Radio wave refractivity deduced from lidar measurements

A high resolution lidar is used to backscatter light from atmospheric aerosols. The actual relative humidity is measured at altitudes corresponding to those from which the backscattered light occurs. A mathematical relationship between the two is then derived and this is used to predict atmospheric relative humidity from subsequent lidar backscatter s measurements. The predicted relative humidity is used with temperature and pressures derived from standard lapse rates to calculate the radio refractivity of the atmosphere. Radio ray coverage is then determined based upon the calculated radio refractivity.

DOCUMENTS INCORPORATED BY REFERENCE 
The following documents are hereby incorporated by reference into this 
specification: 
U.S. Pat. No. 4,125,893 issued on Nov. 14, 1978 to Herbert V. Hitney; 
Juergen H. Richter and Murray H. Schefer titled "Integrated Refractive 
Effects Prediction System; Fitzgerald, James W., "Effect of Humidity on 
the Aerosol Backscattering Coefficient at 0.694- and 10.6-.mu.m 
Wavelengths", printed in applied Optics, Feb. 1, 1984, vol. 23, No. 3, p. 
411-418; Hitney, H. V., A. E. Barrios, and G. E. Lindem, "Engineer's 
Refractive Effects Prediction System (EREPS) Revision 2.0", NOSC Technical 
Document 1342, February 1990, Naval Ocean Systems Center, San Diego, 
Calif.; and Lentz, W. J., "The Visioceilometer: A Portable Visibility and 
Cloud Ceiling Height Lidar", ASL Technical Report 0105, 1982, Atmospheric 
Sciences Laboratory, White Sands Missile Range, N.M. 
BACKGROUND OF THE INVENTION 
1. Field of the Invention 
This invention pertains to the calculation of the refractive index of the 
atmosphere and in particular to the use of a light detection and ranging 
(LIDAR) device to make such calculations. 
2. Description of the Related Art 
Knowledge of radio refractivity within the atmosphere is necessary to 
accurately predict electromagnetic radio propagation. For example, in 
radar applications it is widely known that gradients in the radio 
refractivity of the atmosphere can create ducts which guide radio 
propagation and holes in radio coverage through which objects may travel 
undetected. Plots that illustrate these radio refractivity characteristics 
are known as refractive index profiles. 
In the past refractive index profiles have been assembled through the use 
of balloon radiosondes and aircraft-carried refractometers. These 
instruments provide accurate assessment of factors often used to judge 
radio refractivity such as relative humidity, temperature and pressure. 
Both of these methods, balloon radiosondes and aircraft-carried 
refractometers, are costly, time consuming and may be unsuitable in 
hostile environments. 
Knowledge of relative humidity is essential to the accurate calculation of 
atmospheric radio refractivity. It is known that gradients, or changes in 
relative humidity, will produce gradients in the radio refractivity of the 
atmosphere. These gradients create the ducts and holes in radio 
propagation mentioned above. 
In the past, light detection and ranging (lidar) instruments have been used 
to study atmospheric conditions, including relative humidity. Compared to 
radiosonde launchings or aircraft flights, using a lidar can be a much 
less costly, more time efficient and covert means by which characteristics 
of the atmosphere can be examined. 
A lidar is an instrument that projects a laser light beam and that receives 
backscattered light returned from objects in the path of the projected 
beam. Lidars measure the time elapsed from light transmission to reception 
so that the range at which the projected light beam is backscattered can 
be determined. 
The use of lidars to examine relative humidity has bee based upon the 
strong relationship between atmospheric backscattering and aerosols 
present within the atmosphere. As relative humidity increases, 
condensation of water vapor on the water-soluble aerosol particles causes 
their sizes and consequently their backscattering cross-sections to 
increase. 
Techniques of using lidar instruments to assess atmospheric relative 
humidity have, however, resulted in relative humidity predictions that 
deviate substantially from actual measurements. This less-than-desirable 
result is chiefly due to an insufficient knowledge of the precise 
relationship between atmospheric conditions and aerosol characteristics. 
This has resulted in the making of erroneous assumptions such as those 
pertaining to atmospheric temperature, pressure, backscatter and 
extinction. Further exasperating these problems are inaccuracies in lidar 
measurements, such as those due to lidar readings taken at too large of 
altitude intervals. 
As a result of these shortcomings, lidar-deduced relative humidity 
measurements have not been considered for radio refractivity calculations. 
SUMMARY OF THE INVENTION 
The invention provides a relatively inexpensive, time efficient and covert 
mechanism by which atmospheric radio refractivity may be deduced by lidar 
measurements. Through use of the invention, gradients in radio 
refractivity may be accurately pinpointed. 
The invention incorporates the use of a pulsed laser lidar that has a 
relatively rapid light reception sampling frequency, enabling high 
resolution (small altitude interval) relative humidity measurements to be 
made. Contrary to previous attempts to assess relative humidity from lidar 
measurements, use of the invention permits relative humidity to be 
predicted directly from the lidar return-signals, with temperature and 
pressure factors being derived from standard temperature and pressure 
lapse rates referenced to surface conditions. Precise determination of 
atmospheric backscattering and extinction characteristics, or even the 
relationship between the two is not a requirement of this invention. 
In the invention, lidar return-signals, indicating the amount of light 
backscattered from atmospheric aerosols due to the projecting of a light 
beam from the lidar, are first recorded. The ranges and altitudes at which 
the backscattered light occurs is then determined. 
The lidar calculates range directly by measuring the time elapsed from 
light transmission to reception. Determination of altitude is dependent 
upon the orientation of the lidar. When the lidar is employed to project a 
beam substantially vertically, the altitude will substantially equal the 
range at which the backscattering occurs. When the lidar is used to 
project a beam other than vertically, the altitude at which the 
backscattering occurs can be determined by conventional trigonometric 
techniques. 
The signal-strength of each of the lidar return-signals is I then adjusted 
to compensate for range-induced losses, converting the signals into 
range-compensated return-signals. Actual relative humidity is then 
measured at the altitudes corresponding to the range-compensated 
return-signals and a mathematical relationship is derived from the 
range-compensated return-signals for each calculated altitude and the 
actual relative humidity measured at corresponding altitudes. In a 
preferred embodiment of the invention this mathematical relationship is 
derived using the method of least squares. 
Once this mathematical relationship is derived, radio refractivity is then 
predicted by projecting a subsequent lidar beam into the atmosphere. The 
backscattered light from this subsequent beam is then received by the 
lidar and the ranges and altitudes at which this beam is backscattered are 
calculated. The received backscattered light is converted into additional 
return-signals, and these signals are then range compensated adjusted 
These additional range-compensated return-signals are then used in the 
earlier derived mathematical relationship to predict the relative humidity 
that exists at the altitudes corresponding to these signals. For each of 
the altitudes corresponding to the additional range-compensated 
return-signals, radio refractivity is calculated by using the predicted 
relative humidity, and temperatures and pressures calculated from standard 
temperature and pressure lapse rates reference to surface conditions. 
In this approach no relationship between atmospheric backscatter and 
extinction need be made. Nor are any measurements of actual extinction, 
temperature and pressure required other than those at surface levels. By 
using such an approach a good correspondence between calculated radio 
refractivity and that deduced from actual measurements can be obtained. It 
has been found that through use of the invention, gradients in relative 
humidity could be accurately predicted, and hence gradients in radio 
refractivity calculated through use of the invention correspond very 
closely with those measured by airborne instruments. Accurate prediction 
of radio wave duct and hole position is thus possible. 
OBJECTS OF THE INVENTION 
It is an object of the invention to provide an improved method for 
predicting relative humidity. 
Yet another object of the invention is to provide a method for predicting 
relative humidity that provides an accurate assessment of gradients in 
relative humidity. 
Another object of the invention is to provide a relatively low cost, time 
efficient and covert method for calculating relative humidity. 
Still yet another object of the invention to provide an improved method for 
calculating radio refractivity. 
Yet another object of the invention is to provide a relatively low cost, 
time efficient and covert method for calculating radio refractivity. 
A further object of the invention is to provide a method for calculating 
radio refractivity that provides an accurate assessment of gradients in 
radio refractivity. 
These and other objects of the invention will become more apparent from the 
ensuing specification when considered together with the drawings.

DESCRIPTION OF THE PREFERRED EMBODIMENT 
In FIG. 1 there is shown a computer control lidar system 10 according to a 
preferred embodiment of the invention. In the actual implementation of the 
invention a light detection and ranging (LIDAR) device 12 known as a 
visioceilometer was used. The lidar visioceilometer has been developed by 
the United States Army from a modified AN/GVS-5 Nd:YAG laser range finder. 
The pulse laser of lidar 12 operates at 1.06 micrometers with a nominal 
pulse energy of 13 milli-Joules. Lidar 12 has a pulse half-width of 6 
nano-seconds with a receiver sampling rate of 20 mega-Hertz. Such a 
sampling rate provides a backscatter light sample recording for every 7.5 
meters of altitude above crossover point 14 of FIG. 1. Crossover point 14, 
in the implementation of the invention shown, existed at about 100 meters 
altitude and is the point at which light beam 16 of laser 18 crosses over 
field of view 20 of lidar receiver 22. In Table 1 there are listed the 
characteristics of the specific lidar used offered by way of example. 
These characteristics were compiled by W. J. Lentz referred to earlier in 
this specification. 
TABLE 1 
______________________________________ 
Characteristics of the Visioceilometer Lidar 
______________________________________ 
Beam divergence 1.0 mrad 
Receiver field of view 
3.0 mrad 
Laser energy 13 mJ at 1.06 .mu.m 
Pulse half-width 6 ns 
Receiver aperture 50 mm 
Laser exit diameter 
16 mm 
Optics axis separation 
50 mm 
Full crossover range 
80 m 
Log A slope 10 mV/dB 
Log A zero 80 .mu.V 
Detector noise level 
2 .times. 10.sup.-10 W 
Laser monitor output 
0.75 .+-. 0.25 V 
Sample rate 20 MHz 
A/D converter 10 bits in 2 .mu.s 
Sample device 455-sample dual- 
channel CCD 
(Fairchild #CCD 321) 
Operating temperature 
-5.degree. to 60.degree. C. 
(prototype) 
Sample range 3.3 km 
______________________________________ 
It should be noted that the lidar described here is offered by way of 
example and that other light beam transmitting and receiving devices may 
be used in the invention It should also be noted that in order to 
calculate radio refractivity of useful resolution, a lidar or equivalent 
system should have a relatively high order sample rate so that 
backscattered light may be collected at sufficiently small altitude 
intervals. In the lidar illustrated by way of example, a sample rate of 20 
MHz proved to be sufficient for accurate radio refractivity calculations 
One skilled in the art will realize, however, that even higher sampling 
rates will enhance refractivity calculation accuracy. 
Referring once again to FIG. 1, lidar 12 of system 10 is used to project 
concentrated light beam 16 upwardly into the atmosphere and onto suspended 
aerosol particles 24. Particles 24 lie at various locations in the 
atmosphere and depending on the size of these particles they may appear 
invisible to the eye or may take the form of haze, fog, clouds or the 
like. 
Some of light beam 16 will be reflected or backscattered by aerosol 
particles 24 with the remainder of the beam continuing upward. In the 
illustration shown, aerosol particles 24 backscatter light beam 16 and 
this backscattered light is received by lidar receiver 22, providing that 
the backscattered light falls within receiver field of view 20. 
As previously described, receiver 22 of lidar 12 operates at 20 MHz so that 
a "snapshot" of the backscattered light is taken every 1/20 MHz. This 
provides a high resolution profile of the returning light in 7.5 meter 
altitude graduations. In order to measure the range at which the 
backscattered light is returned, lidar 12 includes time measuring 
capability that notes the elapsed time from the projection of light beam 
16 to the reception of backscattered light from aerosol particles 24. In 
cases in which lidar 12 is used to project a beam substantially vertically 
the range and altitude from which light beam 16 is backscattered will be 
one and the same. In those cases in which lidar beam 16 is projected other 
than substantially vertically, such as for slant range measurements, lidar 
12 will provide the range to the backscattering particles, and the 
altitude of the particles is calculated by traditional techniques. 
The backscattered light collected by receiver 22 of lidar 12 is converted 
into return-signals according to the energy intensity of the light 
received Computer 26 can be designed to include electrical signal 
processors that adjust the signal strength of the returned-signals to 
compensate for range induced signal losses. The return-signals from lidar 
12 will thereby be converted into range-compensated return-signals. 
In a preferred embodiment of the invention computer 26 will control the 
firing of lidar 12's light beam 16 and will read and process the data 
provided by lidar 12. Computer 26 should be programmed to provide for 
input of ground-based measurements such as temperature, pressure and 
relative humidity. These could be used as starting points for standard 
lapse-rate curves of temperature and pressure. In addition, computer 26 
should be programmed to calculate ground-based radio refractivity. 
Computer 26 should also provide a graphic display of range-compensated 
return-signals, radio refractivity profiles provided from the 
lidar-collected data and predicted radio-ray signal coverage. Radio-ray or 
ray-trace computer programs suitable for this application are readily 
available, one such program being known as the Engineer's Refractive 
Effects Prediction System (EREPS) described in U.S. Pat. No. 4,125,893 
incorporated by reference herein. 
Through the course of experimentation leading to the invention it was 
determined that lidar-collected data could be used to predict the presence 
or absence of relative humidity gradients and hence radio refractivity 
conditions. The remaining figures illustrate the basis for this 
conviction. 
Referring to FIG. 2 when lidar 12 is used to project light beam 16 
substantially vertically within the presence of a cloud layer a profile of 
the range-compensated backscattered returned-signal, S(R), shows a rapid 
increase in S(R) near the approximately 500 m base of the cloud. This is 
considered a result of an increase in backscattered light from 
water-bearing aerosol particles within the cloud. A rapid decrease in 
range-compensated return-signals follow as the projected light beam 
penetrates the cloud and is attenuated 
Referring now to FIG. 3, when a temperature inversion exists, and if no 
clouds are present, the range-compensated lidar return-signals S(R) show a 
very rapid decrease at the 900 meter height of the inversion. This is 
considered to result from a decrease in the backscattered 
range-compensated return-signal above the inversion, rather than from an 
increased in atmospheric extinction. The decrease in backscattered 
range-compensated return-signals corresponds to, and results from, a 
decrease in relative humidity through the temperature inversion. An 
example further illustrating this is shown in FIGS. 4A and 4B which are 
comparisons of relative humidity (RH) versus altitude with 
range-compensated return-signals (SR) versus altitude measured on the same 
day. 
In order to predict radio refractivity and hence the presence or absence of 
refractivity ducting conditions a correlation between atmospheric relative 
humidity and range-compensated lidar return-signals was sought. In order 
to make this correlation the method of the invention was devised. 
Referring once again to FIG. 1, the previously described lidar was set up 
upon the southern tip of the Point Loma peninsula in San Diego, Calif. 
This site is about 30 meters above sea level. The light beam of the lidar 
was projected in the vertical direction a number of times and a group of 
five or six profiles of lidar return-signals were received and recorded. 
Note that a large number of collections or profiles of lidar returns were 
made during this experimentation so that a large statistical base could be 
assembled. This should not be interpreted to mean that a number of lidar 
projections are required in all cases, as even a single projection may 
suffice. 
The lidar was used to calculate the ranges from which the light beam 
backscattering occurred As these beams were projected substantially 
vertically, the ranges calculated equalled the altitudes of the 
backscattering aerosol particles. At roughly the same time as the lidar 
measurements, a balloonborne radiosonde was launched and temperature, 
pressure and relative humidity were recorded as functions of time and 
hence altitude as the balloon rose. Lidar and radiosonde measurements were 
then made on an irregular basis, starting on Sep. 12, 1989 and ending Nov. 
21, 1989. Usually only one balloon radiosonde was launched on any given 
day. The dates of these measurements were recorded and are shown among 
other things in Table 2. 
TABLE 2 
______________________________________ 
Least-squares curve fit to a straight line for relative 
humidity (Rh) as a function of lidar S(R) return. 
Curve is Rh = A + B * S(R). 
Maximum 
Date A B Correlation 
Altitude (m) 
______________________________________ 
09/12/89 
241.481 28.390 0.9477 1170 
09/13/89 
178.081 20.755 0.9243 728 
09/18/89 
218.973 21.564 0.7225 660 
09/20/89 
226.310 22.770 0.9305 608 
09/26/89 
270.510 34.270 0.9477 690 
09/27/89 
305.381 35.382 0.9613 1050 
09/28/89 
304.918 34.061 0.9315 420 
09/29/89 
186.333 17.823 0.8232 705 
10/02/89 
234.921 25.084 0.9417 1343 
10/03/89 
328.397 42.097 0.9733 1050 
10/24/89 
161.510 12.123 0.9452 600 
11/13/89 
219.892 22.416 0.7199 750 
11/21/89 
378.925 48.379 0.8534 1100 
______________________________________ 
LIDAR DATA PROCESSING 
The lidar return-signals were adjusted in-light-of lidar calibration data. 
The calibration data were assembled by exposing the lidar receiver to a 
light source of known intensity. After calibration, the return-signals 
were adjusted to compensate for the range-square loss due to distance. 
This provided a profile of range-compensation return-signals, S(R), as a 
function of altitude. 
The S(R) data were then used in a comparison with the radiosonde relative 
humidity data collected at altitudes corresponding approximately to those 
altitudes of the S(R) data. For each particular range-compensated 
return-signal corresponding to a certain altitude, averaging was done with 
those range-compensated return-signals calculated at four altitudes 
intervals above and below the particular signal This averaging was done 
because the lidar return-signals frequently showed small-scale 
irregularities that the radiosonde measurements did not. The radiosondes 
did not detect these irregularities because of their longer sensor 
response time. 
It should be noted that during the course of these measurements, the lidar 
return-signals for 100 meters altitude and below showed a rapid decrease, 
see FIGS. 1 and 2. This occurred because the lidar light beam below this 
altitude is not completely within the field of view of the lidar receiver. 
This 100-meter crossover point is shown in FIG. 1 as "14". Similarly, 
above certain altitudes the lidar return-signals approached background 
noise level. These altitudes depended upon atmospheric conditions and 
ranged from about 600 meters to 1100 meters on various days. 
RADIOSONDE DATA PROCESSING 
The radiosondes provided a record of temperature, pressure and relative 
humidity as a function of time. These data were used to calculate the 
altitudes of the measurements as well as the refractivity existing at the 
calculated altitudes. Profiles of relative humidity versus altitude, 
temperature versus altitude, and modified refractivity units (M-units) 
versus altitude were then generated from these data. 
LIDAR (SR) PROFILES vs RELATIVE HUMIDITY PROFILES 
The range-compensated lidar return-signals were provided for every 7.5 
meters of altitude. These S(R) values were plotted on a horizontal axis 
for each altitude corresponding to the value, with actual measured 
relative humidity for this same altitude being plotted on a vertical axis. 
FIG. 5 is an example of such a plot. 
A least-squares fit to a straight line was then calculated for each of the 
S(R) values versus actual relative humidity plots Staticians will realize 
that other statistical methods could be used. Table 2 is a list of the 
least square fit values in which the curve is Rh=A+B*S(R). In Table 2 the 
maximum altitude calculated at which backscattered light occurred is 
shown. Also shown is the correlation between the actual relative humidity 
measurements as a function of the lidar range-compensated return-signals 
S(R). The results show a good correlation between actual relative humidity 
and the range-compensated lidar return-signals for each day of 
measurement. However, it should be noted that a curve fit for one day may 
be quite different from another day, this most likely attributable to the 
composition or makeup of the atmospheric aerosols varying with time and 
direction of wind. 
It should also be noted that while the lidar-measured data were essentially 
snapshots of conditions at any particular moment, the balloon-borne 
radiosonde rose gradually at approximately 200 meters per minute In 
addition, rather than taking a vertical course like the light beam of the 
lidar, a strong wind caused the radiosondes to drift rapidly, usually 
southeast over land during its rise These factors could have resulted in 
the lidar and radiosondes seeing somewhat different atmospheric 
conditions. 
In FIG. 6 all of the lidar-collected and radiosonde-collected data have 
been plotted on one graph to portray the extent of this variability. A 
least-squares fit to a straight line was calculated for these data. 
LIDAR M-UNIT PROFILES vs RADIOSONDE M-UNIT PROFILES 
The equation of this line, from the actual relative humidity measurements 
and the lidar range-compensated return-signals, was then used to calculate 
predicted relative humidity from the lidar measurements made for each of 
the test days. Radio refractivity corresponding to the altitudes of the 
previously made lidar measurements was then calculated by using the 
predicted relative humidity with temperature and pressure calculated 
through standard-lapse rates referenced to surface measurements made for 
each of the test days. 
The previously made lidar measurements were used so that a comparison 
between radio refractivity based upon predicted relative humidity and 
those based upon relative humidity measured by radiosonde could be made. 
In the ordinary use of the invention however, the lidar would be used again 
to project a subsequent light beam into the atmosphere ultimately 
resulting in additional range-compensated return-signals. The relative 
humidity would then be predicted for each of the altitudes corresponding 
to these additional range-compensated return-signals and radio 
refractivity would be calculated based upon the relative humidity 
predicted at the altitudes corresponding to the additional 
range-compensated return-signals as well as from temperatures and 
pressures calculated as previously described. 
In FIGS. 7A and 7B a comparison of the radio refractivity profiles 
calculated from the radiosonde data is made to corresponding profiles 
calculated from the earlier collected lidar data. In these figures the 
dotted lines correspond to the radiosonde-collected data, with the dashed 
lines corresponding to the lidar measurements. 
The Ray-trace computer program described in U.S. Pat. No. 4,125,893 
previously referred to, was then used to calculate and plot radio signal 
coverage for the radiosonde refractivity profiles and for the lidar 
refractivity profiles. FIGS. 8A and 8B show two of these made from data 
collected on Sep. 12, 1989 when a temperature inversion near 600 meters 
altitude existed. FIG. 8A corresponds to radiosonde collected data with 
FIG. 8B being from the lidar collected data. FIGS. 9A and 9B are similar 
but are made from data collected on Sep. 20, 1989, when an inversion 
existed at 200 meters altitude The plots show closely similar ducting 
conditions. 
Radio refractivity profiles calculated with the use of lidar-measured data 
show quite good agreement with those calculated from radiosonde-measured 
data. Some of the differences observed may have been caused by the 
radiosonde encountering changing atmospheric conditions as it drifted 
horizontally and rose. In addition, day-to-day changes in the composition 
of the atmospheric aerosols can cause some variability in the relationship 
between the lidar-collected data and the actual relative humidity 
profiles. The calculations of radio refractivity profiles, however, do not 
appear to be very sensitive to these differences. The strong gradients 
that occur at inversion levels, in both the lidar-measured returns and the 
actual relative humidity profiles, appear to be the controlling factors in 
calculating atmospheric ducting and radio-ray coverage. 
Though there are obviously many modifications and variations of this 
invention, it should be noted that the lidar described was not optimum for 
the purpose of the invention. It is suggested that an "eye-safe" lidar 
should be used and that this lidar should have a minimum crossover point 
somewhat less than the 80 to 100 meters of the previously described lidar. 
This would allow data to be obtained for elevations closer to ground. 
Ground-based measurements of temperature, pressure and relative humidity 
could be used as starting points for the standard lapse rate curves of 
temperature and pressure. The ground-based radio refractivity value could 
then be calculated and a linear interpolation between this value and the 
minimum altitude lidar return-signal could be used to fill in the lowest 
few meters of the radio refractivity profiles. 
Obviously many modifications and variations of the invention are possible 
in light of the above teachings. It is therefore to be understood that 
within the scope of the appended claims the invention may be practiced 
otherwise than has been specifically described.