An Assessment of Passive Microwave 

and Millimeterwave Sensors for the 

Standoff Detection of Chemical Agents

Gerald T. Fraser

Angela R. Hight Walker

NISTIR 7172
 

NISTIR XXXX

An Assessment of Passive Microwave 

and Millimeterwave Sensors for the 

Standoff Detection of Chemical Agents

Gerald T. Fraser

Angela R. Hight Walker

Optical Technology Division

Physics Laboratory

National Institute of Standards and Technology

Gaithersburg, MD 20899-8441

July 2004

U.S. DEPARTMENT OF COMMERCE

Donald L. Evans, Secretary

TECHNOLOGY ADMINISTRATION

Phillip J. Bond, Under Secretary of Commerce for Technology

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY

Arden L. Bement, Jr., Director
Page 3 of 32 

An Assessment of Passive Microwave and Millimeterwave 

Sensors for the Standoff Detection of Chemical Agents 

Gerald T. Fraser and Angela R. Hight Walker 

Optical Technology Division 

National Institute of Standards and Technology 

Gaithersburg, MD 20899-8441 

ABSTRACT 

A radiative-transfer model is used to assess the potential of ground-based, upwardlooking, passive microwave and millimeterwave radiometry from 5 GHz to 300 GHz for 

the standoff detection of chemical agents. The microwave and millimeterwave spectra of 

the agents and simulants were modeled using published ab initio quantum-mechanical 

predictions of the spectroscopic constants validated by laboratory spectroscopic 

measurements. The pressure-broadened microwave-to-millimeterwave rotational band 

contours vary in intensity, shape, and peak frequency with agent or simulant, providing 

the potential for both identification and quantification. The radiative-transfer calculations 

show that the detection sensitivity is greatest for zenith angles near vertical (θzenith ≈ 0°), 

with changes in brightness temperature upon introduction of a 1 km thick, 1 μmol/mol 

concentration chemical agent cloud of a much as 12 K for soman for a water-vapor 

column of 1 cm (relative humidity ≈ 38 %). The detection sensitivity as measured by the 

magnitude of the change in brightness temperature decreases with increasing water-vapor 

column and with increasing zenith angle till it effectively vanishes at horizontal viewing 

angles (θzenith ≈ 90°). The analysis determines that the performance of a realistic sensor 

will be limited by the atmosphere opacity near horizontal viewing angles of greatest 

interest for standoff detection and by background brightness temperature fluctuations due 

to water-column variations driven by atmospheric turbulence of short and long-term 

duration. It is concluded that ground-based, passive, microwave and millimeterwave 

radiometry is not a viable technology for the sensitive standoff detection and 

identification of chemical agents. 

INTRODUCTION 

Optical sensors are widely employed in the detection of chemical-warfare agents, 

precursors, and decomposition products for application to troop protection, intelligence 

gathering, treaty monitoring, and homeland security. Such sensors are essential for 

providing evidence of the development, manufacture, deployment, testing, or use of 

chemical agents by military adversaries and terrorist groups. Present optical sensors 

suffer from high false-alarm rates, low sensitivity, high maintenance and acquisition 

costs, slow response time, and poor specificity. The unsatisfactory performance of these 

sensors has led to the simultaneous deployment of several different types of sensors in 

real applications to improve overall detection system sensitivity and accuracy. Moreover, 

a significant national research and development effort is being devoted to the 

improvement of sensors for chemical-agent detection. Many of these sensors also have 
Page 4 of 32 

application to the detection of biological agents, and such dual-application sensors are 

attracting wide interest. 

Both point and standoff detection systems for chemical agents have been developed and 

deployed, often in complimentary roles, as part of a complete detection system for troop 

and civilian protection. Standoff systems provide early warning of an attack, usually 

through the detection and identification of a distant chemical-agent cloud or release, and 

require exceptional performance to ensure that the correct response is rapidly 

implemented by the personnel being protected. The warning delivered by a standoff 

detection system is typically verified by a point detection system deployed at the 

perimeter of a site. Because of its close proximity to the personnel being protected, an 

effective point detection system must have sensitivities significantly greater than the 

median incapacitating dose of an agent, approximately 25 mg min/m3

 for sarin by 

inhalation, i.e. 25 mg/m3

 for a 1 min exposure [1]. 

In the present report we assess the potential of passive microwave to millimeterwave 

sensors for the standoff detection of chemical-warfare agents, precursors, and 

decomposition products. Here, we take the microwave and millimeterwave spectral 

region to extend from approximately 5 GHz to 300 GHz. This spectral window is 

characterized by good atmospheric transparency and relatively mature technology. At 

higher frequencies, near 1 THz, the atmospheric transmission decreases significantly due 

to water-vapor absorption, as shown in Figure 1 below. 

0 25 50 75 100 125 150

0.0

0.2

0.4

0.6

0.8

1.0

ln(I

0

/I) Transmittance

Wavenumber / cm-1

Figure 1. Absorption spectrum of water vapor in the atmosphere for a 1 m optical pathlength. The 

water-vapor partial pressure is 0.79 kPa [5.9 Torr], which corresponds to a relative humidity of 

approximately 45 % at 288.15 K. [11] 
Page 5 of 32 

The tremendous success of infrared techniques for the detection of chemical agents, as 

demonstrated by the availability of several deployed military systems using this 

technology, motivates the detailed examination of the microwave and millimeterwave 

spectral region presented in this report. For reconnaissance, surveillance, and targeting, 

microwave and millimeterwave sensors offer potential advantages over infrared sensors 

under conditions of poor visibility, such as when clouds, fog, haze, dust, or smoke are 

present or rain or snow is falling, common occurrences on the battlefield. This advantage 

arises from the general lower scattering and absorption by particulates of long 

wavelength radiation. 

In the infrared, both passive and active standoff detection systems have been 

demonstrated and fielded. Passive infrared sensors generally use thermal imaging within 

atmospheric transmission windows to detect small changes in the thermal background 

due to the presence of a chemical-agent cloud. Such sensors range from relatively 

unsophisticated filter radiometers to highly sophisticated hyperspectral imaging system 

based on interferometry. Examples of passive infrared sensors include the U.S. 

Military’s M21 and JSLSCAD (Joint Service Lightweight Standoff Chemical Agent 

Detector) chemical-agent detectors, both based on a Michelson interferometer operating 

in the infrared fingerprint region between approximately 800 cm-1 and 1200 cm-1. The 

published optical-pathlength-times-concentration (CL) detection sensitivity for the 

JSLCAD of 135 mg/m2 for a nerve agent [2] corresponds to approximately 23 ppb of 

sarin (GB) in a 1 km long cloud along the line of sight, a concentration level which would 

require approximately 3 hours of continuous exposure to incapacitate 50 % of the 

exposed personnel. 

Active infrared sensors typically use infrared lidar to detect the approach of a chemical 

cloud. The backscattered radiation from one or more pulsed lasers is collected and 

analyzed providing information on the composition and range of the cloud. The planned 

Artemis active standoff chemical-agent detection system is one example of an infrared 

lidar sensor, providing new alternatives to passive infrared systems. The expected range 

of the Artemis system of approximately 20 km is four times greater than the 5 km range 

of the M21 and JSLSCAD passive infrared systems. The anticipated sensitivity for a 

nerve agent of 10 mg/m2

 [3], or 2 ppb (2 nmole/mole) for a 1 km long cloud probed by 

the laser, is approximately an order-of-magnitude improvement over thermal imaging. 

In contrast to the infrared, the application of microwave and millimeterwave technology 

to chemical agent sensing is still in its infancy. Several studies have explored the 

potential to use microwave and millimeterwave spectroscopy for chemical-agent 

detection. These studies looked at point detection systems based on Stark-modulation 

microwave spectroscopy [4] and pulsed-molecular-beam Fourier-transform microwave 

spectroscopy [5-7], as well as standoff detection systems [8-10]. 

The study of McIlwain, Smith, and Finnegan [9] is particularly relevant for the present 

investigation as they examined the application of microwave absorption spectroscopy up 

to 100 GHz for the active stand-off detection of chemical agents. McIlwain et al. [9] 

determined detection sensitivities on the order of 10 μmol/mol (10 ppm), which led to the 
Page 6 of 32 

recommendation that “an experimental program be undertaken to accurately 

characterize the microwave spectral contours and intensity profiles of chemical agent 

and simulant molecules. The design and construction of prototype wide area and point 

microwave rotational absorption (MRA) reconnaissance devices are also recommended.” 

The present study expands on the work of McIlwain et al. [9] by extending the spectral 

window examined to 300 GHz, near the peak of the intensity of the pure rotational bands 

of chemical agents at room temperature, and by exploring the potential of passive 

microwave and millimeterwave sensing. The recommendations of McIlwain et al., 

together with the significant advances in millimeterwave technology and in our 

knowledge of the rotational bands of chemical agents, provide significant motivation to 

determine whether the present successes in the infrared sensing of chemical agents can be 

extended to the longer-wavelength microwave and millimeterwave regimes. 

In assessing the potential of microwave and millimeterwave radiometry and spectroscopy 

to detect, identify, and quantify chemical agents we develop below a model for a passive 

sensor operating in this wavelength region. Passive methods offer an advantage over 

active sensors in that no signal is emitted by the sensor, allowing the user to more easily 

avoid detection. The general sensor approach in the microwave and millimeterwave 

region is similar to that used in the infrared, except instead of using vibrational band 

positions and intensities for agent identification and quantification, rotational band 

intensities and profiles are used. 

For in-situ measurements of chemical agents in the ambient air, the microwave to 

millimeterwave spectral regions are at a significant disadvantage relative to the infrared 

spectral region. Atmospheric-pressure-broadened microwave and millimeterwave spectra 

have significantly lower information content than the corresponding infrared spectra, 

restricting the ability of standoff microwave and millimeterwave methods to positively 

identify a specific chemical agent and accurately determine its concentration. Dopplerlimited absorption spectra in the microwave to millimeterwave region and in the infrared 

region, such as might be obtained in the laboratory, provide sufficient information to 

unambiguously identify and quantify a chemical agent. To record such Doppler-limited 

spectra requires sophisticated instrumentation able to deliver high-spectral resolution and 

sensitivity at sample pressures near 1 Pa [7.5 mTorr] in the microwave to millimeterwave 

region and near 1 kPa [7.5 Torr] in the infrared necessary to achieve Doppler-limited 

linewidths. At these low sample pressures, sensitivity will often be insufficient to obtain 

spectra of sufficient intensity for speciation and quantification, even with prior sample 

preparation to increase the concentration of the agent. 

In Figure 2 we show model atmospheric-pressure microwave to submillimeterwave 

spectra for the two nerve agents, sarin (GB) and soman (GD), and the nerve-agent 

simulant, dimethyl methylphosphonate (DMMP). The spectra were calculated assuming 

an atmosphere of air with an agent or simulant concentration of 1 ppm (1 μmole/mole) 

and an optical pathlength of 100 m [11]. The spectral simulations use ab initio quantumchemistry calculations of the rotational spectroscopic constants and electric dipole 

moments [6,7,12]. The rotational constants and electric dipole moments for thermally 

populated vibrationally excited states and conformational isomers were assumed to be 
Page 7 of 32 

unchanged from the ground-state values. Values for the rotational constants, electric 

dipole moments, and the temperature-dependent partition function were used as input to 

the spectral prediction program of Pickett [13] to produce a list of absorption lines and 

their integrated intensities for the spectral modeling. The two approximations, that the 

spectroscopic constants are well predicted by quantum chemistry calculations and that 

their variation between conformations and vibrational states can be ignored, have a 

negligible effect on the present modeling and associated conclusions since individual 

rotational lines are not being measured. It should be noted that good agreement is 

obtained in comparisons between experimentally measured and ab initio calculated 

spectroscopic constants for agents and simulants such as DMMP, sarin, and soman. 

The overall microwave to submillimeterwave band profile as a function of frequency, ν , 

is the sum of the profiles from thousands of individual lines (288,662 in the case of sarin 

with the imposition of a reasonable intensity cutoff) at frequencies, νi, each characterized 

by a van Vleck-Weisskopf lineshape function, 

















+ +

+

− +















 = 2 2 ( ) 2 2 ( )

1 ( , )

ν ν γ

γ

ν ν γ

γ

ν

ν

π

ν ν

i i i

SvVW i

with an assumed air pressure-broadened half-width-at-half-maximum (HWHM) 

linewidth of γ = 3.8 GHz at a total pressure of 101.325 kPa [1 atm, 760 Torr, or 

1013.25 millibar], i.e. 37 MHz/kPa [5 MHz/Torr]. Unlike the Lorentzian lineshape 

function widely used in the infrared, the van Vleck-Weisskopf lineshape function goes to 

zero at zero-frequency. The continuum appearance of the band profiles is the 

consequence of imposing a broad lineshape profile (HWHM = 3.8 GHz) upon the many 

thousands of individual rotational transitions contributing to the spectrum. 

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0 100 200 300 400 500 600 700 800 900 1000

Frequency / GHz

Absorbance (Base e)

Soman

Sarin

DMMP

Figure 2. Simulated absorbance spectra for the chemical agents soman and sarin and the simulant 

dimethyl methyl phosphonate (DMMP) at a concentration of 1 μmol/mol [1 ppm] and a pathlength 

of 100 m. 

, (1)
Page 8 of 32 

The microwave-to-millimeterwave spectra of Figure 2 can be compared with the infrared 

spectra of these agents and simulants shown in Figure 3 [14-16]. In the infrared, a series 

of peaks are observed arising from the various strong fundamental vibrations being 

probed. In some cases, peaks from vibrational combination and overtone bands are also 

observed. In principle, measurement of the frequencies of these various peaks and their 

relative intensities, such as might be obtained from a Fourier-transform infrared 

radiometer deployed in the field, provides sufficient information to identify a chemical 

agent and determine its concentration. The similarity in the chemical structures of sarin, 

soman, and DMMP lead to infrared spectra which are close in appearance, as seen in 

Figure 3, increasing the difficulty of discriminating between these agents and simulants. 

Further challenging the use of microwave and millimeterwave measurements for the 

detection of chemical agents are the small absorption strengths in this spectral window 

relative to the infrared. Near room temperature, the ratio of the infrared to microwave-tomillimeterwave molecular band strengths is approximately given by, 

 

2 2

2

MW MW

IR IR

MW

IR kT

I

I

ν μ

ν μ =

where k is the Boltzmann constant, T is the gas temperature, νIR is the infrared frequency, 

μIR is the magnitude of the infrared transition dipole moment, νMW is the microwave 

frequency, and μMW is the magnitude of the microwave or millimeterwave transition 

dipole moment. For a typical infrared frequency of 1000 cm-1, infrared transition dipole 

moment of 3.3 × 10-31 Cm [0.1 D], microwave-to-millimeterwave band peak frequency of 

10 cm-1, and microwave or millimeterwave transition dipole moment of 3.3 × 10-30 Cm 

[1.0 D], the calculated ratio of IIR/IMW is approximately 20, providing a significant 

advantage to infrared-based techniques over microwave-based techniques for the 

detection of chemical agents. Because the infrared and microwave-to-millimeterwave 

intensities are proportional to agent concentration, approximately 20 times greater agent 

concentration is required in the microwave region relative to the infrared region to 

obtain similar optical thicknesses or opacities.

The spectroscopic signatures of chemical agents dispersed as aerosols will also differ 

significantly between the microwave-to-millimeterwave and infrared spectral regions. 

The scattering will be significantly smaller in the microwave and millimeterwave region 

than in the infrared because of the approximate factor of 100 difference in wavelengths, 

λMW-MM > 1000 μm and λIR ≈ 10 μm and the small size of aerosol particles of typically 

less than 10 μm relative to microwave and millimeterwave wavelengths. Aerosol 

particles will mainly attenuate the microwave and millimeterwave radiation through 

absorption. 

To quantitatively assess the utility of using microwave and millimeterwave spectroscopy 

for the standoff detection of chemical agents, below we theoretically model the detection 

of a chemical-agent gaseous cloud using passive sensing. 

, (2)
Page 9 of 32 

1000 1500 2000 2500 3000

Wavenumber / cm-1

Soman

Absorbance / a.u.

Sarin

DMMP

Figure 3. Infrared spectra of soman, sarin, and dimethyl methylphosphonate (DMMP) [13-15]. 
Page 10 of 32 

RADIATIVE TRANSFER MODELING 

In principle, radiometry can be used to measure the change in the background microwave 

and millimeterwave radiance due to the presence of a gaseous or aerosol chemical-agent 

cloud. The magnitude of this change and its variation with wavelength provides 

quantitative information on the amount and type of chemical agent present. In the 

infrared, radiative-transfer theory has been successfully applied to the modeling of the 

infrared signatures of chemical agents [17-19], as well as biological-agents [20]. The 

present section applies a similar radiative-transfer model to microwave and 

millimeterwave chemical signatures to assess the potential of these techniques for the 

detection of chemical agents. 

The case of an upward-looking passive radiometer peering through the atmosphere is a 

problem of interest to radio astronomers for understanding the effect of atmospheric 

transmission on their measurements. A theoretical investigation of this problem, directed 

at radioastronomy applications, has been presented in detail by Waters [21]. Below, we 

use this radiative-transfer model, which is also described in Reference [22], as a starting 

point for our theoretical analysis. It should be noted that the model of Waters has served 

as a foundation for more sophisticated models by Liebe [23-25] and others. 

Description of Model 

We assume a ground-level microwave or millimeterwave radiometer looking through the 

atmosphere at some zenith angle, θzenith, as pictured in Figure 4. Here, θzenith = 90°

corresponds to a horizontal view. The azimuthal pointing direction is ignored as the 

results are effectively independent of the value of this angle. 

h1

h2

h3

h4

h5

h6

Tb = 3 K

θzenith

h0 s0

s1

s2

s3

s4

s5

s6

si

sf

∆s

Observer

hi

hf

Figure 4. Diagram describing the radiative-transfer model. The shaded area indicates a chemicalagent cloud. 
Page 11 of 32 

 , (5)

 . (3)

 . (4)

As seen in Figure 4, the atmosphere is divided into six horizontal spherical layers in the 

absence of a chemical cloud, and eight when a chemical cloud is present. The six cloudfree layers, {[hi,hi+1] | i = 0,5}, are [0,11.019 km], [11.019 km, 20.063 km], [20.063 km, 

32.162 km], [32.162 km, 47.349 km], [47.349 km, 51.411 km], and [51.411 km, 

60.000 km], where the distances are actual/geometric altitudes above the surface and are 

based on the layer structure of the 1976 U.S. Standard Atmosphere, except that we only 

extend the upper reaches of the atmosphere to 60 km instead of 86 km due to the low gas 

density and concomitant high microwave transparency found above 60 km. It should be 

noted that the U.S. Standard Atmosphere is defined in terms of geopotential altitudes, 

hgeo, which are related to the actual/geometric altitudes, halt ≡ hi shown in the figure by 

hgeo = (halt REarth) / (halt + REarth). Associated with each layer transition, hi, is a point, si, 

along the line of site of the radiometer where, 

2 2 2

i REarth cos zenith REarth cos zenith 2hiREarth hi s = − θ + θ + +

Here, REarth = 6369 km is the radius of the Earth. 

For simplicity, atmospheric refraction is ignored. Refraction adds curvature to the 

atmospheric path, thus increasing the atmospheric pathlength for non-zero zenith angles. 

We have calculated this increase for paths which terminate at an altitude of halt = 60 km 

using, 

∫

−

















+ + 





























 = + − alt h

zenith Earth

h

Earth Earth hR h dh

n

n

s R h R

0

2

1

2 2

2

2 0

max ( ) 1 sin θ 2

Here, nh ≡ n(h) is the index of refraction of the atmosphere at altitude h and is discussed 

by Crane [26], n0 ≡ n(0) is the index of refraction of the atmosphere at ground level, and 

θzenith is the initial zenith angle. Solving for smax numerically, we find that the 

atmospheric pathlength is increased by approximately 0.02 % for a a zenith angle of 50°

and by 4 % for a zenith angle of 90°. The atmospheric pathlength in the absence of 

diffraction is 93 km for a zenith angle of 50° and 876 km for a zenith angle of 90°. 

The radiative-transfer equation for the brightness temperature, Tν (s0), seen by the 

observer looking along a linear optical path from s0 to s6, as pictured in Figure 4, takes 

the form [21], 

T s T s e T s k s e ds s s

s

s

(s s ) ( , )

0 6

0

6

0

0, 6 ( ) ( ) ( ) ( ) ν ν τ

ν ν

τ

ν ν

− −

∫ = +

where τv(sa,sb) is the optical depth or opacity defined by, 
Page 12 of 32 

∫ =

b

a

s

s

(sa ,sb ) k (s)ds ν ν τ

Here, kν (s) is the absorption coefficient, which varies along the path. The subscript ν

indicates that Equations 5 and 6 must be solved for each frequency, ν, in the spectral 

band of interest. 

In formulating Equation 5, the Rayleigh-Jeans approximation has been used to express 

the radiative-transfer equation in terms of radiance temperatures instead of radiances. At 

300 GHz and 288.15 K, radiances calculated by the exact Planck equation and by the 

Rayleigh-Jeans approximation agree within 3 %. At lower frequencies, the agreement is 

even better. For example, at 30 GHz they agree to within 0.25 %. Given a radiance level 

at 300 GHz from a radiating source near 288 K, the Rayleigh-Jeans approximation will 

give a brightness temperature approximately 7 K lower than the exact Plank equation. 

Again, at lower frequencies the error is even smaller, being less than 1 K at 30 GHz. 

That we are primarily interested in differences in brightness temperatures, rather than 

accurate absolute brightness temperatures allows us to use this approximation. 

The physical interpretation of Equation 5 is as follows. The first term in the equation 

gives the reduction in the apparent background radiance temperature, Tν (s6), as seen by 

the observer, due to absorption of the emitted radiance along the optical path. This 

background radiance is assumed to be dominated by the 3 K top-of-the-atmosphere 

cosmic background radiation from space. The second term in Equation 5 accounts for the 

increase in the brightness temperature seen by the observer due to blackbody emission by 

the atmospheric gases and aerosols along the optical path. The blackbody emission from 

point s reaching the observer is characterized by a brightness temperature, Tν (s), 

modulated by the absorption coefficient, kν (s), and reduced by the atmospheric 

absorption between the point of emission, s, and the point of observation, s0. 

The radiative-transfer equation was solved on a frequency grid of 0.25 GHz over the 

frequency interval from 0 GHz to 300 GHz. At each frequency of interest, Equation 5 

was solved by breaking the integrals of Equations 5 and 6 into a sum of integrals over the 

individual spherical layers, [si,si+1], using Gaussian quadratures of order 24 to 108, with 

the higher-order quadratures required for the longer atmospheric paths near θzenith = 90°. 

Tests for convergence included looking for stabilization of the brightness temperature, 

Tν (s0), as the quadrature order was increased and ensuring that Tν (s0) never exceeded the 

maximum value of Tν (s) along the path. 

To calculate Tν (s) and kν (s) necessary for finding a solution to Equation 5 we use the 

1976 U.S. Standard Atmosphere, which is similar to the 1960 version used by Waters. 

The temperature profile, Tν (s), is specified as part of the U.S. Standard Atmosphere. To 

calculate kν (s), we sum at each point s, the absorption coefficients for each atmospheric 

constituent, including any chemical agent present. The critical atmospheric microwave 

. (6)
Page 13 of 32 

and millimeterwave absorbers are water vapor, O2, and the collision-induced continuum. 

Spectrally narrow emission lines from stratospheric ozone characterized by an emission 

temperature near 220 K and an emission altitude between 20 km to 30 km are also 

present. Their weak intensity and narrow pressure broadened widths of less than 

approximately 125 MHz (HWHM, half width at half maximum) together with the 

extinction of their downward emission by atmospheric water vapor limits their 

contribution to the signal from a broad-band microwave or millimeterwave radiometer. 

Both water vapor and O2 have discrete molecular absorptions in the microwave-tomillimeterwave region. The collision-induced continuum arises primarily from collisions 

between water-vapor molecules or between water vapor and O2 and N2 molecules. 

Nitrogen, the major atmospheric constituent, has no significant discrete absorptions in the 

microwave or millimeterwave spectral regions, but does contribute to the collisional 

processes responsible for the pressure broadening of the O2 and water-vapor discrete 

absorptions and for the underlying continuum dominated by collisional processes 

involving water molecules. 

The other minor constituents of the atmosphere, included battleground emission gases, 

were ignored in the present modeling. We note that many of the background gases from 

gasoline and kerosene vapors, such as hydrocarbons, and their combustion products have 

small electric dipole moments and are thus not expected to significantly interfere with the 

detection of chemical agents. At the long wavelengths of the microwave and 

millimeterwave radiation, molecular Rayleigh and background atmospheric aerosol 

scattering and emission are expected to be small and thus were not included in the 

modeling. 

Calculation of the contribution of each of the constituents to kν(s) requires knowledge of 

their partial pressure or density as a function of s and their absorption coefficients at that 

pressure or density. Below we summarize the calculation of the absorption coefficient 

for oxygen, water vapor, and the chemical agent. 

Oxygen 

For oxygen, partial-pressure information is obtained directly from the pressure profile of 

the U.S. Standard Atmosphere by assuming a mole fraction of 0.21. Following Waters 

[21], the absorption coefficient for molecular oxygen in cm-1 is obtained from, 

= × ∑ { + + + + + − −

N odd

N N N N

O k 1.44 10 pT g f ( , ) 2 2 5 3

ν ν φ ν ν ν 

 } E kT

N N N N N N N g f g F e

/ 2 2

( ) 2

1 ( , ) 0 0

−

− φ − ν − ν ν − + φ ν

where p and T are the pressure in millibar and temperature in kelvin, respectively, at 

point s along the optical path, f and F are lineshape functions defined by 

, (7)
Page 14 of 32 

2 2 2 2 2

0

0

0 ( ) 4

1 4 ( , )

ν ν ν ν

ν ν ν

π

ν ν − + ∆

∆ f =

and 

( )

2 ( ) 2 2 ν ν

ν ν

π

ν

+ ∆

∆

F = . 

The gN+|φN+|

2

, gN-|φN-|

2

, and gN0|φN0|

2

 are linestrength factors, including degeneracies, for 

∆J = +1, ∆J = -1, and ∆J = 0 transitions, respectively, where J is the total angular 

momentum and N is the rotational angular momentum for the molecule: 

N

N N

g N N

2 ( +1)(2 −1) − φ − =

1

2 (2 3)

+

+ + + = N

N N

g N φ N

( 1)

2( 1)(2 1) 2 2

0 0 +

+ + + = N NN N N

g N φ N . 

The half-width-half-maximum (HWHM) linewidth parameter, ∆ν, used in Equations 8 

and 9 is also a function of s through its pressure and temperature dependence. ∆ν in 

GHz is given as a function of the pressure p in millibar and temperature T in kelvin by the 

following expressions where ∆νB = 0.0527 GHz: 

1.41 10 (300 / ) 3

p T − ∆ν = × ∆ν ≤ ∆νB

(0.0527) 3

2 (1.41 10 ) (300 / ) 3

1 3 ∆ = × + − ν p T ∆ν ≥ ∆νB

Water Vapor 

For water vapor, we follow Waters [21], and approximate the water-vapor density, 

ρwater(halt), as a function of altitude, halt, by, 

( ) (0) exp( / ) ρ water halt = ρ water −halt hscale 

where the ground-level density is ρwater(0) = 10 g/m3

 and the scale height is hscale = 2 km. 

The ρwater(0) density corresponds to a relative humidity of 77 % at the boundary-layer or 

ground-level temperature of 288.15 K. For altitudes above halt = 15 km, the water-vapor 

, (15)

 (8)

 (9)

 (10)

 (11)

 (12)

 (13)

 (14)
Page 15 of 32 

concentration is assumed to be constant at 2 ppm (2 μmole/mole). Note that the total 

amount of precipitable water vapor is given approximately by ρwater(0) hscale = 2 g/cm2

 or 

just 2 cm since the density of water is 1 g/cm3

. 

The absorption coefficient for water vapor in cm-1 was modeled using, 

∑{ } − − − = − transitions

lm lm

E kT E kT

water

H O k T e e g f l l 1.44 ( , ) / / 2 2 3 / 2

ν ρ ν A φ ν ν , 

where the water-vapor density is in g/m3

, frequencies are in GHz, and f(ν, νlm) is defined 

in Equation 8. Terms which vary with altitude and thus with s include ρwater, T, and 

f(ν,νlm) through the linewidth parameter defined below. Values for the various 

spectroscopic constants characterizing the rotational transitions for water vapor, i.e., the 

state degeneracies gl, the upper and lower state energies, Em and El, and the square 

magnitude of the direction cosine matrix element, |φlm|

2

, have been tabulated by Waters 

[21] and are reproduced in Appendix 2 for convenience. Improved values for many of 

the parameters are available in the literature or from the HITRAN atmospheric database 

[27]. 

The water-vapor linewidth parameter [21] for transition m ↔ l , is approximated by, 































 − ∆

∆  + × 

 









 



 ∆ = ∆ −

−

1 (H O) 1 4.6 10

1013 300 0

0 3 2

lm

water lm

x

lm lm

p

p T T

ν

ρ ν

ν ν , 

where p is the total pressure in millibar, ρwater is the water vapor density in g/m3

, and the 

parameters x, ∆νlm

0

, and ∆νlm(H2O) are as listed in Appendix 2. The correction factor of 

Gaut and Reifenstein [28] discussed by Water’s was also included [21]. This correction 

factor takes the form, 

2

2.1

11

1000

300 1.08 10 ρ ν



 



 



 



 ∆ = × − p

T

kv water , 

where the parameters and their units have been defined above. The correction factor 

accounts for the water-vapor continuum induced by water-water collision (self 

continuum) and by water-nitrogen or water-oxygen collisions (foreign-gas continuum). 

Chemical Agent 

The chemical-agent cloud was modeled by assuming a constant molar atmospheric 

concentration of agent between points si and sf along the atmospheric line of site. Thus, 

the cloud is assumed to be free of any aerosolized chemical agent. The absorption 

coefficient for the chemical agent was calculated with the same model used to generate 

the absorbance curves of Figure 2. The absorption coefficient was generated on a 

 (17)

 (18)

 (16)
Page 16 of 32 

0.1 GHz grid between 0 GHz and 300 GHz. Linear interpolation was used to obtain 

values for the absorption coefficient between points. 

MODEL RESULTS 

In Figure 5 we show the calculated brightness temperature, Tν (0), as a function of 

frequency from 0 GHz to 300 GHz for a ground-based, upward-looking radiometer 

viewing at a zenith angle, θzenith = 0. Calculations are given with and without the 

presence of a cloud of 1 μmol/mol [1 ppm] of the simulant dimethyl methyl phosphonate 

(DMMP) in the background air. The cloud is assumed to reside between 1 km and 2 km 

in altitude above the surface, mimicking a dispersal event from a low-flying airplane. 

The horizontal extent of the cloud is assumed to fill the field-of-view (FOV) of the 

radiometer. 

0 50 100 150 200 250 300

0

50

100

150

200

250

300

Tν(0) / K

ν / GHz

 No Agent

 With Agent

Figure 5. Brightness temperature as a function of frequency for a ground-based, vertical-looking 

radiometer. Temperatures are shown when no chemical agent is present and when 1 μmol/mol 

[1 ppm] DMMP, a nerve-agent simulant, is present as a 1 km thick cloud lying between 1 km and 

2 km in altitude. 

The dominant discrete spectral features in Figure 5 are O2 lines near 60 GHz and 

119 GHz and water-vapor lines near 22 GHz and 183 GHz. The calculated brightness 

temperatures are dominated by contributions from the lowest atmospheric layer, i.e., 

[h0,h1] of Figure 4. This statement is supported by Figure 6 which gives the contributions 

to the total brightness temperature from each of the horizontal layers {[hi, hi+1] | i = 0,5} 

as calculated from the dominant second term of Equation 5. The results from this figure 

support the neglect of atmospheric layers above 60 km in the present modeling, and 

indeed probably above 20 km. 
Page 17 of 32 

0 50 100 150 200 250 300

0

50

100

150

200

250

300

Tν(0) / K

ν / GHz

Layers

 h0 - h1

 h1 - h2

 h2 - h3

 h3 - h4

 h4 - h5

 h5 - h6

Figure 6. Contributions to the total brightness temperature observed by a ground-based radiometer 

looking at a zenith angle of 0°. The layer brightness temperatures are calculated using the dominant 

second term of Equation 5. 

0 50 100 150 200 250 300

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

∆

Tν(0) / K

ν / GHz

Figure 7. The difference between the brightness temperature with DMMP and without DMMP as a 

function of frequency as obtained from the calculations presented in Figure 5. 
Page 18 of 32 

The increased opacity due to the chemical agent leads to a slightly higher calculated 

brightness temperature when the chemical cloud is present, although it is still ≤ 288.15 K, 

the surface-atmosphere boundary-layer temperature which is the highest temperature 

along the optical path. The broad spectral profile of the rotational contour of DMMP 

leaves no obvious characteristic signature in the spectrum of Figure 5 to aid chemical 

identification. 

The effect of the simulant is seen more clearly in Figure 7 where the differences in the 

brightness temperatures between the agent and no agent spectra are plotted as a function 

of frequency. Figure 7 shows that the maximum calculated brightness temperature 

difference occurs near 225 GHz and is slightly greater than 3 K. 

These brightness temperature differences depend upon both the agent concentration and 

the agent type, as illustrated in Figures 8 and 9. In Figure 8, ∆Tν(0) is plotted against the 

concentration-pathlength product, CL, for the two atmospheric windows at ν = 150 GHz 

and ν = 225 GHz. The figure demonstrates that CL is a nearly linear function of agent 

concentration. In Figure 9 the brightness temperature differences under the conditions 

used to produce Figure 7 are plotted for DMMP, sarin, and soman, each at 1 μmol/mol [1 

ppm]. This figure shows that the appearance of the ∆Tν(0) curves are very sensitive to 

agent type. The sarin plot of Figure 9 corresponds to a concentration-pathlength product 

of CL = 5.1 g/m2

, however the overall detection sensitivity will depend on a number of 

factors, including sensor performance and background variation and noise. Recall from 

the Introduction that the JSLCAD has a stated detection sensitivity of CL = 0.135 g/m2

for a nerve agent [2]. In the next section microwave and millimeterwave detection 

sensitivities are more fully discussed. 

The significant differences in the magnitudes of ∆Tν(0) for the various agents or 

stimulants in Figure 9 reflect the differences in the rotational band intensities shown in 

Figure 2. In principle, the shapes of these curves provide information on the identity of 

the chemical agent. To examine this possibility we consider a two-channel radiometer 

with spectral windows coincident with the atmospheric windows at 150 GHz and 

225 GHz. This choice for the two spectral windows allows significant overlap of the 

spectral windows with the rotational bands of the various agents or simulants, while 

minimizing contributions from the water-vapor continuum absorption which grows in 

intensity with frequency. For this two-channel radiometer, the calculated ratio, 

∆T225 GHz(0) / ∆T150 GHz(0), is found to vary significantly with agent, with 

∆T225 GHz(0) / ∆T150 GHz(0) = 1.48, 0.92, and 0.69 for DMMP, sarin, and soman, 

respectively. 
Page 19 of 32 

0 5 10 15 20

0

5

10

15

20

25

30

35

40

45

50

∆

Tν(0) / K

CL / (km μmol/mol)

225 GHz

150 GHz

Figure 8. Plot of the brightness temperature difference versus the product of the agent concentration 

(C) and the optical pathlength (L) through the cloud for DMMP for the atmospheric windows at 150 

GHz and 225 GHz. 

 

0 50 100 150 200 250 300

0

2

4

6

8

10

∆

Tν(0) / K

ν / GHz

 DMMP

 Sarin

 Soman

Figure 9. Plot showing the difference in brightness temperatures with and without agent or simulant 

for DMMP, sarin, and soman. The ground-level water-vapor concentration is 10 g/m3

, which 

corresponds to a relative humidity of 77 % at 288.15 K. 
Page 20 of 32 

Complicating agent identification is the sensitivity of ∆Tν(0) to the magnitude of the 

relative humidity. In Figure 10 we plot the brightness temperature as a function of 

frequency as before except that the water-vapor concentration, and thus relative humidity, 

is reduced by 50 %. This reduction corresponds to a change in ρwater(0) in Equation 15 

from 10 g/m3

 to 5 g/m3. The effect of this reduction is to significantly decrease the 

atmospheric opacity and associated brightness temperature relative to the 10 g/m3

 watervapor level, including in the atmospheric windows used in the calculation of 

∆T225 GHz(0) / ∆T150 GHz(0). This decrease in opacity affects the continuum brightness 

temperatures seen in Figures 5 and 10. For instance, at 300 GHz the brightness 

temperature is 256 K for a 10 g/m3

 water-vapor level versus 195 K for a 5 g/m3 watervapor level. A reduction by two in the lower-atmosphere water-vapor partial pressure 

changes ∆T225 GHz(0) from 3.23 K at 10 g/m3

 to 5.83 K at 5 g/m3 and changes ∆T150 GHz(0) 

from 2.17 K at 10 g/m3

 to 2.89 K at 5 g/m3, as seen from examination of Figures 9 and 

11, where the latter figure is the same as the former except that the water vapor 

concentration has been reduced by 50%. In addition to the changes in the absolute 

temperatures, the ratios ∆T225 GHz(0) / ∆T150 GHz(0) are also affected, becoming 2.01, 1.24, 

and 0.94 at 5 g/m3 for DMMP, sarin, and soman, respectively, where previously they 

were 1.48, 0.92, and 0.69 at 10 g/m3

. The modeling shows that corrections will need to be 

applied for the atmospheric humidity to effectively use such a ratio radiometer for 

chemical-agent identification. 

0 50 100 150 200 250 300

0

50

100

150

200

250

300

Tν(0) / K

ν / GHz

 No Agent

 DMMP

 Sarin

 Soman

Figure 10. Brightness temperature as a function of frequency for a ground-level, vertical looking 

radiometer. The ground-level water-vapor concentration is 5 g/m3

. Spectra are shown without a 

chemical cloud and with 1 μmol/mol [1 ppm] chemical clouds of DMMP, sarin, and soman lying 

between 1 km and 2 km in altitude. 
Page 21 of 32 

The calculations above are for a vertical-viewing sensor, i.e. θzenith = 0°. This viewing 

geometry allows the greatest sensitivity for a chemical agent since it minimizes the 

optical pathlength through the optically thick, higher-density, lower-altitude portion of 

the atmosphere. A realistic sensor will typically be required to look for a chemical-agent 

release near ground level, at a horizontal viewing angle closer to θzenith = 90°. 

Consequently, a large fraction of the measured radiance and associated brightness 

temperature will come from emission from the optically thick, surface-atmosphere 

boundary layer, and not from the colder higher-altitude regions. As the brightness 

temperature saturates at the surface-atmosphere boundary-layer temperature the effective 

dynamic range of the radiometer is reduced, decreasing the sensitivity necessary for 

detection and identification of a chemical-agent cloud. In Figure 12 the predicted 

brightness temperature is plotted as a function of frequency for a series of 5 viewing 

angles between 0° and 90°. In each case, the optical pathlength through the cloud is fixed 

at 1 km by constraining the chemical agent to reside between si = 1 km and sf = 2 km in 

Figure 4. These values correspond to [hi, hf] values of [1 km, 2 km], [0.866 km, 

1.732 km], [0.500 km, 1.000 km], [0.259 km, 0.518 km], and [0.000 km, 0.000 km] for 

θzenith = 0°, 30°, 60°, 75°, and 90°, respectively. Most noteworthy in Figure 12 is the near 

complete saturation of the brightness temperatures at the surface-atmosphere boundarylayer temperature of 288.15 K for the horizontal viewing angle of θzenith = 90°. 

0 50 100 150 200 250 300

0

2

4

6

8

10

12

∆

Tν(0) / K

ν / GHz

 DMMP

 Sarin

 Soman

Figure 11. This plot is similar to that shown in Figure 8 except that the ground-level water-vapor 

concentration has been reduced by 50 % to 5 g/m3

. 

In Figure 13 we examine the sensitivity for the detection of a 1 μmol/mol [1 ppm] 

chemical cloud of soman as measured by ∆Tν(0), the difference in brightness temperature 

with and without an agent present. Results are presented for θzenith = 75.0° and 89.5° and 

for ground-level water vapor concentrations of 5 g/m3

 and 10 g/m3. Because of the near 

saturation of the brightness temperature at θzenith = 89.5° for a water-vapor level of 
Page 22 of 32 

5 g/m3

, we have not included the uninteresting results for a θzenith = 89.5° and a watervapor level of 10 g/m3

. 

Figures 12 and 13 clearly indicate that ground-based passive microwave and 

millimeterwave chemical-agent sensors have a significantly reduced sensitivity, as 

measured by ∆Tν(0), for the detection of chemical-agent clouds near ground level where 

horizontal or near horizontal pointing of the sensor is required. The next section 

examines the measurement of ∆Tν(0) by a realistic sensor, to provide insight into the 

minimum ∆Tν(0) signal required under realistic conditions for chemical agent detection 

and identification. A summary of the key findings from the present section include the 

following: 

• For a ground-level, upward-viewing sensor the signal is dominated by 

contributions from the first 20 km of the atmosphere. 

• ∆Tν(0), the change in brightness temperature due to the presence of a chemical 

agent cloud, increases with decreasing humidity and decreases with increasing 

zenith angle. 

• At a constant pathlength (L), ∆Tν(0) increases approximately linearly with agent 

concentration (C). 

• ∆Tν(0) measurements must be corrected for relative humidity prior to 

determination of the chemical-agent concentration-pathlength product, CL. 

• ∆Tν(0) is predicted to be small, typically < 200 mK, for a horizontal or near 

horizontal viewing sensor, challenging the ability to detect a chemical-agent cloud 

or release near ground level using microwave or millimeterwave passive sensing. 

• For a vertical pointing radiometer, a ∆Tν(0) greater than 12 K is possible for a 

1 km thick, 1 μmol/mol [1 ppm] chemical-agent cloud at 38 % relative humidity. 

• Multiple channel radiometers should be able to discriminate between different 

chemical agents. 
Page 23 of 32 

0 50 100 150 200 250 300

0

50

100

150

200

250

300

Tν(0) / K

ν / GHz

 0°

 30°

 60°

 75°

 90°

Figure 12. Brightness temperature as a function of frequency for various zenith angles from 0° to 

90°. Horizontal viewing corresponds to a zenith angle of 90°. The ground-level water-vapor 

concentration is 10 g/m3

, which corresponds to a relative humidity of 77 % at the surface-atmosphere 

boundary layer temperature of 288.15 K. 

 

0 50 100 150 200 250 300

0

1

2

3

4

5

6

7

8

9

10

∆

Tν(0) / K

ν / GHz

θ zenith Soman H2O Vapor

 75.0° 1 ppm 10 g/m3

 75.0° 1 ppm 5 g/m3

 89.5° 1 ppm 5 g/m3

Figure 13. Plot of the differences in brightness temperatures with and without a cloud of 1 μmol/mol 

[1 ppm] soman in the optical path. The pathlength through the cloud is fixed at 1 km. The cloud is 

assumed to reside between 1 km and 2 km in altitude. Model results are presented for zenith angles 

of 75° and 89.5° and for water-vapor concentrations of 5 g/m3

 and 10 g/m3. 
Page 24 of 32 

Minimum Detectable ∆Tν(0) from a Chemical-Agent Cloud under 

Realistic Conditions 

Information about the radiometer performance and about the stability of the thermal 

background being measured are required to assess the ability of a microwave or 

millimeterwave sensor to identify and quantify a chemical-agent release. The radiometer 

performance is stated in terms of the minimum detectable signal or MDS. MDS is 

calculated from the standard deviation of the radiometer noise [29]. Single-detectorelement radiometers operating near or below 100 GHz can achieve an MDS of better than 

0.05 K with integration times < 100 ms [30]. Imaging systems generally have higher 

MDS values. An inexpensive 94 GHz imaging system has been demonstrated, consisting 

of a 10 x 10 focal-plane array detector with each array element having an MDS < 1 K. 

The imaging system has a frame rate of 60 Hz [31]. For a recent review of 

millimeterwave imaging see Reference [32]. 

Fluctuations in the thermal background also affect overall system performance, 

particularly if they occur at a frequency which makes it difficult to discriminate 

background drift from a chemical-agent release. Typically, the chemical-agent sensor 

will continually view a region of space waiting for the occurrence of a change in the 

background brightness temperature in one or more monitoring channels indicative of a 

chemical agent cloud. Background thermal fluctuations with temporal, spatial, and 

spectral properties similar to that expected for a chemical agent cloud can potentially 

cause false alarms, requiring that the detection threshold be set high, with a concomitant 

reduction in sensitivity. Higher frequency fluctuations will appear as noise, also leading 

to a reduction in detection sensitivity. 

Background thermal fluctuations arise from atmospheric turbulence and winds and local 

solar heating causing temperature, pressure, and humidity drifts. Other sources of 

background fluctuations include smoke, dust, and haze that commonly occur on the 

battlefield. In attempts to understand these atmospheric fluctuations several 

measurements have been made of the ground-level microwave and millimeterwave 

brightness temperature as a function of time. Salmon and Appleby [33], for example, 

examined the brightness temperature fluctuations in two bands, a Ka band from 26 GHz 

to 40 GHz and a W band from 90 GHz to 98 GHz, under cloudy, light-rain conditions. 

They observed short-term temperature fluctuations of 10 mK to 20 mK in the Ka band 

and 20 mK to 30 mK in the W band at a frequency of 1 Hz. At lower frequencies 

(10 mHz), the temperature fluctuations were significantly greater, as much as 5 K for the 

Ka band and 10 K for the W band. These large temperature fluctuations were attributed 

to cloud movement. 

Clear-sky measurements at 20.6 GHz by Han et al. [34] over a period of 12 hours reveal 

changes in brightness temperature of nearly 7 K out of approximately 24 K over a period 

of 12 hours. These changes were well modeled by radiative transfer models using Raman 

lidar, radiosonde, and radio-acoustic sounding to provide input water-vapor and 

temperature profiles. Hogg et al. [35] performed measurements over a period of a week 

and observed changes in the water vapor column by a factor of four, from approximately 
Page 25 of 32 

2 cm to 0.5 cm. Smoot et al. [36] observed dramatic changes in brightness temperature, 

approximately 11 K over the period of 68 min, during poor weather conditions. They 

note that changes in the magnitude of the water-vapor column amounts drive the changes 

in brightness temperatures [36]. Changes in atmospheric pressure can also affect the 

contribution of oxygen to the brightness temperature, but this effect is significantly 

smaller than changes in water vapor [37], primarily since factional changes in 

atmospheric pressure are significantly smaller than fractional changes in water-vapor 

column amounts. We note that at 90 GHz the brightness temperature change with watervapor column change for a vertical-looking radiometer is approximately 29 K/cm [36]. 

Figures 5 and 10 demonstrate that the ground-level brightness temperature is sensitive to 

the amount of water vapor along the optical path. 

In summary, the minimum detectable ∆Tν(0) will generally be limited by fluctuations in 

the atmospheric brightness temperature. Integration of a companion sensor with the 

radiometer, such as a Raman lidar instrument, together with accurate real-time radiative 

transfer modeling may improve the situation. 

CONCLUSIONS 

The present modeling results demonstrate that the direct application of ground-based, 

upward-looking, microwave-to-millimeterwave radiometry will be challenged by the 

poor atmospheric opacity at near horizontal viewing angles of greatest interest for 

standoff detection of a chemical-agent release. The small changes in brightness 

temperature expected for realistic chemical-agent release scenarios will likely be masked 

by temporal changes in the background brightness temperature induced by atmospheric 

turbulence giving rise to fluctuations in the water-vapor column, the major contributor to 

the background radiance. Options to circumvent this problem include only operating the 

sensor in low-humidity weather conditions and providing sufficient spectral coverage of 

the microwave-to-millimeterwave band to obtain data to correct for water-vapor 

fluctuations. Alternatively, the radiometer could be used in tandem with other sensors, 

such as lidar, which can provide accurate water-vapor spectral and temporal profiles for 

correcting the microwave-to-millimeterwave measurements. 

Many of these limiting issues are reduced or eliminated when the cloud being sensed has 

a temperature significantly above that of the background gas. Such a situation may arise 

following the explosive release of an agent or when an agent, precursor, or 

decomposition produced is suspected in a smoke-stack plume. Sensitivity will depend on 

the size of the hot column of gas and the time for the temperature to return to that of the 

ambient background air. 

The present modeling has not addressed other potential applications of microwave-tomillimeterwave technology for the standoff detection of chemical agents. Such 

application could include active lidar or radar technology [38] and passive downwardlooking radiometers mounted on airplanes, unmanned aerial vehicles (UAVs), or 

satellites. Radar and lidar technologies will require high sensitivity due to the weak 

backscatter from chemical agent clouds at these long wavelengths. Downward sensing is 
Page 26 of 32 

likewise difficult due to the brightness temperature noise arising from ground clutter. 

Such noise has been found to be on the order of several kelvin, and is subject to 

significant variation [39]. The magnitude of this variation will depend on whether the 

surface has natural structures with typically high emissivities such as vegetation and 

water or man-made structures with emissivities varying from near zero to near unity such 

as concrete and metal. Future studies will examine more closely these potential 

applications of microwave and millimeterwave methods. 
Page 27 of 32 

REFERENCES

1. A table of Chemical Warfare Agent Characteristics is available form the U.S. 

Army Chemical School website at 

http://www.wood.army.mil/cmdoc/WFS/CWA/CW%20agent%20characteristics.

PDF. 

2. Joint Service Lightweight Standoff Chemical Agent Detector (JSLSCAD) 

brochure from General Dynamics (2004). Available at 

http://www.gdatp.com/products/detection/jslscad/JSLSCAD.pdf. 

3. M. Abaie, “Artemis Advance Planning Briefing for Industry,” DoD CB Defense 

Advance Planning Briefing for Industry, National Defense Industrial Association, 

April 8-9, 2003. (Available at 

http://proceedings.ndia.org/dod_cb/Wednesday_AM/Abaie.pdf) 

4. J. Friend and C. Gelman, “Preliminary Study of the Microwave Spectrum of GB 

for Possible Application to Detection,” Chemical Warfare Labs, Army Chemical 

Center, Md (1956). Available from STINET using Accession Number: 

AD00884802. 

5. C. W. Gillies, M. B. Tabacco, T. G. DiGiuseppe, “Development of a Microwave 

Rotational Detector for Chemical Agent Warning and Detection,” Geo-Centers, 

Inc., Newton Centre, MA (1986). Available from STINET using Accession 

Number: ADB108314. 

6. A. R. H. Walker, R. D. Suenram, A. Samuels, J. Jensen, M. W. Ellzy, J. M. 

Lochner, D. Zeroka, “Rotational spectrum of Sarin,” Journal Of Molecular 

Spectroscopy 207, 77-82 (2001). 

7. R. D. Suenram, R. S. DaBell, A. R. H. Walker, R. J. Lavrich, D. F. Plusquellic. 

M. W. Ellzy, J. M. Lochner, L. Cash, J. O. Jensen, and A. C. Samuels, 

“Rotational spectra of the Diastereorners of Soman,” Journal Of Molecular 

Spectroscopy, 224, 176-184 (2004). 

8. S. D. Christesen, “Microwave Detection of Chemical Agents: A Review,” 

Chemical Research Development and Engineering Center, Aberdeen Proving 

Ground, MD, (1986). Available from STINET using Accession Number: 

ADA171472. 

9. M. McIlwain, D. Smith, and E. Finnegan, “Theoretical Evaluation of Microwave 

Absorption for Chemical Agent Detection,” Geo-Centers, Inc., Newton Center, 

MA (1983). Available fro STINET using Accession Number ADB075312. 

10. D. F. Flanigan, “The Spectral Signatures of Chemical Agent Vapors and 

Aerosols,” Chemical Research and Development Center, Aberdeen Proving 
Page 28 of 32 

Grounds, MD (1985). Available from STINET using Accession Number: 

ADB092373. 

11. For a description of how to perform these simulations see, V. B. Podobedov, R. J. 

Lavrich, T. M. Korter, G. T. Fraser, D. F. Plusquellic, and A. C. Samuels, 

“Submillimeter and THz Detection of Dimethyl Methyl Phosphonate in Air,” 

NIST Internal Report, NISTIR 7134 (2004). 

12. R. D. Suenram, F. J. Lovas, D. F. Plusquellic, A. Lesarri, Y. Kawashima, J. O. 

Jensen, and A. C. Samuels, “Fourier Transform Microwave Spectrum and ab 

Initio Study of Dimethyl Methylphosphonate,” Journal of Molecular 

Spectroscopy 211, 110-118 (2002). 

13. H. M. Pickett, “The Fitting and Prediction of Vibration-Rotation Spectra with 

Spin Interactions,” Journal of Molecular Spectroscopy 148, 371-377 (1991). The 

program is available at http://spec.jpl.nasa.gov/. 

14. S. W. Sharpe, T. J. Johnson, J. Kleimeyer and B. Rowland, The DPG/PNNL 

InfraRed Spectral Library, Pacific Northwest National Laboratory, Richland, WA 

9932 and Dugway Proving Grounds, Dugway, UT 84022, Version 1.1 (2002). 

15. S. W. Sharpe, T. J. Johnson, P. M. Chu, J. Kleimeyer and B. Rowland, 

“Quantitative, Infrared Spectra of Vapor Phase Chemical Agents,” in Chemical 

and Biological Sensing IV. Edited by P.J. Gardner, Proceedings of the SPIE, 

5085, 19-27 (2003). 

16. S. W. Sharpe, R. L. Sams, T. J. Johnson, P. M. Chu, G. C. Rhoderick, C. George, 

F. R. Guenther, “Creation of 0.10-cm-1 resolution quantitative infrared spectral 

libraries for gas samples,” in Vibrational Spectroscopy-based Sensor Systems, 

edited by S. D. Christesen and A. J. Sedlacek, Proceedings of the SPIE, 4577,12-

24 (2002). The database is available at the following website: 

https://secure.pnl.gov/nsd/NSD.nsf/Welcome?OpenForm. 

17. D. F. Flanigan, “Hazardous cloud imaging: a new way of using passive infrared,” 

Applied Optics 36, 7027-7036 (1997). 

18. D. F. Flanigan, “Prediction of the limits of detection of hazardous vapors by 

passive infrared with the use of MODTRAN,” Applied Optics 35, 6090-6098 

(1996). 

19. D. F. Flanigan, “Detection of organic vapors with active and passive sensors: a 

comparison,” Applied Optics 25, 4253-4260 (1986). 

20. D. A. Ligon, A. E. Wetmore, and P. S. Gillespie, “Simulation of the passive 

infrared spectral signatures of bioaerosol and natural fog clouds immersed in the 

background atmosphere,” Optics Express 10, 909-919 (2002). 
Page 29 of 32 

21. J. W. Waters, “Atmospheric Effects: Absorption and Emission by Atmospheric 

Gases,” in Methods of Experimental Physics, Vol. 12 Astrophysics, Part B: Radio 

Telescopes, edited by M.L. Meeks, Academic Press, New York (1976), pp. 142-

176. 

22. F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing, Active 

and Passive, Volume I, Microwave Remote Sensing: Fundamentals and 

Radiometry, Addison-Wesley, Reading MA, (1981). 

23. H.J. Liebe, “A contribution to modeling atmospheric millimeter-wave properties,” 

FREQUENZ 41, 31-36 (1987). 

24. H.J. Liebe, “An updated model for millimeter wave propagation in moist air,” 

Radio Science 20, 1069-1089 (1985). 

25. H.J. Liebe, “MPM - an atmospheric millimeter-wave propagation model,” 

International Journal of Infrared and Millimeter Waves 10, 631-650 (1989). 

26. R. K. Crane, “Atmospheric Effects: Refraction Effects in the Neutral 

Atmosphere,” in Methods of Experimental Physics, Vol. 12 Astrophysics, Part B: 

Radio Telescopes, edited by M.L. Meeks, Academic Press, New York (1976), pp. 

186-200. 

27. L.S. Rothman, A. Barbe, D. Chris Benner, L.R. Brown, C. Camy-Peyret, M.R. 

Carleer, K. Chance, C. Clerbaux, V. Dana, V.M. Devi, A. Fayt,J.-M. Flaudi, R.R. 

Gamachej, A. Goldmank, D. Jacquemarta, K.W. Jucksa, W.J. Lafferty, J.-Y. 

Mandin, S.T. Massie, V. Nemtchinov, D.A. Newnham, A. Perrin, C.P. Rinsland, 

J. Schroeder, K.M. Smith, M.A.H. Smith, K. Tang,R.A. Toth, J. Vander Auwera, 

P. Varanasi, and K. Yoshino, Journal of Quantitative Spectroscopy & Radiative 

Transfer 82 5–44 (2003). 

28. N. E. Gaut and E. C. Reifenstein III, Environmental Research and Technology 

Report No. 13, Lexington, MA (1971). 

29. R. M. Price, “Radiometers: Radiometer Fundamentals,” in Methods of 

Experimental Physics, Vol. 12 Astrophysics, Part B: Radio Telescopes, edited by 

M.L. Meeks, Academic Press, New York (1976), pp. 201-224. 

30. N. A. Salmon, “Passive Millimetre Wave Imaging and Absorption from Exhaust 

Gasses,” Proceedings of the International University Conference, Electronics and 

Radiophysics of Ultra-High Frequencies, Saint Petersburg, May 1999, pp. 472-

474. 

31. R. Augeri, J. Whelehan, J. Taub, and R. Rudish, “Low Cost Passive Millimeter 

Wave Imaging System,” JJW Consulting, Inc. Smithtown, NY and AIL Systems, 
Page 30 of 32 

Inc. Deer Park, NY. (1998). Available from STINET using Accession Number: 

ADB280115. 

32. L. Yujiri, M. Shoucri, and P. Moffa, “Passive Millimeter-Wave Imaging,” IEEE 

Microwave Magazine 4, 39-50 (2003). 

33. N. A. Salmon and R. Appleby, “Millimetre Wave Sky Radiation Temperature 

Fluctuations,” Proceedings of the 25th International Conference on Infrared and 

Millimeter Waves, Beijing, China, Sept. 2000, pp. 463 – 464. 

34. Y. Han, J. B. Snider, E. R. Westwater, S. H. Melfi, and R. A. Ferrare, 

“Observations of water vapor by ground-based microwave radiometers and 

Raman lidar,” Journal of Geophysical Research 99, 18695-18702 (1994). 

35. G. F. Smoot, S. M. Levin, A. Kogut, G. De Amici, and C. Witebsky, 

“Atmospheric emission variation measurements at 3, 0.9, and 0.33 cm 

wavelength,” Radio Science 22, 521-528 (1987). 

36. D. C. Hogg, F. O. Guiraud, and W. B. Sweezy, “The Short-Term Temporal 

Spectrum of Precipitable Water Vapor,” Science 213, 1112-1113 (1981). 

37. M. Bersanelli, M. Bensadoun, L. Danese, G. Deamici, A. Kogut, S. Levin, M. 

Limon, D. Maino, G. F. Smoot, and C. Witebsky, “Measurements of The 

Atmospheric Emission and Variations in the 1-90 GHz Range,” Planetary and 

Space Science 43, 1467-1472 (1995). 

38. N. Gopalsami and A. C. Raptis, “Millimeter-Wave Radar Sensing of Airborne 

Chemicals,” IEEE Transactions on Microwave Theory and Techniques,” 49, 646-

653 (2001). 

39. W. J. Wilson, R. J. Howard, A. C. Ibbott, G. S. Parks, and W. B. Ricketts, 

“Millimeter-Wave Imaging Sensor,” IEEE Transactions on Microwave Theory 

and Techniques,” 34, 1026-1035 (1986). 
Page 31 of 32 

APPENDIX 1. 

Microwave Transition Frequencies for Molecular Oxygen (16O2) from Waters 

[21] 

N ν N- / GHz ν N+ / GHz 

1 118.750343 56.264766 

3 62.486255 58,446580 

5 60.306044 59.590978 

7 59.164215 60.434776 

9 58.323885 61.150570 

11 57.612488 61.800169 

13 56.968180 62.411223 

15 56.363393 62.997991 

17 55.783819 63.568520 

19 55.221372 64.127777 

21 54.671145 64.678914 

23 54.1302 65.22412 

25 53.5959 65.764744 

27 53.0669 66.30206 

29 52.5424 66.83677 

31 52.0214 67.36951 

33 51.50302 67.90073 

35 50.9873 68.4308 

37 50.4736 68.9601 

39 49.9618 69.4887 
Page 32 of 32 

APPENDIX 2 

Spectroscopic parameters for rotational transitions of H2

16O contributing to the 

atmospheric opacity between 0 and 300 GHz from Waters [21] 

Transition Frequency 

(GHz) 

gl |φlm|

2 Em

(cm-1) 

El 

(cm-1) 

∆νlm

0

(GHz) 

∆νlm(H2O) 

(GHz) 

x 

616-523 22.23515 3 0.0549 447.30 446.56 2.85 13.68 0.626 

313-220 183.31012 1 0.1015 142.27 136.16 2.68 14.49 0.649 

1029-936 323. 3 0.0870 1293.80 1283.02 2.30 12.04 0.420 

515-422 32.1538 1 0.0891 326.62 315.78 3.03 15.21 0.619 

414-321 380.1968 3 0.1224 224.84 212.16 3.19 15.84 0.630 

1037-11210 390. 1 0.0680 1538.31 1525.31 2.11 11.42 0.330 

753-660 436. 1 0.0820 1059.63 1045.03 1.50 7.94 0.290 

643-550 438. 3 0.0987 756.76 742.11 1.94 10.44 0.360 

752-661 442. 3 0.0820 1059.90 1045.11 1.51 8.13 0.332 

423-330 448.0008 3 0.1316 300.37 285.42 2.47 14.24 0.510