical model. The prediction efciency ranges from negative innity to 1. A prediction
efciency of 1 implies a perfect model performance, while a prediction efciency of 0 means that the model
performance is as accurate as the mean of the observed data. A negative value indicates that the observed
mean is a better predictor than the model. Ratio of the Maximum Change in Magnitudes and Ratio of the Maximum Magnitudes
The rootmeansquare error and prediction efciency measure how well time series observed data and mod-
eled values are correlated with each other. Metrics based on ratio are used to quantify the model capability to
produce peak values or shortterm variations during a certain period of time, even though performance of
Space Weather
model is poor in term of the RMS error and/or prediction efciency. The two types of ratio selected
were the ratio of the maximum change (maximum minus minimum values; max min, also called
prediction yield) and the ratio of the maximum (max) values of models to those of observations during
a certain time interval:
ratio maxminðÞ¼
ratio maxðÞ¼
where (x
and (x
are the maximum values of the observed and modeled quantities during a cer-
tain time window. Perfect models have a ratio of 1. The ratio of maxmin and the ratio of max larger than 1
overestimate maximum variations and maximum values. Note that the two ratios depend on the length of
time window. Ratio of the Event (or Over a Certain Duration) Sum
ratio sumðÞ¼
Such metrics may be used for comparing total accumulated dose type of quantities, say the dose over a ight. Relative Deviation and Mean Deviation
As used in Meier et al. (2018), the relative deviation of observed quantity to the modeled one can be dened
as follows:
The mean deviation Δfor a given event/interval (with n measurements) can be dened as
n Correlation Coefcient
It is a numerical measure of a statistical relationship between two variables. The Pearson correlation
coefcient, r, is often used, dened as the covariance of the variables divided by the product of their standard
q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
rcan take a range of values from +1 to 1, with 0 indicating that there is no association, a value greater than
0 indicating a positive association, and a value less than 0 indicating a negative association.
It should be mentioned that for ux (data that cover orders of magnitude) type of model and data
comparison, the metrics above should be performed after applying the logarithmic calculation. For dose type
of quantities, there is no such need. Categorical Skill Scores
Threshold based (yes/no prediction)For example, for surface charging, whether the >10keV ux exceeds
a certain threshold 1.5 × 10
1. Heidke Skill Score (HSS)This is suitable when there are many events.The HSS calls for generation of a
contingency table of hit (H), miss (M), false positive (F), and correct negative (N) model predictions.
Their denition is as follows.
Space Weather
Hit: both observation and model exceed the threshold at least once in a time window
Miss: observation exceeds the threshold but model does not exceed threshold at least once in a window
False positive: model does exceed threshold at least once in a window, but observation does not
Correct negative: both observation and model do not exceed threshold in a window
Probability of detection: H/(H + M)
Probability of false detection: F/(F + N)
Heidke Skill Score: HSS = 2(HN MF)/[(H + M) (M + N) + (H + F) (F + N)]The HSS measures the
fractional improvement of the forecast over the standard forecast. Like most skill scores, it is normalized
by the total range of possible improvement over the standard, which means that Heidke Skill scores can
safely be compared on different data sets. The range of the HSS is to 1. Negative values indicate that
the chance forecast is better, 0 means no skill, and a perfect forecast obtains a HSS of 1. The HSS is a
popular score, partly because it is relatively easy to compute and perhaps also because the standard
forecast, chance, is relatively easy to beat.
For example, Ganushkina et al. (2015, 2019) used HSS (it is called binary event tables/analysis there) for eval-
uating the performance of their nowcast model for lowenergy electrons in the inner magnetosphere that
could constitute surface charging risks.
4.2.2. Novel Metrics Metrics Based on the Log Accuracy Ratio
In Morley et al. (2018), metrics based on the log accuracy ratio have been suggested. Two useful ones are the
Median Symmetric Accuracy and the Symmetric Signed Percentage Bias (SSPB). The advantages of them
include the following: (1) they are meaningful for data spanning several orders of magnitude, (2) underpre-
diction and overprediction by the same factor are penalized equally, (3) they are easy to interpret, and (4)
they are robust to the presence of outliers and bad data. The Median Symmetric Accuracy
ς¼100 exp MlogeQi
where Qi¼xmod
xobs is the ratio of predicted versus observed, x
is the model, x
is the observation, and M is
the median value.
The median symmetric accuracy (ζ) is equivalent to the median percentage error. The SSPB
SSPB ¼100 sgn MlogeQi
exp MlogeQi
where Sgn is the signum function and M is the median value.
The SSPB can therefore be interpreted similarly to a mean percentage error but is not affected by the
likely asymmetry in the distribution of percentage error and robustly estimates the central tendency of
the error. Statistical Metrics
Given the chosen environmental quantity, the 75th and 97th percentiles (or other values) can be
selected as the threshold values for dening green and red (hazard indicators) type of risks (by these
denitions, the environment is green 75% of the time, yellow 22% of the time, and red 3% of the time).
Computing the percentile value for both the observed and modeled quantity and examining their
difference are the required steps. This type of metrics assesses both observed and modeled quantity's
statistical signicance in their entire distribution space/time. This type of metrics validates the modeled
quantities' role from a longterm, missionaveraged perspective (its current percentile over a long period
of time, whether it is in a green, yellow, or red zone), not just to validate a ux value, dose rate, or
induced current. Its principle is similar to what is done in O'Brien (2009). Such metrics is not likely
to be a rst choice as it requires data of a long time period and running a model over the same long
period correspondingly.
Space Weather
4.2.3. Selecting Proper Metrics
Which metrics to use depends on the physical quantities (whether varying over several orders of magnitude
or not) and types of models. However, for models of similar nature, same sets of metrics should be applied
and compared. For example, the initial model validation work (Yu et al., 2019) on surface charging used
the 1050keV electron ux as the quantity for comparison. Different types of metrics were employed to
evaluate model performance, including crosscorrelation, prediction efciency, rootmeansquare error,
prediction yield, and the symmetric signed percentage bias but all were done to the logarithmic value of
uxes. In contrast, the initial validation work (Meier et al., 2018) of aviation radiation models used the
simple relative deviation in the ambient dose equivalent rate dH*(10)/dt and in the absorbed dose rate in sili-
con dD
/dt. The outcome/measure of metrics also depends on other factors such as boundary conditions
and whether additional data are used in a particular model. All factors should be kept in mind for the
fairness of the validation results. The CAMEL (Comprehensive Assessment of Models and Events based
on Library tools) system to be discussed next (section 5) will provide choices of different metrics (could be
one or more) that are suitable for quantities/models at consideration (Rastätter et al., 2019). Besides metrics
evaluation for individual events, evaluation of a model performance over multiple events or an extended
time interval will also be carried out. Statistical signicance of different metrics will depend on the duration
of an individual event or whether metrics itself is dened/based on many events (such as Heidke Skill Score
or Statistical Metrics mentioned above).
The example below demonstrates that the choice of appropriate metrics depends on the chosen physical
quantities or applications at hand.
Figure 4 shows the absorbed dose in silicon computed from the Numerical Optimizations, Visualizations,
and Integrations on Computer Aided Design/Constructive Solid Geometry Edices (also known as
NOVICE) model (Jordan, 1976) for different thicknesses (indicated by different colors) of aluminum shield-
ing for the year 2012. Particle spectra used in the NOVICE model are taken from GOES measurements. From
the plot, we can see clearly the episodic nature of several SEP events during the year.
Figure 5 shows the accumulated dose for the same year for different levels of shielding using the same GOES
spectra data. We can see that the accumulated dose prole does not change much after the major SEP events
in January and March of 2012.
Obviously, metrics suitable for model validation efforts for doses of individual events in Figure 4 and for the
accumulated dose over a long time period in Figure 5 will be rather different with the former calling for
median symmetric accuracytype of metrics and the latter calling for mean deviationtype ones.
Additionally, to reect a model's performance from different perspectives, different metrics should be
explored. For example, one model on SEPs that captures well the thinshielding situations may not perform
well for thick shielding. Similarly, a model may perform well in terms of capturing the highenergy tail but
may suffer at the lower energy end.
5. Community Coordinated Modeling Center Resource: CAMEL
One resource relevant to model validation is the CAMEL system that has been under development. It is a
framework to combine tools to perform model execution, postprocessing, and model evaluation. For details,
please see the CAMEL paper of this special issue (Rastätter et al., 2019). This tool stores model outputs and
observations for all validation studies, plots model and observations together, has builtin variety of metrics,
and is to incorporate features of the National Center for Atmospheric Research's Model Evaluation Tool
(MET) through partnership with Tara Jensen, Barb Brown et al. (Jensen & Brown, 2018). MET is a verica-
tion toolkit designed for exible yet systematic evaluation for terrestrial weather forecast.
6. NASA Standard for Models and Simulations
NASASTD7009A ( is a Technical Standard
published by the National Aeronautics and Space Administration (NASA) to provide uniform engineering
and technical requirements for processes, procedures, practices, and methods endorsed as standard for
models and simulations developed and used in NASA programs and projects, including requirements for
selection, application, and design criteria of an item.Although this document is more or less intended for
Space Weather
the engineering community, a majority of the elements covered (e.g., Data Pedigree, Verication, Validation,
Input Pedigree, Uncertainty Characterization, Robustness) are also applicable to space environment/space
science model verication and validation endeavors and can serve as a starting point. Figures 6 and 7 are
different representations of Credibility Assessment taken from the document. These elements are also
important considerations with validations of space environment models, facilitating standardization of the
model assessment processes.
Figure 5. The accumulated dose prole in silicon for different level of aluminum shielding (image credit: JeanPaul Breuer).
Figure 4. The absorbed dose in silicon for aluminum shielding of different thicknesses (image credit: JeanPaul Breuer).
Space Weather
7. Initial Set of Space Environment Models and Effect Models
To start the validation efforts, the working team has identied an initial set of potential space
environment models for each subtopic area. It should be noted that this is intended to be an evolving
community effort. Models do not need to be hosted at Community Coordinated Modeling Center
(CCMC) to participate. New models or newer versions of existing models with more capabilities are
expected/urged to join once ready. All participating models will be documented (with the version control)
and archived at CCMC's Metadata Registry (,
constantly being updated.
Effect models are not the focus except those atmospheric radiation models for aviation. Once an effect model
is chosen, it should/will be used as the unifying translation tool to be applied across all space
environment models.
7.1. Surface Charging
Space environment models of initial focus are the following: Ovation Prime of the CCMC implementation
( for characterizing aurora
(Newell et al., 2010); the Ring currentAtmosphere interaction Model and SelfConsistent Magnetic Field (B)
(e.g., Jordanova et al., 2010), and its variants (such its coupling with the
Space Weather Modeling Framework; e.g., Yu et al., 2016); the
Comprehensive InnerMagnetosphere Ionosphere Model (Fok et al.,
2014), and its variants (Glocer et al., 2011, 2013); and the Inner
Magnetosphere Particle Transport and Acceleration Model (e.g.,
Ganushkina et al., 2015). Yu et al. (2019) showcase the initial progress in
surface charging related validation effort.
7.1.1. Spacecraft Charging Models
Known charging codes include the NASCAP2k (NASA/Air Force
Spacecraft Charging Analyzer Program; Rubin et al., 1980; Davis &
Mandell, 2014); SPIS (Spacecraft Plasma Interaction Software; http://, MUSCAT (MultiUtility
Spacecraft Charging Analysis Tool; e.g., Muranaka et al., 2007; Hosoda
et al., 2008), and other small group ones. Other engineering effect codes
relevant to surface charging can be found at SPENVIS (the Space
ENVironment Information System;
Figure 6. Bar graph of credibility assessment.
Figure 7. Spider plot or radar plot of credibility assessment.
Space Weather
7.2. Internal Charging
7.2.1. Environment Models
The models that have high probability of running benchmarks soon include physicsbased models such as
the Comprehensive InnerMagnetosphere Ionosphere (Fok et al., 2014), Versatile Electron Radiation Belt
code (Shprits et al., 2009; Subbotin and Shprits, 2009), DREAM (a data assimilative model, Reeves et al.,
2012), the British Antarctic Survey model (Glauert et al., 2014; Horne et al., 2018), and Salammbo (e.g.,
Beutier et al., 1995; Bourdarie et al., 2005) and empirical models such as CRRESELE (Brautigam & Bell,
1995). Other more orbitspecic (e.g., GEO) models include the Geosynchronous Radiationbelt Electron
Empirical Prediction model (Kellerman et al., 2013), Relativistic Electron Forecast Model (running at the
Space Weather Prediction Center;
model , Baker et al., 1990), the Ukhorskiy model (Ukhorskiy et al., 2004), the model using Nonlinear
Autoregressive Moving Average modeling algorithm (
model=SNB3GEO; Balikhin et al., 2011; Boynton et al., 2013), and the Li et al. model (e.g., Li et al.,
7.2.2. Effect Models
Internal charging codes such as NUMIT (Jun et al., 2008; Kim et al., 2017), DICTAT (Rodgers, 1999), and
SHIELDOSE2 (Seltzer, 1994) can be used as the translation tool. DICTAT is to be superseded by MCICT
(Monte Carlo Internal Charging Tool; Lei et al., 2016).
7.3. Total Dose
Since the main contributors for total dose are electrons >100 keV and protons >1 MeV, with the former
mostly of trapped electrons in the Earth's radiation belts and the latter mostly of solar origin, the correspond-
ing initial set of environment models are as follows.
7.3.1. Environment Models
The empirical ones for the trapped population include AE8/AP8 (e.g., Sawyer & Vette, 1976; Vampola, 1996;
Vette, 1991), AE9/AP9/SPM (Ginet et al., 2013), IGE2006/POLE (Boscher et al., 2003; SicardPiet et al., 2006,
2008), CRESSELE, and CRESSPRO (Gussenhoven et al., 1994). The empirical ones for particles of solar ori-
gin are the King (King, 1974) model, JPL91 (Feynman et al., 1993), Emission of Solar Protons/Prediction of
Solar particle Yields for CHaracterizing Integrated Circuits (ESP/PSYCHIC model, Xapsos et al., 1999, 2000,
2007), and Solar Accumulated and Peak Proton and Heavy Ion Radiation Environment (SAPPHIRE
model, Jiggens et al., 2018).
The physicsbased models for the trapped population are the same as those for the internal charging
(section 7.2). For the solar population modeling, there is SOLar Particle ENgineering Code
(SOLPENCO, Aran et al., 2005, 2006). Other potential SEP models include those participating in the SEP
scoreboard (, such as COronal Mass Ejections and Solar
Energetic Particles (COMESEP model, Crosby et al., 2012), SEPForecast, Forecasting Solar Particle Events
and Flares (FORSPEF model, Anastasiadis et al., 2017; Papaioannou et al., 2018), UMASEP (Núñez, 2011,
2015; Núñez et al., 2017), PREDICCS (; Schwadron et al., 2010), AER SEP model
(Winter et al., 2015), SPRINTS (Engell et al., 2017), and REleASE/HighEnergy Solar Particle Events
foRecastIng and Analysis (e.g., Posner et al., 2007; Malandraki and Crosby, 2018).
Empirical (climatological) models are typically used for total dose calculation for a mission, for example,
AP9/AE9 for trapped particles and JPL/ESP for solar protons
7.3.2. Effect Models
Effect models include the NOVICE code (Jordan, 1976), SHIELDOSE2 for total ionizing dose calculation,
and EQFLUX and MCSCREAM (as done in Hands et al., 2018) for displacement damage dose estimate.
7.4. SingleEvent Effects
7.4.1. Environment Models
For the trapped protons, we have AP9 (also AP8 still used in some standards), PSB97, and its updated version
(based on SAMPEX/PET data; Heynderickx et al., 1999). For SEP models, we have the ESP/PSYCHIC, JPL,
MSU (Nymmik, 1999, 2007), and SAPPHIRE models. As mentioned above, there are a variety of models
involved in the SEP Scoreboard activities and the SEP Working Team of the International forum (https:// Commonly used GCR models include the ISO15390
GCR model, BadhwarO'Neill (Badhwar & O'Neill, 1996; O'Neill et al., 2011), and the Deutsches Zentrum
Space Weather
für Luftund Raumfahrt‐‐German Aerospace Center GCR model (Matthiä et al., 2013). Existing models to
assess the SEPs and GCRs' access to the nearEarth region due to magnetic eld shielding include the
Energetic Solar Heavy Ion Environment ModelsMagnetospheric Shielding Model (Lei, 2017) and the
Smart and Shea model (e.g., Smart & Shea, 1994, 2001, 2003).
7.4.2. Effect Models
For the SEE rate calculation, the CRÈME96 software package can be used (https://creme.isde.vanderbilt.
edu/; Tylka et al., 1997). Other models include those at SPENVIS.
7.5. Radiation Effects at Aviation Altitudes
7.5.1. Environment Models
All the models discussed above regarding SEPs and GCRs also apply here.
7.5.2. Effect Models
First steps have been taken in verication of models assessing radiation exposure at aviation altitudes (Meier
et al., 2018, this special issue). The participating models are CARI7A at Federal Aviation Administration
(e.g., Copeland, 2017), PANDOCA (e.g., Matthiä et al., 2014), and NAIRAS (Mertens et al., 2010, 2013).
Other possible models include AVIDOS (e.g., Latocha et al., 2009), QARM (e.g., Lei et al., 2006), KREAM
(Hwang et al., 2014), EPCARD. Net (the European Program Package for the Calculation of Aviation Route
Doses) e.g., Mares et al., 2009; Schraube et al., 2002), and MAIRE (
Additional ones are also mentioned in Tobiska et al. (2015) and Matthiä et al. (2014).
8. Initial Progress
The working team has made some initial progress. In the area of surface charging, some preliminary model
validation work has been carried out using the identied physical quantity and a corresponding paper is
included in this special issue (Yu et al., 2019). Ganushkina et al., 2019 (also in this special issue) presents
validation work done with IMPTAM where the HSStype binary event analysis metrics, the median
symmetric accuracy, and symmetric signed percentage bias were employed. In the area of internal charging,
two major events/periods where internal charging anomalies occurred have been selected. Two
manuscripts in the area of radiation effects at aviation altitudes have been published as part of this special
issue (Meier et al., 2018; Tobiska et al., 2018). In Meier et al. (2018), the mean deviation was used as a
metrics for validating models for the assessment of the radiation exposure at aviation altitudes. For total
dose effects, due to its longterm and accumulative nature, the team has decided to start with how changes
in orbit, such as electric orbit raising (usually taking about six months), affect total dose a satellite receives
Figure 8. The importance of the model validation efforts with two sets of physical quantities.
Space Weather
during the duration. In comparison to the other subtopic areas, total dose has some unique aspects in that it
is a climatological quantity, not so much a space weather quantity. Total dose estimate for a mission uses a
longterm average environment, not the worstcase environment. Quantities that are needed for computing
total dose include trapped electron and proton uence spectra and SEP uence spectra for the duration of a
mission. For singleevent effects, we will start with assessment of rigidity cutoff models. Presentations and
relevant documents can be found in our Google drive (
0Bxc9VBElGQoga2JxRVkta1ZIVXM). In general, the focus team has recognized the importance of energy
spectra in leading to a quantitative estimate of engineering impacts discussed in this paper.
9. Summary and Future Outlook
With recognition of the complexity (needs knowledge of environment, shielding characteristics, device
effects/response, and so on) involved in assessing how space environment affects space assets (both technol-
ogy and humans), we mainly focus on performance of space environment models but with potential impacts
in mind. The quantities chosen for validation have impact bearing and can be qualitatively translated into
impact information. Besides calling the community's attention to this rather new type of validation, we hope
the quantities identied in Table 1 can serve as a starting point (eventually leading to denition of the stan-
dard) in tracking space environment models' usefulness and performance in space weather operations.
Figure 8 summarizes the goal of the effort and puts its importance in a global context in terms of bridging
different communities (users in the diagram bears a more general meaning) together. Such effort is indispen-
sable in the research to operations and operations to research arena. Spurring from such initiative, CCMC is
building a model inventory (through its Metadata Registry as mentioned above) where specics of space
environment models are documented, such as the version, input, output, language, running platforms,
usage/capability, and caveats. Such validation efforts are expected to be archived, either linked to the model
inventory or be part of the Metadata Registry, with the ultimate goal of tracking model performance over
time for the benet of different types of users.
Anastasiadis, A., Papaioannou, A., Sandberg, I., Georgoulis, M., Tziotziou, K., Kouloumvakos, A., Jiggens, P. (2017). Predicting Flares and
Solar Energetic Particle Events: The FORSPEF Tool. Solar Physics,292(134).
Aran, A., Sanahuja, B., & Lario, D. (2005). Fluxes and uences of SEP events derived from SOLPENCO. Annales de Geophysique ,23,
Aran, A., Sanahuja, B., & Lario, D. (2006). SOLPENCO: A solar particle engineering code. Advances in Space Research,37(6), 12401246.
Badhwar, G. D., & O'Neill, P. M. (1996). Galactic cosmic radiation model and its applications. Advances in Space Research,17(2), 717.
Baker, D. N., McPherron, R. L., Cayton, T. E., & Klebesadel, R. W. (1990). Linear prediction lter analysis of relativistic electron properties
at 6.6 R
.Journal of Geophysical Research,95(A9), 15,13315,140.
Balikhin, M. A., Boynton, R. J., Walker, S. N., Borovsky, J. E., Billings, S. A., & Wei, H. L. (2011). Using the NARMAX approach to model the
evolution of energetic electrons uxes at geostationary orbit. Geophysical Research Letters,38, L18105.
Beutier, T., & Boscher, D. (1995). A threedimensional analysis of the electron radiation belt by the Salammbô code, J. Geophys. Res.,100, 14,
Bodeau, M. (2005). Going beyond anomalies to engineering corrective action, new IESD guildelines derived from a rootcause investigation.
In 2005 Space Environmental Effects Working Group. Aerospace Corporation, El Segundo, CA.
Bodeau, M. (2010). High energy electron climatology that supports deep charging risk assessment in GEO, AIAA 20101608. Paper pre-
sented at 48
AIAA Aerospace Sciences Meeting, Orlando, FL.
Boscher, D. M., Bourdarie, S. A., Friedel, R. H. W., & Belian, R. D. (2003). A model for the geostationary electron environment: POLE. IEEE
Transactions on Nuclear Science,50(6), 22782283.
Bourdarie, S., Friedel, R. H. W., Fennell, J., Kanekal, S., & Cayton, T. E. (2005). Radiation belt representation of the energetic electron
environment: Model and data synthesis using the Salammbô radiation belt transport code and Los Alamos geosynchronous and GPS
energetic particle data, Space Weather, 3, S04S01,
Boynton, R. J., Balikhin, M. A., Billings, S. A., & Amariutei, O. A. (2013). Application of nonlinear autoregressive moving average exogenous
input models to geospace: Advances in understanding and space weather forecasts. Annales de Geophysique,31(9), 15791589. https://
Brautigam, D. H., & Bell, J. T. (1995). CRRES electron omnidirectional ux models and CRRESELE utility, Proceedings of 1995 IEEE
Nuclear and Space Radiation Effects Conference (NSREC'95), Madison, WI, USA, 1995, pp. 90.doi:
Chancellor, J., Scott, G., & Sutton, J. (2014). Space radiation: The number one risk to astronaut health beyond low Earth orbit. Life,4(3),
Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009). Powerlaw distributions in empirical data. SIAM Review,51(4), 661703.
Cochran, D. J., Buchner, S. P., Chen, D., Kim, H. S., LaBel, K. A., Oldham, T. R., et al. (2009). Total ionizing dose and displacement damage
compendium of candidate spacecraft electronics for NASA, 2009 IEEE Radiation Effects Data Workshop, Quebec City, QC, pp. 2531.
Space Weather
The authors thank the two reviewers
and the Editor for their detailed and
constructive comments that have
improved this paper. W. K. Tobiska
acknowledges the nancial support for
DECISION Project Contract
NNL07AA00C, NASA SBIR Phase I and
Phase II Program Contracts
NNX11CH03P and NNX12CA78C,
NASA AFRC Phase III Contracts
NND14SA64P and NND15SA55C, the
80NSSC18K0187, the NASA FO
ARMASHi Project NND17AP04A, the
80NSSC18P2111, and South Korean
Space Weather Center matching funds
for the ARMAS SBIR Phase IIE. The
work of N. Ganushkina at the
University of Michigan was partly
supported by the National Aeronautics
and Space Administration under Grant
Agreement NNX17AI48G and by the
National Science Foundation under
Grant Agreement NSF 1663770. The
data and materials (mostly just
materials that are needed for
clarication and enhancement of the
paper) presented in the paper are
provided with links/references for their
Copeland, K. (2017). CARI7A: Development and validation. Radiation Protection Dosimetry,178(4), 419431.
Copeland, K. (2018). MIRA 2017: A CARI7 based solar radiation alert system (Report DOT/FAA/AM18/6). Washington, DC: Federal
Aviation Administration Ofce of Aerospace Medicine.
Copeland, K., Matthiä, D., & Meier, M. M. (2018). Solar cosmic ray dose rate assessments during GLE 72 using MIRA and PANDOCA. Space
Weather,16, 969976.
Crosby, N. B., Veronig, A., Robbrecht, E., Vrsnak, B., Vennerstrom, S., Malandraki, O., et al., & Comesep Consortium (2012). Forecasting
the space weather impact: The COMESEP project. In Q. Hu, et al. (Eds.), Space Weather: The Space Radiation Environment: 11th Annual
International Astrophysics Conference, Am. Inst. of Phys. Conf. Ser. (Vol. 1500, pp. 159164). College Park, Md: Am. Inst. Phys. https://
Daly, E., Glover, A., & Hilgers, A. (2007). Effects on spacecraft hardware and operations. In Space Weather: Physics and effects. Springer
Praxis Books (Chapter 12, pp. 353381). Berlin, Heidelberg: Springer.
Davis, V. A., & Mandell, M. J. (2014). Nascap2k scientic documentation for version 4.2, rev. ed, (p. 4). San Diego, CA: Leidos.
Dyer, C., Hands, A., Ryden, K., & Lei, F. (2018). Extreme atmospheric radiation environments and single event effects. IEEE Transactions on
Nuclear Science,65(1), 432438.
Dyer, C. S., & Truscott, P. R. (1999). Cosmic radiation effects on avionics. Microprocessors and Microsystems,22(8), 477483.
Edmonds, L. D., Barnes, C. E., & Scheick, L. Z. (2000). An introduction to space radiation effects on microelectronics, JPL publication 0006.
Pasadena, California: Jet Propulsion Laboratory, California Institute of Technology.
Engell, A. J., Falconer, D. A., Schuh, M., Loomis, J., & Bissett, D. ( 2017). SPRINTS: A framework for solardriven event forecasting and
research. Space Weather,15, 13211346.
Ferguson, D. C. (1985). Ramwake effects on plasma current collection of the PIX 2 Langmuir probe, Spacecraft Environment Interactions
Technology, pp. 349357.
Ferguson, D. C., & Hillard, G. B. (2003). Low Earth orbit spacecraft charging design guidelines. NASA/TP2003212287.
Ferguson, D. C., Hilmer, R. V., & Davis, V. A. (2015). Best geosynchronous Earth orbit daytime spacecraft charging index. Journal of
Spacecraft and Rockets,52(2), 526543.
Feynman, J., & Gabriel, S. B. (2000). On space weather consequences and predictions. Journal of Geophysical Research,105(A5),
Feynman, J., Spitale, G., Wang, J., & Gabriel, S. ( 1993), Interplanetary proton uence model: JPL 1991, Journal Geophys. Res.,98( A8),
Fok, M.C., Buzulukova, N. Y., Chen, S.H., Glocer, A., Nagai, T., Valek, P., & Perez, J. D. (2014). The Comprehensive Inner Magnetosphere
Ionosphere Model. Journal of Geophysical Research: Space Physics,119, 75227540.
Frooninckx, T. B., & Sojka, J. J. (1992). Solar cycle dependence of spacecraft charging in low Earth orbit. Journal of Geophysical Research,
97(A3), 29852996.
Ganushkina, N., Jaynes, A., & Liemohn, M. (2017). Space weather effects produced by the ring current particles. Space Science Reviews,
212(34), 13151344.
Ganushkina, N. Y., Amariutei, O. A., Welling, D., & Heynderickx, D. (2015). Nowcast model for lowenergy electrons in the inner mag-
netosphere. Space Weather,13,1634.
Ganushkina, N. Y., Sillanpää, I., Welling, D. T., Haiducek, J. D., Liemohn, M. W., Dubyagin, S., & Rodriguez, J. V. (2019). Validation of
Inner Magnetosphere Particle Transport and Acceleration Model (IMPTAM) with longterm GOES MAGED measurements of keV
electron uxes at geostationary orbit. Space Weather,17, 687708.
Garrett, H. B. (2016). In G. V. Khazanov (Ed.), Spacecraft charging in the book of Space Weather Fundamenta ls. eBook ISBN
9781498749084 Boca Raton, London, New York: CRC Press, Taylor & Francis Group
Garrett, H. B., & Whittlesey, A. C. (2012). Guide to mitigating spacecraft charging effects. Hoboken, NJ: John Wiley & Sons.
Ginet, G. P., O'Brien, T. P., Huston, S. L., Johnston, W. R., Guild, T. B., Friedel, R., et al. (2013). AE9, AP9 and SPM: New models for spe-
cifying the trapped energetic particle and space plasma environment. Space Science Reviews,179(14), 579615.
Glauert, S. A., Horne, R. B., & Meredith, N. P. (2014), Threedimensional electron radiation belt simulations using the BAS Radiation Belt
Model with new diffusion models for chorus, plasmaspheric hiss, and lightninggenerated whistlers. Journal of Geophysical Research:
Space Physics,119, 268289,
Glocer, A., Fok, M., Meng, X., Toth, G., Buzulukova, N., Chen, S., & Lin, K. (2013). CRCM + BATSRUS two way coupling. Journal of
Geophysical Research: Space Physics,118, 16351650.
Glocer, A., Fok, M.C., Nagai, T., Tóth, G., Guild, T., & Blake, J. (2011). Rapid rebuilding of the outer radiation belt. Journal of Geophysical
Research,116, A09213.
Gussenhoven, M. S., Mullen, E. G., & Brautigam, D. H. (1994). NearEarth radiation model deciencies as seen on CRRES. Advances in
Space Research,14, 927491.
Hands, A. D. P., Ryden, K. A., Meredith, N. P., Glauert, S. A., & Horne, R. B. (2018). Radiation effects on satellites during extreme space
weather events. Space Weather,16, 12161226.
Heynderickx, D., Kruglanski, M., Pierrard, V., Lemaire, J., Looper, M. D., & Blake, J. B. (1999). A low altitude trapped proton model for solar
minimum conditions based on SAMPEX/PET data. IEEE Transactions on Nuclear Science,46(6), 14751480.
Horne, R. B., Phillips, M. W., Glauert, S. A., Meredith, N. P., Hands, A. D. P., Ryden, K., & Li, W. (2018). Realistic worst case for a
severe space weather event driven by a fast solar wind stream. Space Weather,16, 12021215.
Horne, R. B., & Pitchford, D. (2015). Space weather concerns for allelectric propulsion satellites. Space Weather,13, 430433. https://doi.
Hosoda, S., Muranaka, T., Kuninaka, H., Kim, J., Hatta, S., Kurahara, N., et al. (2008). Laboratory experiments for code validation of
Multiutility Spacecraft Charging Analysis Tool (MUSCAT). IEEE Transactions on Plasma Science,36(5), 23502359.
Hwang, J., Dokgo, K., Choi, E., Kin, K.C., Kim, H.P., & Cho, K.S. (2014). Korean Radiation Exposure Assessment Model for aviation route
dose: KREAM, KSS Fall meeting, Jeju, Korea, 2931 Oct.
Jensen, Tara, & Brown, B. (2018). Terrestrial weather forecast verication and model evalua tion tools, 2018 CCMC Workshop, College Park,
MD, 2327 April 2018.
Space Weather
Jiggens, P., Heynderickx, D., Sandberg, I., Truscott, P., Raukunen, O., & Vainio, R. (2018). Updated model of the solar energetic proton
environment in space. Journal of Space Weather and Space Climate,8, A31.
Jordanova, V. K., Zaharia, S., & Welling, D. T. (2010). Comparative study of ring current development using empirical, dipolar, and self
consistent magnetic eld simulations. Journal of Geophysical Research,115, A00J11.
Jordan, T. M. (1976), An adjoint charged particle transport method. IEEE Transactions on Nuclear Science,23(6), 18571861. https://doi.
Jun, I., Garrett, H. B., Kim, W., & Minow, J. (2008). Review of an internal charging code, NUMIT. IEEE Transactions on Plasma Science,36,
Jun, I., Xapsos, M. A., Messenger, S. R., Burke, E. A., Walters, R. J., Summers, G. P., & Jordan, T. (2003). Proton nonionizing energy loss
(NIEL) for device applications. IEEE Transactions on Nuclear Science,50(6), 19241928.
Kellerman, A. C., Shprits, Y. Y., & Turner, D. L. (2013). A Geosynchronous Radiationbelt Electron Empirical Prediction (GREEP) model.
Space Weather,11, 463475.
Kim, W., Chinn, J. Z., Katz, I., Garrett, H. B., & Wong, K. F. (2017). 3D NUMIT: A general 3D internal charging code. IEEE Transactions on
Plasma Science,45(8), 22982302.
King, J. H. (1974), Solar proton uences for 19771983 space missions. Journal of Spacecraft and Rockets,11, 401.
Koons, H. C., Mazur, J. E., Selesnick, R. S., Blake, J. B., Fennell, J. F., Roeder, J. L., & Anderson, P. C. (2000). The impact of space weather
environment on space systems. In 6
Spacecraft Charging Technology Conference. AFRL/USAF, Bedford, MA.
Latocha, M., Beck, P., & Rollet, S. (2009). AVIDOSA software package for European accredited aviation dosimetry. Radiation Protection
Dosimetry,136(4), 286290.
Lei, F. (2017). (RadMod Research), ESHIEM Project (ESA Contract 4000107025/12/NL/GLC): Technical Note 2a Magnetosphere Sh ielding
Model (MSM),v1.5.
Lei, F., Hands, A., Dyer, C., & Truscott, P. (2006). Improvements to and validations of the QinetiQ Atmospheric Radiation Model (QARM).
IEEE Transactions on Nuclear Science,53(4), 18511858.
Lei, F., Rodgers, D., & Truscott, P. (2016). MCICTMonte Carlo Internal Charging Tool, 14th Spacecraft Charging Techno logy Conference,
ESA/ESTEC, Noordwijk, NL, 0408 April 2016
Li, X., Temerin, M., Baker, D. N., Reeves, G. D., & Larson, D. (2001). Quantitative prediction of radiation belt electrons at geostationary orbit
based on solar wind measurements. Geophysical Research Letters,28(9), 18871890.
Malandraki, O. E., & Crosby, N. B. (2018). The HESPERIA HORIZON 2020 project and book on solar particle radiation storms forecasting
and analysis. Space Weather,16, 591592.
Mares, V., Maczka, T., Leuthold, G., & Rühm, W. (2009). Air crew dosimetry with a new version of EPCARD. Radiation Protection
Dosimetry,136(4), 262266.
MatéoVélez, J.C., Sicard, A., Payan, D., Ganushkina, N., Meredith, N. P., & Sillanpäa, I. (2018). Spacecraft surface charging induced by
severe environments at geosynchronous orbit. Space Weather,16,89106.
Matthiä, D., Berger, T., Mrigakshi, A. I., & Reitz, G. (2013). A readytouse galactic cosmic ray model. Advances in Space Research,51(3),
329338, ISSN 02731177.
Matthiä, D., Meier, M. M., & Reitz, G. (2014). Numerical calculation of the radiation expos ure from galactic cosmic rays at aviation altitudes
with the PANDOCA core model. Space Weather,12, 161171.
Mazur, J. E., Zeitlin, C., Schwadron, N., Looper, M. D., Townsend, L. W., Blake, J. B., & Spence, H. (2015). Update on radiation dose from
galactic and solar protons at the Moon using the LRO/CRaTER microdosimeter. Space Weather,13, 363364.
Meier, M. M., Copeland, K., Matthiä, D., Mertens, C. J., & Schennetten, K. (2018). First steps toward the verication of models for the
assessment of the radiation exposure at aviation altitudes during quiet space weather conditions. Space Weather,16, 12691276. https://
Meier, M. M., & Matthiä, D. (2014). A space weather index for the radiation eld at aviation altitudes. Journal of Space Weather and Space
Climate,4, A13.
Meier, M. M., & Matthiä, D. (2018). Classication and Communication of Aviation Related Space Weather RadiationEvents. SF J Aviation
Aeronaut Sci., (2018) 1(1),
Mertens, C. J., Kress, B. T., Wiltberger, M., Blattnig, S. R., Slaba, T. C., Solomon, S. C., & Engel, M. (2010). Geomagnetic inuence on aircraft
radiation exposure during a solar energetic particle event in October 2003. Space Weather,8, S03006.
Mertens, C. J., Meier, M. M., Brown, S., Norman, R. B., & Xu, X. (2013). NAIRAS aircraft radiation model development, dose climatology,
and initial validation. Space Weather,11, 603635.
Messenger, S. R., E. A. Burke, M. A. Xapsos, G. P. Summers and R. J. Walters (2004), The Simulation of damage tracks in silicon, IEEE
Transactions on Nuclear Science, Vol. 51, no. 5, pp. 28462850.
Messenger, S. R., Summers, G. P., Burke, E. A., Walters, R. J., & Xapsos, M. A. (2001). Modeling solar cell degradation in space: A com-
parison of the NRL displacement damage dose and the JPL equivalent uence approaches. Progress in Photovoltaics: Research and
Applications,9(2), 103121.
Messenger, S. R., Wong, F., Hoang, B., Cress, C. D., Walters, R. J., Kluever, C. A., & Jones, G. (2014). LowThrust Geostationary Transfer
Orbit (LT2GEO) radiation environment and associated solar array degradation modeling and ground testing. IEEE Transactions on
Nuclear Science,61(6), 33483355.
Mewaldt, R. A. (2006). Solar energetic particle composition, energy spectra, and space weather. Space Science Reviews,124, 303316.
Morley, S. K., Brito, T. V., & Welling, D. T. (2018). Measures of model performance based on the log accuracy ratio. Space Weather,16,6988.
Muranaka, T., Hosoda, S., Hatta, S., Kim, J., Ikeda, K., Hamanaga, T., et al. (2007). Development of multiutility spacecraft charging analy sis
tool (MUSCAT), in Proc. 10th Spacecr. Charging Technol. Conf., Biarritz, France.
NASAHDBK4002A w/CHANGE 1 (2017). Mitigating inspace charging effectsA guideline. NASAHDBK4002A. Washington, DC:
National Aeronautics and Space Administration. Approved: 03032011; REVALIDATED 20171019
NASAHDBK4006 (2007). Low Earth orbit spacecraft charging design handbook, NASA TECHNICAL HANDBOOK, Approved: 06032007.
National Aeronautics and Space Administration, Washington, DC.
Newell, P. T., Sotirelis, T., & Wing, S. (2010). Seasonal variations in diffuse, monoenergetic, and broadband auroratransient displays of light,
often displaying as moving curtains and rays, at high latitudes associated with geomagnetic disturbances. Journal of Geophysical
Research,115, A03216.
Space Weather
Normand, E. (1996). Single event effects in avionics. IEEE Transactions on Nuclear Science,43, 461.
Núñez, M. (2011). Predicting solar energetic proton events (E > 10 MeV). Space Wea ther,9, S07003.
Núñez, M. (2015). Realtime prediction of the occurrence and intensity of the rst hours of >100 MeV solar energetic proton events. Space
Weather,13, 807819.
Núñez, M., ReyesSantiago, P. J., & Malandraki, O. E. (2017). Realtime prediction of the occurrence of GLE events. Space Weather,15,
Nymmik, R. A. (1999). Probabilistic model for uences and peak uxes of solar energeti c particles, Radiation Measurements,30(3), 287296,
Nymmik, R. A. (2007). Improved environment radiation models, Advances in Space Research,40(3), 313320,
O'Brien, T. P. (2009). SEAESGEO: A spacecraft environmental anomalies expert system for geosynchronous orbit. Space Weather,7,
O'Bryan, M. V., LaBel, K. A., Pellish, J. A., Lauenstein, J.M., Chen, D., Marshall, C. J., et al. (2009). Single event effects compendium of
candidate spacecraft electronics for NASA, 2009 IEEE Radiation Effects Data Workshop, Quebec City, QC, 2009, pp. 1524. https://doi.
O'Neill, P. M., Foster, C. C., & BadhwarO'Neill (2011), Galactic cosmic ray ux model description, Tech. Rep. NASA/TP2013217376.
Papaioannou, A., Anastasiadis, A., Kouloumvakos, A., Paassilta, M., Vainio, R., Valtonen, E., et al. (2018). Nowcasting Solar Energetic
Particle Events Using Principal Component Analysis. Solar Physics.293(7), 1.
Posner, A. (2007). Up to 1hour forecasting of radiation hazards from solar energetic ion events with relativistic electrons. Space Weather,5,
Rastätter, L., Wiegand, C., Mullinix, R. E., & MacNeice, P. J. (2019). Comprehensi ve Assessment of Models and Events Using Library Tools
(CAMEL) framework: Time series comparisons. Space Weather,17.
Reeves, G. D., Chen, Y., Cunningham, G. S., Friedel, R. W. H., Henderson, M. G., Jordanova, V. K., et al. (2012), Dynamic Radiation
Environment Assimilation Model: DREAM. Space Weather,10, S03006,
Rodgers, D. J. (1999). DICTAT Software: Users' manual, Issue 3.0, DERA/CIS (CIS3).
Rubin, A. G., Katz, I., Mandell, M. J., Scnuelle, G., Steen, P., Parks, D., et al. (1980). A 3dimensional spacecraft charging computer code. In
H. B. Garrett, & C. P. Pike (Eds.), Space Systems and their interactions with the Earths Space Environment, Progress in Astronautics and
Aeronautics (Vol. 71). (Chapter 12, pp. 318336). New York: AIAA.
Sawyer, D. M., & Vette, J. I., (1976). AP8 trapped proton environment for solar maximum and solar minimum, NSSDC/WDCAR&S
Schraube, H., Leuthold, G., Heinrich, W., Roesler, S., Mares, V. & Schraube, G. (2002). EPCARDEuropean program package for the
calculation of aviation route doses, User's manual. GSFNational Research Center, Neuherberg, Germany. ISSN 07211694. GSFReport
Schwadron, N. A., Townsend, L., Kozarev, K., Dayeh, M. A., Cucinotta, F., Desai, M., et al. (2010). EarthMoonMars radiation environment
module framework. Space Weather,8, S00E02.
Seltzer, S. M. (1994). Updated calculations for routine spaceshielding radiation dose estimates: SHIELDOSE2.Gaithersburg, MD, NIST
Publication NISTIR 5477.
Shprits, Y. Y., Subbotin, D., & Ni, B. (2009). Evolution of electron uxes in the outer radiation belt computed with the VERB code. Journal of
Geophysical Research,114, A11209.
SicardPiet, A., Bourdarie, S., Boscher, D., & Friedel, R. H. W. (2006). A model for the geostationary electron environment: POLE, from 30
keV to 5.2 MeV. IEEE Transactions on Nuclear Science,53(4), 18441850.
SicardPiet, A., Bourdarie, S., Boscher, D., Friedel, R. H. W., Thomsen, M., Goka, T., et al. (2008). A new international geostationary electron
model: IGE2006, from 1 keV to 5.2 MeV. Space Weather,6, S07003.
Smart, D. F., & Shea, M. A. (1994). Geomagnetic cutoffs: A review for space dosimetry applications. Advances in Space Research,14(10),
Smart, D. F., & Shea, M. A. (2001). A comparison of the Tsyganenko model predicted and measured geomagnetic cutoff latitudes. Advances
in Space Research,28(12), 17331738.
Smart, D. F., & Shea, M. A. (2003). The spacedeveloped dynamic vertical cutoff rigidity model and its applicability to aircraft radiation dose.
Advances in Space Research,32(1), 103108.
Srour, J. R., & McGarrity, J. M. (1988). Radiation effects on microelectronics in space. Proceedings of the IEEE,76(11), 14431469.
Subbotin, D. A., & Shprits, Y. Y. (2009). Threedimensional modeling of the radiation belts using the Versatile Electron Radiation Belt
(VERB) code. Space Weather,7, S10001.
Thomsen, M. F., Henderson, M. G., & Jordanova, V. K. (2013). Statistical properties of the surfacecharging environment at geosynchronous
orbit. Space Weather,11, 237244.
Tobiska, W. K., Atwell, W., Beck, P., Benton, E., Copeland, K., Dyer, C., et al. (2015). Advances in atmospheric radiation measurements and
modeling needed to improve air safety. Space Weather,13, 202210.
Tobiska, W. K., Bouwer, D., Smart, D., Shea, M., Bailey, J., Didkovsky, L., et al. (2016). Global realtime dose measurements using the
Automated Radiation Measurements for Aerospace Safety (ARMAS) system. Space Weather,14, 10531080.
Tobiska, W. K., Didkovsky, L., Judge, K., Weiman, S., Bouwer, D., Bailey, J., et al. (2018). Analytical representations for characterizing the
global aviation radiation environment based on model and measurement databases. Space Weather,16, 15231538.
Tylka, A. J., Adams, J. H., Boberg, P. R., Brownstein, B., Dietrich, W. F., Flueckiger, E. O., et al. (1997). CREME96: A revision of the cosmic
ray effects on microelectronics code. IEEE Transactions on Nuclear Science,44(6), 21502160.
Ukhorskiy, A. Y., Sitnov, M. I., Sharma, A. S., Anderson, B. J., Ohtani, S., & Lui, A. T. Y. (2004). Dataderived forecasting model for
relativistic electron intensity at geosynchronous orbit. Geophysical Research Letters,31, L09806.
Vampola, A. L. (1996). Outer zone energetic electron environment update, Final Report of ESA/ESTEC Contract No..
Velazco, R., Fouillat, P., & Reis, R. (Eds) (2007). Radiation effects on embedded systems. Netherlands: Springer. 07/978
1402056468 ISBN 9781402056468 (online) 9781402056451 (print)
Vette, J. I. (1991). The NASA/National Space Science Data Center Trapped Radiation Environment Model Program (19641991),
Space Weather
Winter, L. M., & Ledbetter, K. (2015). Type II and type III radio bursts and their correlation with solar energetic proton events. The
Astrophysical Journal,809, 105.
Wrenn, G. L., Rodgers, D. J., & Ryden, K. A. (2002). A solar cycle of spacecraft anomalies due to internal charging. Annales de Geophysique,
20(7), 953956.
Wrenn, G. L., & Smith, R. J. K. (1996). Probability factors governing. ESD effects in geosynchronous orbit. IEEE Transactions on Nuclear
Science,NS43(6), 27832789.
Xapsos, M. A., Summers, G. P., Barth, J. L., Stassinopoulos, E. G. & Burke, E. A. (1999). Probability model for worst case solar proton event
uences, IEEE Transactions on Nuclear Science,46(6), 14811485,
Xapsos, M. A., Summers, G. P., Barth, J. L., Stassinopoulos, E. G., & Burke, E. A. (2000). Probability Model for Cumulative Solar Proton
Event Fluences, IEEE Trans. Nucl. Sci.,47, 486490, 2000.
Xapsos, M. A., Stauffer, C., Jordan, T., Barth, J. L., & Mewaldt, R. A. (2007). Model for cumulative solar heavy ion energy and linear energy
transfer spectra. IEEE Transactions on Nuclear Science,54(6), 19851986.
Xiao, F., Shen, C., Wang, Y., Zheng, H., & Wang, S. (2008). Energetic electron distributions tted with a relativistic kappatype function at
geosynchronous orbit. Journal of Geophysical Research,113, A05203.
Yu, Y., Jordanova, V. K., Ridley, A. J., Albert, J. M., Horne, R. B., & Jeffery, C. A. (2016). A new ionospheric electron precipitation module
coupled with RAMSCB within the geospace general circulation model. Journal of Geophysical Research: Space Physics,121, 85548575.
Yu, Y., Rastätter, L., Jordanova, V. K., Zheng, Y., Engel, M., Fok, M .C., & Kuznetsova, M. M. (2019). Initial results from the GEM challenge
on the spacecraft surface charging environment. Space Weather,17, 299312.
Zhao, H., Baker, D. N., Jaynes, A. N., Li, X., Kanekal, S. G., Blum, L. W., et al. (2017). Radiation belt electron energy spectra characterization
and evolution based on the Van Allen Probes measurements, American Geophysical Union, Fall Meeting 2017, abstract #SM22C01.
Space Weather
... The proposed model is developed in Section 2 based on Van Allen Probes electron flux data in 2012-2017 (Baker et al., 2013) and GPS satellite data in (Morley et al., 2016, and its performance is tested using various metrics (Zheng et al., 2019). A key advantage of the proposed model is that it can approximately predict both the start of each 10-day period of high 2-MeV electron flux and the total time-integrated electron flux that will be accumulated over these next 10 days. ...
... Hereafter, several quantitative measures of the model performance are provided. To quantify the performance of the model on all days of each year, we first provide in Table 1 the True Skill Statistics (TSS), Heidke Skill Score (HSS), probability of detection (POD), probability of false detection (POFD), and false alarm ratio (FAR) (Woodcock, 1976;Zheng et al., 2019) of the Int(aa H ) model for the prediction of days of high daily-averaged 2-MeV electron flux higher than 3.5 ⋅ 10 5 e/cm 2 /sr/MeV/s at L* ∼ 4.5. Such high daily fluxes have been observed only ∼15% of the time in 2001-2017. ...
... Such high daily fluxes have been observed only ∼15% of the time in 2001-2017. The POD (also known as hit rate) is equal to the fraction of actual events (here, days of high flux) correctly predicted, and the POFD is the fraction of non-event days that were incorrectly forecast as events (Zheng et al., 2019). The FAR is the fraction of predicted events that turn out to be non-events. ...
Full-text available
Since the advent of the Space Age, the importance of understanding and forecasting relativistic electron fluxes in the Earth’s radiation belts has been steadily growing due to the threat that such particles pose to satellite electronics. Here, we provide a model of long‐duration periods of high time‐integrated 2‐MeV electron flux deep inside the outer radiation belt, based on the significant correlation obtained in 2001–2017 between time‐integrated electron flux measured by satellites and a measure of the preceding time‐integrated homogenized aaH geomagnetic index. We show that this correlation is likely due to a stronger cumulative chorus wave‐driven acceleration of relativistic electrons and a stronger cumulative inward radial diffusion of such electrons during periods of higher time‐integrated geomagnetic activity. Return levels of 2‐MeV electron flux are provided based on Extreme Value analysis of time‐integrated geomagnetic activity over 1868–2017, in rough agreement with estimates based on 20‐year data sets of measured flux. A high correlation is also found between our measure of time‐integrated geomagnetic activity averaged over each solar cycle and averaged sunspot numbers, potentially paving the way for forecasts of time‐integrated relativistic electron flux during future solar cycles based on predictions of solar activity.
... This radiation, including protons and electrons, will interfere with the communication data, which is likely to produce wrong negotiation data during the mission negotiation process [16]. At the same time, the satellite communication may also be maliciously attacked or tampered with at any time during the mission negotiation process, resulting in an inconsistent system communication, which is primarily contributed to the collapse of the whole satellite cluster communication system [17]. The traditional distributed mission planning is also difficult to solve the above problems, so the decentralized consistency algorithm is needed to solve the problems. ...
Full-text available
The traditional on-board centralized-distributed mission negotiation architecture has poor security and reliability. It can easily give rise to the collapse of the whole system when the master node is attacked by malicious nodes. To address this issue, the decentralized consistency algorithms commonly used in the internet world are referred to in this paper. Firstly, four typical consistency algorithms suitable for the Internet and which are named RAFT, PBFT, RIPPLE and DPOS are selected and modified for a multi-satellite autonomous mission negotiation. Additionally, based on the above modified consistency algorithms, a new double-layer decentralized consistency algorithm named DDPOS is proposed. It is well known that the above four common consistency algorithms cannot have both a low resource occupation and high security. The DDPOS algorithm can integrate the advantages of four common consistency algorithms due to its freedom of choice attribute, which can enable satellite clusters to flexibly adopt different appropriate consistency algorithms and the number of decentralized network layers. The DDPOS algorithm not only greatly improves the security and reliability of the whole satellite cluster, but also effectively reduces the computing and communication resources occupation of the satellite cluster. Without the presence of a malicious node attack, the resource occupation of the DDPOS algorithm is almost the same as that of the RAFT algorithm. However, in the case of a malicious node attack, compared with the RAFT algorithm, the total computation and total bandwidth occupation of the DDPOS algorithm have decreased by 67% and 75%, respectively. Moreover, it is surprising that although the DDPOS algorithm is more complex, its code size is only about 8% more than the RAFT algorithm. Finally, the effectiveness and feasibility of the DDPOS algorithm in the on-board practical application are analyzed and verified via simulation experiments.
... Specific examples of metrics and community best practices were provided in the Space Weather Capabilities Assessment topical issue of Space Weather and should be followed if possible. This includes the forecast of geomagnetic indices (Liemohn et al., 2018), thermospheric neutral densities (Bruinsma et al., 2018), radiation and plasma environment (Zheng et al., 2019), and arrival time of coronal mass ejections (Verbeke et al., 2019), among others. When appropriate, specific thresholds should be defined in order to develop a binary classification. ...
Full-text available
Plain Language Summary Manuscripts based on machine‐learning techniques have significantly increased in Space Weather over the past few years. We discuss which manuscripts are within the journal's scope and emphasize that manuscripts focusing purely on a forecasting technique (rather than on understanding and forecasting a phenomenon) must correspond to a substantial improvement over the current state‐of‐the‐art techniques and present this comparison. All manuscripts shall include information about data preparation, including splitting of data between training, validation and testing sets. The software and/or algorithms used for to develop the machine‐learning technique should be included in a repository at the time of submission. Comparison with published results using other methods must be presented, and uncertainties of the forecast results must be discussed.
... Using OGO 3 satellite data, Frank [239] reported about 25% of ring current energy during storm time. These electrons may not penetrate the satellite surface but could pose serious risk though surface charging [240]. The interaction of ring current-charged particles and solar UV radiation with the surface material leads to the generation of secondary electrons. ...
Full-text available
In the last few decades, solar activity has been diminishing, and so space weather studies need to be revisited with more attention. The physical processes involved in dealing with various space weather parameters have presented a challenge to the scientific community, with a threat of having a serious impact on modern society and humankind. In the present paper, we have reviewed various aspects of space weather and its present understanding. The Sun and the Earth are the two major elements of space weather, so the solar and the terrestrial perspectives are discussed in detail. A variety of space weather effects and their societal as well as anthropogenic aspects are discussed. The impact of space weather on the terrestrial climate is discussed briefly. A few tools (models) to explain the dynamical space environment and its effects, incorporating real-time data for forecasting space weather, are also summarized. The physical relation of the Earth's changing climate with various long-term changes in the space environment have provided clues to the short-term/long-term changes. A summary and some unanswered questions are presented in the final section.
Full-text available
A long-term annealing experiment was performed using 850 nm vertical-cavity surface-emitting lasers (VCSELs) irradiated with 10 MeV protons. Static parameters such as the threshold current, slope efficiency, and light output power were tested using annealing currents above and below the threshold. The experimental results indicated that these parameters gradually recovered with annealing time, and the degree of recovery was proportional to the annealing current. In addition, curve fitting was performed to obtain the direct relationship between the slope efficiency and annealing current. A comprehensive investigation of the annealing behavior of VCSELs is crucial for device applications in harsh radiation environments.
Solar radiation is the external driving force of the Earth's climate system. In different spatial and temporal scales, meteorological elements have different responses and lag periods to solar activity (SA), climatic oscillation (CO), geographic factors (GF) and other influencing factors. However, such studies are not abundant and in-depth in the world. To further understand the “solar-climate-water resource” system, this study considers China as the study area and investigates the monthly data of temperature (T) and precipitation (P) during 1900–2020 that were obtained from 3836 grid stations. The strong interaction and lag distribution between T or P with SA and CO were studied and influence weights of SA, CO, and geographical factors (GF) of each grid station were calculated. A multivariate hysteretic decomposition model was established to simulate and quantitatively decompose the periodic lag considering the factors of the earth's revolution. It is found that the strong interaction/lag periods obtained in a long-time scale can be decomposed into several periods shorter than the SA period. The distribution of strong interaction/lag periods is nested with topography and echoes with cities. The underlying surface conditions and urbanization are also important factors affecting the T and P lag. There are two distinct dividing lines in the lag period and influencing factor pattern of T and P. The T dividing line moves through valleys where water or mountain ranges meet, where the gap facilitates monsoon movement across regions, while the P dividing line is a zone of dramatic terrain, where tall mountains block water vapor transport. In the lag trend of T, the northern region of China has the longest lag period, and the lag period of surrounding regions tends to converge to the northern region. The lag period caused by SN in southwest China is larger than that in northwest China, while the lag effect of CO is opposite in the above two regions. The lag trend of P also has the above characteristics, but the difference is that the lag period in central China is the longest.
The least number of proton events and ground-level enhancements was recorded in the solar cycle 24 which corresponds with the least smoothed sunspot number compared to the last three previous solar cycles. This was attributed to the weak sun’s polar field and decreasing strength of the interplanetary magnetic field at the start of the solar cycle. The majority contribution to background radiation dose within our earth’s atmosphere is galactic cosmic rays and trapped particles in the Van Allen Belts. However, solar proton events cause sudden spikes in radiation doses, and this depends on the fluence and energy spectra of the events. While these doses are least detected in the lower atmosphere, they have significant radiation damage to spacecraft electronic components and astronauts on long space missions and at higher atmospheric altitudes. Therefore, the prediction of such events and estimation of their effective radiation damage is an important consideration for planning long space missions and spacecraft design materials.
Full-text available
The Earth's outer radiation belt response to geospace disturbances is extremely variable spanning from a few hours to several months. In addition, the numerous physical mechanisms, which control this response depend on the electron energy, the timescale, and the various types of geospace disturbances. As a consequence, various models that currently exist are either specialized, orbit‐specific data‐driven models, or sophisticated physics‐based ones. In this paper, we present a new approach for radiation belt modeling using Machine Learning methods driven solely by Solar wind speed and pressure, Solar flux at 10.7 cm, and the angle controlling the Russell‐McPherron effect (θRM). We show that the model can successfully reproduce and predict the electron fluxes of the outer radiation belt in a broad energy (0.033–4.062 MeV) and L‐shell (2.5–5.9) range, and moreover, it can capture the long‐term modulation of the semi‐annual variation. We also show that the model can generalize well and provide successful predictions, even outside of the spatio‐temporal range it has been trained with, using > $ > $0.8 MeV electron flux measurements from GOES‐15/EPEAD at a geostationary orbit.
Full-text available
Electron variability at geosynchronous orbit (GEO) plays a key role in satellite operations especially concerning the low energies which can lead to surface charging effects on spacecraft. In this work, we use 9 years (2011–2019) of electron measurements from GOES‐13, 14 and 15 satellites to study the evolution of electron fluxes with respect to various solar, solar wind, and magnetospheric parameters. The source electron fluxes are shown to be well correlated with AE index and Newell's function, while the seed electron fluxes are shown to be well correlated with solar wind speed. Based on these findings, we have developed a predictive multiple regression model for electron fluxes in the 30–600 keV energy range which uses solely solar wind parameters' measurements. The model may have a variety of applications related to the nowcasting/forecasting of the distribution of electron fluxes at GEO including serving as low‐energy boundary conditions for studying electron acceleration to relativistic energies or providing information for predicting surface and/or internal charging effects on spacecraft.
Cette thèse s'inscrit dans le contexte de la fiabilité des satellites soumis à un environnement spatial. En effet, les satellites sont constitués de matériaux diélectriques qui sous certaines conditions peuvent se charger de particules électriques environnantes (électrons et ions). L'accumulation locale de ces particules constitue la charge d'espace qui tend à modifier les propriétés chimiques, mécaniques et électriques des matériaux diélectriques. Ces modifications sont à l'origine de phénomènes physiques qui peuvent endommager des éléments du satellite et rendre obsolètes certaines de ses fonctions comme lors de décharges électrostatiques (DES). Dans le but d'éviter de tels scénarios, le Centre national d'études spatiales (CNES) et le Laboratoire plasma et conversion d'énergie (LAPLACE) cherchent à caractériser les matériaux diélectriques utilisés en environnement spatial à l'aide, notamment, de techniques de mesure capable de sonder la charge en temps et en espace. La méthode PEA, ou Pulsed Electro-Acoustic method, fait partie de ces techniques de mesure. L'objectif de cette thèse est d'optimiser le traitement de données d'un banc de mesure PEA afin d'estimer au mieux la répartition spatio-temporelle de la charge dans un échantillon diélectrique. Pour ce faire, une méthode originale, basée sur un modèle PSpice optimisé du banc de mesure PEA, a été développée permettant de caractériser une fonction de transfert du banc de mesure. A l'aide d'une technique de régularisation de type Tikhonov, les résultats montrent que les charges retrouvées lors d'une étape de calibration sont en parfaite adéquation avec les charges réellement imposées. Ce nouveau traitement développé lors de cette thèse permettra d'étudier des échantillons : (i) qui présentent une atténuation et une dispersion des ondes acoustiques importantes ; (ii) multicouches ; (iii) dont les charges sont localisées proches de la surface irradiée ; (iv) trop fin (de quelques dizaines de microns).
Full-text available
The Comprehensive Assessment of Models and Events using Library Tools (CAMEL) framework leverages existing Community Coordinated Modeling Center services: Run‐on‐Request postprocessing tools that generate model time series outputs and the new Community Coordinated Modeling Center Metadata Registry that describes simulation runs using Space Physics Archive Search and Extract metadata. The new CAMEL visualization tool compares the modeled time series with observational data and computes a suite of skill scores such as Prediction Efficiency, Root‐Mean‐Square Error, and Symmetric Signed Percentage Bias. Model‐data pairs used for skill calculations are obtained considering a user‐selected maximum difference between the time of observation and the nearest model output. The system renders available data for all locations and time periods selected using interactive visualizations that allow the user to zoom, pan, and pick data values along traces. Skill scores are reported for each selected event or aggregated over all events for all participating model runs. Separately, scores are reported for all locations (satellites or stations) and for each location individually. We are building on past experiences with model‐data comparisons of magnetosphere and ionosphere model outputs from GEM2008, GEM‐CEDAR Electrodynamics Thermosphere Ionosphere, and the SWPC Operational Space Weather Model challenges. The CAMEL visualization tool is demonstrated using three validation studies: (a) Wang‐Sheeley‐Arge heliosphere simulations compared against OMNI solar wind data, (b) ground magnetic perturbations from several magnetosphere and ionosphere electrodynamics models as observed by magnetometers, and (c) electron fluxes from several ring current simulations compared to Radiation Belt Storm Probes Helium Oxygen Proton Electron instrument measurements, integrated over different energy ranges.
Full-text available
Surface charging by keV (kiloelectron Volt) electrons can pose a serious risk for satellites. There is a need for physical models with the correct and validated dynamical behavior. The 18.5-month (2013–2015) output from the continuous operation online in real time as a nowcast of the Inner Magnetosphere Particle Transport and Acceleration Model (IMPTAM) is compared to the GOES 13 MAGnetospheric Electron Detector (MAGED) data for 40, 75, and 150 keV energies. The observed and modeled electron fluxes were organized by Magnetic Local Time (MLT) and IMPTAM driving parameters; the observed Interplanetary Magnetic Field (IMF) BZ, BY, and |B|; the solar wind speed VSW; the dynamic pressure PSW; and Kp and SYM-H indices. The peaks for modeled fluxes are shifted toward midnight, but the ratio between the observed and modeled fluxes at around 06 MLT is close to 1. All the statistical patterns exhibit very similar features with the largest differences of about 1 order of magnitude at 18–24 MLT. Based on binary event analysis, 20–78% of threshold crossings are reproduced, but Heidke skill scores are low. The modeled fluxes are off by a factor of 2 in terms of the median symmetric accuracy. The direction of the error varies with energy: overprediction by 50% for 40 keV, overprediction by 2 for 75 keV, and underprediction by 18% for 150 keV. The revealed discrepancies are due to the boundary conditions developed for ions but used for electrons, absence of substorm effects, representations of electric and magnetic fields which can result in not enough adiabatic acceleration, and simple models for electron lifetimes.
Full-text available
Spacecraft surface charging during geomagnetically disturbed times is one of the most important causes of satellite anomalies. Predicting the surface charging environment is one prevalent task of the geospace environment models. Therefore, the Geospace Environment Modeling (GEM) Focus Group “Inner Magnetosphere Cross-energy/Population Interactions” initiated a community-wide challenge study to assess the capability of several inner magnetosphere ring current models in determining surface charging environment for the Van Allen Probes orbits during the 17 March 2013 storm event. The integrated electron flux between 10 and 50 keV is used as the metrics. Various skill scores are applied to quantitatively measure the modeling performance against observations. Results indicate that no model consistently perform the best in all of the skill scores or for both satellites. We find that from these simulations the ring current model with observational flux boundary condition and Weimer electric potential driver generally reproduces the most realistic flux level around the spacecraft. A simple and weaker Volland-Stern electric field is not capable of effectively transporting the same plasma at the boundary toward the Earth. On the other hand, if the ring current model solves the electric field self-consistently and obtains similar strength and pattern in the equatorial plane as the Weimer model, the boundary condition plays another crucial role in determining the electron flux level in the inner region. When the boundary flux spectra based on magnetohydrodynamics (MHD) model/empirical model deviate from the shape or magnitude of the observed distribution function, the simulation produces poor skill scores along Van Allen Probes orbits.
Full-text available
The Nowcast of Atmospheric Ionizing Radiation for Aviation Safety climatological model and the Automated Radiation Measurements for Aerospace Safety (ARMAS) statistical database are presented as polynomial fit equations. Using equations based on altitude, L shell, and geomagnetic conditions an effective dose rate for any location from a galactic cosmic ray (GCR) environment can be calculated. A subset of the ARMAS database is represented by a second polynomial fit equation for the GCR plus probable relativistic energetic particle (REP; Van Allen belt REP) effective dose rates within a narrow band of L shells with altitudinal and geomagnetic dependency. Solar energetic particle events are not considered in this study since our databases do not contain these events. This work supports a suggestion that there may be a REP contribution having an effect at aviation altitudes. The ARMAS database is rich in Western Hemisphere observations for L shells between 1.5 and 5; there have been many cases of enhanced radiation events possibly related to effects from radiation belt particles. Our work identifies that the combined effects of an enhanced radiation environment in this L shell range are typically 15% higher than the GCR background. We also identify applications for the equations representing the Nowcast of Atmospheric Ionizing Radiation for Aviation Safety and ARMAS databases. They include (i) effective dose rate climatology in comparison with measured weather variability and (ii) climatological and statistical weather nowcasting and forecasting. These databases may especially help predict the radiation environment for regional air traffic management, for airport overflight operations, and for air carrier route operations of individual aircraft.
Full-text available
Satellite charging is one of the most important risks for satellites on orbit. Satellite charging can lead to an electrostatic discharge resulting in component damage, phantom commands, and loss of service and in exceptional cases total satellite loss. Here we construct a realistic worst case for a fast solar wind stream event lasting 5 days or more and use a physical model to calculate the maximum electron flux greater than 2 MeV for geostationary orbit. We find that the flux tends toward a value of 10⁶ cm⁻²·s⁻¹·sr⁻¹ after 5 days and remains high for another 5 days. The resulting flux is comparable to a 1 in 150-year event found from an independent statistical analysis of electron data. Approximately 2.5 mm of Al shielding would be required to reduce the internal charging current to below the National Aeronautics and Space Administration-recommended guidelines, much more than is currently used. Thus, we would expect many satellites to report electrostatic discharge anomalies during such an event with a strong likelihood of service outage and total satellite loss. We conclude that satellites at geostationary orbit are more likely to be at risk from fast solar wind stream event than a Carrington-type storm.
Conference Paper
Full-text available
Exposure from cosmic radiation at aviation altitudes can be elevated during solar energetic particle events compared to the omnipresent galactic cosmic ray background. The largest of these events can be measured on ground as so-called Ground Level Enhancements (GLE). GLE 72, which occurred 10 Sept. 2017, was the more recent of the two solar particle induced ground level enhancements in solar cycle 24 in which GLEs have been unusually rare. Larger GLEs can significantly increase ionizing radiation dose rates at aviation altitudes for hours to days, leading to concern among crewmembers. One way to provide real time monitoring and preliminary evaluation of solar particle events, including GLEs, in regard to effective dose rates at aviation altitudes is to use real time measurements of the cosmic ray intensity, for instance GOES proton measurements, in combination with numerical models for the calculation of radiation exposure at aviation altitudes. In this work, the PANDOCA and MIRA models which have been developed for this purpose are compared. PANDOCA has been developed by the German Aerospace Center (DLR) and can be applied both to galactic cosmic radiation and solar energetic particle events. MIRA is based on CARI-7A and part of the latest Solar Radiation Alert System (SRAS) of the U.S. Federal Aviation Administration. For GLE72, the models consistently predict increases in the radiation exposure at aviation altitudes, i.e. below 50000 ft, which were on the order of or below the galactic cosmic ray background. Increases in dose rate were limited to high latitudes as the primary solar particles were strongly suppressed by the geomagnetic shielding.
High-energy trapped electrons in the Van Allen belts pose a threat to the survivability of orbiting spacecraft. Two key radiation effects are total ionizing dose and displacement damage dose in components and materials, both of which cause cumulative and largely irreversible damage. During an extreme space weather event, trapped electron fluxes in the Van Allen belts can increase by several orders of magnitude in intensity, leading to an enhanced risk of satellite failure. We use extreme environments generated by modeling and statistical analyses to estimate the consequences for satellites in terms of the radiation effects described above. A worst-case event could lead to significant losses in power generating capability—up to almost 8%—and cause up to four years' worth of ionizing dose degradation, leading to component damage and a life-shortening effect on satellites. The consequences of such losses are hugely significant given our increasing reliance on satellites for a vast array of services, including communication, navigation, defense, and critical infrastructure.
Space weather is an important driver of the exposure of aircrew and passengers to cosmic rays at flight altitudes, which has been a matter of concern for several decades. The assessment of the corresponding radiation doses can be realized by measurements or model calculations that cover the whole range of the radiation field in terms of geomagnetic shielding, atmospheric shielding, and the effects of space weather. Since the radiation field at aviation altitudes is very complex in terms of particle composition and energy distribution, the accurate experimental determination of doses at aviation altitudes is still a challenging task. Accordingly, the amount of data with comparatively small uncertainties is scarce. The Community Coordinated Modeling Center invited the Federal Aviation Administration, the German Aerospace Center, and the National Aeronautics and Space Administration to make their radiation models for aviation CARI‐7A, PANDOCA, and NAIRAS available for interested users via the Community Coordinated Modeling Center web site. A concomitant comparison of model calculations with measuring data provided information on the predicting capabilities and the uncertainties of the current versions of these models under quiet space weather conditions.
Ground level enhancement (GLE) 72, which occurred 10 September 2017, is the most recent of two solar particle-induced enhancements in ground level measurements of cosmic radiation secondary neutrons in solar cycle 24. GLEs have been unusually rare in this solar cycle. GLEs can significantly increase ionizing radiation dose rates at aviation altitudes for hours to days, leading to concern among crewmembers. Real-time monitoring and preliminary evaluation of solar proton events, including GLEs, in regard to effective dose rates at aviation altitudes has been ongoing since the U.S. Federal Aviation Administration began operating its Solar Radiation Alert System (SRAS) in 2002. Since then, SRAS has been revised multiple times. In this report, model calculations of dose rates during GLE 72 from Maps of Ionizing Radiation in the Atmosphere (MIRA), the latest SRAS software based on CARI-7A, are compared with those from the model Professional Aviation Dose Calculator (PANDOCA) developed by the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt). At very low cutoff rigidities model calculations agree within 40% and indicate no significant increase in radiation exposures at commercial aviation altitudes. The larger than expected differences at very low cutoff rigidities indicate Geostationary Orbiting Environmental Satellite particle flux data alone that are insufficient to produce consistent solar particle dose estimates.