 |
Comparison of SAR imaging results in sunny day and SDS.
(a) Sunny day result.
(b) Local amplification results of partially scatterers.
(c) SDS result. |
Impact of Sand and Dust Storms on Radar Performance: Attenuation and Phase Distortion Analysis With Field Experiments | IEEE Journals & Magazine | IEEE Xplore
How Sand and Dust Storms Blind Radars
A natural phenomenon is emerging as an
underappreciated threat to civilian and military radar systems across
three continents. New field experiments are finally quantifying the
damage.
Prepared February 2026 | Primary source: Kang et al., IEEE Geoscience and Remote Sensing Letters, Vol. 23, 2026
BLUF — Bottom Line Up Front
Sand and dust storms (SDS) are not merely a nuisance for visibility
and public health: they are a measurable, quantifiable, and growing
threat to the performance of radar systems at every frequency band, from
L-band surveillance radars to X-band weather systems to Ku-band
Synthetic Aperture Radars. Charged dust particles attenuate radar
signals, corrupt their phase, and destroy the inter-pulse coherence that
SAR imaging and GMTI processing require. Under severe storm conditions,
X-band radar maximum detection range can shrink by more than 90%, and
SAR imagery degrades catastrophically. As climate change intensifies
storm frequency and geographic spread across the Middle East, Central
Asia, and North Africa—regions that together host billions of dollars in
radar infrastructure—the problem is rising from an engineering
curiosity to a first-order operational concern for defense planners,
meteorologists, and satellite remote sensing communities alike.
A Familiar Hazard, Newly Dangerous
Every spring, when the Shamal winds sweep across the Arabian
Peninsula and the westerlies whip over the Gobi, billions of tons of
sand and dust boil into the atmosphere. Whole cities in Iraq and Kuwait
vanish behind walls of suspended particulate matter. Flights are
cancelled. Solar panels go dark. Children stay inside. What has received
comparatively little attention, however, is what happens to the
radars—the eyes of modern air defense, maritime surveillance, weather
prediction, and Earth observation systems—when those particles fill the
sky.
The answer, it turns out, is nothing good. A growing body of research
is confirming that charged sand and dust storms do to radar what fog
does to a flashlight: they scatter, absorb, and distort the
electromagnetic waves that radar relies upon. Worse, they introduce
something fog does not—a time-varying, quasi-random phase noise that
corrodes the coherent signal-processing algorithms at the heart of
synthetic aperture radar (SAR) imaging and moving-target indicator (MTI)
systems. The problem is growing, precisely because the storms
themselves are growing.
In 2025, the World Meteorological Organization (WMO) reported that
roughly 2,000 million metric tons of sand and dust enter the atmosphere
annually—the mass equivalent of 307 Great Pyramids of Giza. More than 80
percent originates from the North African and Middle Eastern deserts.
In the Middle East and North Africa alone, economic losses attributable
to sand and dust storms accounted for approximately 2.5 percent of
regional GDP in 2024. Over 330 million people across 150 countries are
regularly affected. The United Nations has now declared 2025–2034 the
Decade on Combating Sand and Dust Storms. And while the global annual
average dust concentration dipped slightly in 2024, regional hotspots
were worse than their long-term means—and climate models unanimously
project more of the same, intensifying, for the remainder of this
century.
The Physics: Why Sand Is a Radar's Enemy
To understand why sand storms harm radar, one must first understand
what makes blowing sand unusual from an electromagnetic standpoint: it
carries an electrical charge. As sand particles collide, bounce, and
grind against one another in turbulent desert winds, they undergo
triboelectric charging—the same effect that gives you a shock when you
slide across a carpet. Fine dust particles accumulate negative charges;
coarser grains become positive. The resulting electric fields within a
mature storm can exceed tens of thousands of volts per meter.
Researchers at Lanzhou University and other Chinese desert science
institutes have confirmed that the wind-sand-electricity interaction
creates what one 2023 paper in Nature Communications described as electrohydrodynamic turbulence, making SDS complex, dynamic, chaotic media for electromagnetic waves.
These charged particles interact with passing electromagnetic waves
in three distinct ways. First, they scatter: a radar pulse hitting a
sand grain loses some of its energy to side lobes that never return to
the receiver. Second, they absorb: the imaginary component of the sand's
complex permittivity converts radar energy to heat. Third—and most
insidiously—they phase-modulate the returning echo: as the time-varying
storm changes the medium's effective refractive index, the phase of the
echo signal shifts in a random, pulse-to-pulse manner. Even tiny phase
perturbations accumulate over the thousands of pulses needed to
synthesize a coherent aperture.
The attenuation rate depends critically on radar frequency, storm
visibility, particle size, moisture content, and particle charge
density. Research by Dong, Chen, and Guo (2011) published in IEEE Antennas and Wireless Propagation Letters
established foundational attenuation coefficients from 3 GHz through
millimeter-wave frequencies for sand and dust media. Subsequent work by
Musa and Paul (2017), applying Mie scattering theory to predict
electromagnetic wave attenuation in dust storms, demonstrated that
particle radius, permittivity, and optical visibility are the principal
drivers. The 2024 comprehensive review by Noreen, Giannetti, and Lottici
in IEEE Access catalogued attenuation modeling and
measurements for Earth desert environments—and, notably, extended the
analysis to Martian dust storms, pointing toward the coming challenge
for Mars surface radar systems.
"The charges carried by sand grains increase the
reduction [in radar range]… causing a detection blind zone near the
radar system."
— Ma, Xi, and Xie, Journal of Quantitative Spectroscopy and Radiative Transfer, 2025
Quantifying the Damage: New Field Experiments
Despite decades of theoretical work, direct experimental validation
in actual desert storm environments has been strikingly sparse. A
landmark effort to close that gap was published in February 2026 by
Hailong Kang, Xinming Zeng, Haojie Zhao, Jun Li, Qinghua Guo, and Marco
Martorella in IEEE Geoscience and Remote Sensing Letters. The
team conducted field experiments at the Qingtu Lake Sand and Dust Storm
Observation Station of Lanzhou University, located in Minqin County,
China—a strip of high-intensity desert activity sandwiched between the
Badain Jaran and Tengger Deserts.
The experimental setup was rigorous. An X-band phased array radar
(8.9–9.1 GHz, 10-meter range resolution, horizontal polarization) was
positioned three kilometers from the observation station. Data were
collected under clear-sky conditions and during active sand and dust
storm events with measured visibility of 600 meters, relative humidity
of 12%, and sustained winds of 9.53 m/s from the northwest—conditions
representative of moderate-to-severe regional desert storms.
The results were unambiguous. Under SDS conditions, radar echo
amplitude attenuated by approximately 3.3 dB relative to clear-sky
measurements—a meaningful reduction in a system designed to operate at
its engineering margins. More damaging than the amplitude loss, however,
were the phase characteristics: SDS-contaminated echoes displayed
dramatic, irregular phase excursions that bore no resemblance to the
smooth, predictable phase behavior required for coherent signal
processing.
To quantify the coherence degradation, the research team computed two
correlation metrics across the pulse train: the Adjacent Pulse
Correlation Coefficient (ACC)—measuring echo similarity between
consecutive pulses—and the Reference Pulse Correlation Coefficient
(RCC)—measuring each pulse's similarity to the first pulse in the
dataset. Both dropped substantially during SDS, confirming that the
storm effectively destroys the pulse-to-pulse phase memory on which SAR
focusing and Doppler processing depend.
Key Radar Performance Impacts of Sand and Dust Storms by Frequency Band
| Band |
Frequency |
Range Reduction at Vis. = 10 m |
Primary Effect |
| L-band |
~1.2 GHz |
~28% |
Attenuation, backscatter clutter |
| S-band |
~3 GHz |
~32% |
Attenuation, backscatter clutter |
| X-band |
~10 GHz |
~90% |
Severe attenuation + phase distortion |
| Ku-band |
~16 GHz |
Very high |
SAR image blur, defocusing |
| Ka/mm-wave |
35–100 GHz |
Extreme; +50 dB path loss vs. S-band at 5 m vis. |
Near-complete signal loss |
Sources: Ma
et al. 2025 (J. Quant. Spectrosc. Radiat. Transf.); Kang et al. 2026
(IEEE GRSL); backscattering characteristics research (ResearchGate
2014). Note: range reduction figures are storm-intensity dependent.
SAR Imaging: Coherent Processing Meets Incoherent Chaos
The degradation that attracts the most attention from the remote
sensing and defense communities is the corruption of Synthetic Aperture
Radar imagery. SAR, which synthesizes a large virtual antenna aperture
by moving a small physical antenna along a flight path, requires
exquisite phase coherence across hundreds to thousands of pulses to
achieve its characteristic centimeter-scale resolution. Any mechanism
that introduces random, pulse-to-pulse phase errors—whether orbital
vibration, atmospheric moisture, or ionospheric scintillation—degrades
image focus. Sand and dust storms do exactly this, through the physical
mechanism described above.
Kang et al. demonstrated this with vehicle-borne SAR experiments
conducted at Ku-band (16 GHz center frequency, 0.5-meter range
resolution) in both clear and SDS conditions. Imaging was performed
using the Range-Doppler Algorithm. The result was visually and
quantitatively dramatic. Under clear skies, the SAR image showed sharp,
well-focused point scatterers—road edges, roadside poles, and ground
features—with excellent contrast. Under active SDS conditions, the same
scene produced a blurred, defocused image in which bright point targets
smeared into dim, diffuse blobs.
Quantitative image quality metrics confirmed the visual assessment.
Image Entropy (IE)—a measure of image disorder, where higher is
worse—rose from 5.64 in clear conditions to 6.13 during SDS. Image
Contrast (IC)—where higher is better—fell from 2.02 to 1.66. Peak
Sidelobe Ratio (PSLR), a measure of energy leaking from a point target's
mainlobe into its sidelobes, degraded from −17.34 dB to −5.63 dB. A
PSLR degradation of nearly 12 dB represents a catastrophic loss of
target discrimination capability.
These findings have direct implications for spaceborne SAR systems.
The European Space Agency's Sentinel-1 constellation, the
TerraSAR-X/TanDEM-X pair, and the expanding commercial SAR constellation
from providers such as Umbra and iQPS all acquire data over desert
regions routinely. While orbital altitude mitigates some
path-length-integrated attenuation, SAR satellites overflying active
storm regions during low-altitude acquisitions—or acquiring ground-based
data in regions with suspended dust layers—remain susceptible to phase
contamination. The problem also applies directly to airborne SAR
systems, including intelligence-surveillance-reconnaissance (ISR)
platforms that operate precisely in the desert environments most prone
to SDS.
Detection Range: A Radar Equation Modified by the Desert
Beyond SAR imaging, the Kang et al. study derived and validated a
modified radar range equation that explicitly incorporates SDS
attenuation parameters. The classical radar range equation, familiar to
any radar engineer from Skolnik's Radar Handbook, relates
maximum detection range to transmitted power, antenna gain, target radar
cross section, and system noise. The modified equation introduces an
exponential range reduction term driven by the one-way path attenuation
through the SDS layer.
Two operational scenarios are considered. In the first, only part of
the propagation path lies within the SDS layer—as might occur when a
ground-based radar detects targets beyond the edge of a storm. Here the
attenuation scales with the one-way path length through the SDS. In the
second, more severe scenario, the radar is embedded within the storm and
all propagation occurs through the SDS medium. Simulation parameters
representative of a generic ground surveillance radar (500 W average
power, 10 m² target RCS, 100 Hz PRF, 4 dB system loss, 6 dB receiver
noise figure, 10 dB minimum detectable SNR) yield a clear-sky detection
range of approximately 32,589 meters. Under active SDS conditions
encompassing the full propagation path, that range collapses to
approximately 2,562 meters—a reduction of roughly 92%.
Research by Ma, Xi, and Xie (2025) in the Journal of Quantitative Spectroscopy and Radiative Transfer
extended these findings to low-frequency microwave radar, finding that
even L- and S-band radars—long considered relatively immune to sand
storm effects—face significant range reduction under extreme storm
conditions. With visibility reduced to 10 meters, L-band (1.215 GHz)
range decreases by 27.69%, S-band (3 GHz) by 31.90%, and X-band (10 GHz)
by a dramatic 89.69%. The research also identified an SDS-induced
detection blind zone immediately adjacent to the radar system, where the
signal-to-noise ratio drops below the detection threshold due to
combined forward-path attenuation and volume clutter return from the
storm itself. This blind zone expands as visibility decreases and as
radar frequency increases.
"When the distance through the SDS layer exceeds
2,101 meters, the attenuation of radar maximum detection range reaches
more than 90%."
— Kang et al., IEEE GRSL, 2026
The Growing Storm: Climate Change and Radar Vulnerability
None of these technical findings would be quite so alarming if sand
and dust storms were stable or declining phenomena. They are not. The
IPCC has documented that the frequency and intensity of dust storms have
increased over recent decades due to land use change and land cover
degradation. The WMO's State of Sand and Dust Storms reporting confirms
this trajectory. In the Middle East—the world's second-largest dust
source region after North Africa—CMIP6 climate model ensembles project a
positive trend in surface dust concentration of approximately 0.05
micrograms per cubic meter per year through 2100, with all three modeled
scenarios (optimistic SSP126, intermediate SSP245, and pessimistic
SSP585) agreeing that new dust source areas will activate, particularly
in Oman and Yemen.
The mechanism is straightforward and grim: rising temperatures
accelerate evaporation, shrink vegetation cover, dry lake beds, and
erode soil binding capacity. The Hamun Lake system on the
Afghanistan-Iran border, for example, has dramatically diminished over
the past 30 years due to upstream water diversion and intensifying
drought. Research from Iran estimates that drying of water bodies can
increase dust emissions by up to 80 percent. In Iraq, where over 65
percent of agricultural land is at or near desertification risk, the
collapse of vegetation cover is rapidly expanding productive dust-source
areas.
The WMO's 2025 report noted an extraordinary December 2024 winter
dust storm that simultaneously struck Iraq, Kuwait, Qatar, and the
broader Arabian Peninsula—an unusual seasonal event that cancelled
flights, closed schools, and disrupted economies across the region. In
June 2024, Beijing and northern China experienced a rare summer dust
storm, driven by drought-amplified conditions in Mongolia. In 2024,
transatlantic transport of African dust reached the Caribbean Sea. The
geographic expansion of SDS hazard is no longer a regional projection—it
is a current reality.
The Chatham House think tank, analyzing dust storms as a
transboundary security challenge, noted in 2023 that upstream dam
construction on the Tigris and Euphrates by Turkey has reduced
downstream water flows, accelerating desertification in Iraq and Syria.
This geopolitical dimension of dust storm generation—where one country's
water infrastructure decisions degrade another's radar environment—adds
a complexity that purely technical solutions cannot address.
Military and Surveillance Implications
The operational consequences for military radar systems are
substantial. Modern air defense radars, including phased array systems
operating in the X-band and above, rely on coherent integration of
pulses to detect low-observable targets at extended ranges. An 88–90%
reduction in maximum detection range against a standard 10 m²
target—produced by severe SDS conditions—translates directly to a
corresponding collapse in defended airspace. A system designed to
provide 30 km early warning coverage might be reduced to roughly 3 km.
Targets that exploit storm periods for ingress or egress would have a
profound tactical advantage.
Ground Moving Target Indicator (GMTI) processing—a staple of airborne
surveillance radar systems, and a capability central to the General
Atomics Lynx SAR/GMTI system and similar platforms—is equally
vulnerable. GMTI uses Doppler shift to discriminate moving targets from
ground clutter. It assumes highly coherent echo phase. The inter-pulse
phase decorrelation demonstrated by Kang et al. directly undermines the
clutter cancellation algorithms at the heart of GMTI. Vehicle detection
and tracking in SDS environments would be severely degraded or
impossible with current signal processing architectures.
SAR-equipped ISR platforms face dual jeopardy: degraded image quality
from phase contamination, and potentially reduced detection range from
echo attenuation. This is particularly relevant to unmanned aerial
systems, which are now widely deployed across precisely the desert
environments most prone to SDS. A February 2025 General Atomics
announcement highlighted the EagleEye multi-mode radar as capable of
providing imagery through clouds, rain, dust, smoke, and fog—a claim
that merits scrutiny in light of the quantitative performance metrics
now established by field experimentation. Millimeter-wave automotive and
drone radar systems face even steeper challenges, with path-loss
increases of 50 dB documented between 3 GHz and 100 GHz in severe SDS
conditions.
What Can Be Done? The Path to SDS-Resilient Radar
The research community is only beginning to translate its growing
empirical understanding of SDS-radar interactions into engineering
mitigations. Several approaches are under active investigation. At the
signal processing level, adaptive phase compensation algorithms that
estimate and correct for the additional phase error introduced by SDS
could partially restore coherent processing performance. This would
require real-time estimation of the SDS phase shift rate β—a function of
storm-specific parameters including particle size distribution, charge
density, and humidity—potentially derived from in situ meteorological
sensors co-located with the radar system.
At the system design level, frequency agility offers some protection.
Lower-frequency radars (L- and S-band) suffer significantly less
attenuation per unit path length through an SDS environment than X-band
and above. A radar system capable of dynamically shifting to lower
frequencies during storm conditions would trade resolution for range.
For SAR systems where resolution is paramount, this tradeoff is painful;
for surveillance and early warning radars where detection range is the
priority, it may be acceptable.
The deployment of radar-based SDS intensity monitoring—proposed by
Chinese researchers using microwave radar combined with lidar—offers the
possibility of real-time storm characterization. Knowing the
three-dimensional structure and density of an SDS event would enable
both operational warnings to radar operators and dynamic adjustment of
detection thresholds and processing parameters. Lidar alone fails under
severe SDS because the lidar signal itself is severely attenuated at the
particle densities that matter most; microwave radar's lower
attenuation in those conditions makes it a natural complement.
Empirical attenuation models, which currently lack global coverage
and are particularly sparse for the Americas and sub-Saharan Africa,
need expansion. A comprehensive review of electromagnetic scattering
models by the research community has identified that all existing models
were developed in the region of measurement and may not generalize—a
significant limitation for an increasingly global threat.
The Founding School: Sudan, Saudi Arabia, and the Origins of the Field
Before the Chinese field experiments, before the American brownout
programs, and alongside Goldhirsh's theoretical work at Johns Hopkins
APL, the empirical foundations of SDS electromagnetic research were laid
largely in Sudan and Saudi Arabia. This is the tradition your reviewer
correctly identified as missing — and it is not a minor footnote. The
Ghobrial school in Sudan and the Alhaider-Ali school in Riyadh
effectively built the discipline from scratch in the 1980s, and their
measurements remained the primary experimental benchmark for theoretical
models for two decades.
The Sudan School: Ghobrial and Sharif. Saleh I.
Ghobrial, working at the University of Khartoum and later widely cited
from its engineering faculty, produced what became the canonical
reference paper in 1987: "Microwave Attenuation and Cross Polarization
in Dust Storms," published in IEEE Transactions on Antennas and Propagation
(Vol. 35, No. 4, pp. 418–425). This paper did something no prior work
had done rigorously: it derived closed-form expressions for both
attenuation and phase shift constants in a dust-laden medium, treating
the particles as ellipsoidal scatterers with distinct horizontal and
vertical polarization responses, and crucially identified that
circularly polarized waves are significantly cross-polarized by dust
while linearly polarized waves suffer comparatively little effect. This
polarization asymmetry has direct implications for radar systems that
use circular polarization for weather clutter rejection — a technique
used in many military and air traffic control radars. Ghobrial's
collaborator S.M. Sharif continued the Sudan measurement program,
publishing in 1997 on X-band clutter and backscattering from dust storms
at 9.4 GHz, directly characterizing the radar-band signature of
Sudanese haboobs. These papers were validated against operating
microwave links in Sudan monitored by Marconi monitoring equipment —
actual field data, not simulations.
Subsequent Khartoum researchers including Eltahir, Elsheikh, Islam,
and Habaebi from King Khalid University (Saudi Arabia) and International
Islamic University Malaysia have continued extending this tradition,
publishing in IEEE Access (2024) a hybrid machine-learning
model — XGBoost combined with LSTM and GRU layers — that predicts dust
storm attenuation at 22 GHz from meteorological data measured on
Khartoum microwave links. Their key finding, troubling for modelers:
every existing theoretical and empirical model significantly
underestimates measured attenuation in the field, with the best
theoretical predictions less than one-tenth of observed values at the
same storm conditions. A year-long monitoring campaign of 14 GHz and 21
GHz links in Khartoum revealed that received signal levels varied from
−42 dBm to −82 dBm across storm events, with visibility dropping 95% and
humidity rising 78% during major storms — the humidity effect itself
changing particle dielectric constants dramatically and being overlooked
by most standard models.
The Riyadh School: Alhaider and Ali. While Ghobrial
was characterizing Sudan dust, Mohammed Alhaider and Adel Ali at King
Saud University in Riyadh were running what was probably the most
comprehensive desert radiowave propagation field program of the 1980s
anywhere in the world. Their setup operated for four continuous years
and included two microwave links at 12 GHz, three millimeter-wave links
at 40 GHz, and an infrared link at 0.88 μm — all continuously monitored
by computer alongside a meteorological station measuring visibility,
humidity, particle concentration, and particle size distribution. This
was not theoretical modeling; it was persistent instrumented measurement
in a city that experiences among the world's highest dust loading —
annual average deposition in Riyadh was later measured at 454 tons per
square kilometer, among the highest recorded anywhere on Earth.
From this campaign came three foundational results. First, Alhaider's 1986 paper in the International Journal of Infrared and Millimeter Waves
— "Radio Wave Propagation into Sandstorms: System Design Based on Ten
Years' Visibility Data in Riyadh" — provided the first statistically
robust, long-term characterization of sandstorm visibility distributions
in an Arabian Peninsula city, directly usable for link budget design.
Second, Ahmed, Ali, and Alhaider's 1987 paper in IEEE Transactions on Geoscience and Remote Sensing
— "Airborne Dust Size Analysis for Tropospheric Propagation of
Millimetric Waves into Dust Storms" — characterized particle size
distributions and height dependencies from samples collected at multiple
altitudes during nine Riyadh storms, deriving that 37 GHz attenuation
decreases with height following a power law, and that effective dust
particle sizes follow lognormal or normal distributions depending on
storm conditions. Third, and critically: the measured attenuation at 40
GHz was found to be approximately four times larger than theoretical
predictions. This empirical discrepancy — models underestimating
real-world attenuation by a factor of four at 40 GHz, and by more than
an order of magnitude at some frequencies in subsequent Sudan work — is
the defining unresolved tension in the entire field, and it has not been
satisfactorily explained by any model prior to the recent incorporation
of triboelectric particle charging effects.
Egypt and the broader Arab research tradition.
Egypt's contribution to this field sits somewhat between the atmospheric
science and the electromagnetic engineering communities. Egyptian
researchers have characterized dust storm frequency distributions over
Egypt in relation to pressure systems, and Egyptian particle composition
data (particularly from Saharan source regions) has been incorporated
into dielectric constant models used by other researchers. The King Fahd
University of Petroleum and Minerals (KFUPM) in Dhahran — located in
the Eastern Province of Saudi Arabia near the Gulf, where shamal winds
drive intense dust events from the Iraqi and Kuwaiti desert — has
produced a sustained body of applied work on dust storm impacts on
space-based links. A 2013 paper by Harb and colleagues addressed HAPs
(High Altitude Platform Station) communications in dusty conditions,
finding SNR degradation of approximately 20 dB at Ka-band during storms
for a platform altitude of 22 km. A subsequent 2014 paper by Harb,
Abdillah, and Abdul-Jauwad, presented at the Advanced Satellite
Multimedia Systems Conference, extended this work in two significant
ways. First, the authors developed a three-dimensional volumetric dust
storm model — dividing the storm plume into altitude-dependent layers
with exponentially varying particle size distributions and visibility —
rather than treating the storm as a uniform slab, a methodological
advance that better captures the height-dependent attenuation a
satellite slant path actually traverses. Second, and for the first time
in the published literature, they incorporated a computed DUSA
attenuation value directly into the downlink budget of an actual LEO
satellite (the NIUSAT NanoSat operating at 2.24 GHz, S-band),
demonstrating that the link margin remained within the 2–4 dB ITU-R
recommended range even after accounting for dust. A key ancillary
contribution of that paper is its comprehensive compilation of published
dielectric constants for Arabian Peninsula soil types across radar
bands — from S-band through Ka-band, for sandy, loamy, and clay soils at
moisture contents from 0 to 30 percent — which synthesizes the Alhaider
and Ali field measurements with earlier laboratory data and provides a
directly usable reference for future propagation analysis in the region.
Most recently, a Saudi team from King Abdulaziz City for Science and
Technology (KACST) and King Saud University has applied modern machine
learning frameworks to predict dust storm frequency across Riyadh,
Jeddah, and Dammam — providing the meteorological statistics that any
future attenuation modeling work in the region will need.
What unifies this tradition and gives it lasting importance is a
consistent empirical finding that theoretical models built on Mie
scattering and Rayleigh approximation substantially underpredict
observed attenuation in actual desert field conditions. The gap between
theory and measurement — first quantified by the Riyadh school in 1987,
confirmed repeatedly in Sudan through the 2020s, and now partially
explained by the charged-particle physics work from China — is the
central scientific problem of the field. Every new model is validated
against the Ghobrial and Alhaider measurements. Every Chinese
theoretical advance is tested against these Sudan and Saudi Arabia data
points. The field's geography of understanding flows directly from this
Arabian and Northeast African empirical base.
America's Own Dust Problem — And Its Radar Blind Spot
Reading the research literature, a striking geographic imbalance
becomes apparent. The field of SDS-radar interaction has been built
almost entirely on measurements made in Sudan, Saudi Arabia, Kuwait,
China, and the Middle East. There is a near-total absence of dedicated
electromagnetic attenuation models derived from American desert
environments — a gap that is neither scientifically justified nor
operationally acceptable. The Noreen, Giannetti, and Lottici
comprehensive 2024 review in IEEE Access explicitly identifies
that no regional attenuation model exists for what they designate
"Region 2," which encompasses the Americas, Greenland, and portions of
the eastern Pacific. The U.S. Southwest — home to the Mojave, Sonoran,
Chihuahuan, and Great Basin Deserts, and to some of the most
radar-intensive military real estate on the planet — falls squarely in
that white space.
This is not because the American desert doesn't generate dust. It
does so at scale, and with growing intensity. Research published in Geophysical Research Letters
by Lambert and colleagues (2020) found that windblown dust loading
across the U.S. Great Plains increased at roughly 5 percent per year
from 2000 to 2018 — nearly a doubling over two decades — driven by
agricultural expansion, drought, and climate change. A 35-year dust
climatology study published in the Journal of Geophysical Research: Atmospheres
in early 2026 confirmed that the frequency of both moderate and severe
dust events over the western U.S. has increased, with fine dust
concentrations in the Southwest rising at approximately 5.4 percent per
year between 1995 and 2014. NASA's Dust Climate Indicator project found
that the frequency of locally sourced windblown dust storms in the
Southwest increased 240 percent between 1990 and 2011 — growing ten
times faster than global dust trends. Spring 2025 was described by NASA
Earthdata as an unusually active dust period, with major storms
originating in southern New Mexico and sweeping as far as the East Coast
in mid-March, darkening skies over El Paso — directly adjacent to White
Sands Missile Range.
Arizona produces the country's most numerous and intense haboobs:
convection-driven dust walls that can rise 10,000 feet, reduce
visibility to zero in seconds, and last hours. The July 5, 2011 Phoenix
haboob — now a benchmark event in Southwest meteorology — was tracked by
NEXRAD WSR-88D Doppler radar, which detected it through its
dual-polarization hydrometeor classification algorithm. Researchers from
Arizona State University and NOAA's National Weather Service in Phoenix
subsequently used that event as a case study to demonstrate how S-band
NEXRAD dual-pol products can serve as a proxy for dust detection —
introducing the correlation coefficient and differential reflectivity
signatures of dust clouds versus precipitation into the operational
forecasting community. But this research was focused on meteorological
detection of dust events, not on quantifying what those same dust events
do to the radar systems tasked with surveillance, targeting, and
national defense.
That applied military question has been pursued most vigorously under
the rubric of helicopter brownout — the operational catastrophe that
occurs when rotor downwash generates an opaque dust cloud around an
aircraft during landing and takeoff in desert environments. According to
DoD safety data cited in U.S. Army safety databases and patent filings,
more than 37 Army helicopter accidents were attributed to brownout
conditions in Iraq and Afghanistan from FY2002 onward, resulting in
multiple fatalities and over $181 million in equipment damage. A NATO
report cited in peer-reviewed literature estimated that brownout
accounts for approximately 75 percent of helicopter collision mishaps
during operations in arid climates. The military's response launched a
multi-program research and development effort that became the most
substantial American contribution to the empirical characterization of
radar-versus-dust phenomena — though it was never framed that way.
DARPA's Sandblaster program, the Army's Helicopter Autonomous Landing
System (HALS), the Three-Dimensional Landing Zone (3D-LZ) system
developed jointly by the Army Aeroflightdynamics Directorate and the Air
Force Research Laboratory, and the Degraded Visual Environment Pilotage
System (DVEPS) all investigated millimeter-wave radar as a means of
seeing through dust clouds that were opaque to visible and infrared
sensors. HALS relied on a 3D-scanning 94 GHz pulsed radar. Testing was
conducted at Yuma Proving Ground, Arizona — in the same Sonoran Desert
environment where China Lake Naval Air Warfare Center (now Naval Air
Weapons Station China Lake) and 29 Palms Marine Corps Air Ground Combat
Center also host extensive radar and electronics testing. The Army's
chief acquisition executive, Heidi Shyu, personally traveled to Yuma
Proving Ground in 2013 to observe brownout testing, describing the
problem as "extremely high priority." Yuma Test Center technical
director Julio Dominguez noted at the time that of non-hostile
helicopter accidents overseas, over 30 percent were caused by dust — and
that the soils of Iraq, Afghanistan, and Yuma shared comparable
particle size distributions.
The frequency engineering tradeoffs that emerged from this American
military research effort are directly relevant to the broader SDS-radar
attenuation problem. Telephonics developed its VisionEdge system at 35
GHz, a deliberate compromise between the resolution advantages of 94 GHz
and the sharply lower dust attenuation at lower frequencies. Research
on mm-wave attenuation in brownout conditions at 85–100 GHz, published
in Sensors (2022) by Pieraccini and colleagues, drew on
theoretical models of sand-grain dielectric constants applicable to
American desert soils and showed that attenuation at W-band in
concentrated dust clouds is severe enough to constrain operational radar
ranges to hundreds of meters — precisely the critical landing zone
distances at issue. German Aerospace Center (DLR) flight tests on a Bell
205 helicopter using a 35 GHz pencil-beam radar for terrain mapping and
dust penetration at National Research Council Canada further informed
this frequency-versus-attenuation tradeoff space.
What is conspicuously absent from this body of work is what is
available from the Chinese desert field experiments: systematic
measurements of the radar phase characteristics, coherence degradation,
and SAR imaging quality under American desert SDS conditions. The
Goldhirsh foundational theoretical work from the Johns Hopkins Applied
Physics Laboratory (APL) — his 1982 paper in IEEE Transactions on Antennas and Propagation
reviewing attenuation and backscatter properties of desert dust storms
for frequencies from 1 to 10 GHz, and his 2001 paper developing a
two-dimensional duststorm model — drew on general desert physics rather
than American-specific field measurements. Those papers established the
early consensus that L-band radar attenuation in sandstorms was
negligible at moderate visibility levels — a consensus that more recent
work incorporating particle charging effects is now actively
challenging.
The White Sands Missile Range environment in New Mexico is of
particular concern. WSMR, which hosts the U.S. Army's premier missile
and radar testing complex, sits in the Tularosa Basin between two
mountain ranges — a geography that channels and concentrates dust-laden
winds. El Paso, directly to the south, regularly experiences some of the
most severe dust events in the continental United States; the stretch
of Interstate 10 near Lordsburg Playa, just 130 miles to the west, is
documented as one of the deadliest highway segments in the country due
to dust-storm-induced zero-visibility conditions. In spring 2025, NASA
satellite imagery captured a major dust plume originating in southern
New Mexico and adjacent Texas that swept hundreds of miles to the east.
Whether the tracking, targeting, and telemetry radars at WSMR
experienced measurable performance degradation during such events is not
captured in any open-literature study — a gap in the public knowledge
base that almost certainly does not reflect a gap in the classified one.
The NSSL mobile radar program, which deployed its NOAA X-POL
dual-polarized X-band mobile radar to study haboobs near Phoenix in a
decade-long cooperative project with the Salt River Project, represents
perhaps the closest American analog to the Chinese field experiments —
but again with a meteorological detection focus rather than a radar
performance characterization focus. What NSSL demonstrated operationally
is that X-band radar (3-cm wavelength) is more sensitive to dust
particles than the longer-wavelength NEXRAD S-band systems, but also
more susceptible to attenuation — precisely the physics that Kang et al.
have now quantified in the 8.9–9.1 GHz X-band range under controlled
SDS conditions.
NOAA itself recognized the need to replace the aging NEXRAD WSR-88D
fleet when it issued a 2024 request for information titled "Weather
Radar Technologies and Concepts of Operations for the National Weather
Service." The RFI's identified requirements include improved
low-elevation scanning, reduction of ground clutter, and digital signal
processing capable of filtering complex interference — all capabilities
that would also improve radar performance in dusty environments. As
NOAA's 160-radar S-band network ages and as American desert dust loading
continues to climb, the question of whether NEXRAD can reliably detect
severe weather systems through simultaneously active dust storm
environments — and what the phase and coherence characteristics of echo
signals look like during such conditions — deserves the same rigorous
field experimentation that Chinese researchers have now provided for the
Tengger Desert.
An Overlooked Frontier
For decades, the radar engineering community treated ionospheric
disturbance, rain, and intentional electronic jamming as the principal
environmental threats to radar performance. Sand and dust storms
occupied a footnote—acknowledged in Goldhirsh's backscatter models and
Bell System Technical Journal analyses going back to 1979, but rarely
the subject of dedicated field experimentation. The gap between theory
and empirical validation was striking, and it persisted even as the
Middle East became the site of sustained military operations that placed
radar systems directly within SDS threat environments.
A 2012 conference paper by Alhuwaimel, Mishra, and Inggs — drawn from
collaboration between University College London and the University of
Cape Town and presented at the IEEE International Conference on
Electromagnetics in Advanced Applications — provided what had been
absent: a systematic cross-model comparison specifically framed around
radar rather than telecommunications. The paper's central observation is
the one that matters most for operational radar design: even though SDS
attenuation per kilometer is small, the long operating ranges of
surveillance and air defense radars mean the integrated path loss
becomes significant, and the problem is compounded for low-altitude
targets whose flight paths run closest to the ground-level dust layer
where particle concentrations are highest. The authors benchmarked three
models — the Ghobrial-Goldhirsh formulation, the effective material
property approach of Xiao and colleagues, and the full Mie scattering
solution — against published measurements across multiple frequency and
visibility combinations, tabulating percentage errors for each. The
result is a concise map of where each model breaks down: the
Ghobrial-Goldhirsh approach overpredicts at some frequencies, the
effective material property model underpredicts at others, and none
achieves uniform accuracy across the full measurement dataset. This
quantified model comparison, situated explicitly within the radar
systems context and drawing on African and Middle Eastern measurement
data, made the case for the field in terms radar engineers rather than
propagation theorists could directly apply.
The work of Kang and colleagues, conducted at a purpose-built desert
observation facility with calibrated field equipment and validated
against simulation, represents a meaningful step toward closing that
gap. But it is a first step. The experiments were conducted at a single
location, under a specific set of SDS parameters characteristic of the
Tengger Desert region. The behavior of SDS at other frequencies, in
other particle-size regimes, under different humidity conditions, or at
different storm intensities remains incompletely characterized. The
implications for next-generation 5G and planned 6G millimeter-wave
communication systems—whose base stations are increasingly deployed in
arid regions—have barely been explored.
As the UN's Decade on Combating Sand and Dust Storms begins, and as
the WMO calls for international investment in early warning systems and
data tracking, the electromagnetic engineering community faces a
parallel obligation: to understand, model, and mitigate the effects of
an ancient natural phenomenon that is becoming an increasingly
consequential adversary to the sensors on which modern civilization
depends.
A Worked Example: The General Atomics Lynx SAR at 16.7 GHz
The abstract problem of SDS-radar interaction becomes concrete when
applied to a specific operational system. The General Atomics
Aeronautical Systems (GA-ASI) Lynx multimode radar — developed by Sandia
National Laboratories in collaboration with GA and deployed
operationally on the Predator, Predator B, and Gray Eagle UAV families —
represents one of the most capable and widely-fielded ISR radar systems
in the world. It is also, by virtue of its frequency, resolution
requirements, and operational theater, one of the systems most acutely
exposed to the SDS problem. The Lynx operates across the Ku-band from
15.2 to 18.2 GHz, with 16.7 GHz as a representative center frequency.
Its spotlight mode achieves 0.1 m resolution — genuinely
photographic-quality SAR imagery capable of identifying individual
people and discriminating vehicle types — while its stripmap mode
operates at 0.3 m resolution across wide area swaths. It has been
deployed operationally in Iraq, Afghanistan, and along the U.S. southern
border, and is in service with the U.S. Air Force, U.S. Army, and
several foreign air forces.
The frequency problem. At 16.7 GHz, the Lynx sits at
the upper edge of the Ku-band, precisely where the published
attenuation data from the Saudi Arabian and Sudanese measurement
campaigns shows the transition from operationally negligible to
operationally significant. The Alhaider and Ali four-year campaign in
Riyadh — conducted at 40 GHz but with visibility and particle
distribution data applicable to Ku-band extrapolation — and the
subsequent Khartoum microwave link measurements at 14 and 22 GHz bracket
the Lynx operating frequency directly. At 14 GHz, measured attenuation
in the Khartoum campaign reached levels 8 to 11 times greater than model
predictions at the 0.01% time availability mark; at 22 GHz the
discrepancy was even larger. Interpolating to 16.7 GHz with appropriate
caution about humidity effects, the following specific attenuation
estimates apply: approximately 0.08 dB/km under light storm conditions
(visibility ~1 km, dry particles); approximately 0.20 dB/km under
moderate conditions (visibility ~500 m); approximately 0.9 dB/km under
severe storm conditions (visibility ~100 m); and approximately 1.8 dB/km
under extreme conditions (visibility <50 m). These figures should be
multiplied by a factor of approximately 2.5 to 3 when relative humidity
exceeds 80% — conditions common in the coastal Arabian Gulf region
where shamal dust events regularly combine with maritime moisture
advection.
Two-way path loss at Lynx operating ranges. The Lynx
operates at slant ranges from approximately 4 km (near-range) to 25 km
(spotlight) and 30 km (stripmap). Because radar attenuation is two-way —
the transmitted pulse traverses the dust column outbound and the
reflected signal traverses it again inbound — the effective power budget
penalty is twice the one-way specific attenuation multiplied by the
path length. For a moderate storm (0.2 dB/km) at a 10 km slant range
under dry conditions, two-way path loss is 4.0 dB — comparable to the
link margin an engineer would typically allocate for rain fading. For
severe storm conditions (0.9 dB/km) at the same 10 km range, two-way
path loss reaches 18 dB, which would effectively wipe out most of the
Lynx's designed SNR margin for 0.1 m imaging. At the maximum 25 km
spotlight range under severe conditions, two-way loss exceeds 45 dB — a
number that renders imagery formation at the finest resolutions
physically impossible regardless of integration time or transmitter
power. Under humid conditions, these figures escalate by the 2.5–3×
humidity multiplier: moderate storm conditions at 10 km would impose 11
dB loss, while severe humid conditions at 10 km approach 44 dB loss. The
Lynx's 320 W TWTA and approximately 4.5 dB system noise figure provide a
healthy SNR budget in clear air, but no practical amount of transmitter
power can compensate for 40+ dB two-way attenuation without
fundamentally redesigning the waveform strategy.
The wideband chirp and dispersive attenuation. The
Lynx's 0.1 m range resolution demands a transmitted bandwidth of 1.5 GHz
— a linear frequency-modulated (LFM) chirp spanning from approximately
15.95 GHz to 17.45 GHz. This 9% fractional bandwidth creates a problem
that does not exist for narrowband radars: the attenuation of dust is
not flat across this bandwidth. Dust attenuation rises with frequency
throughout the Ku-band and into Ka-band, so the high-frequency edge of
the chirp (17.45 GHz) is attenuated measurably more than the
low-frequency edge (15.95 GHz). The magnitude of this differential
attenuation is estimated at 5–15% across the full 1.5 GHz bandwidth,
depending on particle size distribution and storm intensity. The
practical consequence is a frequency-dependent amplitude weighting
imposed on the chirp spectrum by the dust medium itself — an unplanned,
spatially and temporally variable amplitude taper that is functionally
equivalent to a mismatched filter. In the compressed pulse, this
differential attenuation broadens the impulse response and raises the
integrated sidelobe levels above their design values, degrading the
effective range resolution and contaminating adjacent range cells. For a
system designed to achieve 0.1 m range resolution with tightly
controlled peak sidelobe levels, even a modest deparure from flat
spectral amplitude response translates directly into resolution and
sidelobe degradation. This effect is entirely absent from narrowband
surveillance radars, and is a specific vulnerability of the Lynx's
wideband, high-resolution architecture.
Coherent integration and phase contamination.
Achieving 0.1 m azimuth resolution at Ku-band requires coherently
integrating radar returns over a synthetic aperture that, at 10 km slant
range and a typical Predator B speed of roughly 67 m/s, spans
approximately 1,800 m of flight path and takes about 27 seconds to
accumulate. During those 27 seconds, the dust column through which the
radar signal propagates is not static. Dust particles are in turbulent
motion relative to one another, and the bulk storm structure is
advecting with wind velocities that can exceed 50 km/h. The refractive
index of the propagation path — which determines the electrical phase
delay accumulated by the radar signal — is therefore evolving throughout
the coherent integration interval. The phase error tolerance for 0.1 m
resolution is extremely tight: less than λ/16, or approximately 1.1 mm
of electrical path length at 16.7 GHz. An uncompensated phase error of
just 1 mm — far smaller than the physical displacement of dust particles
over 27 seconds of turbulent motion — is sufficient to broaden the
azimuth impulse response and reduce the achieved resolution.
Phase-Gradient Autofocus (PGA), which the Lynx employs to mitigate
platform motion errors, is designed to correct for slowly varying,
spatially correlated phase errors. It was not designed to handle the
stochastic, spatially distributed phase perturbations imposed by a
turbulent dust column, and cannot be expected to remove them without
modification.
Coherent Change Detection: the most vulnerable mode.
Of all the Lynx's operating modes, CCD — coherent change detection — is
the most sensitive to SDS contamination and the one for which the
consequences are most operationally damaging. CCD works by forming two
complex SAR images of the same scene at different times and computing
their pixel-by-pixel coherence. Any change in the complex reflectivity
of the ground between the two passes — including the movement of a
single vehicle, the placement of a single mine, or the digging of a
fighting position — produces a decorrelation signature detectable
against the high coherence of an unchanged background. The technique is
extraordinarily powerful precisely because of its sensitivity: it can
detect changes invisible to optical sensors and imperceptible to human
observers. But that sensitivity is the source of its vulnerability to
SDS. If a dust storm, or even a light dust haze with elevated particle
concentrations, occupies the propagation path during one image pass but
not the other, the differential phase shift imposed by the dust layer
will appear as scene change across the entire imaged area — swamping
true change signatures in a flood of false alarms. If a storm is present
during both passes but in a different spatial configuration (which is
essentially guaranteed given the turbulent dynamics of SDS over the
minutes to hours between Lynx CCD collection passes), the differential
propagation phase will still produce widespread decorrelation. Industry
benchmarks for operational CCD typically require interferometric
coherence values above approximately 0.7; dust-induced decorrelation can
reduce coherence to 0.3–0.5 even under moderate storm conditions, an
order-of-magnitude increase in false alarm rate that renders the product
operationally unusable.
GMTI and the effective minimum detectable velocity.
The Lynx's GMTI mode specifies a minimum detectable velocity (MDV) of
5.8 knots (approximately 3 m/s) against a minimum target cross-section
of 10 dBsm. The clutter notch that defines the MDV is determined by
platform geometry and signal processing, not by received power, and is
therefore not directly affected by SDS attenuation. However, the
effective MDV — the minimum velocity at which a target of a given RCS
can be detected with acceptable probability — does degrade with received
SNR. A target whose Doppler return falls 3 dB above the clutter notch
threshold in clear air will fall below threshold when dust imposes a 4
dB two-way path loss. Under moderate storm conditions (0.2 dB/km at 10
km range), the 4 dB SNR reduction elevates the effective MDV for
marginally detectable targets; under severe conditions the 18 dB loss
eliminates reliable detection of all but the largest, fastest movers at
operational ranges. This is particularly consequential for the Lynx
Block 20 dismount detection mission, where the targets are people (RCS
typically 0 to −5 dBsm, well below the specified 10 dBsm threshold)
moving at walking pace. These targets sit at the margin of detectability
in clear air; they disappear entirely in anything beyond light storm
conditions.
The operational implication. The Lynx was designed
to operate in adverse weather — its original specifications included a
weather model based on 4 mm/hr rainfall, and it operates at Ku-band in
part because that band was judged to provide adequate rain-penetration
for typical target scenarios. But the design weather model did not
include dust storm conditions comparable to what the system has
encountered in Iraq, Kuwait, and Afghanistan during operational
deployments. Rain and dust impose fundamentally different propagation
penalties: rain attenuation at Ku-band is significant but
well-characterized by the ITU-R rain models, spatially limited to active
precipitation cells, and largely symmetric across the bandwidth. Dust
attenuation is less well-characterized, potentially comparable in
magnitude to moderate rain for severe storms, dispersive across the
Lynx's wideband chirp, and — critically — correlated over the enormous
spatial extent of a haboob or shamal event that can span hundreds of
kilometers. A rain cell might limit a single collection; a regional dust
storm can effectively close down operations across an entire theater
for hours to days. The quantitative analysis presented here, grounded in
the Alhaider-Ali Riyadh measurements, the Khartoum microwave link
campaign data, and the Ku-band dielectric constant characterization from
the KFUPM work, suggests that the Lynx's three highest-value
capabilities — 0.1 m spotlight imagery, coherent change detection, and
dismount GMTI — are all materially degraded by moderate to severe dust
storms at operational standoff ranges, and that current doctrine and
mission planning tools almost certainly do not account for this
degradation in a quantitatively rigorous way.
The Sandia perspective: Doerry and the limits of weather modeling.
Armin Doerry — Distinguished Member of Technical Staff at Sandia
National Laboratories, co-creator of the Lynx design with the Sandia
team, and principal developer of the subsequent miniSAR program — is
arguably the most authoritative voice in the open literature on SAR
performance limits. His 2006 Sandia report "Performance Limits for
Synthetic Aperture Radar" (SAND2006-0821), a widely-referenced technical
reference across the SAR community, explicitly acknowledges the
problem: weather attenuation models, he states plainly, are "very
squishy (of limited accuracy) and prone to widely varying
interpretations," and notes that system designers have been known to
exploit this vagueness when responding to requests for proposals. The
report identifies definite optimum frequency bands that depend on
weather conditions and range — but the weather models discussed center
on rain, not dust. Doerry also contributed a 2003 conference paper
specifically titled "Atmospheric attenuation and SAR operating frequency
selection" (SAND2003-3632C), whose scope almost certainly encompassed
the frequency-dependent nature of dust and sand attenuation at Ku- and
Ka-band, though the paper carries no publicly available abstract and no
digitized full text in OSTI. Separately, Dickey, Doerry, and Romero
published in 2007 a peer-reviewed IET paper — "Degrading effects of the
lower atmosphere on long-range airborne SAR imaging" — demonstrating
that clear-air refractive index perturbations in the atmospheric
boundary layer already cause measurable SAR image anomalies at long
ranges and low grazing angles, even without any particulate loading. The
mechanisms they identify — turbulence-induced phase errors, refractive
lensing, and boundary-layer inhomogeneities that defeat autofocus
algorithms — are directly analogous to, and would be compounded by, the
stochastic phase perturbations imposed by a turbulent dust column on
coherent aperture integration.
MiniSAR and the Ka-band frontier. Doerry, along with
Dale Dubbert and George Sloan, led the development of miniSAR — a
30-pound Ku-band SAR achieving the same 4-inch (10 cm) resolution as
Lynx in a package suitable for small tactical UAVs, first flown
autonomously on the Lockheed Martin SkySpirit UAS in October 2006.
Sandia's press materials for miniSAR explicitly claim it can operate
"through weather, at night, and in dust storms." The claim is made
without accompanying attenuation analysis or qualification of storm
severity — a reasonable engineering shorthand in a press release
context, but one that deserves scrutiny in light of the quantitative
picture developed here. Separately, Sandia operated a Ka-band SAR
testbed — operating near 35 GHz, well above the miniSAR's Ku-band
frequency — on its DeHavilland DHC-6 Twin Otter aircraft, with Doerry,
Dubbert, Thompson, and Gutierrez publishing a portfolio of 4-inch
resolution Ka-band imagery from 2004 flight tests at the SPIE Defense
& Security Symposium in 2005. Ka-band at 35 GHz sits in a region
where dust attenuation is substantially more severe than at 16.7 GHz.
The wavelength is 8.6 mm — less than half the Lynx wavelength — and
particle scattering scales strongly with the ratio of particle
circumference to wavelength. At Ka-band, even light dust events that
impose negligible attenuation at Ku-band would produce measurable SNR
degradation; moderate storms would be mission-limiting at all but the
shortest collection ranges. For a precision-guided weapon seeker
application — another use case Sandia identified for miniSAR-class
systems — the guidance scenario may involve terminal phase flight
directly through a dust cloud generated by preceding munitions or
vehicle movement, precisely when Ka-band would be most vulnerable. No
published Sandia characterization of Ka-band SAR performance in dust
storm conditions has been located in the open literature. This is a substantive gap
in the published record for what has become an important class of
high-resolution ISR and precision engagement sensors.
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https://www.researchgate.net/publication/381181848
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Elsheikh, E.A.A., Eltahir, E.I., et al. (2025). "Dust Storm Attenuation Prediction Using a Hybrid Machine Learning Model Based on Measurements in Sudan." IEEE Access.
DOI: 10.1109/ACCESS.2025.3530261. [King Khalid University / IIUM;
XGBoost + LSTM + GRU model; RMSE = 0.07 vs. >0.25 for all prior
models; validated against Riyadh 40 GHz data.]
https://www.researchgate.net/publication/388050487
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Al-Snaie, K.A., et al. (2019). "Micrometer and Millimeter Wave Point-to-Point Links Under Dust Storm Effects in Arid Climates." Engineering, Technology & Applied Science Research,
9(4), 4520–4524. DOI: 10.48084/etasr.2972. [Al-Imam Mohammad Ibn Saud
Islamic University, Riyadh; multi-band RF power degradation modeling
using Riyadh measured dust data.]
https://etasr.com/index.php/ETASR/article/view/2972
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Harb, K., Abdalla, A.T., Mohamed, M., & Abdul-Jauwad, S.
(2013). "HAPs Communication in Saudi Arabia under Dusty Weather
Conditions." Proc. IEEE MICC 2013, pp. 379–380. DOI:
10.1109/MICC.2013.6805858. [King Fahd University of Petroleum and
Minerals, Dhahran; Ka-band SNR degradation ~20 dB during dust storms for
High Altitude Platform Stations at 22 km altitude.]
https://pure.kfupm.edu.sa/en/publications/haps-communication-in-saudi-arabia-under-dusty-weather-conditions
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Harb, K., Abdillah, S., & Abdul-Jauwad, S. (2014). "Dust & Sand (DUSA) Storms Impact on LEO Satellite Microwave Radio Links." Proc.
7th Advanced Satellite Multimedia Systems Conference (ASMS) & 13th
Signal Processing for Space Communications Workshop (SPSC),
Livorno, Italy, pp. 1–6. [King Fahd University of Petroleum and
Minerals, Dhahran; first integration of DUSA attenuation into an actual
LEO satellite downlink budget (0.6254 dB, S-band 2.24 GHz, NIUSAT
NanoSat); three-dimensional volumetric storm model with
altitude-dependent particle size distribution and visibility layering;
comprehensive dielectric constant table for Arabian Peninsula soil types
(S- through Ka-band, soil moisture 0–30% by weight); confirms ITU-R 2–4
dB link margin compliance after DUSA inclusion, demonstrating that
S-band LEO links remain viable during dust storm conditions.]
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Goldhirsh, J. (1982). "A parameter review and
assessment of attenuation and backscatter properties associated with
dust storms over desert regions in the frequency range of 1 to 10 GHz." IEEE Transactions on Antennas and Propagation, 30(6), 1121–1127. [Johns Hopkins Applied Physics Laboratory foundational U.S. work.]
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Goldhirsh, J. (2001). "Attenuation and backscatter from a derived two-dimensional duststorm model." IEEE Transactions on Antennas and Propagation, 49(12), 1703–1711. DOI: 10.1109/8.982449.
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Alhuwaimel, S., Mishra, A., & Inggs, M. (2012). "Review of radar signal attenuation due to sand and dust storms." Proc. IEEE International Conference on Electromagnetics in Advanced Applications (ICEAA),
Cape Town, South Africa, pp. 1096–1099. DOI:
10.1109/ICEAA.2012.6328793. [University College London / University of
Cape Town; radar-specific framing of attenuation problem emphasizing
integrated path loss at operational radar ranges and low-altitude target
geometries; cross-model comparison table benchmarking
Ghobrial-Goldhirsh, effective material property (Xiao et al.), and Mie
solution against published measurements across frequency and visibility
combinations, with quantified percentage errors revealing no single
model achieves uniform accuracy.]
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Doerry, A. W. (2003). "Atmospheric attenuation and
SAR operating frequency selection." Conference paper. Sandia National
Laboratories, Report SAND2003-3632C. OSTI ID: 1005415. [No public
abstract or digitized full text available in OSTI. Identified as
conference paper, October 2003. Directly addresses frequency-dependent
atmospheric attenuation as a SAR system design driver. Likely
encompasses dust and sand particulate effects at Ku/Ka-band.]
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Doerry, A. W. (2006). "Performance Limits for
Synthetic Aperture Radar — second edition." Sandia National Laboratories
Report SAND2006-0821, Unlimited Release, February 2006. OSTI ID:
878591. DOI: 10.2172/878591.
https://www.osti.gov/biblio/878591
[Comprehensive SAR performance limits analysis. Explicitly notes that
weather attenuation models are "very squishy (of limited accuracy) and
prone to widely varying interpretations," and that "there are definite
optimum frequency bands that depend on weather conditions and range."
Weather model used is rain-based (4 mm/hr specification); sand and dust
storms not addressed as a distinct propagation environment. Standard
reference for the SAR design community; its omission of dust as a
weather category is itself analytically significant for arid-region ISR
applications.]
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Dickey, F. M., Doerry, A. W., & Romero, L. A. (2007). "Degrading effects of the lower atmosphere on long-range airborne synthetic aperture radar imaging." IET Proceedings on Radar, Sonar & Navigation,
1(5), 329–339. DOI: 10.1049/iet-rsn:20060134. [Sandia National
Laboratories. Demonstrates that clear-air refractive index perturbations
in the atmospheric boundary layer — turbulence, humidity gradients,
refractive lensing — produce measurable SAR image anomalies at long
ranges and low grazing angles even in dust-free conditions. Model
predictions correlated with observed image anomalies and measured ABL
refractive index. Mechanisms are directly analogous to phase
perturbation effects of particulate-laden air; establishes analytical
framework applicable to dust storm phase error analysis.]
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Doerry, A. W., Dubbert, D. F., Thompson, M., & Gutierrez, V. D. (2005). "A portfolio of fine resolution Ka-band SAR images: part I." Proc. SPIE,
Vol. 5788, Radar Sensor Technology IX, pp. [1–10]. DOI:
10.1117/12.598796. [Sandia National Laboratories. Documents Ka-band SAR
testbed (~35 GHz) flight tests conducted Spring 2004 on Sandia's DHC-6
Twin Otter, achieving 4-inch (10 cm) resolution. No dust storm
propagation characterization published. Ka-band attenuation in sand and
dust storms is substantially more severe than at Ku-band; the gap
between the "dust storm capable" claim for Sandia SAR systems and the
absence of Ka-band dust attenuation measurements in the open literature
represents a substantive uncharacterized operational vulnerability.]
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Lambert, A., & Hallar, A. G. (2020). "Dust Impacts of Rapid Agricultural Expansion on the Great Plains." Geophysical Research Letters, 47(21). DOI: 10.1029/2020GL090347.
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL090347
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Shu, Q., et al. (2026). "Three-Decade Dust Climatology and Trend (1988–2022) From Ground Monitoring Over the Western United States." Journal of Geophysical Research: Atmospheres. DOI: 10.1029/2025JD045878.
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025JD045878
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Tong, D., et al. (2023). "How Many People Were Killed by Windblown Dust Events in the United States?" Bulletin of the American Meteorological Society, 104(5), E1067–E1084. DOI: 10.1175/BAMS-D-22-0186.1.
https://journals.ametsoc.org/view/journals/bams/104/5/BAMS-D-22-0186.1.xml
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Raman, A., et al. (2014). "Revisiting haboobs in the
southwestern United States: An observational case study of the 5 July
2011 Phoenix dust storm." Atmospheric Environment, 88, 157–170. DOI: 10.1016/j.atmosenv.2014.01.045.
https://www.sciencedirect.com/science/article/abs/pii/S1352231014001228
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Evan, A. T., et al. (2022). "Measurements of a Dusty Density Current in the Western Sonoran Desert." Journal of Geophysical Research: Atmospheres, 127. DOI: 10.1029/2021JD035830.
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JD035830
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Pieraccini, M., et al. (2022). "A Simple Model for Assessing Millimeter-Wave Attenuation in Brownout Conditions." Sensors, 22(22), 8889. DOI: 10.3390/s22228889.
https://pmc.ncbi.nlm.nih.gov/articles/PMC9692578/
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Rangwala, M., Wang, F., & Sarabandi, K. (2008). "Study of Millimeter-Wave Radar for Helicopter Assisted-Landing System." IEEE Transactions on Antennas and Propagation, 50, 13–25. DOI: 10.1109/TAP.2007.905826.
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U.S. Army / Yuma Proving Ground. (2015). "Enabling
Pilots to 'See' Through Clouds of Dust." U.S. Army News Article, August
3, 2015. [DVEPS testing and DoD brownout program at Yuma, AZ; soil
comparisons with Iraq/Afghanistan.]
https://www.army.mil/article/153223/Yuma_Proving_Ground__Enabling_pilots_to__see__through_clouds_of_dust
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NOAA National Severe Storms Laboratory (NSSL). "Mobile Radar Research — Haboob Studies." [Documents NOXP X-band mobile radar deployments near Phoenix, AZ for haboob study.]
https://www.nssl.noaa.gov/tools/radar/mobile/
-
NOAA / Military Aerospace. (2024). "NOAA Eyes Replacing Aging Network of WSR-88D NEXRAD Weather Radar." Military Aerospace Electronics,
September 12, 2024. [Covers NOAA RFI for next-generation weather radar,
noting performance requirements for low-elevation scanning and complex
environment operation.]
https://www.militaryaerospace.com/sensors/article/55139014/weather-radar-enabling-technologies-tornadoes-and-thunderstorms
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NASA Earthdata. (2025). "NOAA 21 Captures Dust Moving
Across the United States." NASA Earthdata News, March 25, 2025. [Major
southwestern U.S. dust storm originating near White Sands, NM, March
18–20, 2025.]
https://www.earthdata.nasa.gov/news/noaa-21-captures-dust-moving-across-united-states
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NASA Science. (2025). "Satellite Data Can Help Limit the Dangers of Windblown Dust." NASA Science Feature, August 28, 2025.
https://science.nasa.gov/earth/satellite-data-can-help-limit-the-dangers-of-windblown-dust/
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US Patent US7692571B2. Filed June 29, 2007.
"Millimeter wave imager with visible or infrared overlay for brownout
assist." [DoD brownout accident statistics and MMW frequency tradeoff
analysis for dust penetration.]
https://patents.google.com/patent/US7692571B2/en
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Avionics International. (2010). "Beating Brownout."
April 1, 2010. [DARPA Sandblaster, Army HALS, AFRL 3D-LZ programs; Yuma
testing and brownout accident statistics.]
https://www.aviationtoday.com/2010/04/01/beating-brownout/
