SPACE WEATHER PHASE 1

BENCHMARKS

A Report by the

Space Weather Operations, 

Research, and Mitigation Subcommittee

Committee on Homeland and National Security

of the

NATIONAL SCIENCE & TECHNOLOGY COUNCIL

JUNE 2018
SPACE WEATHER PHASE 1 BENCHMARKS

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About the National Science and Technology Council

The National Science and Technology Council (NSTC) is the principal means by which the Executive 

Branch coordinates science and technology policy across the diverse entities that make up the Federal 

research and development enterprise. A primary objective of the NSTC is to ensure science and 

technology policy decisions and programs are consistent with the President's stated goals. The NSTC 

prepares research and development strategies that are coordinated across Federal agencies aimed at 

accomplishing multiple national goals. The work of the NSTC is organized under committees that 

oversee subcommittees and working groups focused on different aspects of science and technology. 

More information is available at http://www.whitehouse.gov/ostp/nstc. 

About the Office of Science and Technology Policy

The Office of Science and Technology Policy (OSTP) was established by the National Science and 

Technology Policy, Organization, and Priorities Act of 1976 to provide the President and others within 

the Executive Office of the President with advice on the scientific, engineering, and technological 

aspects of the economy, national security, homeland security, health, foreign relations, the 

environment, and the technological recovery and use of resources, among other topics. OSTP leads 

interagency science and technology policy coordination efforts, assists the Office of Management and 

Budget with an annual review and analysis of Federal research and development in budgets, and serves 

as a source of scientific and technological analysis and judgment for the President with respect to major 

policies, plans, and programs of the Federal Government. More information is available at

http://www.whitehouse.gov/ostp.

About the Space Weather Operations, Research, and Mitigation (SWORM) 

Subcommittee

The Space Weather Operations, Research, and Mitigation (SWORM) Subcommittee, an interagency 

group organized under the NSTC Committee on Homeland and National Security. The SWORM 

Subcommittee seeks to coordinate Federal Government departments and agencies to enhance 

national preparedness by achieving the goals and objectives identified in National Space Weather 

Strategy. 

About this Document 

This document was developed by the SWORM Subcommittee. It was released in draft for public 

comment on the Federal Register (82 FR 7801).

Copyright Information

This document is a work of the United States Government and is in the public domain (see 17 U.S.C. 

§105). Subject to the stipulations below, it may be distributed and copied with acknowledgment to 

OSTP. Copyrights to graphics included in this document are reserved by the original copyright holders 

or their assignees and are used here under the government’s license and by permission. Requests to 

use any images must be made to the provider identified in the image credits or to OSTP if no provider 

is identified. Printed in the United States of America, 2018.
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SPACE WEATHER OPERATIONS, RESEARCH, AND MITIGATION 

SUBCOMMITTEE

CO-CHAIRS

Department of Commerce, National Oceanic and Atmospheric Administration

Department of Homeland Security

Office of Science and Technology Policy

MEMBERS

Departments

Department of Commerce

Department of Defense

Department of Energy

Department of Homeland Security

Department of the Interior

Department of Justice

Department of State

Department of Transportation

Agencies and Service Branches

Federal Aviation Administration

Federal Bureau of Investigation

Federal Communications Commission

Federal Emergency Management Agency

Federal Energy Regulatory Commission 

Federal Railroad Administration

National Aeronautics and Space Administration 

National Oceanic and Atmospheric Administration

National Science Foundation

Nuclear Regulatory Commission 

Office of the Director of National Intelligence 

United States Air Force

United States Geological Survey

United States Navy

United States Postal Service

Executive Office of The President

National Security Council 

Office of Management and Budget

Office of Science and Technology Policy

White House Military Office
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Table of Contents

Introduction .................................................................................................................................................1

Summary of Benchmarks.............................................................................................................................3

Benchmarks for Induced Geo-electric Fields ..............................................................................................9

Benchmarks for Ionizing Radiation ........................................................................................................... 12

Benchmarks for Ionospheric Disturbances............................................................................................... 23

Benchmarks for Solar Radio Bursts........................................................................................................... 30

Benchmarks for Upper Atmosphere Expansion........................................................................................ 34

Bibliography ............................................................................................................................................... 43

Abbreviations ............................................................................................................................................. 46
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Introduction

The 2017 National Security Strategy1 calls for the promotion of American resilience and space 

commerce, and for the U.S. to maintain the lead in space exploration. Space weather, a natural source 

of electromagnetic pulse (EMP), can disrupt, degrade, or damage infrastructure and technology 

systems, including the electrical power grid. For example, in 1989, a space weather event caused a 

blackout in Quebec that affected more than six million customers. Space weather can blackout air 

traffic control and high-frequency communications systems. Beyond terrestrial systems, space 

weather can affect satellite systems, interfere with GPS service, endanger the lives of humans in 

space, and delay the launch of space missions. This makes preparing for space weather events critical 

to national security, infrastructure services, space missions, and technology innovations (such as 

autonomous vehicles) that rely on communications systems and GPS for positioning, navigation, and 

timing services.

Recent executive actions identify priorities that necessitate the need to further prepare the Nation for 

effects of space weather phenomena, including EMP. Efforts to address the threat of space weather 

through enhanced research, operations, and mitigation activities can be leveraged to help address the 

threat of adversarial EMP. Benchmarks for space weather will enhance awareness of threats among 

critical infrastructure owners and operators in the private sector, and will serve as an input for 

academic and private sector innovation towards building a space weather- and EMP-ready Nation.

In accordance with these priorities and the existing policy on enhancing resilience to space weather, 

this report identifies initial benchmarks for five phenomena associated with space weather events: 

1. Induced geo-electric fields

2. Ionizing radiation

3. Ionospheric disturbances

4. Solar radio bursts

5. Upper atmospheric expansion

The benchmarks are designed to capture an event’s ability to affect the Nation, and provide clear and 

consistent descriptions of space weather events based on current scientific understanding and the 

historical record. The purpose of the benchmarks is to provide input for creating engineering 

standards, developing vulnerability assessments and risk estimates, establishing decision points and 

thresholds for action, understanding risk, developing more effective mitigation procedures and 

practices, and enhancing response and recovery planning. Their purpose is not to categorize or 

classify a space weather event’s degree of impact on a technology system. 

For each of the five phenomena, this report identifies benchmark values for two different scales: those 

that are likely to occur once in 100 years and those associated with the theoretical maximum. Each of 

the benchmarks specifies the physical characteristics and conditions against which a space weather 

event can be measured. The benchmarks describe the nature and intensity of extreme space weather 

events, providing a point of reference from which to improve understanding of the effects of space 

weather. 

 1

 Executive Office of the President, National Security Strategy of the United States of America, December 2017, 

https://www.whitehouse.gov/wp-content/uploads/2017/12/NSS-Final-12-18-2017-0905.pdf
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For each benchmark, critical assumptions are described with their associated uncertainties. The 

benchmarks presented in this report are at different levels of maturity based on the current scientific 

understanding and available data. Where provided, benchmarks seek to supply parameters that are 

most useful to the relevant audience.

The Action Plan identifies a two-phase approach with different timelines to balance immediate needs 

for benchmarks with requirements for scientifically and statistically rigorous benchmarks. For Phase 

1, the results of which are captured in this report, working groups associated with the SWORM 

Subcommittee conducted a quick-turnaround analysis to develop benchmarks using existing data 

sets and studies, where available. This report is informed by public feedback received from the 

Federal Register Notice and request for comment on a January 2017 draft of the Phase 1 benchmarks.2

For Phase 2, the Subcommittee will more rigorously analyze cases where a quick-turnaround analysis 

did not yield benchmarks of sufficient precision for their intended uses. In addition, the approach for 

Phase 2 will also be informed by public feedback received on the draft of the Phase 1 benchmarks. The 

Action Plan3 states that all benchmarks will be re-examined at least once every 5 years or when 

significant new data or models become available. 

 2

 Department of Commerce (DOC), “Notice of Availability of and Request for Public Comment on Space Weather Phase 1 

Benchmarks,” National Oceanic and Atmospheric Administration (NOAA), Federal Register 82, no. 13 (January 23, 2017): 

7801–7802. 3

 NSTC, SWORM Subcommittee, National Space Weather Action Plan, October 2015, 

https://www.sworm.gov/publications/2015/swap_final__20151028.pdf
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Summary of Benchmarks

Table 1. Phase 1 benchmarks, parameters, and methods

Benchmarks for Induced Geo-Electric Fields 

Environmental parameter Intense magnetic storms may induce geo-electric fields of sufficient strength to drive 

quasi-direct currents in electric power grids, sometimes causing blackouts and 

damaging transformers.

Methodology for determining 

benchmarks

Benchmarking for induced geo-electric field amplitudes used two geophysical 

quantities: the surface impedance relationship between geomagnetic variation and 

the induced geo-electric field, as well as a measure of geomagnetic activity at Earth’s 

surface. Surface impedance values are obtained by magnetotelluric surveys, which 

have been completed for about half of the continental United States. Surface 

geomagnetic activity is routinely measured at magnetic observatories and 

variometer stations, and geomagnetic variations during a once-per-century event

are estimated by a statistical analysis.

1-in-100-year benchmarks The median once-per-century geo-electric exceedance amplitude among surveyed 

sites (see Figure 1) is 0.26 volts per kilometer (V/km), with amplitudes exceeding 14 

V/km in Minnesota. One standard-deviation error, the result of statistical variance in 

the geomagnetic data, is estimated to be about 30 percent, which is small compared 

to the site-to-site differences. The full benchmark of once-per-century geo-electric 

amplitudes across the United States, where data is available, is displayed in Figure 1.

Theoretical maximum 

benchmarks

Not feasible to compute benchmarks. Higher frequency amplitudes cannot be 

reasonably estimated from the observatory data, and while lower frequency 

harmonics generally yield smaller geo-electric amplitudes, additional investigation 

would help inform this issue. 

Benchmarks for Ionizing Radiation 

Environmental parameter Solar energetic particle (SEP) events, galactic cosmic rays (GCRs), and radiation belts 

around Earth can interact in complicated ways to augment the flux and fluence of 

protons, electrons, and other particles near Earth.

Methodology for determining 

benchmarks

Statistical information on the probability versus intensity of fluxes or fluences from 

spacecraft were used to set benchmarks for solar particle events and the radiation 

belts. Estimates of the probability and severity of extreme flux events were obtained 

using extreme value theory (EVT), which is aimed at characterizing the lowfrequency/high-severity tail of the probability distribution.

Approximations of variations in galactic cosmic rays (GCRs) caused by total solar 

wind and magnetic field intensity in the heliosphere were used to benchmark GCR 

ionizing radiation. As a theoretical maximum, the GCR fluence is equal to that of the 

local interstellar spectra (LIS), essentially assuming that there is no solar modulation 

(i.e. that the heliopause has moved inside 1 AU)
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Benchmarks for Ionizing Radiation (continued)

1-in-100-year benchmarks Benchmarks for SEP events, GCRs, and electron radiation belts (at selected 

locations) are provided below. For more benchmarks, see the full chapter on Ionizing 

Radiation.

Differential GCR Flux (particles/(cm2 sr s MeV/n)) at 1 AU, φa = 200 MV

Uncertainties are 10%-25%

Energy/nucleon Hydrogen Oxygen Iron

10 MeV 1.3 × 10-4 5.3 × 10-7 1.2 × 10-7

100 MeV 5.3 × 10-4 1.5 × 10-6 2.9 × 10-7

1 GeV 1.2 × 10-4 2.4 × 10-7 3.7 × 10-8

100 GeV 1.2 × 10-6 2.1 × 10-9 3.9 × 10-10

1000 GeV 2.9 × 10-9 5.0 × 10-12 1.2 × 10-12

Electron Radiation Belt Flux

Location Energy Differential Flux

(cm-2 s-1 sr-1MeV-1)

INTEGRAL/IREM at L*=6.0

2.05 MeV 1.58 × 105

1.82 MeV 1.76 × 105

1.27 MeV 5.99 × 105

690 keV 4.68 × 106

INTEGRAL/IREM at L*=4.5

2.05 MeV 5.79 × 105

1.82 MeV 7.29 × 105

1.27 MeV 2.52 × 106

690 keV 1.46 × 107

Proton Radiation Belt Flux

Energy (MeV)

1-in-100-Year Flux 

(protons/(cm2 s MeV)) 

based on Gumbelb Fit

Estimate of Uncertainty 

(ratio of exponential and 

Gumbelb fit)

1 2.53 × 1010 ~ 2

3 1.97 × 1010 ~ 25

5 1.23 × 108 ~ 2

10 4.49 × 1010 ~ 20
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Benchmarks for Ionizing Radiation (continued)

Theoretical maximum 

benchmarks

Differential GCR Flux (particles/(cm2 sr s MeV/n)) at 1 AU, φa = 0 MV

Uncertainties are 10%-25%

Energy/nucleon Hydrogen Oxygen Iron

10 MeV 2.6 × 10-1 6.8 × 10-5 2.3 × 10-5

100 MeV 9.4 × 10-3 6.8 × 10-5 1.4 × 10-6

1 GeV 2.2 × 10-4 3.2 × 10-7 4.9 × 10-8

100 GeV 1.3 × 10-6 2.2 × 10-9 4.0 × 10-10

1000 GeV 2.9 × 10-9 5.1 × 10-12 1.2 × 10-12

Statistical maximum benchmarks Integral SEP Fluence (protons/cm2) at Geostationary Orbit (GEO)c

Energy (MeV) Upper Limit

Maximum value with 

uncertainty (Upper Limit 

+1 sigma)

1 1.56 × 1011 3.54 × 1011

10 3.49 × 1010 7.93 × 1010

100 1.45 × 109 3.30 × 109

500 2.21 × 107 5.01 × 107

Electron Radiation Belt Flux

Location Energy Differential Flux (cm-2 s-1

sr-1MeV-1)

GEO (LANL)

2.65 MeV 5.9 [4.7, 7.3] × 104

625 keV 4.1 [3.2, 5.2] × 106

270 keV 2.0 [1.5, 2.6] × 107

INTEGRAL/IREM at L*=6.0

1.82 MeV 1.98 x 105

1.27 MeV 8.11 x 105

690 keV 4.82 x 106

INTEGRAL/IREM at L*=4.5

2.05 MeV 6.08 x 105

1.82 MeV 8.10 x 105

1.27 MeV 4.74 x 106

690 keV 1.51 x 107
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Benchmarks for Ionospheric Disturbances

Environmental parameter The ionosphere is highly variable and is driven externally by solar extreme ultraviolet 

(EUV) and X-ray irradiance, geomagnetic storms, and the neutral atmosphere. These 

drivers can affect the total electron content (TEC), highest affected frequency (HAF), 

maximum usable frequency (MUF), height of the F2 layer (hmF2), peak density of the 

F2 layer (NmF2), and the phase and amplitude scintillation indices Sigma-Phi and S4. 

Ionospheric variability affects radio signal propagation by changing the reflection, 

refraction, absorption, and delay of the radio signal.

Methodology for determining 

benchmarks

The extreme impacts of ionospheric disturbances on communication, navigation, 

and positioning systems would be fully described by global maps of a number of 

parameters, including the TEC, HAF, MUF, hmF2, NmF2, and the phase and amplitude 

scintillation indices Sigma-Phi and S4.

1-in-100-year benchmarks Not feasible to compute benchmark. Complexities in modeling ionospheric 

disturbances prevented setting useful benchmarks with reasonable uncertainty.

Theoretical maximum 

benchmarks

Not feasible to compute benchmarks. Advances in scientific understanding of the 

entire magnetosphere-ionosphere-thermosphere (MIT) system and its response to 

extreme geomagnetic storms would improve the ability to quantify benchmarks for 

ionospheric disturbances.

While benchmarks were not developed, values were measured during the intense 

2003 Halloween event for ionospheric disturbances. These include a vertical TEC of 

250 TECu (where 1 TECu = 1 ×1016 electrons m-2

) with an associated error of 

approximately 3 TECu, a TEC spatial range gradient of 40 cm/km, and a TEC 

temporal range gradient of 15 cm/s.d, e, f These conditions could last up to several 

days.g

Benchmarks for Solar Radio Bursts 

Environmental parameter Solar radio bursts are emitted from the Sun during solar flares; on average, solar 

flares occur every 3.5 days at solar maximum and every 18.5 days at solar minimum. 

Solar flux units (sfu) are used to describe the intensity of the incident solar flare at 

Earth.

Methodology for determining 

benchmarks

The 1-in-100-year benchmarks were defined using the peak flux distribution of solar 

radio bursts for the frequency bands that align with standard usage, as presented by 

Nita et al.h The authors’ analysis relies on the most extensive data set on solar radio 

bursts, collected by the United States Air Force’s Radio Solar Telescope Network 

(RSTN), with data from 1960 to present. 

1-in-100-year benchmarks Frequency Band Name Frequency 

(megahertz)

Benchmark 

(sfu)i Error Bars (sfu)i

Very High Frequency (VHF) 30–300 2.8 × 109 [-2.5 × 109, +0]

Ultra High Frequency (UHF) 300–3000 1.2 × 107 [-1 × 107, +0]

Global Positioning System 

(GPS)

1,176–1,602 1.2 × 107 [-1 × 107, +0]

F10.7 2,800 1.3 × 107 [-1 × 107, +0]

Microwave 4,000–20,000 3.7 × 107 [-3 × 107, +0]

Theoretical maximum 

benchmarks

Not feasible to compute benchmarks. Identifying the theoretical maximum intensity 

from solar radio bursts in each frequency band could be informed by determining 

the maximum feasible brightness temperature and area of the burst source.
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Benchmarks for Upper Atmosphere Expansion

Environmental parameter Upper atmosphere expansion refers to changes in the thermosphere that can affect 

satellite drag at low Earth-orbit (LEO). The primary expansion effect arises from an 

increase in temperature, which can be driven by solar or geomagnetic activity, which 

causes an increase in neutral density at a fixed altitude in Earth’s upper atmosphere. 

This heating can be driven by solar or geomagnetic activity. This is quantified by the 

percent neutral density increase.

Methodology for determining 

benchmarks

The benchmark from solar extreme ultraviolet (EUV) and far ultraviolet (FUV) 

radiation on timescales greater than one day was determined using the 

NRLMSISE-00 empirical neutral density model.j The neutral density response is 

defined at 250 kilometers, 400 kilometers, and 850 kilometers altitude as percent 

increases relative to empirical model reference values using 240 and 200 solar flux 

units (sfu) for the F10.7 daily and 81-day mean, respectively.

The benchmark from the impact of EUV enhancement during impulsive events, such 

as solar flares, estimates a 100-year flare as an X30 and a theoretical maximum as an 

X40.k The values quoted are peak dayside neutral density increases relative to the 

background before the flare. The values are quoted at 400 kilometers altitude only 

and are the response at a median F10.7 solar flux level of 150 sfu.

The benchmark from the impact of coronal mass ejections (CMEs) uses the predicted 

1-in-100-year solar wind values to drive the Weimer empirical magnetospheric 

convection model with expected magnetospheric saturation. The percent increase is 

relative to the values experienced during the Halloween or Bastille Day storms as 

predicted by the CTIPe physical model.

1-in-100-year benchmarks Cause of Upper Atmosphere 

Expansion

Altitude 

(km)

Benchmark

(percent neutral 

density increase)m

Associated 

Uncertainty

Solar Extreme Ultraviolet and 

Far Ultraviolet Radiation

250 50% ± 30%

400 100% ± 30%

850 200% ± 30%

Solar EUV Radiation 

Enhancement during Solar 

Flares

400 75% factor of 2

Coronal Mass Ejections Driving 

Geomagnetic Storms 400 400% ± 100%

Theoretical maximum 

benchmarks Solar Extreme Ultraviolet and

Far Ultraviolet Radiation

250 100% factor of 2

400 160% factor of 2

850 300% factor of 2

Solar EUV Radiation 

Enhancement during Solar 

Flares

400 135% factor of 2

Coronal Mass Ejections Driving 

Geomagnetic Storms

400 Not feasible to 

compute benchmarks

± 100%

Notes: INTEGRAL/IREM is the Radiation Environment Monitor (IREM) on board the International Gamma Ray Astrophysical 

Laboratory (INTEGRAL) spacecraft, which is in an elliptical orbit with perigee of 9,000 km and apogee of 155,000 km. L* = n

describes a set of planetary magnetic field lies which cross the Earth’s magnetic equator at n earth radii from the center of 

the Earth, e.g., L*= 6 describes the set of magnetic field lines three earth radii from the center.
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a

 φ is the modulation parameter used in the force-field modulation approximation.

b

 The Gumbel function is a particular case of the generalized extreme value distribution function.

c

 Integrated over all energies greater than the nucleon energy of interest

d A. J. Mannucci, “Global Ionospheric Storms,” white paper submitted to the Space Studies Board of the U.S. National 

Research Council for its 2010 “decadal survey” in solar and space physics (heliophysics), 2010.

e

 P. Stephens et al., “New leveling and bias estimation algorithms for processing COSMIC/FORMOSAT-3 data for slant total 

electron content measurements,” Radio Science 46 (2011): RS0D10. doi: 10.1029/2010RS004588

f

 S. Datta-Barua, “Ionospheric Threats to Space-Based Augmentation System Development,” presented at the 

Proceedings of the 17th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION 

GNSS 2004), Long Beach, California, 2004.

g

 M. Hapgood et al., Summary of Space Weather Worst-Case Environments, RAL technical report, Revised edition, RAL-TR2016-006, May 2016.

h

 G. M. Nita et al. “The Peak Flux Distribution of Solar Radio Bursts.” Astrophysical Journal 570 (May 2002): 423–438.

i

 Solar flux is measured in watts (W) per square meter (m2) per hertz (Hz), with 1 sfu equal to 10-22 W m-2 Hz-1.

j

 J. M. Picone et al., “NRLMSISE-00 Empirical Model of the Atmosphere: Statistical Comparisons and Scientific Issues,” 

Journal of Geophysical Research 107 (2002): A12, 1468; J. T. Emmert, “Thermospheric Mass Density: A Review,” Advances 

in Space Research 56 (2015): 773–824.

k C. J. Schrijver et al. “Estimating the Frequency of Extremely Energetic Solar Events, Based on Solar, Stellar, Lunar, and 

Terrestrial Records.” Journal of Geophysical Research 117 (2012): A08103, based on an NRC study.

l

 D. R. Weimer, “Improved Ionospheric Electrodynamics Models and Application to Calculating Joule Heating Rates,” 

Journal of Geophysical Research 110 (2005): A05306.

m See methodology description above for details on benchmarked values for each cause of upper atmosphere expansion.
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Benchmarks for Induced Geo-electric Fields

1. Space Weather Action Plan 1.1.1

Action 1.1.1 of the Space Weather Action Plan states: “The Department of the Interior (DOI), the 

Department of Commerce (DOC), and the National Aeronautics and Space Administration (NASA), in 

coordination with the Department of Homeland Security (DHS), the Department of Energy (DOE), and 

the National Science Foundation (NSF), will: (1) assess the feasibility of establishing functional 

benchmarks [for induced geo-electric fields] using currently available storm data sets, existing models, 

and published literature; and (2) use the existing body of work to produce benchmarks [for induced 

geo-electric fields] for specific regions of the United States.”

2. Induced Geo-electric Fields

Geo-electric fields are induced in Earth’s electrically conducting interior by time-dependent 

geomagnetic field variation. During intense magnetic storms, induced geo-electric fields can drive 

quasi-direct currents of electricity of sufficient strength to interfere with operation of the power grid, 

sometimes causing blackouts and damaging transformers. Geomagnetic disturbances have affected 

power grids in the past. For example, in March 1989, an intense magnetic storm caused the collapse of 

the entire Hydro-Quebec power grid in Canada. More recently, in October 2003, a magnetic storm 

caused disturbances in power grids in Scotland and Sweden. According to some scenarios, the future 

occurrence of an extremely intense magnetic storm could result in widespread and possibly cascading 

failures if the power grid is not sufficiently resilient to the effects of space weather. Even for brief periods 

of time, loss of power can prove disruptive for communities.

3. Methodology for Establishing Benchmarks for Induced Geo-electric Fields

This task focused on the development of a formal statistical product in terms of maps of geo-electric 

hazard. For practical evaluation of geo-electric hazards, estimates of two geophysical quantities are 

needed: (1) the surface impedance relationship between geomagnetic variation and the induced 

geo-electric field and (2) a measure of geomagnetic activity realized at Earth’s surface.

Surface impedance is a function of the three-dimensional conductivity structure of the solid Earth and 

ocean. It is usually expressed in the Fourier-transformed frequency domain as a tensor. Impedance can 

differ greatly from one geographic location to another; it is not readily estimated from geological and 

tectonic models. Impedance is measured, however, during magnetotelluric surveys, such as the one 

sponsored by the NSF’s EarthScope program,4 which has, so far, been completed for about half of the 

contiguous United States.

Surface geomagnetic activity is measured at magnetic observatories, such as those operated within the 

INTERMAGNET consortium,5 or at variometer stations, such as those of the ULTIMA consortium.6 For 

purposes of hazard assessment, analysis of magnetometer time series can be focused on either the 

time-autocorrelated waveform nature of the data, or it can be focused on statistical analysis of 

 4 A. Schultz et al. “USArray TA Magnetotelluric Transfer Functions: REU60, 2006–2018,” doi:10.17611/DP/11455918. 

Retrieved from the IRIS database August 16, 2017. 5 J. J. Love and A. Chulliat, “An International Network of Magnetic Observatories,” EOS, Transactions, American Geophysical

Union 94, no. 42 (2013): 373–384, doi:10.1002/2013EO42 6 K. Yumoto et al., “ULTIMA of Ground-Based Magnetometer Arrays for Monitoring Magnetospheric and Ionospheric 

Perturbations on a Global Scale,” presented at 2012 Fall Meeting, AGU, San Francisco, California.
SPACE WEATHER PHASE 1 BENCHMARKS

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characteristic features identified in the data. These two approaches are orthogonal, but knowing the 

results of both is useful. This report takes a statistical approach for benchmarking induced geo-electric 

field amplitudes that are unlikely to occur more than once in 100 years.

To use the measured impedances and to perform a statistical analysis of observatory data, Love et al. 

focused on sinusoidal variation over a finite window of time.7 Analysis of geomagnetic variation is 

limited on the high-frequency end of the spectrum by the one-minute sampling rate of the historical 

magnetic observatory data. For specificity, the amplitudes of geomagnetic activity Fourier waveforms 

having period of 240 seconds and persisting over a duration of 600 seconds were estimated from 

approximately 30 years of observatory data. This was done for both north-south (px) and east-west (py) 

magnetic vector components. These amplitudes were then extrapolated using a simple statistical 

model to once-per-hundred-year values. The frequency domain multiplication of a Fourier magnetic 

field amplitude with an impedance tensor gives a geo-electric amplitude.

4. Benchmarks

For the one-in-100-year benchmark, detailed results are discussed in Love et al.8 A map of once-percentury geo-electric exceedance amplitudes (Ee

x

) for px is shown in Figure 1. Depending on location, 

once-per-century geo-electric exceedance amplitudes can exceed 1 volt per kilometer (V/km) in many 

places across the northern Midwest United States and some places in the Eastern United States. Among 

the surveyed sites, the median geo-electric amplitude is 0.26 V/km, but because of the combination of 

geographic differences in geomagnetic activity and Earth-surface impedance, geo-electric amplitudes 

differ by over two orders of magnitude. At some sites in Minnesota, for example, once-per-century 

amplitudes exceed 3.00 V/km. Across other areas, such as in Florida, these amplitudes are less than 0.1 

V/km. In northern Minnesota, once-per-century amplitudes exceed 14.00 V/km, while just over 100 

kilometers away, amplitudes are only 0.08 V/km. One standard-deviation error, the result of statistical 

variance in the geomagnetic data, is estimated to be about 30 percent, which is small compared to the 

differences.

At some sites in the northern Midwest United States, once-per-century geo-electric amplitudes exceed 

2 V/km, which is the level inferred to have been realized in Quebec during the March 1989 storm. As a 

point of reference only, amplitudes in some regions of northern Minnesota exceed the once-per-century 

baseline amplitude of 8 V/km (without latitude corrections) used by the North American Electric 

Reliability Corporation (NERC) in its benchmark study using synthetic Earth impedances.9

 7

 J. J. Love et al., “Geoelectric Hazard Maps for the Continental United States,” Geophysical Research Letters, 43, no. 18

(2016.): 9415–9424, doi:10.1002/2016GL070469 8

 Ibid.

9

 NERC, “Benchmark Geomagnetic Disturbance Event Description” (2014): 1–26.
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Source: Love et al., “Geoelectric Hazard Maps for the Continental United States,” Geophysical Research Letters 43 (18, 2016), 

9415–9424, doi:10.1002/2016GL070469

Note: No estimates are available outside of survey sites shown.

Figure 1. Once-per-century geo-electric exceedance amplitudes10 (E in V/km), for north-south geomagnetic variation at 240 

seconds (and over 600 seconds)

No theoretical maximum benchmarks were computed for the following reason: higher frequency 

amplitudes cannot be reasonably estimated from the observatory data; lower frequency harmonics, or 

those persisting for long periods of time, will generally yield smaller geo-electric amplitudes; additional 

investigation could inform understanding of the issue. Phase 2 activities will be directed toward 

(1) developing hazard maps for other frequencies, (2) developing scenario studies for individual 

magnetic storms (time-dependent maps of induced geo-electric fields), (3) investigating “coast effects” 

related to the conductivity contrast between the solid Earth and ocean, (4) developing a theoretical 

benchmark, and (5) assessing hazards at additional locations on the map, potentially including Canada.

 10 Statistical values for 100 years are often reported as exceedances for all types of geophysical benchmarks. Specific values 

are usually not described in terms of statistics; what is described is a range of values, in this case 100-year “value” is a 

value that is exceeded, on average, once per 100 years.
SPACE WEATHER PHASE 1 BENCHMARKS

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Benchmarks for Ionizing Radiation

1. Space Weather Action Plan 1.2.1

Action 1.2.1 of the Space Weather Action Plan states: “NASA and DOC, in coordination with NSF, the 

Department of Transportation (DOT), the Department of Defense (DOD), and the Federal 

Communications Commission (FCC), will: (1) assess the feasibility and utility of establishing functional 

benchmarks for ionizing radiation using the existing models and body of literature for this 

phenomenon; and (2) use the existing body of work to produce benchmarks.”

2. Ionizing Radiation

A major consideration in setting environmental benchmarks for ionizing radiation is to select the types 

of particles and energy ranges that have detrimental effects on vulnerable infrastructures and assets 

critical to the Nation’s economic and social well-being. The immediate need is to develop plans for 

coping with low-frequency/high-intensity space weather events, thus the focus on benchmarking 

worst-case and 1-in-100-year fluxes for each type of radiation at locations of interest. Worst-case fluxes 

for solar energetic particles and radiation belt populations are estimated with statistical maxima using 

observational data, because it is not feasible to calculate theoretical maxima, the maximum values 

these populations could possibly achieve, until a stronger theoretical foundation has been developed. 

However, a theoretical maximum for galactic cosmic rays is provided later in this chapter by assuming 

the flux distribution in the Local Interstellar Medium is not attenuated during transport to Earth.

Different types and energy ranges of ionizing radiation present hazards for satellites, astronauts, airline 

crews, and aviation communication. Exposure of satellites to enhanced fluxes of ionizing radiation can 

cause operational anomalies and damage to electronics, solar arrays, and optical systems. On the 

international space station this exposure presents a significant hazard to astronauts. The highest 

energy radiation penetrates deeply into the atmosphere and can be hazardous to airline crews on polar 

routes and significantly degrade high frequency (HF) communication via polar cap absorption events. 

Each of the specific species and energy ranges of ionizing radiation at key altitudes and locations 

responds differently to disruptions in the space environment. Environmental benchmarks for ionizing 

radiation must capture this complexity to maximize their future usefulness in designing around or 

mitigating hazards to vulnerable technologies, astronauts, and aviation crews during an extreme event.

Presented here is a set of benchmark values for only the most representative species, locations, and 

energy ranges along with explanatory material needed to understand how these extreme values were 

estimated. Due to the complexity and diversity of ionizing radiation hazards, the literature survey,

undertaken to produce the Phase 1 benchmarks, captured more extensive information on the 

characteristics of each type of radiation and enabled a description of worst case and 1-in-100-year 

values on a finer energy resolution and covering more species of GCRs and SEPs. 

A summary of the major types of radiation is given in Table 2.
SPACE WEATHER PHASE 1 BENCHMARKS

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Table 2. Three types of ionizing radiation, their sources, and the hazards they pose

Solar Energetic Particles (SEPs)

Type

Ions: 1 megaelectron volts (MeV) to 20 gigaelectron volt (GeV) hydrogen 

(H), helium (He), and heavy ions

Electrons: Smaller fluxes with particle energies up to 10 MeV 

Source Accelerated in flares, at shocks in the corona, and at interplanetary 

shocks, in particular those ahead of fast coronal mass ejections

Hazard

Ions: 

> 500 MeV: penetrate to ground level

> 100 MeV: radiation hazard for airplane crews and passengers

>30 MeV hazard for space tourism 

Electrons

Penetrate into polar cap region. Reach the moon and the Lagrangian 

points where space missions reside.

Cosmic Rays

Type

Near-constant isotropic flux. Nuclei of all natural elements in the 

Periodic Table: 90% H. 9% He, 1% heavier elements

Energy Range: 1MeV–1 billion MeV 

Source

Not fully understood. Thought to originate in the large, expanding shells 

of supernovas. A small portion are accelerated at neutron stars or black 

holes.

Hazard

Radiation hazard for astronauts and airplane crews. Effects of the 

primary cosmic rays scale with the square of the atomic number. Small 

percentages of heavy ions have large effects. Generate particle showers 

passing through thin mechanical shielding or the atmosphere. The 

atmosphere absorbs most of the cosmic ray energy before reaching the 

surface. However, protection from cosmic rays in space is the most 

difficult of all ionizing radiation.

Radiation Belts

Type

Inner belt (1.2-3 Earth radii): > 1 MeV H+ (proton), <MeV electrons

Outer belt (3-10 Earth radii): 0.1–10 MeV electrons, < 1 MeV H+ (proton)

Source

Intensity determined by the balance among radial diffusion, waveparticle interactions, non-adiabatic processes and (inner belt) neutrino 

decay. Outer electron belt drops out & reforms in response to space 

weather. New belts rapidly formed in response to interplanetary 

shocks/SEPs

Hazard

Inner belt: Greatest hazard is > 30 MeV protons that can penetrate space 

suits & spacecraft walls. An expanded inner belt may encompass the 

International Space Station and low Earth orbit (LEO) satellites. 

Outer belt: 0.1–10 MeV electrons cause surface charging, arcing & 

phantom commands on satellites
SPACE WEATHER PHASE 1 BENCHMARKS

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3. Benchmarks for Ionizing Radiation

Solar Particle Events

Worst-case fluence is estimated by dividing the SEP energy spectra up into energy bins and performing 

a worst-case analyses for each bin using the methods of the Maximum Entropy Principle and Extreme 

Value Theory to construct an upper limit energy spectrum. The concepts behind Extreme Value Analysis 

are described in a later section on radiation belt benchmarks. As discussed by Xapsos et al. “A 

reasonable interpretation for the upper limit fluence parameter is that it is the best value that can be 

determined for the largest possible event fluence, given limited data. It is not an absolute upper limit 

but is a practical and objectively determined guideline for use in limiting design costs.”11 This upper 

limit or statistical maximum spectra along with the +1 sigma estimate is given in Table 3.

Table 3. Integral SEP fluence for the statistical maximum case

SEP Integral Fluence (protons/cm2) at Geostationary Orbit (GEO)a

Energy 

(MeV/ 

nucleon)

Upper 

Limit

Upper 

Limit 

+ 1 Sigma

Energy 

(MeV/ 

nucleon)

Upper 

Limit

Upper 

Limit

+ 1 Sigma

Energy 

(MeV/ 

nucleon)

Upper 

Limit

Upper 

Limit + 1 

Sigma

1 1.56 × 1011 3.54 × 1011 9 3.84 × 1010 8.71 × 1010 80 2.37 × 109 5.39 × 109

2 1.09 × 1011 2.46 × 1011 10 3.49 × 1010 7.93 × 1010 90 1.82 × 109 4.14 × 109

3.2 8.44 × 1010 1.92 × 1011 20 1.94 × 1010 4.39 × 1010 100 1.45 × 109 3.30 × 109

4 7.41 × 1010 1.68 × 1011 32 1.24 × 1010 2.82 × 1010 200 2.80 × 108 6.36 × 108

5 6.46 × 1010 1.47 × 1011 40 9.48 × 109 2.15 × 1010 250 1.57 × 108 3.56 × 108

6.3 5.30 × 1010 1.20 × 1011 50 6.38 × 109 1.45 × 1010 320 7.87 × 107 1.79 × 108

7.1 4.76 × 1010 1.08 × 1011 63 4.00 × 109 9.09 × 109 400 4.17 × 107 9.46 × 107

8 4.27 × 1010 9.70 × 1010 71 3.11 × 109 7.07 × 109 500 2.21 × 107 5.01 × 107

a Integrated over all energies greater than the nucleon energy of interest

Note that fluence and flux units are both used to describe ionizing radiation in this chapter. Flux is the 

rate at which particles flow through a unit area, given in units of particles/(cm2 s). Fluence is the

time-integrated flux or the number of particles delivered to a unit area over some interval of time, given 

in units of particles/cm2

. This quantity does not contain information about the directionality of the flux. 

The differential flux contains directional information expressed as a function of solid angle and may 

also be expressed as a function of energy, given in units of particles/(cm2 s sr) or particles/(cm2 s sr keV),

respectively.

 11 M. A. Xapsos et al., “Probability Model for Worst Case Solar Proton Event Fluences,” IEEE Transactions on Nuclear Science

46, no. 6 (December 1999): 1481–5, 1485.
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Solar energetic particles with energies >100 MeV will penetrate down to the altitude of commercial 

polar flight routes (~30,000 feet or 10 kilometers) exposing airplane crew and passengers to increased 

radiation. SEP with energies above 30 MeV will increase radiation doses at the “edge” of space (100,000 

feet or 30 kilometers) now accessed by space tourism aboard high altitude balloons.

Galactic Cosmic Rays (GCRs)

The primary variation in GCRs is due to the modulation stemming from the 11-year solar cycle12, with a 

minimum of GCRs during the solar maximum, and a maximum during periods of low solar activity. The 

most recent solar minimum, with its exceptionally low levels of solar activity, led to the highest GCR 

fluxes observed in the space age (i.e., since the late 1950s).

The 1-in-100-year benchmarks for GCR fluxes shown in Table 4 were derived using local interstellar 

spectra (LIS) parameters from O’Neill, Golge, and Slaba, modulated with φ = 200 megavolts (MV).13

Transport of GCRs through the heliosphere is a complicated problem, but a simple one-parameter 

“force-field approximation”14 can be used to benchmark GCR ionizing radiation. The modulation 

parameter (φ) in the force-field modulation approximation15 may be used to account for attenuation of 

the GCRs due to the state of the heliosphere. The φ is in units of rigidity (MV), and is proportional to the 

momentum/charge required for a particle to penetrate the heliosphere. Larger values of φ represent 

lower GCR fluxes reaching Earth. The value of φ can be approximated from solar activity indices, such as 

sunspot number, but a more accurate derivation can be obtained from current GCR data, such as those 

available from the Advanced Composition Explorer (ACE) spacecraft.

As a completely worst-case GCR intensity, one can take the local interstellar spectra (LIS), essentially 

assuming that there is no solar modulation or φ=0 (i.e. that the heliopause has moved inside 1 AU) (Table 

5). The LIS does change with time, but the time scales are tens of thousands of years or longer. Tables

4 and 5 give the most important elements, but GCRs composed of all the other elements will be present, 

and their fluxes can be calculated.

 12 The solar cycle refers to the periodic increase and decrease in solar activity caused by the reversal of the Sun’s magnetic 

poles. The solar cycle takes place over approximately 11 years and is tracked by counting the number of sunspots, an 

indicator of solar activity. For more information, see NASA’s primer on the solar cycle: 

https://www.nasa.gov/mission_pages/sunearth/news/solarcycle-primer.html. Plots of the official count of daily and 

monthly sunspot numbers are available at the World Data Center - Sunspot Index and Long-term Solar Observations: 

http://sidc.be/silso/datafiles#total 13 P. M. O’Neill, S. Golge, and T. C. Slaba, “Badhwar-O’Neill 2014 Galactic Cosmic Ray Flux Model Description,” NASA/TP-2015-

218569 (Houston, Texas: NASA Johnson Space Center, March 2015). 14 L. J. Gleeson and W. I. Axford, “Solar Modulation of Galactic Cosmic Rays,” Astrophysical Journal 154 (December 1968):

1011–26. 15 Gleeson and Axford. “Solar Modulation of Galactic Cosmic Rays.”
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Table 4. Differential GCR fluxes for 

the most abundant elements at 1 astronomical unit (AU) for the 1-in-100-year case

Differential GCR Flux (particles/(cm2 sr s MeV/n)) at 1 AU, φ = 200 MV

Uncertainties are 10%-25%

Energy/nucleon Hydrogen Helium Carbon Oxygen Iron

10 MeV 1.3 × 10-4 1.7 × 10-5 6.1 × 10-7 5.3 × 10-7 1.2 × 10-7

30 MeV 3.2 × 10-4 3.7 × 10-5 1.3 × 10-6 1.1 × 10-6 2.5 × 10-7

100 MeV 5.3 × 10-4 4.7 × 10-5 1.7 × 10-6 1.5 × 10-6 2.9 × 10-7

300 MeV 4.1 × 10-4 2.7 × 10-5 9.9 × 10-7 8.9 × 10-7 1.5 × 10-7

1 GeV 1.2 × 10-4 6.9 × 10-6 2.6 × 10-7 2.4 × 10-7 3.7 × 10-8

30 GeV 1.7 × 10-5 8.1 × 10-7 3.3 × 10-8 3.0 × 10-8 4.9 × 10-9

100 GeV 1.2 × 10-6 5.4 × 10-8 2.4 × 10-9 2.1 × 10-9 3.9 × 10-10

300 GeV 6.4 × 10-8 2.6 × 10-9 1.3 × 10-10 1.1 × 10-10 2.3 × 10-11

1000 GeV 2.9 × 10-9 1.1 × 10-10 6.2 × 10-12 5.0 × 10-12 1.2 × 10-12

Table 5. Differential GCR fluxes for

the most abundant elements for LIS for the theoretical maximum benchmarks

Differential GCR Flux (particles/(cm2 sr s MeV/n)) for LIS, φ = 0 MV

Uncertainties are 10%-25%

Energy/nucleon Hydrogen Helium Carbon Oxygen Iron

10 MeV 2.6 x 10-1 2.5 x 10-3 8.8 x 10-5 6.8 x 10-5 2.3 x 10-5

30 MeV 5.0 x 10-2 7.7 x 10-4 2.7 x 10-5 2.2 x 10-5 5.8 x 10-5

100 MeV 9.4 x 10-3 2.2 x 10-4 7.9 x 10-6 6.8 x 10-5 1.4 x 10-6

300 MeV 1.6 x 10-3 5.4 x 10-5 2.0 x 10-6 1.8 x 10-6 3.0 x 10-7

1 GeV 2.2 x 10-4 9.3 x 10-6 3.5 x 10-7 3.2 x 10-7 4.9 x 10-8

30 GeV 2.1 x 10-5 9.2 x 10-7 3.7 x 10-8 3.4 x 10-8 5.4 x 10-9

100 GeV 1.3 x 10-6 5.6 x 10-8 2.5 x 10-9 2.2 x 10-9 4.0 x 10-10

300 GeV 6.6 x 10-8 2.6 x 10-9 1.3 x 10-10 1.1 x 10-10 2.3 x 10-11

1000 GeV 2.9 x 10-9 1.1 x 10-10 6.2 x 10-12 5.1 x 10-12 1.2 x 10-12
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Radiation Belts

For the radiation belt benchmarks, the main objective is to provide thresholds for the upper limit worstcase and 1-in-100-year fluxes at strategic locations. The locations are important because the radiation 

belts do not respond uniformly to strong driving, but individual populations and locations may respond 

differently to the same space weather event. Even at a single location, different portions of the energy 

spectra of worst-case fluxes are contributed by different events. Each is a spectrum of worst cases 

rather than a worst-case spectrum.

Estimates of the probability and severity of extreme flux events are obtained using extreme value theory 

(EVT). EVT analysis is aimed at characterizing the low-frequency tail of the probability distribution, 

which is not well represented by the “parent” probability distribution used to describe the full 

population of radiation belt flux observations. This parent distribution, which provides the probability 

that the flux will be less than or equal to a selected value, underestimates the value and/or frequency 

of the events in the low-frequency tail. Extreme value analysis constructs a separate probability 

distribution for the extreme tail. There are two main methods for EVT analysis: one involves block 

maxima, the other is based on exceedances over a high threshold. The “block maximum” method used 

by O’Brien et al. to calculate results in Table 6, divides the observations into non-overlapping time 

blocks of identical duration and takes the maximum flux value in each time block.16 The “exceedances 

over a high threshold” method, used by Meredith et al. for results in Table 6, takes all flux values in the 

distribution exceeding a specified threshold flux value.17 The resulting set of extreme flux values in each 

case is fit with a function to characterize the probability distribution in the low-frequency high-severity 

tail. Depending on the observations, the tail distribution can have an upper limit cut-off value, a power 

law fall off or an exponential fall off towards decreasing occurrence probabilities. Combining this with 

the parent probability distribution, an estimate of the radiation belt flux in a 1-in-100 year event, which 

has a 1% probability of occurring in any given year, is provided by the flux value at the 99th percentile. 

The flux value at the 100th percentile is an estimate of the worst-case flux or statistical maximum. 

O’Brien et al. find an upper limit flux cutoff in the low-frequency high-severity tail, which represents a 

true worst-case flux. 

 16 T. P. O’Brien et al., “Extreme Electron Fluxes in the Outer Zone,” Space Weather 5, no. 1 (January 2007),

doi:10.1029/2006SW000240

17 N. P. Meredith et al., “Extreme Relativistic Electron Fluxes at Geosynchronous Orbit: Analysis of GOES E >2 MeV Electrons,”

Space Weather 13, no. 3 (March 2015): 170–184, doi:10.1002/2014SW001143
SPACE WEATHER PHASE 1 BENCHMARKS

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Table 6. Estimates of 1-in-100-year and statistical maximum electron radiation belt fluxes compared to the most extreme 

observed values

Location Energy Integral Electron Fluxes

1-in-100-Years 

(units = cm−2 s−1 sr−1)

Most Extreme Observed (date)

(units = cm−2 s−1 sr−1)

GEO (GOES-West)a >2 MeV 7.68 × 105 4.92 × 105

(7/29/2004 - 1 in 50 yrs)

GEO (GOES-East)a >2 MeV 3.25 × 105 1.93 × 105

(7/29/2004 - 1 in 50 yrs)

Worst Case (Statistical Maximum)

(units = cm−2 s−1 sr−1)

Most Extreme Observed (date)

(units = cm−2 s−1 sr−1)

HEO1 at L = 4.0b

>8.5 MeV 3.5 [1.7, 7.3] × 102 2.4 × 102 (8/30/1998)

>4.0 MeV 4.5 [1.7, 12] × 104 2.6 × 104 (8/5/2004)

>1.5 MeV 2.6 [2.2, 3.0] × 105 2.4 × 104 (8/30/1998)

HEO3 at L = 6.0b >630 keV 1.0 [0.34, 3.3] × 105 6.0 × 104 (6/27/1998)

at L = 4.0 >630 keV 4.5 [4.0, 5.1] × 105 4.3 × 105 (8/29/1998)

at L = 2.25 >630 keV 2.1 [1.4, 3.1] × 105 1.9 × 105 (11/13/2004)

Location Energy Differential Electron Fluxes

1-in-100-Years 

(units = cm−2 s−1 sr−1 MeV-1)

Most Extreme Observed (date)

(units = cm−2 s−1 sr−1keV-1)

INTEGRAL /IREM at L*=6.0c

2.05 MeV 1.58 × 105 1.4 × 105 (05/11/2016)

1.82 MeV 1.76 × 105 -

1.27 MeV 5.99 × 105 5.74 × 105 (05/11/2016)

690 keV 4.68 × 106 4.60 × 106 (05/11/2016)

INTEGRAL/IREM at L*=4.5c

2.05 MeV 5.79 × 105 5.65 × 105 (04/15/2006)

1.82 MeV 7.29 × 105 -

1.27 MeV 2.52 × 106 2.30 × 106 (04/15/2006)

690 keV 1.46 × 107 1.42 × 107 (04/15/2006)

Worst Case (Statistical Maximum)

(units = cm−2 s−1 sr−1MeV-1)

Most Extreme Observed (date)

(units = cm−2 s−1 sr−1MeV-1)

GEO (LANL)b 2.65 MeV 5.9 [4.7, 7.3] × 104 5.1 × 104 (7/30/2004)

625 keV 4.1 [3.2, 5.2] × 106 3.4 × 106 (7/29/2004)

270 keV 2.0 [1.5, 2.6] × 107 1.6 × 107 (6/5/1994)
SPACE WEATHER PHASE 1 BENCHMARKS

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Location Energy Differential Electron Fluxes (continued)

Worst Case (Statistical Maximum)

(units = cm−2 s−1 sr−1MeV-1)

Most Extreme Observed (date)

(units = cm−2 s−1 sr−1MeV-1)

INTEGRAL/IREM at L*=6.0c

1.82 MeV 1.98 x 105 -

1.27 MeV 8.11 x 105 5.74 × 105 (05/11/2016)

690 keV 4.82 x 106 4.60 × 106 (05/11/2016)

INTEGRAL/IREM at L*=4.5c

2.05 MeV 6.08 x 105 5.65 × 105 (04/15/2006)

1.82 MeV 8.10 x 105 -

1.27 MeV 4.74 x 106 2.30 × 106 (04/15/2006)

690 keV 1.51 x 107 1.42 × 107 (04/15/2006)

Notes: INTEGRAL/IREM is the Radiation Environment Monitor (IREM) on board the International Gamma Ray 

Astrophysical Laboratory (INTEGRAL) spacecraft, which is in an elliptical orbit with perigee of 9,000 km and apogee of 

155,000 km. HEO1-HEO3 are three communication satellites in highly elliptical orbit with perigee of a few hundred 

kilometers and apogee 

of ~7 RE.

a Meredith et al., “Extreme Relativistic Electron Fluxes at Geosynchronous Orbit: Analysis of GOES E >2 MeV Electrons.” 

b O’Brien et al, “Extreme Electron Fluxes in the Outer Zone.”

c Meredith et al. (2017), “Extreme Relativistic Electron Fluxes in the Earth’s Outer Radiation Belt: Analysis of INTEGRAL 

IREM Data”, Space Weather, 15, 917-933, doi:10.1002/2017SW001651

1-in-100-Year and Worst-Case Electron Radiation Belt Fluxes

Studies of LANL geosynchronous or highly elliptical orbit (HEO) observations18 over one solar cycle or 

more suggest that the relativistic electron fluxes in GEO and HEO have upper limits—equivalent to 

1-in-100-year fluxes—that do not vary with solar wind speed. Another study, using GOES-East and 

GOES-West fluxes over 19.5 years, does not find such an upper limit and speculates that confirming its 

existence would require a longer data set.19 All of these studies use extreme value analysis to get

estimates of worst-case fluxes20 or 1-in-10-year, -20-year, -50-year, -100-year, and -150-year fluxes.21

A more global estimate over energy and altitude of extreme integral flux values can be obtained using 

the statistical AE9 reference model. Figure 2 is a plot of the mean annual electron fluence from the 

statistical AE9 reference model. As indicated in the figure, the mean integral fluence at > 2 MeV in 

geostationary orbit is approximately 1012 cm-2

. Assuming the AE9 flux is constant and isotropic over 4π 

steradian over one year, the mean annual fluence would correspond to a flux of approximately 2.5x103

(cm2 s sr)-1

. The estimated GOES West 1-in-100 year flux (approximately 7.68 × 105 (cm2 s sr)-1) is roughly 

300 times larger than the AE9 flux. This can be used to develop a scaling factor for the AE9 model, which 

would then provide a crude estimate of the altitude distribution of 1-in-100 year fluxes with the 

 18 H. C. Koons, “Statistical Analysis of Extreme Values in Space Science,” Journal of Geophysical Research 106 (June 2001): A6, 

10915–21, doi:10.1029/2000JA000234; O’Brien et al., “Extreme Electron Fluxes in the Outer Zone”; and G. D. Reeves et al., 

“On the Relationship Between Relativistic Electron Flux and Solar Wind Velocity: Paulikas and Blake revisited,” Journal of

Geophysical Research 116 (February 2011): A2. 19 Meredith et al., “Extreme Relativistic Electron Fluxes at Geosynchronous Orbit.” 20 Koons, “Statistical Analysis of Extreme Values in Space Science”; O’Brien et al. “Extreme Electron Fluxes in the Outer 

Zone.” 21 Meredith et al., “Extreme Relativistic Electron Fluxes at Geosynchronous Orbit.”
SPACE WEATHER PHASE 1 BENCHMARKS

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assumption that the energy spectra given by AE9 don’t change with time or the level of magnetic 

activity. More sophisticated methodologies will be the subject of future updates to the benchmarks.

Figure 2. Integrated electron fluence versus energy in GEO from the AE9 model

Figure 2 shows the mean, median, and 90th percentile integral fluence from the AE9 model over the 

time interval of a year. The 1-in-100-year flux can be estimated from Figure 2 by dividing the mean 

integral fluence values by 4 × 108 to convert to units of (cm2/(s sr))

-1

, and then multiplying by 300 to scale 

the values to estimated 1-in-100-year flux levels indicated by GOES West observations.

Worst-Case <10s of KeV Substorm Electron Injections

One of the major causes of spacecraft anomalies is electrostatic discharge caused by electrical charges 

built up on satellite surfaces. Major sources of this surface charging are energetic electron injections 

produced by substorms. Substorm injections extend over a limited time interval (typically 1–2 hours) 

and cover regions that are localized in space. The electron flux at <100 keV energies within a substorm 

interval varies significantly on time scales of minutes. The relationship between the severity of the 

substorm activity and the energy spectra or flux of these energetic electron injections is not yet 

understood. Substorms occur during both magnetic storms and non-storm conditions. However, most 

of the largest auroral disturbances in the interval 1996–2012 as indicated by AL (< –3000 nT) or 

AU (> 1500 nT), listed in Nakamura et al.,22 occurred during major storm events and high-speed solar 

wind. Less severe but longer-lived intervals of substorm injections (and thus extended periodic 

enhancements of the magnetospheric electron environment) are associated with high-speed streams. 

An initial search of the scientific literature provided no estimates of extreme substorm flux values at 

relevant spatial and temporal scales. 

1-in-100-Year Proton Radiation Belt Fluxes

The most significant energy ranges for proton radiation belts depends on the application of interest. 

For example, photovoltaics are lightly shielded with cover glasses, and proton degradation occurs 

 22 M. Nakamura et al., “Statistical analysis of extreme auroral electrojet indices,” Earth, Planets and Space 67 (2015): doi:

10.1186/s40623-015-0321-0
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mainly from incident protons having energies less than 10 MeV. There is interest in higher energy 

protons. For example, electronics in the interior of spacecraft can degrade from cumulative exposure 

to protons, either from a total ionizing dose or displacement damage. High-energy protons can also 

cause single event effects in some of the more sensitive electronic devices. 

A new empirical proton belt model (PolarP) characterizes trapped proton fluxes (protons/(cm2 s MeV)) 

along medium Earth orbit (MEO) during nearly a full solar cycle from 2000 to 2010. The model is based 

on a statistical treatment of proton fluxes from the NASA Polar mission, operating over the period 

1996–2007, which are further scaled along MEO using GPS ns41 observations. This scaling expands the 

energy range to include 50 keV–6 MeV and enables temporal features to be preserved. A more detailed 

description of the model is given by Chen et al.23

The PolarP data set spans only ten years but can be used to derive a crude estimate of 1-in-100-year 

proton fluxes. Proton fluxes (with 24-second resolution) at all magnetic latitudes and L values between

2 and 4 were combined into a single probability distribution to improve the statistics in each energy bin 

of interest. The resulting probability distribution was scaled upward by a factor of ten to approximate 

the 100-year occurrence probabilities. The “low probability/high flux” tail of the distribution was fit with

two functions, a truncated Gumbel function and a simple exponential function. The Gumbel probability 

distribution function is a particular case of the generalized extreme value distribution24 function used 

to model extreme events. The ratio of the exponential/Gumbel fits provides a measure of the 

uncertainty. The 1-in-100 year fluxes based on the Gumbel fit are given in Table 7 with an estimate of 

the uncertainty.

Table 7. Estimates of 1-in-100-year proton radiation belt fluxes in the region L=2-4 including all magnetic latitudes.

Energy (MeV)

1-in-100-Year Proton Flux

(protons/(cm2 s MeV))

based on Gumbel fitting function

Estimate of Uncertainty (ratio of 

exponential and Gumbel fit)

1 2.53 x 1010 ~2

3 1.97 x 1010 ~25

5 1.23 x 108 ~ 2

10 4.49 x 1010 ~20

Source: Yue Chen, LANL

4. Caveats

Occurrence frequency analysis, which is currently the best technique for obtaining extreme value 

estimates for many of the ionizing radiation benchmarks, assumes that the physical processes that 

produced the available time series of a benchmark parameter are the same physical processes 

responsible for the occurrence of extreme events that fall in the distant tail of the probability 

distribution with low frequency but high intensity. It is possible that entirely different system-level 

behaviors (i.e., emergent processes or the chance interaction between two normally separated features 

within the system) may actually trigger extreme but rare events. In other words, an extreme event may 

 23 Y. Chen et al., “Trapped Proton Environment in Medium-Earth Orbit (2000–2010),” Los Alamos National Laboratory (31 

March 2016). 24 The generalized extreme value distribution is a family of continuous probability distributions used in extreme value theory. 

For more information, see https://gmao.gsfc.nasa.gov/research/subseasonal/atlas/GEV-RV-html/GEV-RV-description.html
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not just be a bigger version of a major event. This introduces uncertainty into an extrapolation based 

on fits to major but not extreme events.

As an example, the most extreme observed values of >2 MeV electron radiation belt fluxes in 

geosynchronous orbit (estimated to be a 1-in-50-year event) were observed on July 29–30, 2004. These 

extreme fluxes appeared not during an extreme magnetic storm event but after a sequence of three 

coronal mass ejections (CMEs) that were interspersed with high-speed solar wind hit Earth, producing 

progressively larger magnetic storms. The highest > 2MeV electron fluxes at GEO occurred in the 

recovery phase of the third storm. The working hypothesis is that a rarefaction was produced between 

the leading edge of the high-speed solar wind and a faster moving CME ahead of it.25 The rarefaction 

arrived as the outer belt was rebuilding, allowing the magnetospheric cavity to expand to an unusual 

size, providing an exceptionally large containment vessel for building the outer radiation belt. 

In a second example, a preliminary simulation of a superstorm slightly larger than the Carrington event 

indicates that a major reconfiguration of the electron radiation belts is a possibility in a 1-in-100-year 

event.26 When the boundary of the plasmasphere is pushed below 1.5 Earth radii (RE) in the model and 

is no longer presenting a barrier through its role in enhanced losses, electrons in the outer belt are 

accelerated deeply inward to intensify the inner belt and deplete the outer belt. The region of the inner 

belt is home to the International Space Station and LEO satellites, which would be subjected to extreme 

levels of radiation for possibly years after the event in this scenario.

 25 Y. Miyoshi et al., “Flux enhancement of the outer radiation belt electrons after the arrival of stream interaction regions,” 

Magnetospheric Physics, 113 (2008): A3, doi:10.1029/2007JA012506 26 Y. Shprits et al., “Profound change of the near-Earth radiation environment caused by solar superstorms,” Space Weather, 

9 (2011): 8, doi:10.1029/2011SW000662
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Benchmarks for Ionospheric Disturbances

1. Space Weather Action Plan 1.3.1 

Action 1.3.1 of the Space Weather Action Plan states: “DOC and DOD, in coordination with NASA, DOI, 

NSF, and FCC, will: (1) assess the feasibility and utility of establishing functional benchmarks using the 

existing models and body of literature for this phenomenon; and (2) use the existing body of work to 

produce benchmarks.” It further states that “benchmarks and associated confidence levels will define 

at least the following: 

• Ionospheric radio absorption and duration as a function of frequency; 

• Total electron content (slant, vertical, and rate of change);

• Ionospheric refractive index; and

• Peak ionospheric densities and the height of the peak [of the layer].”

2. Ionospheric Disturbances 

The ionosphere is highly variable and is driven externally by solar extreme ultraviolet (EUV) and X-ray 

irradiance, geomagnetic storms, and the neutral atmosphere. Figure 3 shows the approximate vertical 

profiles of the ionosphere electron density and the neutral atmosphere temperature for solar minimum 

and solar maximum daytime conditions. Ionospheric variability affects radio signal propagation by 

changing the reflection, refraction, absorption, and delay of the radio signal. The impact on radio 

signals depends on the vertical profile of the ionospheric electron density, and the angle of incidence 

of the radio wave on the ionosphere. The ionosphere can become turbulent due to small-scale 

irregularities that alter the amplitude and phase of transmitted signals. The term ionospheric 

scintillation is often used to describe this phenomenon. 

Different layers in the ionosphere are labeled the D, E, and F regions as defined by the relative altitude 

above Earth’s surface. These layers transmit, absorb, reflect, and refract radio signals depending on the 

density of the layer and frequency of the radio wave. The signal delay of transionospheric signals is 

directly related to the total electron content (TEC), which is the number of electrons between the 

transmitter and receiver. For satellite navigation systems including Global Positioning Systems (GPS) 

and Global Navigation Satellite System (GNSS), a delay in arrival time results in positioning errors unless 

corrections are made. Strong horizontal gradients in the ionosphere can induce errors as the 

positioning information from each satellite varies with the TEC along the line of sight between the 

satellite and the receiver.

Three primary types of space weather phenomena affect the ionosphere and radio signal propagation. 

Solar flares enhance the low- and mid-latitude D region, essentially blocking high frequency (HF)

communication on much of the dayside of Earth. Similarly, solar proton events enhance the D region at 

high latitudes, disrupting signal propagation and HF communication in polar regions. Geomagnetic 

storms modify the ionosphere in several ways, degrading satellite communication, navigation, and 

positioning capabilities, often on global scales. During an extreme storm, these effects on radio 

communication may extend to very high frequency (VHF) and ultra-high frequency (UHF). A very deep 

solar minimum with extremely low solar EUV irradiance could also affect HF communication and radar 

systems, but these conditions will be discussed in future updates to the benchmarks document. A brief 

discussion of these extreme space weather conditions and their potential effects on the ionosphere 

follow.
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Figure 3. Height profiles of the neutral atmosphere temperature and the ionosphere

electron density for daytime solar minimum and solar maximum conditions 

(layers of the neutral atmosphere and ionosphere are labeled for reference)

Solar Flares

Solar X-ray photons penetrate to the lower part of the ionosphere, or D region of the ionosphere 

(60–90 kilometers) creating a narrow enhanced layer that absorbs HF radio waves (3–30 megahertz 

[MHz]), affecting maritime sea-to-shore services, over-the-horizon radar systems, Global Maritime 

Distress and Safety System communications, and several other military and government 

communication systems. The stronger the solar flare, the more enhanced the D region, the larger the 

affected area, and the higher the affected radio frequency. For an extreme flare (e.g., a flare classified 

as X30 or greater, in a classification system where X is the highest class), the D region would completely 

absorb HF radio signals up to about 40 MHz or higher on much of the dayside of Earth. Solar X-ray flares 

typically last for tens of minutes, but the EUV portion of the flare can last 1–3 hours. The effects on HF 

communication last as long as the solar X-ray flux levels remain high. 

Flare effects on the D region are well understood, and the empirical models used to specify these effects 

work well for moderate and large flares. The relationship between flare magnitude and HF radio 

frequency is empirical, and extreme X-ray flares (> X30) have never been accurately observed or 

recorded.27 Thus, a number of uncertainties exist in determining not only the magnitude of the largest 

possible flare, but also the response of the D region and the subsequent impact on HF propagation. 

Flares can modify other regions of the ionosphere, but these effects are relatively small compared to 

other variations caused by geomagnetic storms and solar cycle variations. Flares are sometimes 

associated with radio emissions at frequencies near the GPS frequencies and could potentially affect 

communication and navigation systems (as discussed in Action 1.4 of the Space Weather Action Plan). 

 27 For example, see N. R. Thomson, C. J. Rodger, and R. L Dowden, “Ionosphere Gives Size of Greatest Solar Flare.”

Geophysical Research Letters 31 (2004): L06803.
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Solar Energetic Protons

Energetic protons, accelerated near the Sun and in the solar wind by solar eruptive events such as 

coronal mass ejections (CMEs) and flares, are guided by the geomagnetic field and penetrate to the 

lower D region of the high-latitude ionosphere, creating a narrow enhanced layer that, like the 

enhancement caused by solar flares, absorbs radio waves. During large geomagnetic storms, the 

affected regions extend towards the equator. In an extreme storm, the proton effects on 

communication may reach ±25 degrees geomagnetic latitude from the magnetic pole. The effects of 

protons on the ionosphere and on communication are well understood and can be predicted, assuming 

the energy distribution of the precipitating protons is known. Proton events can last for several days, 

and the loss of HF communication from these events may last just as long. An extreme solar proton 

event may disrupt HF communications at high latitudes for a week or more. Protons do not change the 

other parts of the ionosphere where the bulk of the electrons reside; therefore, the impact of solar 

proton events on the ionosphere is limited.

Geomagnetic Storms

Extreme geomagnetic storms, which are usually caused by CMEs, have complex and different effects on 

the ionosphere at high, mid-, and low latitudes. The auroral effects associated with geomagnetic storms 

increase ionospheric density and heat and expand the thermosphere. The auroral zone moves toward 

the equator, and the equatorial ionospheric scintillation region can expand toward the poles, bringing 

the ionospheric structures, which are prevalent in the equatorial and polar regions, to mid-latitudes. 

Auroral particles and current systems deposit energy into the ionosphere-thermosphere system, which, 

in turn, creates structures such as waves, gradients, and traveling ionospheric disturbances that 

enhance the variability of the mid-latitude ionosphere and disrupt HF through VHF signal propagation. 

The effect on GPS navigation of the resulting sharp electron density gradients can be quite severe and 

can last for hours or even days.28 At low latitudes, ionospheric irregularities and plasma bubbles 

interfere with satellite communication, navigation, positioning, and radar systems that depend on 

trans-ionospheric radio links. These structures can be enhanced during geomagnetic storms (e.g., if the 

storm creates higher than normal plasma vertical uplift). 

One additional consideration is the possibility of several simultaneous events, due to repeated 

eruptions from the same active region on the Sun, which is common. During major geomagnetic storms, 

solar flares, protons, X-rays, and radio bursts are likely to occur simultaneously over the course of 

several days. The severity and extent of their effects will depend on solar activity, the season, and the 

state of the system at the onset of the storm. Some of the effects on the ionosphere may be additive, 

but non-linear effects are common and are not fully understood. The ionosphere is tightly coupled to 

the thermosphere and magnetosphere, which also respond to solar activity and further influence the 

response of the ionosphere. 

Figure 4 shows an estimate of the global extent of the various ionospheric disturbances during extreme 

space weather conditions. 

 28 A. J. Mannucci et al.. “Dayside Global Ionospheric Response to the Major Interplanetary Events of October 29–30, 2003 

Halloween Storms.” Geophysical Research Letters 32, no. 4 (May 2005): L12S02; Rodriguez Bilbao et al. "Precise point 

positioning degradation in the presence of a SITEC." 2015 1st URSI Atlantic Radio Science Conference (URSI AT-RASC) (2015): 

1-1. doi: 10.1109/URSI-AT-RASC.2015.7303132
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Source: From a private communication with Air Force Research Laboratory staff member.

Figure 4. Estimate of the global extent of various ionospheric 

disturbances during extreme space weather conditions

Table 8 provides descriptions of some of the phenomena described here and their effects on the 

ionosphere.

Table 8. Effect of extreme events on the ionosphere

Phenomenon Event Location Environmental Effect

Flare D Region 

Enhancement

Sunlit side of 

Earth

Absorbs RF signals from HF to VHF in the lower 

ionosphere

Energetic 

Protons

D Region 

Enhancement

High and midlatitudesAbsorbs RF signals from HF to VHF in the lower 

ionosphere

Geomagnetic 

Storms

Polar Cap and 

Aurora

High and midlatitudesPatches, plasma structures, and ionospheric gradients 

refract radio waves.

Traveling 

Ionospheric 

Disturbances and 

Storm-Enhanced 

Densities 

Mid-latitude 

region on the 

dayside of Earth

Creates large TEC enhancements (up to 200 TEC units) 

and strong gradients in TEC

Equatorial 

Scintillation

Latitudes ±20 

degrees of 

geomagnetic 

equator

Large-scale plasma depletions and associated smallscale ionospheric structures observed just after sunset 

and generally up to midnight. Scintillation of 

transmitted radio signals.

3. Methodology for Establishing Benchmarks for Ionospheric Disturbances

A number of parameters are used to describe the ionospheric properties that typically affect 

communication, navigation, and positioning. In addition to the TEC, these parameters include the 

highest affected frequency (HAF), the maximum usable frequency (MUF), the height in kilometers of the 

F2 layer (hmF2), the peak density of the F2 layer (NmF2), as well as the phase and amplitude scintillation 
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indices Sigma-Phi and S4.29 Global maps of each of these parameters would fully describe the extreme 

event impacts on communication, navigation, and positioning systems.

The ionospheric response to flares and proton events is reasonably well understood and predictable. It 

is possible to quantify the impacts of severe solar flares and extreme solar energetic proton events on 

HF communication. Empirical models driven by solar X-ray data and energetic proton data (from GOES), 

geomagnetic activity (from U.S. Geological Survey and Defense Meteorological Satellite Program 

magnetometers), and solar wind, interplanetary magnetic field (IMF) data (from ACE and the Deep 

Space Climate Observatory [DSCOVR]) provide reasonable estimates of the ionospheric response and 

the effects resulting from extreme events. The ability of current empirical models to predict extreme 

conditions is uncertain because they reflect only what has been observed previously; however, 

reasonable estimates may be readily achievable.

The ionospheric response to extreme geomagnetic storms is much more complex. Because it is not yet 

fully understood how extreme solar wind conditions propagate through the magnetosphere, it is 

difficult to characterize the resulting modifications to the ionosphere. Conditions associated with highlatitude and equatorial scintillation are not well captured in most models. Ionospheric features, such 

as traveling ionospheric disturbances and irregularities and storm enhancements in plasma density, 

may have severe effects on technologies that are difficult to predict. The ionospheric effects on systems 

could be specified by a detailed analysis of storm conditions that have been observed and additional 

research and development to couple physics-based ionospheric models with magnetosphere and 

thermosphere models.

Extreme events are difficult to define given only a few decades of data. An extremely large storm 

occurred in 1859 (the Carrington event), but there were few measurements of the space environment 

at that time. There are extensive observations of more recent storms, such as those in March 1989, July 

2000, June 2001, and October-November 2003, all of which were significant, but these events do not 

necessarily represent a 1-in-100-year storm. Estimates of the magnitude of space weather parameters 

during the Carrington event are shown in Table 9, along with the largest observed events from more 

recent records. This table references the Disturbance Storm Time (Dst)30 index as a measure of the 

geomagnetic activity.

Table 9. Magnitude of the largest space weather storms

Phenomenon Carrington Event (Estimates) Largest Observed

Flare X35 to X45 X28-X45 (estimated)a

Proton Event 2.7 to 11.0 × 109 protons cm-2 @30 MeV 10 × 1010 protons cm-2 @30 MeVb

Geomagnetic Storm Dst –900 to –1760 nT Dst –589 nTc

a November 2003; GOES XRS saturated so flare magnitude is an estimate (e.g., Thomson, Roger, and 

Dowden, “Ionosphere Gives Size of Greatest Solar Flare”).

b Proton flux observed at 30 MeV for the August 1972 event 

c March 1989.

 29 S. Basu et al., “250 MHz/GHz Scintillation Parameters in the Equatorial, Polar and Auroral Environments,” IEEE 

Transactions on Selected Areas in Communications, SAC-5 (1987): 102–115. 30 M. Sugiura, “Hourly Values of Equatorial Dst for the IGY.” In Annals of the International Geophysical Year, Part 1: 9–45

(Pergamon Press, 1964).
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Although much is known about the local characteristics of the ionosphere that change in response to 

geomagnetic storm conditions, it is still difficult to quantify the global response to an extreme storm. 

Earth’s ionosphere is a dynamic system that is strongly coupled to both the magnetosphere and the 

neutral atmosphere. Estimating the ionospheric response to extreme geomagnetic storms is informed 

by an understanding of the entire magnetosphere-ionosphere-thermosphere (MIT) system. The current 

uncertainties on estimating this benchmark are very large. The larger scale responses could be twice 

what has been observed to date, whereas features such as gradients could be 10 times larger if they are 

localized and cover a smaller region. It is also possible that the response to an extreme event is actually 

no greater than what has been observed during some of the largest storms (e.g., the large geomagnetic 

storms of October 2003, referred to as the Halloween storm, 31 or of July 2000, referred to as the Bastille 

Day storm32). 

It will be important to take advantage of available data from observations from previous large storms, 

ground-based GPS and magnetometers, GOES and DSCOVR missions, and the multi-national COSMIC 

constellation of satellites. Solar, solar wind, and magnetosphere observations will play key roles in the 

data analysis as well. Analysis of these observations will provide a sound basis for estimates of the 

extreme conditions on the Sun and in the solar wind, including solar EUV and X-ray irradiance as well 

as solar wind speed, density, and the IMF. Other variables include the state of the 

thermosphere/ionosphere at the beginning of the storm, the Universal Time Coordinated start of the 

storm, and to some extent, the wave field (tides, planetary waves, and gravity waves) propagating from 

the lower atmosphere. Once the ranges of all the input drivers are determined, these drivers can be fed 

into models of the MIT system. It may then be possible to model the range of responses in the 

ionosphere to extreme events, but new phenomena, such as non-linear effects, may not be captured 

adequately by current models. 

The equatorial ionosphere responds to changes in geomagnetic activity, but it is not as tightly coupled 

to geomagnetic storm drivers as high- and mid-latitude regions. At low latitudes, the effects are 

typically due to penetration electric fields, whereas responses at high latitudes are more complex. 

Under extreme conditions, the extent of equatorial ionospheric scintillation could expand both in local 

time and in latitude. The number and intensity of the plasma bubbles could also increase, thus 

expanding and amplifying the phenomena and their effects on navigation and communication systems. 

4. Benchmarks

Because of the complexities in modeling ionospheric disturbances, no quantitative benchmarks were 

set for this section. Although much is known about the local characteristics of the ionosphere that 

change in response to geomagnetic storm conditions, it is still difficult to quantify the global response 

to an extreme storm. Earth’s ionosphere is a dynamic system, and is strongly coupled to both the 

magnetosphere and the neutral atmosphere. A better understanding of the entire MIT system and its 

response to extreme geomagnetic storms would help inform this benchmark. Providing benchmark 

estimates of various extremes in the ionospheric parameters with uncertainties approaching 100% 

could potentially be misleading and may lead to severe under or over estimation of disturbances to the 

 31 S. Basu et al., “Response of the Equatorial Ionosphere to Prompt Penetration Electric Fields during Intense Magnetic 

Storms,” Journal of Geophysical Research 112 (2007): A08308. doi:1029/2006JA012192 32 S. Basu et al., “Response of the Equatorial Ionosphere in the South Atlantic Region to the Magnetic Storm of July 15, 2000,” 

Geophysical Research Letters 28, no. 3 (2001): 577.
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ionosphere. Phase 2 of the benchmarks effort (NSWAP 1.3.3) will seek to refine the benchmark values 

and uncertainties.

In the absence of benchmarks, measured values from the intense 2003 Halloween event for ionospheric 

disturbances are reported here. Mannucci reports a vertical TEC of 250 TECu (where 1 TECu = 1 ×1016

electrons m-2

) with an associated error of approximately 3 TECu, and Datta-Barua reports a TEC spatial 

range gradient of 40 cm/km and a TEC temporal range gradient of 15 cm/s. 33, 34, 35 These conditions could 

last up to several days.36

 33 A.J. Mannucci, “Global Ionospheric Storms,” white paper submitted to the Space Studies Board of the U.S. National 

Research Council for its 2010 “decadal survey” in solar and space physics (heliophysics), 2010. 34 P. Stephens et al., “New leveling and bias estimation algorithms for processing COSMIC/FORMOSAT-3 data for slant total 

electron content measurements,” Radio Science 46 (2011): RS0D10. doi: 10.1029/2010RS004588 35 S. Datta-Barua, “Ionospheric Threats to Space-Based Augmentation System Development,” presented at the Proceedings 

of the 17th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2004), Long 

Beach, California, 2004. 36 M. Hapgood et al., Summary of Space Weather Worst-Case Environments, RAL technical report, Revised edition, RAL-TR2016-006, May 2016. 
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Benchmarks for Solar Radio Bursts

1. Space Weather Action Plan 1.4.1

Action 1.4.1 of the National Space Weather Action Plan states: “DOC, DOD, and NASA, in coordination 

with DOI and FCC, will: (1) assess the feasibility and utility of establishing functional benchmarks [for 

solar radio bursts] using the existing models and body of literature for this phenomenon; and (2) use 

the existing body of work to produce benchmarks [for solar radio bursts].”

2. Solar Radio Bursts

Solar radio bursts are emitted from the Sun during solar flares; on average, solar flares occur every 

3.5 days at solar maximum and every 18.5 days at solar minimum. Solar radio bursts are significant 

because use of the radio spectrum has become as ubiquitous as the use of electricity. The Nation relies 

on the radio spectrum in so many ways that it is impossible to enumerate them all or to completely 

understand how disruptive disturbance to the radio spectrum could be. Note the radio bursts 

considered here include those from flare-associated phenomena, including coronal mass ejections, 

thus covering all the bright emissions likely to impact users of the radio spectrum.

In a review of Federal radio uses, the DOC National Telecommunications and Information 

Administration (NTIA) looked at frequency ranges from low frequency (30 kilohertz [kHz]) to extra high 

frequency (300 gigahertz [GHz]).37 The list of uses is extremely broad and includes such diverse areas as 

air traffic control, radio navigation, ship safety and security, space, law enforcement, environmental 

monitoring, and national defense.

3. Methodology for Establishing Benchmarks for Solar Radio Bursts 

The most extensive data set on solar radio bursts is the U.S. Air Force’s Radio Solar Telescope Network 

(RSTN), with data from 1960 up to the present. The RSTN network is a global network of stations, thus 

ensuring continuous observations of the Sun. The RSTN data have observations over all of the 

frequency bands of interest, making it the ideal data set for setting benchmarks. The next best data 

source is the Nobeyama Radio Observatory, which observed for 45 years only in the higher frequency 

bands (>1000 MHz) from a single site, thereby missing any bursts occurring after sunset. These data can 

be used as a consistency check against any analysis performed with the RSTN data.

The main users of the radio spectrum that can be affected by solar events can be sorted into the 

standard frequency bands of VHF, UHF, and microwaves. In addition, due to their common usage, GPS 

frequencies and the F10.7 index (2800 MHz) are specifically highlighted.

Figure 5 shows the spectrum for a typical radio burst. Note the two peaks in the spectrum with a 

minimum around 2–3 GHz. Figure 5 does not provide an instantaneous view of a radio burst; the radio 

burst spectrum varies significantly both within a burst and from burst to burst.

 37 Department of Commerce (DOC), Federal Radar Spectrum Requirements, National Telecommunications and Information 

Administration (NTIA), NTIA Special Publication 00-40, May 2000.
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Source: G. M. Nita, D. E. Gary, and L. J. Lanzerotti, “Statistics of Solar Microwave Radio 

Burst Spectra with Implications for Operations of Microwave Radio Systems.” Space 

Weather 2 (2004): S11005, Figure 1. 

Figure 5. Typical radio spectrum burst

For the 1-in-100-year benchmarks, the results presented in Nita et al. were used.38 Defined frequency 

bands that align with standard usage for the benchmarks were chosen, whereas Nita et al. and the RSTN 

data are at discrete frequencies within each band. The benchmarks can be assumed to be valid over the 

full range of frequencies covered by a band. In cases where multiple discrete frequencies fall within a 

single band, the largest computed intensity was used as the benchmark. 

Three different emission mechanisms are important in assessing the theoretical maximum intensity 

expected from solar radio bursts. Theoretical maximum intensities for microwave frequency bands 

(4,000–20,000 MHz) are the most straightforward to compute, because these are dominated by 

synchrotron emission, which are incoherent emissions produced by electrons accelerated in solar 

flares. The radio flux from a burst source can be expressed as the product of the effective brightness 

temperature of the source and its area, scaled by the square of the frequency. These factors can be 

estimated and used to derive a frequency-dependent theoretical maximum. 

At lower frequencies, the situation is more complicated because the two emission mechanisms active 

there are coherent, and placing upper limits on their effective brightness temperatures is difficult. 

Between 600 MHz and 3 GHz, covering the range of UHF, GPS, and F10.7, electron cyclotron maser 

emissions can operate with very high brightness temperatures (e.g., 1014 K), albeit with upper limits 

likely to be similar to the source size. At frequencies below 600 MHz (the band), the plasma emission 

mechanism from the conversion of Langmuir waves dominates, again with the potential for very high 

brightness temperatures, but also with the potential for much larger source sizes.

 38 G. M. Nita et al. “The Peak Flux Distribution of Solar Radio Bursts.” Astrophysical Journal 570 (May 2002): 423–438.
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4. Benchmarks

Table 10 shows 1-in-100-year benchmarks for solar radio bursts. These Phase 1 benchmarks are obvious 

overestimates. This is because both the Nita et al. paper and the 2012 paper with Nobeyama data39

show that the frequency of radio bursts falls off much more rapidly than a simple linear extrapolation 

implies. Inspection of the plots in Figure 5 (above) and Figure 6 (below) indicate the phase 1 benchmarks 

overestimate the 1-in-100-year flux by at least an order of magnitude. The 1-in-100-year benchmarks 

were derived by extrapolating the power-law fits in Nita et al. (the solid and dashed (corrected) lines) to 

an event rate of 1 event every 36,525 days, or 2.7379 × 10-5 events per day.

Table 10. Benchmarks for solar radio bursts

Frequency Band Name Frequency Band 

(MHz)

1-in-100-Year 

Benchmark (sfu)a

Error Bars 

(sfu)a

Theoretical 

Maximum

Very High Frequency (VHF) 30–300 2.8 × 109 [-2.5 × 109, +0] Phase 2

Ultra High Frequency (UHF) 300–3,000 1.2 × 107 [-1 × 107, +0] Phase 2

Global Positioning System (GPS) 1,176–1,602 1.2 × 107 [-1 × 107, +0] Phase 2

F10.7 2,800 1.3 × 107 [-1 × 107, +0] Phase 2

Microwave 4,000–20,000 3.7 × 107 [-2.5 × 109, +0] Phase 2

a Solar flux is measured in watts (W) per square meter (m2

) per hertz (Hz), with 1 solar flux unit (sfu) equal to 10-22 W m-2 Hz1.

Source: G. M. Nita et al., “The Peak Flux Distribution of Solar Radio Bursts.” 

Astrophysical Journal 570 (May 2002): 423–438, Figure 10(a).

Figure 6. Cumulative number of bursts per day at frequencies 

above 2 GHz during solar maximum years

 39 Q. Song, G. Huang, and B. Tan, “Frequency Dependence of the Power-Law Index of Solar Radio Bursts.” Astrophysical 

Journal 750 (May 2012): 160–163.
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No theoretical maximum benchmarks were computed because no published work addresses the 

question of the theoretical maximum intensity expected from solar radio bursts. 

To assess the theoretical maximum intensity expected from solar radio bursts, it is important to note 

that there are three different emission mechanisms that play a role and are typically dominant in 

different frequency bands. The radio flux from a burst source can generally be expressed as the product 

of the effective brightness temperature of the source and its area, scaled by the square of the frequency. 

The key to identifying the theoretical maximum in each frequency band is the ability to determine the 

maximum feasible brightness temperature and area.

Theoretical maximum intensities for the microwave frequency bands (4-20 GHz) are the most 

straight-forward to compute, but at lower frequencies the situation is more complicated because the 

two emission mechanisms active there are coherent, and it is difficult to place upper limits on their 

effective brightness temperatures. An estimate of the maximum brightness temperature of plasma 

emission may be possible upon further investigation of the literature.

With the current state of knowledge, we therefore believe that we can provide plausible theoretical 

maxima at high and low frequencies, with somewhat less certainty in the UHF-GPS-F10.7 frequency 

bands. 
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Benchmarks for Upper Atmosphere Expansion

1. Space Weather Action Plan 1.5.1

Action 1.5.1 of the National Space Weather Action Plan states: “DOC, DOD, NSF, and NASA, in 

coordination with DOI and FCC, will: (1) assess the feasibility and utility of establishing functional 

benchmarks [for upper atmosphere expansion] using the existing models and body of literature for this 

phenomenon; and (2) use the existing body of work to produce benchmarks [for upper atmosphere 

expansion].

2. Upper Atmosphere Expansion

Upper atmosphere expansion refers to changes in the thermosphere that can affect satellite drag at low 

Earth-orbit (LEO).40 The primary effect arises from an increase in temperature, which causes an increase 

in neutral density at a fixed altitude in Earth’s upper atmosphere; temperature is therefore considered 

synonymous with density for the purposes of the benchmark. Figure 3 (in the section on benchmarks 

for ionospheric disturbances) illustrates the vertical profile of the atmospheric temperature with the 

layers of the atmosphere labeled. Upper atmospheric heating can be driven by solar or geomagnetic 

activity. The neutral density response also depends on neutral composition. Composition varies as a 

function of altitude and solar cycle. At 400 kilometers, the thermosphere at mean levels of solar activity 

is dominated by atomic oxygen (O). At 250 kilometers molecular nitrogen (N2) contributes a significant 

fraction, and at 850 kilometers helium (He) begins to dominate. 

Figure 7 shows the vertical profile of the number densities (a) and mass density (b) of the atmospheric 

species from 100 to 1000 kilometers altitude for low solar activity (dashed lines) and high solar activity 

(solid lines). The relative composition of species at a fixed altitude is also dependent on the level of solar 

activity, as the atmosphere expands and contracts. To reflect this altitude dependence, the 

Benchmarks consider the environmental conditions and response to energy injection at three height 

levels: 250 kilometers, 400 kilometers, and 850 kilometers. While assets at 850 kilometers are not 

typically considered vulnerable to significant satellite drag force magnitudes, the variability in these 

forces may still constitute the largest contribution to overall orbital errors and drag effects above 700 

kilometers altitude during storms have been reported. Moreover, during extreme events the neutral 

density experienced at 850 kilometers during storms would be comparable to the drag at 600 

kilometers altitude during quiet times. In addition, during extreme storms at higher altitudes, ions can 

become a significant component of the atmosphere causing drag in addition to the neutrals.

At these altitudes, 250 kilometers, 400 kilometers, and 850 kilometers, neutral species (e.g., N2, O, He) 

tend to dominate. However, at 850 kilometers altitude the ionized atmosphere can contribute a 

significant fraction (a few percent) of the total density and impacts drag just as a neutral constituent 

would. In addition, the ions can be transported to higher altitudes not just by thermal expansion, but 

also through transport by electric fields. During an extreme geomagnetic storm, vertical plasma drift at 

low and mid latitudes are expected to exceed 200 m/s, which if present for an hour or two, could raise 

the relative density concentration of the ionized atmosphere at 850 kilometers to greater than 10%. To 

date, there has been no direct evidence that the vertical motion of the ions driven by prompt 

penetration electric fields has an appreciable effect on vertical transport of neutral density. Quantifying 

 40 J. T. Emmert, “Thermospheric Mass Density: A Review,” Advances in Space Research 56 (2015): 773–824; NRC, Orbital 

Debris: A Technical Assessment (Washington, D.C.: National Academies Press. 1995); NRC, Continuing Kepler’s Quest: 

Assessing Air Force Space Command’s Astrodynamics Standards (Washington, D.C.: National Academies Press. 2012).
SPACE WEATHER PHASE 1 BENCHMARKS

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the impact of the ionized atmosphere on the drag of satellites and orbital prediction of debris will be 

considered in Phase 2.

Source: T.J. Fuller-Rowell et al. "How the Thermosphere and Ionosphere Might React to an Extreme Space Weather 

Event,” Extreme Events in Geospace. Edited by N. Buzulukova. Elsevier Inc., Cambridge, MA, 2017.

Figure 7. The vertical profile of the number densities (a) and mass density (b) of the different atmospheric species from 100 to 

1000 kilometers altitude for low solar activity (dashed lines) and high solar activity (solid lines)

In-track winds change apparent density along an orbit, and cross-track winds influence orbit trajectory. 

The benchmark therefore considers neutral density, and winds, as the primary parameters impacting 

satellite drag during extreme events. The additional impact of possible ion density enhancements on 

total density at higher altitude will be considered in Phase 2.

Neutral density and winds at LEO altitudes pose two distinct risks to operational spacecraft: 

1. The direct effect of enhanced drag on the spacecraft, changing its orbit, increasing the 

uncertainty of its position, and reducing its orbital lifetime.

2. The indirect effect of atmospheric expansion and winds on the ability to monitor the 

trajectories of debris, including objects with high area-to-mass ratios, for collision 

avoidance. 

The issue of the predicting the orbits of debris is of particular concern for collision avoidance. Debris 

objects tend to be much smaller than operational satellites, some have a larger area-to-mass ratio, have 

eccentric orbits that are affected by thermosphere horizontal and vertical structure, and consequently 

are more diversely affected by atmospheric drag. Collision avoidance has become of increasing concern 

due to the recent proliferation of space debris in LEO. One cause of the rapid inflation in the number of 

objects was the collision of one of the satellites in the Iridium constellation with a spent Russian satellite 

COSMOS 2251. Incidents like this create a large cloud of debris. 
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The issue of space debris, which has been brought to a new level of awareness, highlights the need for 

accurate orbit predictions and tracking of space objects. The Space Surveillance Network (SNN) 

currently tracks more than 20,000 objects greater than 10 cm. These objects consist of everything from 

active satellites, defunct satellites, and spent rocket stages, to smaller debris arising from erosion, 

explosion, and collision fragments. Since the orbits of these objects often overlap the trajectories of 

newer operational spacecraft, collision of the debris with active satellites is of serious concern. 

The number of objects in may reach a critical density where the creation of new debris occurs faster 

than various natural forces can remove them from orbit. Beyond this point, a runaway chain reaction 

can occur that quickly reduces all objects in orbit to debris in a phenomenon known as the Kessler 

Syndrome.41 The biggest uncertainty in orbit prediction in low Earth orbit is estimating the likely drag 

on the satellite due to neutral density in the upper atmosphere. The long-term trend of reduced density 

in the upper atmosphere due to carbon dioxide (CO2) cooling42 will tend to increase the lifetime of debris 

and exacerbate the issue of collision avoidance.

The upper atmosphere expansion benchmark quantifies the response to three main sources driving 

upper atmospheric density or winds. These are:

1. Ultraviolet radiation from the Sun, which is produced continuously and changes day-today; 

2. Ultraviolet radiation from the Sun, which is produced by a solar flare transient event; and

3. Coronal mass ejections from the Sun, which are transient events driving geomagnetic 

storms.

3. Methodology for Establishing Benchmarks for Upper Atmosphere Expansion

The methodology for establishing the benchmarks is described for the three main causes of upper 

atmosphere expansion: 

1. The impact from extreme solar ultraviolet (EUV; wavelength < ~100 nm) radiation and far 

ultraviolet (FUV; wavelengths > 100 nm) radiation on the upper atmosphere on timescales 

greater than one day;

2. EUV enhancement of the upper atmosphere during impulsive events, such as solar flares; 

and 

3. CMEs that drive geomagnetic storms that lead to the expansion of the upper atmosphere. 

 41 D. J. Kesslerand B. G. Cour-Palais, “Collision Frequency of Artificial Satellites: The Creation of a Debris Belt,” Journal of 

Geophysical Research 83 (1978): A6, 2637–2646, doi:10.1029/JA083iA06p02637 42 J. Emmert, J. Picone, S. Lean, and S. Knowles, “Global Change in the Thermosphere: Compelling Evidence of a Secular 

Decrease in Density,” Journal of Geophysical Research 109 (2004); R. Akmaev and V. Fomichev, “Cooling of the Mesosphere 

and Lower Thermosphere Due to Doubling of CO2,” Annales Geophysicae 16 (1998): 1501–1512.
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Effects of Solar Extreme Ultraviolet (EUV) and Far Ultraviolet Radiation (FUV) on Upper 

Atmosphere Expansion on Timescales Greater than One Day

This benchmark uses the NRLMSISE-00 empirical neutral density model.43 Alternative empirical models 

such as JB200844 or DTM-201345 could equally be used. The accuracy of the available empirical models 

is similar, all having been constructed with similar observed neutral density or tracking data. During 

quiet times, their accuracy is ~20% increasing to ~50% during geomagnetic storms. Their uncertainty is 

expected to increase further when extrapolating for extreme events, to ~100%, since clearly no 

observational data is available during these times. Empirical neutral density models of the effects of 

solar EUV and FUV on upper atmosphere expansion are driven by proxies, such as the average of the 

daily solar 10.7-centimeter solar radio flux (F10.7) and its 81-day mean. The F10.7 radio emission originates 

high in the chromosphere and low in the corona of the solar atmosphere; it correlates well with the 

number of sunspots as well as ultraviolet (UV) fluxes.46 Since thermospheric neutral winds do not 

increase substantially in response to increases in solar ultraviolet radiation, the benchmark for this 

category is restricted to neutral density.

The benchmarks are defined relative to the NRLMSISE-00 empirical neutral density reference model in 

order to extrapolate the response to a 1-in-100-year and theoretical maximum event. Benchmark values 

are determined assuming the enhanced ultraviolet flux has persisted for at least 1 to 5 days, guided by 

the fact that the neutral density response to EUV has an e-folding time (interval for an exponentially 

growing quantity to increase by expansion factor e) of about 1 day, and the response to the longer FUV 

wavelengths that penetrate more deeply into the upper atmosphere is 3 to 5 days.

Over the past 68 years, the daily F10.7 at 1 AU exceeded 300 solar flux units (sfu)47 on only 80 days 

(0.3 percent occurrence rate). The peak value of the daily F10.7 was 377 sfu at 1 AU and 390 sfu at the

minimum Earth-Sun distance. These high values are usually associated with an active region rotating 

with the Sun; they could be elevated for a few days on either side of these peak values; and they could 

recur on the next solar rotation. These observations of the daily and 81-day mean F10.7 radio flux were 

analyzed to estimate a likely 100-year value of 390 sfu for the daily mean and 280 sfu for the 81-day 

mean. No theoretical maximum benchmark is available, but a value of 500 sfu was used, with 390 sfu as

the 81-day mean. These daily F10.7 and 81-day mean values were used to drive the empirical NRLMSISE00 neutral density model, and the values compared (as a percentage change) to reference values using 

240 and 200 sfu for the daily and 81-day mean, respectively. Because events at these levels are clearly 

outside the parameters used to develop the empirical model, the estimates are likely to have large 

uncertainties. Physical models will be used in Phase 2 to support the estimates. Also in Phase 2, 

extreme-value methods will be explored for estimates of 1-in-100-year event likelihood.

 43 J. M. Picone et al., “NRLMSISE-00 Empirical Model of the Atmosphere: Statistical Comparisons and Scientific Issues,”

Journal of Geophysical Research 107 (2002): A12, 1468; J. T. Emmert, “Thermospheric Mass Density: A Review,” Advances in 

Space Research 56 (2015): 773–824. 44 B. R. Bowman et al., “A New Empirical Thermospheric Density Model JB2008 Using New Solar and Geomagnetic Indices,”

presentation at AIAA/AAS Astrodynamics Specialist Conference, 2008, AIAA 2008-6438. 45 S. Bruinsma, “The DTM-2013 Thermosphere Model,” Journal of Space Weather and Space Climate 5 (2015): A1. 46 Space Weather Prediction Center, u.d. “F10.7 cm Radio Emissions.” National Oceanic and Atmospheric Administration. 

http://www.swpc.noaa.gov/phenomena/f107-cm-radio-emissions 47 Solar flux is measured in watts (W) per square meter (m2

) per hertz (Hz), with 1 sfu equal to 10-22 W m-2 Hz-1
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Although not a direct consequence of atmospheric expansion, extended periods of enhanced EUV 

radiation will raise the fluence of atomic oxygen in LEO and degrade optics, organic films, advanced 

composites, and metallic surfaces.48

Note that during solar radio bursts, the F10.7 flux can be more than five orders of magnitude greater (see 

discussion in the section on benchmarks for solar radio bursts). During these times, the 10.7 solar radio 

flux no longer correlates with EUV and is no longer a suitable proxy.

Effects of Solar EUV Radiation Enhancement during Solar Flares on Upper Atmosphere Expansion

Solar flares are categorized by the power in the wavelength range of 0.1 to 0.8 nanometers (nm) in the 

X-ray part of the solar spectrum. The highest category of flare (X-class) has a power greater than 

10-4 W m-2

. The largest solar flare on record is an X28 (2.8 × 10-3 W m-2 in the 0.1 to 0.8 nm wavelength 

range), which was recorded by the GOES satellite on November 6, 2003, before the GOES instrument 

became overloaded. The ionospheric response at the time indicated the flare might have been closer 

to X45. The X-rays themselves have a small impact on neutral density because the energy is deposited 

deep within the atmosphere (~90–100 kilometers altitude). However, the EUV part of the solar spectrum 

can also increase during a flare, which does have a neutral density response, although the EUV flux is 

not always well correlated with the X-ray flux by which the flare is categorized. The Challenging 

Minisatellite Payload (CHAMP) satellite has recorded the neutral density response to numerous solar 

flares categorized as greater than X5.49 The peak neutral density response was generally small, 5 to 10 

percent at 400 kilometers altitude. On October 28, 2003 during an X17 flare, CHAMP recorded a peak 

density increase of 50 percent on the dayside of Earth for a few hours.50

For the purposes of the neutral density response to the EUV part of the solar spectrum, the estimates 

for a 100-year flare is X30 and that for a theoretical maximum is X40.51 Palowsky and Ridley indicated 

that the density response at 400 kilometers altitude is linearly dependent on the total incident energy 

of the flare.52 The benchmark is established assuming a similar EUV spectrum and duration compared 

with the October 2003 event. The typical short duration of a flare of a few hours naturally constrains the 

temperature and density response, limiting the potential impact on satellite drag and orbit prediction. 

Therefore, the benchmark is restricted to the response at 400 kilometers at a median F10.7 solar flux level 

of 150 sfu. In Phase 2, the need to expand the benchmark to the response at 250 and 850 kilometers 

altitude, and at extremely low (F10.7 = 65 sfu) and high (F10.7 = 300 sfu) solar activity, will be reassessed.

Similar to daily EUV, since the thermospheric neutral winds do not increase substantially in response 

to a solar flare, the benchmark for this category is restricted to neutral density.

 48 A. C. Tribble et al., “Low Earth Orbit Thermal Control Coatings Exposure Flight Tests: A Comparison of U.S. and Russian 

Results,” NASA Contractor Report 4647, March 1995. 49 H. Lühr et al., “Thermospheric Up-Welling in the Cusp Region: Evidence from CHAMP Observations.” Geophysical Research 

Letters 31 (2004): L06805. 50 E. K. Sutton et al. “Neutral Density Response to the Solar Flares of October and November, 2003.” Geophysical Research 

Letters 33 (2006): L22101; B. T. Tsurutani et al., “The October 28 2003 Extreme EUV Flare and Resultant Extreme Ionosphere 

Effects: Comparison to Other Halloween Events and the Bastille Day Event,” Geophysical Research Letters 32 (2005): 

L03S09. 51 C. J. Schrijver et al. “Estimating the Frequency of Extremely Energetic Solar Events, Based on Solar, Stellar, Lunar, and 

Terrestrial Records.” Journal of Geophysical Research 117 (2012): A08103, based on an NRC study. 52 D. J. Pawlowski and A. J. Ridley, “The Effects of Different Solar Flare Characteristics on the Global Thermosphere,” Journal 

of Atmospheric and Solar-Terrestrial Physics 73 (2011): 1840–1848.
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Effects of Coronal Mass Ejections Driving Geomagnetic Storms on Upper Atmosphere Expansion

Geomagnetic storms drive winds through ion drag and heat the thermosphere through auroral 

precipitation and Joule heating. Observations of neutral density and winds during large storms are 

available, and reasonable estimates of the response can be predicted using empirical models (e.g., 

NRLMSISE-00; DTM-2013; and JB2008) and simulated with physical models (e.g., the Coupled 

Thermosphere Ionosphere Plasmasphere Electrodynamics [CTIPe] model53). Some of the largest 

storms in recent history stretch the limits of the empirical models, so estimates from the empirical 

models are likely to be uncertain by at least 50 percent for significant storms and nearly 100 percent for 

the largest storms. However, the empirical models, together with observations of large storms and 

guidance from the CTIPe physical model, can be used to extrapolate the response to more extreme 

events.

Although more likely to occur at the apex or descending phase of the solar cycle, large geomagnetic 

disturbances can theoretically occur during any level of background solar activity. In Phase 1, the 

neutral density and wind response to geomagnetic storms was estimated for mean solar activity

(F10.7 ~ 150 sfu) for this benchmark. Since the relative response to storms depends on solar activity, the 

benchmark needs to be expanded in Phase 2 to quantify the response at very low (F10.7 = 65 sfu) and very 

high (F10.7 = 300 sfu) solar activity. The same storm drives a larger relative density at low solar activity, 

even though the absolute density is lower. Relative density and structure is particularly important for 

orbit prediction of debris and collision avoidance, which is a hazard at all levels of solar activity. 

Estimates of the response to a 1-in-100-year geomagnetic storm are referenced to predictions of the 

solar wind condition of the important drivers, for example, the interplanetary magnetic field (IMF) 

magnitude and direction (particularly the magnitude of southward Bz),54 and solar wind velocity and 

density. Two events are often cited in the literature characterizing an extreme event: the Carrington 

storm of 1859 and an event observed by NASA’s Solar Terrestrial Relations Observatory Ahead (STEREOA) spacecraft in July 2012.55 For the Carrington event, Li et al.56 estimate Bz to be in a southward 

direction between 60 to 70 nanotesla (nT), together with a solar wind speed exceeding 2,000 

kilometers/second, and solar wind density exceeding ~60 cm-3

. The magnetic storm disturbance index, 

Dst,57 was predicted to be less than –1500 nT, although Siscoe, Crooker, and Clauer58suggested a more 

modest value of –850 nT. Note that the lowest measured Dst on record was recorded in March 1989 of 

‒589 nT, during an event when the Hydro-Quebec and the U.S. East Coast power grids were severely 

disrupted. Geomagnetic storms can also be driven by high-speed streams in the solar wind, but they 

tend to be smaller in magnitude than CME driven storms so are not addressed specifically in the 

Benchmark estimates. 

 53 T. J. Fuller-Rowell et al., “Storm-Time Response of the Thermosphere-Ionosphere System,” in Aeronomy of the Earth’s 

Atmosphere and Ionosphere, IAGA Special Sopron Book Series, vol. 2, chap. 32, edited by M. A. Abdu, D. Pancheva, and A. 

Bhattacharyya (Springer 2011). 54 Bz indicates the north-south direction of the IMF. Solar wind particles will more easily enter Earth’s magnetosphere and 

develop into a geomagnetic storm with a southward Bz than with a northward Bz. 55 D. N. Baker et al., “A Major Solar Eruptive Event in July 2012: Defining Extreme Space Weather Scenarios,” Space Weather

11 (2013): 585–591. 56 X. Li et al., “Modeling of 1–2 September 1859 Super Magnetic Storm,” Advanced Space Research 38 (2006): 273–279. 57 M. Sugiura, “Hourly Values of Equatorial Dst for the IGY.” In Annals of the International Geophysical Year, Part 1: 9–45

(Pergamon Press, 1964). 58 G. Siscoe, N. U. Crooker, and C. R. Clauer, “Dst of the Carrington Storm of 1859.” Advanced Space Research 38 (2006): 173–

179.
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Empirical models of magnetospheric convection59 estimate Joule heating of 14,000 GW globally, with a 

Bz of –60 nT, Vsw of 1,500, and solar wind density of 10 cm-3

. This Joule heating power has significant 

uncertainty and is expected to be reduced by magnetospheric saturation.60 Magnetospheric model 

simulations of extreme solar wind conditions could potentially reduce this uncertainty. In addition to 

producing the large-scale and global increase in neutral density, geomagnetic storms are also expected 

to produce significant structure, particularly at high and mid-latitudes. Polar orbiting objects and those 

in eccentric orbits are likely to be particularly vulnerable to neutral density structure.

4. Benchmarks

Solar Extreme Ultraviolet and Far Ultraviolet Radiation 

The upper atmosphere expansion benchmarks for the neutral density response to the EUV flux on 

timescales greater than one day are shown in Table 11. The neutral density response is defined at 

250 kilometers, 400 kilometers, and 850 kilometers altitude as percent increases relative to empirical 

model reference values using 240 and 200 sfu for the F10.7 daily and 81-day mean, respectively. The F10.7 

levels for the 100-Year Benchmark are not too far outside the parameter space used to develop the 

empirical model, so are likely to have uncertainties of around 30%, not that much greater than the 

uncertainties in neutral density at F10.7 peak levels of ~300 sfu. On the other hand, the uncertainties for 

the neutral density increases for the theoretical maximum are well outside parameter space of the 

empirical neutral density models and therefore the uncertainty increases to about a factor of 2.

Table 11. Benchmarks for global mean neutral density response to solar EUV on > 1-day timescales

Altitude

100-Year Benchmark

(percent increase in neutral density 

in response to a daily F10.7 of 390 sfu a with an 81-day mean of 280 sfu)

Theoretical Maximum

(percent increase in neutral density 

in response to a daily F10.7 of 500 sfu 

with an 81-day mean of 390 sfu)

250 kilometers 50% 100%

400 kilometers 100% 160%

850 kilometers 200% 300%

a One solar flux unit (sfu) = 10-22 W m-2 Hz-1

Effects of Solar EUV Radiation Enhancement during Solar Flares on Upper Atmosphere Expansion

The upper atmosphere expansion benchmarks for the neutral density response to the EUV flux during 

a solar flare are shown in Table 12. The benchmarks assume a similar EUV spectrum and duration 

compared with the October 2003 X-class flare. The values quoted are peak dayside neutral density 

increases relative to the background before the flare. The values are quoted at 400 kilometers altitude 

only and are the response at a median F10.7 solar flux level of 150 sfu. The EUV flux during a flare is not 

always well correlated with the X-ray flux by which the flare is categorized so the neutral density 

enhancements are uncertain to a factor of 2.

 59 See, for example, D. R. Weimer, “Improved Ionospheric Electrodynamics Models and Application to Calculating Joule 

Heating Rates,” Journal of Geophysical Research 110 (2005): A05306. 60 J. Raeder and G. Lu. “Polar Cap Potential Saturation during Large Geomagnetic Storms,” Advanced Space Research 36 

(1804, 2005).
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Table 12. Benchmarks for peak dayside neutral density response to solar EUV on flare timescales

Altitude

100-Year Benchmark

(peak dayside percent increase in 

response to an X30 flare)

Theoretical Maximum

(peak dayside percent increase in 

response to an X40 flare)

400 kilometers 75% 135%

Phase 2 analyses will evaluate if the benchmark needs to be expanded to other altitudes and levels of 

solar activity. Considerable variability and uncertainty in the magnitude and duration of the EUV part 

of the spectrum exists during a solar flare.61 Prudent estimates for uncertainty in the peak density 

change and flare duration are at least a factor of two.

Coronal Mass Ejections Driving Geomagnetic Storms 

In Phase 1, the neutral density benchmark in response to a 1-in-100-year CME was estimated at 

400 kilometers altitude and at median F10.7 solar flux level of 150 sfu. This benchmark is defined for peak 

global mean values of neutral density, but the values could equally be applied to orbit average density 

along polar or equatorial trajectories. The benchmark magnitude is the percent increase referenced to 

the NRLMSISE-00 values using solar and geomagnetic indices for the large geomagnetic storms of 

October 2003 (Halloween storm) or of July 2000 (Bastille Day storm).

Using the predicted 1-in-100-year solar wind values to drive the Weimer62 empirical magnetospheric 

convection model with expected magnetospheric saturation,63 the benchmark assumes a peak global 

Joule heating rate of 10,000 GW. This energy was used to scale the input to a physics-based 

thermosphere-ionosphere model for the geomagnetic storm of December 2006. It was assumed the 

storm has a similar duration of about 12-hours. In that simulation, the thermospheric temperature 

exceeded 4,000 K, and neutral density exceeded empirical model estimates of the response to a Bastille 

Day- or Halloween-like storm by a factor of five (Table 13). 

Table 13. Benchmarks for global mean neutral density response to CME.

Altitude

100-Year Benchmark

(percent increase in response 

above values experienced during 

Halloween or Bastille Day storms)

Theoretical Maximum

(percent increase in response 

above values experienced during 

Halloween or Bastille Day storms)

400 kilometers 400% Phase 2

Results from the physical model simulation were used to estimate peak neutral winds, which are 

expected to exceed 2,000 meters/second, modulating the benchmark in-track apparent density by 

about 50 percent. Cross-track winds of these magnitudes will also significantly impede debris orbit 

prediction.

 61 T. N. Woods et al., “New Solar Extreme-Ultraviolet Irradiance Observations during Flares.” Astrophysical Journal 739, no. 2

(2011): 59. 62 Weimer, “Improved Ionospheric Electrodynamics Models and Application to Calculating Joule Heating Rates,” Journal of 

Geophysical Research 110 (2005): A05306. 63 J. Raeder et al., “Global Simulation of Space Weather Effects of the Bastille Day Storm.” Solar Physics 204 (2001): 325.
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The following are some additional characteristics in the neutral density response expected to impact 

orbit prediction of satellites and debris:

• Neutral composition mixing during a geomagnetic storm, changing the O/N2 ratio, is expected 

to modulate the neutral density response by ±50 percent. 

• Auroral heating is expected to increase global neutral density by 20 to 30 percent.

• Neutral density structure is expected to be produced during a geomagnetic storm. A small-scale 

structure (< 100 kilometer scale size) has only a small impact on satellite drag due to the brief 

period of influence (~10 seconds) for typical satellite orbit velocities (~8 kilometers/second). 

Wave structure (peaks and troughs) at this resolution will also have only a small impact on

satellite orbit since the net drag integrates through the peaks and troughs. A larger scale 

structure (~500 kilometers), such as associated with the cusp64 or auroral oval, however, can 

have an impact on particular orbit trajectories and eccentric orbits. During an extreme event, a 

500-kilometer structure and waves would be expected to deviate 50 to 100 percent from the 

background.65

The uncertainties of the benchmark estimates for the neutral density response to an extreme CME are 

currently at least 100 percent, meaning the response could be up to twice the current estimates. It is 

also possible that the magnetosphere saturates, and the response to an extreme event would actually 

be no more than the response to some of the super-storms of the past (e.g., Halloween or Bastille Day 

storms). Extreme storms are likely to occur at the same time as elevated EUV flux, so the effects could 

be additive. In these circumstances, the cumulative effect could increase density above previously 

observed storms by a factor of ten. Finally, extreme storms are likely to occur at the same time as 

elevated EUV flux so the effects could be additive. In these circumstances the cumulative effect could 

increase density above previously observed storms by a factor of ten.

A significant uncertainty in the geomagnetic storm simulation is the cooling rates in the model, which 

were calculated based on an extrapolation of the Marsh, Solomon, and Reynolds SNOE NO empirical 

model (NOEM).66 The cooling rates in CTIPe come firstly from vertical heat conduction from the upper 

to lower thermosphere, and then from infrared radiative cooling, primarily from the 5.3-μm nitric oxide 

(NO) band. NO cooling has been referred to as a “thermostat” for the system by Mlynczak et al.67 Auroral 

precipitation, as well as serving as a source of heat and ionization during a geomagnetic storm, also 

produces NO and increases the cooling rate. Since the Marsh NOEM code has to be extrapolated from 

lower geomagnetic storm levels to these more extreme conditions, inevitably the cooling rate estimates 

have significant uncertainty. In addition, Knipp et al. have reported that the NO production during a 

storm can persist into the recovery and drive an “overcooling” and collapse of the thermosphere, 

leading to significant drag reduction.68

 64 H. Lühr et al., “Thermospheric Up-Welling in the Cusp Region: Evidence from CHAMP Observations,” Geophysical Research 

Letters 31 (2004): L06805. 65 R. L. Anderson, G. H. Born, and J. M. Forbes, “Sensitivity of Orbit Predictions to Density Variability,” Journal of Spacecraft 

and Rockets 46, no. 6 (2009). 66 D. R. Marsh, S. C. Solomon, and A. E. Reynolds, Empirical Model of Nitric Oxide in the Lower Thermosphere,” Journal of 

Geophysical Research 109 (2004): A07301, doi:10.1029/2003JA010199 67 M. Mlynczak et al. “The Natural Thermostat of Nitric Oxide Emission at 5.3 μm in the Thermosphere Observed During the 

Solar Storms of April 2002,” Geophysical Research Letters, 30, no. 21 (2003), doi:10.1029/2003GL017693 68 D. J., Knipp et al., “Data and Model Views of Energy Input to the Dayside Thermosphere, When the East-West Interplanetary 

Magnetic Field Is Large,” presented at AGU Fall Meeting, San Francisco, California, 2011.
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Abbreviations

ACE Advanced Composition Explorer

ACR anomalous cosmic ray

AU astronomical unit

C carbon

CHAMP Challenging Minisatellite Payload

cm Centimeter

CME coronal mass ejection

CO2 carbon dioxide

CTIPe Coupled Thermosphere Ionosphere Plasmasphere Electrodynamic

DHS Department of Homeland Security

DOC Department of Commerce

DOD Department of Defense

DOE Department of Energy

DOI Department of the Interior

DOT Department of Transportation

DSCOVR Deep Space Climate Observatory

Dst Disturbance storm time index

EUV extreme ultraviolet

eV electron volt

EVT extreme value theory.

F10.7 10.7-centimeter solar radio flux

FCC Federal Communications Commission 

Fe iron

FUV far ultraviolet

GCR galactic cosmic ray

GEO geostationary Earth orbit

GeV gigaelectron volt

GHz gigahertz

GLE ground-level event

GNSS Global Navigation Satellite System

GOES Geostationary Operational Environmental Satellite 

GPS Global Positioning System
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HAF highest affected frequency

H hydrogen

He helium

HEO highly elliptical orbit 

HF high frequency

hmF2 height of the F2 layer

IEEE Institute of Electrical and Electronics Engineers

IMF Interplanetary Magnetic Field

keV kiloelectron volts

kHz kilohertz

km kilometer

LANL Los Alamos National Laboratory

LEO low Earth orbit

LIS local interstellar spectra

MEO medium Earth orbit

MeV megaelectron volts

MHz megahertz

MIT magnetosphere-ionosphere-thermosphere

MUF maximum usable frequency

MV megavolt

N2 molecular nitrogen

NASA National Aeronautics and Space Administration

NERC North American Electric Reliability Corporation

NmF2 peak density of the F2 Layer

NOAA National Oceanic and Atmospheric Administration

NSF National Science Foundation

NSTC National Science and Technology Council

nT nanotesla

NTIA National Telecommunications and Information Administration

O oxygen

OSTP Office of Science and Technology Policy

R&D research and development

RE radius of Earth 
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RSTN Radio Solar Telescope Network

SEP solar energetic particle

sfu solar flux unit

SIS Solar Isotope Spectrometer

SPENVIS Space Environment Information System

STEM Science, Technology, Engineering, and Mathematics

TEC Total Electron Content

U.S.C. United States Code

UHF Ultra High Frequency

VHF very high frequency

V/km volt(s) per kilometer