Boundary Layer Turbulence (BLT) Water Vapor

Measurement Test: Summary of Current Results

September 2009

by Sean O’Brien, David Tofsted, Jimmy Yarbrough,

D. Scott Elliott, David Quintis, and Robert Brice

ARL-TR-4991 October 2009

Approved for public release; distribution is unlimited.
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Army Research Laboratory

White Sands Missile Range, NM 88002-5513

ARL-TR-4991 October 2009

Boundary Layer Turbulence (BLT) Water Vapor

Measurement Test: Summary of Current Results

September 2009

Sean O’Brien, David Tofsted, Jimmy Yarbrough,

D. Scott Elliott, David Quintis, and Robert Brice

Computational Information Sciences Directorate, ARL

Approved for public release; distribution is unlimited.
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May 2007–October 2007

4. TITLE AND SUBTITLE

Boundary Layer Turbulence (BLT) Water Vapor Measurement Test: Summary of 

Current Results September 2009

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6. AUTHOR(S)

Sean O’Brien, David Tofsted, Jimmy Yarbrough, D. Scott Elliott, David Quintis, 

and Robert Brice

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7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

U.S. Army Research Laboratory

Computational and Information Sciences Directorate

Battlefield Environment Division (ATTN: RDRL-CIE-D)

White Sands Missile Range, NM 88002-5501

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14. ABSTRACT

The propagation of terahertz (THz) band radiation through the atmosphere is a topic of increasing interest as advanced passive 

and active imaging systems are developed that operate in this spectral region. Much of the impact of the boundary layer 

atmosphere in this band is due to water vapor absorption, which is very strong even in atmospheric window regions. Most of 

the effect of atmospheric water vapor is due to the average integrated path value. However, absorption effects are so extreme in 

the THz band that short-path variability of water vapor can play a mitigating role. To study this variability in the boundary 

layer we performed a field measurement experiment. The purpose of this experiment was to measure atmospheric water vapor 

temporal and spatial structure with the highest practical resolution. We describe the setup of the test sensors, data collection 

procedures, and analysis results below.

15. SUBJECT TERMS

turbulence, stability, surface layer, characterization, terahertz, water vapor

16. SECURITY CLASSIFICATION OF: 

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OF ABSTRACT

UU

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OF PAGES

38

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Sean O’Brien

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Unclassified

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Unclassified

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(575) 678-1570

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iii

Contents

List of Figures v

List of Tables vi

Preface vii

Acknowledgments viii

Executive Summary ix

1. Introduction 1

2. Background and Review of Test Methodology 2

2.1 Summary of Prior Results and Motivation for the Current Work...................................2

2.2 Geographical Description of the Test Site ......................................................................3

2.3 Climatological Description of Test Site Region..............................................................5

2.4 Test Setup and Schedule..................................................................................................6

2.4.1 Phase 1A (25 May 2007 to 29 Jun 2007)............................................................7

2.4.2 Phase 1B (29 Jun 2007 to 25 Jul 2007)...............................................................7

2.4.3 Phase 2A (25 Jul 2007 to 15 Aug 2007) .............................................................8

2.4.4 Phase 2B (15 Aug 2007 to 30 Sep 2007) ............................................................8

3. Data Collection and Reduction 10

3.1 Data Collection Hardware and Methodology................................................................10

3.2 Auditing Data Quality ...................................................................................................11

3.3 Daily Meteorological Summary Specifications ............................................................12

4. Data Analysis Products 13

4.1 Water Vapor Spatial Power Spectra..............................................................................13

4.2 Cross-Correlation Temporal Results for Horizontal Array...........................................16

4.3 Heat and Momentum Fluxes .........................................................................................17

5. Discussion 21
iv

6. References 22

List of Symbols, Abbreviations, and Acronyms 23

Distribution 24
v

List of Figures

Figure 1. Wide field satellite image of the Bldg. 19472 test site and its environs, which are located at 

the terminus of a road spur. Note the light-colored dry wash at lower left of picture. North is 

the vertical upward direction............................................................................................................3

Figure 2. Higher spatial resolution image of test site. Note that the larger patches of vegetation are 

primarily mesquite, with smaller patches consisting of mesquite, creosote bush, and yucca. The 

largest plants are of the order of 2 m tall.........................................................................................4

Figure 3. At the highest resolution, the compound perimeter fence is clearly seen, along with the 

approximate location of the horizontal measurement array (which is indicated by the red dot)......4

Figure 4. Phase 1A test layout for horizontal array of Li-Cor and sonic sensors. .................................7

Figure 5. Phase 1B test layout for horizontal array (also used in Phase 2A). ........................................8

Figure 6. Horizontal array layout for Phase 2B (asymmetric placement)..............................................9

Figure 7. Tower layout for Phases 2A and 2B. Side and plan views of tower and surroundings are 

shown. ..............................................................................................................................................9

Figure 8. Photograph of the Phase 2A setup, 31 July 2007, looking approximately south..................10

Figure 9. Li-Cor humidity sensor records for 25 May 2007, beginning at 2000 Mountain Daylight 

Time (MDT). Sensors were in a horizontal linear array, with a 2 m separation (Phase 1A 

configuration). Sensor 1 was the northernmost, sensor 3 was the southernmost. .........................11

Figure 10. El Paso (EPZ) Doppler radar base reflectivity maps for 2000, 2100, and 2200 MDT, 25 

May 2007. White diamond symbol on 2000 MDT plot depicts approximate location of observing 

site. .................................................................................................................................................12

Figure 11. Example spatial power spectrum for 1700–1800 MDT on 25 May 2007. The red curve 

represents the full spectrum of about 72000 points, with the green curve representing a 5-point 

running mean..................................................................................................................................14

Figure 12. Spatial power spectrum for absolute humidity fluctuations for 1800–1900 MDT on 29 

May 2007. Green line is –5/3 power law curve that intersects average near spatial frequency of 

1 m-1

................................................................................................................................................15

Figure 13. Spatial power spectrum for absolute humidity fluctuations for 0900–1000 MDT on 1 Aug 

2007. Green line is –5/3 power law curve that intersects average near spatial frequency of 1 m-1

.15

Figure 14. Example unnormalized cross-correlation for absolute humidity fluctuations for 1500–1600 

MDT on 25 May 2007....................................................................................................................16

Figure 15. Comparison of diurnal variation of sensible and latent heat fluxes for the three sonic 

anemometer-hygrometer horizontal array sensor pairs for a typical dry day.................................18

Figure 16. Comparison of sensible and latent heat flux time series with wind speed and absolute 

humidity for sensor pair 3 in the horizontal array. .........................................................................18

Figure 17. Radar base reflectivity maps for 28 Jul 2007 showing progress of compact storm cell near 

observation site. Approximate observation site location is indicated by white triangle on 1700 

MDT plot, with approximate cell motion indicated by arrow........................................................19
vi

Figure 18. Comparison of sensible and latent heat fluxes for 28 May 2007........................................20

Figure 19. Sensible heat flux event during the night of 29–30 May 2007...........................................21

List of Tables

Table 1. C-Station climatological means for air temperature (°F).........................................................5

Table 2. C-Station climatological means for wind speed and direction.................................................5

Table 3. C-Station climatological means for relative humidity at 0500 and 1700 local........................5

Table 4. C-Station climatological means for precipitation. ...................................................................5

Table 5. C-Station climatological means for percentage daytime and 24-hour (h) sky cover...............6

Table 6. C-Station 2007 observed means and extremes for air temperature (°F). C-Station 

climatological values are shown in parentheses...............................................................................6

Table 7. C-Station 2007 observed means and extremes for wind speed and direction. C-Station 

climatological values are shown in parentheses...............................................................................6

Table 8. C-Station 2007 observed means and extremes for precipitation. C-Station climatological 

values are shown in parentheses.......................................................................................................6
vii

Preface

The Boundary Layer Turbulence (BLT) test was designed as part of a larger effort to 

characterize the propagation properties of the near-surface atmosphere for simulation and 

prediction of terahertz frequency band system performance. The principal limiting factor for 

system performance in field operation is the strong absorption of terahertz energy by 

atmospheric water vapor. Earlier studies using scanned Light Detection and Ranging (LIDAR)

probes indicated that significant turbulent water vapor density structure exists in the boundary 

layer. This turbulent structure will modulate the spatial frequency composition of terahertz band 

imagery and will also temporally modulate terahertz band communication links. As a first step 

toward a truly comprehensive treatment, it was decided to characterize the boundary layer water 

vapor turbulence for the desert environment with an instrument suite consisting of high speed 

hygrometers and anemometers.

The results of this field experiment were first used to refine the spatial frequency spectrum 

model used by a terahertz image propagation model that we had developed as part of our 

contribution to the Defense Advanced Research Projects Agency (DARPA)-sponsored Terahertz 

Imaging Focal-plane-array Technology (TIFT) project. However, many other applications for 

the BLT data and their analytical products are possible. For example, high energy laser systems 

operating at wavelengths where water vapor is a significant absorber can experience significant 

blooming behavior within regions where the absolute humidity is stochastically high. Another 

application for the BLT data is in the assessment of short range covert terahertz communication 

links that may be intermittently visible at longer ranges due to spatial fluctuations in the water 

vapor density field. 

The report that follows is our initial documentation that describes the test design, physical layout, 

configuration sequence, and preliminary analytical results. We intend to build on this effort with 

future analytical reports that will examine the data in greater detail.
viii

Acknowledgments

The authors wish to thank Dr. Mark Rosker of DARPA for his support of our modeling effort 

under the TIFT program. We also wish to thank other TIFT team members for their support of 

our effort, including Dr. Eddie Jacobs of U.S. Army Night Vision and Electronic Sensors 

Directorate (NVESD) (now with the University of Memphis), Mr. Richard Espinola of NVESD, 

Dr. Stephen Griffin of the University of Memphis, Dr. Frank De Lucia of Ohio State University, 

and Mr. Douglas Petkie of Wright State University.
ix

Executive Summary

The Battlefield Environment Division, part of the U.S. Army Research Laboratory’s (ARL) 

Computational and Information Science Directorate, has worked extensively in boundary layer 

meteorology to further the Army’s knowledge of atmospheric influences on various types of 

battlefield sensing technologies. Two such emergent technologies are imaging sensors operating 

in the terahertz (THz) band of the electromagnetic spectrum, and short-haul THz-based 

communications systems. Water vapor absorption in this band is significant in the boundary 

layer. Even the best THz window regions feature absorption coefficients near 0.1 dB/m for midrange values of absolute humidity. Consequently, the effective range of THz active sensor 

illumination may vary greatly, depending on the nature of the ambient atmosphere under 

different propagation conditions, since the amount of water vapor present in the atmosphere is a 

strong function of terrain and weather.

There is considerable uncertainty regarding the overall degree of variation possible in the path 

integrated moisture, which is dependent on fluctuations in atmospheric humidity levels due to 

turbulent influences.1

We designed the Boundary Layer Turbulence (BLT) experiment

 Although some knowledge of these fluctuations is available, based on 

studies of “conservative passive additive” scalar fluctuation effects featured in dynamic 

similarity theory, humidity influences must be treated somewhat differently from temperature 

influences. In particular, temperature fluctuations are derived from heat transfers at the surface 

using a similar mechanism for all surfaces, whereas humidity sources can arise from spatially 

localized vegetative or surface water features, and can have more complex emission rate 

scenarios. 

2

The BLT experiment was conducted during the late spring (from mid-May) and summer (to early 

August) seasons at our White Sands Missile Range (WSMR), NM, observation site. The 

to provide a database of 

simultaneous wind, temperature, and absolute humidity turbulence measurements by multiple 

sensor platforms at high temporal and spatial resolution over an extended period of weeks. The 

experiment proceeded in phases, from an initial intercomparison of sensors, to a series of 

horizontal (single level) array configurations, to a two-dimensional triangular pattern, and finally 

to vertical tower-horizontal array combination. As we proceeded, we applied “lessons learned” 

to our data acquisition and reduction procedures to fix problems due to instrumental and 

environmental phenomena.

 1 O’Brien, S. G.; Tofsted, D. H. Terahertz Target Fluctuation Estimates Derived from Field Measurements of Atmospheric 

Water Vapor. Proceeding of the SPIE Defense and Security Conference, Vol. 6949, Orlando, FL, 2008.

2 O’Brien, S. G.; Tofsted, D. H.; Yarbrough, J.; Scott Elliott, D.; Quintis, D. Analysis of Boundary Layer Meteorological 

Data Collected at the White Sands Missile Range. Proceeding of the SPIE Defense and Security Conference, Vol. 6239, 

Orlando, FL, 2006., Las Cruces, NM, December 2007. 
x

precipitation patterns that occurred during the experimental period deviated from the expected 

climatological norms. The main departure from the climatology was in late May 2007, where 

the frequency of thunderstorm activity was higher than usual. This pattern persisted into the 

normally quiet early June period that precedes the more active late June to early September 

monsoonal season at WSMR. As a result, we observed unusually high latent heat fluxes in our 

early results and also observed heavy rain conditions that caused our fast hygrometer sensors to 

produce intermittently erroneous measurements. 

The primary analysis products that we generated to understand the results of our experiment are 

spatial frequency spectra for the absolute humidity turbulence, inner and outer size scales for this 

turbulence, and correlation of latent heat flux behavior with local weather conditions. Secondary 

products include spectral and flux correlations for wind speed, wind direction, and temperature 

with local weather conditions and shortwave/longwave radiative fluxes.

The database that we have developed should provide valuable insight into the effect of the 

atmosphere (particularly the desert boundary layer) upon THz imaging and communication 

systems. In parallel, we have developed an imaging tool3

 3 O’Brien, S. G.; Tofsted, D. Development of a Terahertz Short Range Imaging Model. Proceeding of the SPIE Defense and 

Security Conference, Vol. 6239, Orlando, FL, 2006.

that may be used to simulate the 

effects of a turbulent environment upon the image quality observed by prospective passive and 

active THz imagers. In our initial investigations, we have seen many interesting daytime and 

nocturnal boundary layer phenomena; we expect that more will be uncovered as we perform 

additional detailed spectral, cross correlation, and covariance analyses. Data auditing software 

has been developed to uncover problems with instruments and atmospheric conditions that might 

complicate or invalidate results from analysis software during the data reduction process. We 

will continue to refine our reduction and analysis software to capture the temporal and spatial 

scales that can be observed for humidity fluctuations, and compare these with what might be 

expected from theory.
1

1. Introduction

The Battlefield Environment Division of the U.S. Army Research Laboratory (ARL) has worked 

extensively in boundary layer meteorology to further the Army’s knowledge of atmospheric 

influences on various types of battlefield sensing technologies. One of these emerging 

technologies is imaging sensors operating in the terahertz (THz) band of the electromagnetic 

spectrum. This band features radiation wavelengths of approximately 1⁄2 mm (500 microns) or 

less. Of particular interest is propagation in the band around 650 GHz, a so-called atmospheric 

window region, yet even this band features significant water vapor absorption. 

To be more specific, the absorption in this waveband is approximately 0.1 dB/m for mid-range 

absolute humidity levels, which yields effective ranges for imaging equipment of 50–200 m. 

However, the degree of the variation in this effective range may greatly depend on the nature of 

the ambient atmosphere under different propagation conditions, since the amount of water vapor

present in the atmosphere is a strong function of terrain and weather conditions.

There is also considerable uncertainty regarding the overall degree of variation possible in the 

path-integrated moisture that depends on fluctuations in atmospheric humidity levels due to 

turbulent influences. Although some knowledge of these fluctuations is available, based on 

studies of “conservative passive additive” scalar fluctuation effects featured in dynamic 

similarity theory, humidity influences must be treated somewhat differently from temperature 

influences. In particular, temperature fluctuations are derived from the sensible heat flux at 

various surfaces (e.g., Oke, 1978), whereas humidity sources can arise from spatially localized 

vegetative or surface water features, and can have more complex emission rate scenarios. One 

such feature is the plant stomatal resistance that depends on the ambient humidity level and 

available soil moisture (e.g., Deardorff, 1977). The diversity of water vapor sources raises 

several questions that are relevant to the structure of humidity fluctuations. For instance, are 

there feasible methods (such as air mass trajectory analysis) to determine source points of an 

observed water vapor field? What is the mechanism that creates humidity fluctuations from 

standing pools of water, grass, shrubbery, and trees as they are ventilated by ambient winds? 

Can these sources somehow be characterized by identifiable signatures? What are the influences 

of anthropogenic artifacts such as buildings and landscaping on water source emissions? Must 

such effects be treated on an individualized basis or is it possible to aggregate the results into 

more general models? 

Most studies of water effects are motivated mainly by agricultural concerns. As a result, much 

of the available information is based on evaporation of water from roughly uniform crop 

surfaces. In contrast, our interest is primarily in urban or other short-range settings that may 

feature individualized (non-uniform) water sources. We are interested in both horizontal and 

vertical fluctuations, since particularly at night there may be a highly concentrated moist layer 
2

near the ground due to the suppression of vertical mixing. We are also interested in the spatial 

spectral nature of these fluctuations since fluctuations at different size scales will have different 

influences on the propagation problem. For instance, large fluctuations at small scales may be 

significant, leading to image degradation, whereas large scale fluctuations may not be significant 

at all, since they merely contribute to the overall extinction along a path. 

With these considerations in mind, we proposed, designed, and executed the Boundary Layer 

Turbulence (BLT) experiment (O’Brien et al., 2007) to provide a body of information that can be 

used to examine the impact of boundary layer water vapor turbulence upon THz systems.

2. Background and Review of Test Methodology

2.1 Summary of Prior Results and Motivation for the Current Work

Our initial high speed water vapor fluctuation measurements were collected in March (near 

Building 19472 at White Sands Missile Range [WSMR], NM) and August 2005 (at a suburban 

site in Las Cruces, NM), using a single Li-Cor transmissometer sensor flanked by multiple UVW 

ultrasonic anemometers (R. M. Young model 81000). Because only one Li-Cor humidity probe 

was available for these earlier measurements, analysis was limited to stream-wise fluctuations at 

a fixed spatial point. The spectra that were derived from these data were used to create threedimensional (3D) synthetic stochastic humidity fields in a THz image propagation model that 

was developed in collaboration with the Defense Advanced research Projects Agency (DARPA)-

sponsored THz Imaging Focal-plane-array Technology (TIFT) project.

Since those initial measurements, two additional Li-Cor probes were purchased for the BLT 

measurement effort. Consequently, we can now answer a wider range of characterization 

questions than was previously possible. In particular, we can now obtain simultaneous data at 

three points with variable spatial separations, so correlation analysis of these data can produce 

valuable insights into the nature of the water vapor fluctuation spectra in both vertical and 

horizontal planes. Additionally, we can, as before, deduce correlations between the humidity 

fluctuations and other meteorological data such as wind speed and direction that are collected 

simultaneously. We developed the measurement program described below to at least partially 

address the following questions:

• How do the humidity spectrum inner and outer scales vary as a function of height?

• What is the vertical structure of humidity fluctuations, and how does it vary with time of 

day?

• How does the vertical flux of latent heat vary with time of day and prevailing wind 

direction?
3

• How well can we determine outer scale for the humidity fluctuations in the vertical and 

horizontal by varying sensor separation and cross-correlating the sensor data?

• Can we correlate long and short range airmass trajectories over terrain and vegetation 

features with the mean value of humidity and the strength of humidity fluctuations?

• How good is the Taylor’s “frozen field” hypothesis for humidity fluctuations?

• Can we see gravity wave or drainage flow effects in the humidity mean or fluctuation data?

The information provided by these inquiries will improve the accuracy of THz image 

propagation and sensor performance models, particularly in situations where the sensor system 

has a large field of view or field of regard.

2.2 Geographical Description of the Test Site

The test site was chosen on the basis of convenience and security for the ARL staff who 

conducted and maintained the experiment. The measurement instruments were sited next to the 

Building 19472 compound, outside of the fence perimeter and placed up-wind from the fence by 

approximately 20 m to avoid influences of the fence on the measurements obtained. The terrain 

at the test site is nearly flat, but slightly slopes downhill toward the northeast. Spurs of the 

Organ Mountains are nearby to the west and southwest of the test site. Figures 1, 2, and 3 are 

satellite images of the terrain near the test site extracted from the Internet with some contrast 

enhancement to reduce atmospheric haze. These are presented in order of increasing detail, with 

the last image depicting the approximate location of the horizontal measurement array. 

Figure 1. Wide field satellite image of the Bldg. 19472 test site

and its environs, which are located at the terminus 

of a road spur. Note the light-colored dry wash at 

lower left of picture. North is the vertical upward direction.
4

Figure 2. Higher spatial resolution image of test site. Note 

that the larger patches of vegetation are primarily 

mesquite, with smaller patches consisting of mesquite,

creosote bush, and yucca. The largest plants are 

of the order of 2 m tall.

Figure 3. At the highest resolution, the compound perimeter 

fence is clearly seen, along with the approximate

location of the horizontal measurement array 

(which is indicated by the red dot).

The area is sparsely vegetated with plants that are typical of the Chihuahuan desert environment: 

mesquite, creosote bush, and various species of yucca. There are also smaller plants and grasses 

present on the test site terrain whose density varies greatly with the amount of recent rainfall 

during the growing season. The rainfall (mostly in the form of thunder showers) was noticeably 

greater than usual during the early part of the test period, resulting in enhanced small plant 

coverage and growth.

The soil at the test site is primarily sand, with some amendment of organic matter from plant 

decay. Small mammals, reptiles, insects, and other animals recycle this plant material through 

various levels of the food chain. As an indirect result, much of the soil is well aerated and is 
5

permeated by burrows, scrapes, and mounds. Much of the soil is so well-aerated that it is very 

soft; requiring much care in setting up and securing instrument tripods and towers.

2.3 Climatological Description of Test Site Region

We wished to conduct our measurement series over an extended period during which moisture 

levels were expected to range from very dry to fairly moist (relative to the norms for the U.S.

desert Southwest). The dry period was expected to range from the beginning of the experiment 

in mid May through the end of June, with monsoonal enhancement of the moisture levels 

beginning in mid July. Surface temperatures for this period are also quite warm, with highs

ranging between 85 and 100 °F and lows generally between 60 and 70 °F. Most of the rains that 

occur during this period are associated with thunderstorms. These are typically localized in 

nature, causing irregular patterns of wind speed and direction. Typical thundershower activity 

involves late morning cloud building and mid-afternoon to evening thundershower activity. 

Tables 1–5 show climatological means associated with the WSMR C-Station meteorological site, 

approximately 5 miles from our site.

Table 1. C-Station climatological means for air temperature (°F).

May Jun Jul Aug Sep Oct Nov

Low 55 62 66 65 58 46 34

High 87 96 96 93 87 78 66

Mean 71 79 81 79 73 62 50

Table 2. C-Station climatological means for wind speed and direction.

May Jun Jul Aug Sep Oct Nov

Spd (kts) 6.0 5.0 4.6 3.9 3.6 3.8 4.3

Dir W S S S S S S

Table 3. C-Station climatological means for relative humidity at 0500 and 1700 local.

May Jun Jul Aug Sep Oct Nov

0500 45% 50% 65% 74% 71% 65% 62%

1700 17% 19% 31% 35% 34% 31% 34%

Table 4. C-Station climatological means for precipitation.

May Jun Jul Aug Sep Oct Nov

Amt (in) 0.56 0.99 1.43 2.54 1.75 0.88 0.55

Pct of Yr 5% 8% 12% 22% 15% 7% 5%
6

Table 5. C-Station climatological means for percentage daytime and 24-hour (h) sky cover.

May Jun Jul Aug Sep Oct Nov

Day 41% 37% 52% 55% 42% 43% 40%

24 h 35% 37% 51% 55% 41% 33% 34%

Our actual experience as the late spring to summer seasons unfolded was a pronounced departure 

of precipitation amounts from the climatological averages. Significant thunderstorm activity 

began in May and continued throughout the remainder of the test period, with some multi-day 

respites of dry weather interspersed. Tables 6–8 list the temperature, wind, and precipitation 

averages observed for May–July 2007, with the associated climatological averages shown for 

comparison.

Table 6. C-Station 2007 observed means and extremes for air temperature (°F). C-Station climatological 

values are shown in parentheses. 

May Jun Jul

Extreme Low 43 (55) 54 (62) 62 (66)

Extreme High 93 (87) 105 (96) 101 (96)

Mean 70 (71) 81 (79) 80 (81)

Table 7. C-Station 2007 observed means and extremes for wind speed and direction. C-Station 

climatological values are shown in parentheses.

May Jun Jul

Speed (kts) 6.1 (6.0) 6.1 (5.0) 5.2 (4.6)

Direction 208° SW (W) 274° W (S) 135° SE (S)

Table 8. C-Station 2007 observed means and extremes for precipitation. C-Station climatological

values are shown in parentheses.

May Jun Jul

Total (in) 1.38 (0.56) 0.76 (0.99) 2.36 (1.43)

2.4 Test Setup and Schedule

We arranged our test series in phases to make the most of the available instrumentation and to 

allow our personnel time to streamline experimental procedures. Roughly, these phases were 

organized into three episodes: pre-test (Phase 0) calibrations and shakedown tests of sensors; 

horizontal array measurements (Phases 1A and 1B) and combined horizontal array and vertical 

tower (Phases 2A and 2B) measurements. The analysis results provided in this report will 

primarily deal with the horizontal array measurement series, so that we shall only describe the 

Phase 1 and 2 setup geometry. Figures 4–7 depict the test layout for Phases 1A, 1B, 2A, and 2B 

respectively.
7

2.4.1 Phase 1A (25 May 2007 to 29 Jun 2007)

This phase of the experiment was the initial operational period, though some hardware and 

software configuration refinements continued to be set in place. Only the horizontal array was 

used for this phase, with the south-to-north line of the array oriented 330° from north. The three 

Li-Cor and sonic anemometer sensor pairs were separated by 2 m and were mounted at a height 

of 2 m on tripods. Each Li-Cor and sonic pair were separated by a horizontal distance of 20 cm, 

with the sonic upwind (for our initial prevailing wind direction estimate of 240° from north) 

from the Li-Cor.

Figure 4. Phase 1A test layout for horizontal array of Li-Cor and sonic sensors.

2.4.2 Phase 1B (29 Jun 2007 to 25 Jul 2007)

The measurements collected during Phase 1A indicated that the prevailing wind was more 

northwesterly than our initial estimate for the observing period. We therefore took the 

opportunity to reorient the array line to a bearing of 20° from north as we increased the array 

spacing to 4 m between sensor pairs.
8

Figure 5. Phase 1B test layout for horizontal array (also used in Phase 2A).

2.4.3 Phase 2A (25 Jul 2007 to 15 Aug 2007)

This phase marked the initial operation of the 8 m vertical tower array of T107 thermistor 

temperature sensors and sonic anemometers, as well as the Kipp and Zonen Model CNR1 

radiometric sensor set. The tower was populated at 2, 4, 6, and 8 m above ground level (AGL) 

with pairs of sonic and T107 sensors. This combination was selected to provide a vertical profile 

of temperature and wind fluctuations, with the T107s providing a more accurate estimate of the 

long-term mean temperature at each level. Additional T107 sensors were situated within 2 m of 

the tower at 1 m and 0.15 m (about 6 inches) AGL. Prior to the start of the BLT test, the T107 

sensors were intercompared and calibrated by placing them in close proximity at the same (2 m 

AGL) height. The tower was set up to be in line with the horizontal array line, at a distance of 

approximately 20 m from the south end of the horizontal array line. The CNR1 sensor set was 

situated about 4 m from the base of the tower, at a height of 2 m, on a line between the tower and 

the horizontal array. The horizontal array configuration was unchanged from Phase 1B for this 

phase of the experiment. A photograph of this setup is shown in figure 8.

2.4.4 Phase 2B (15 Aug 2007 to 30 Sep 2007)

The horizontal array central sensor pair was moved 2 m to the south along the existing array line 

for this phase, to create an asymmetric 2 m, 6 m pair spacing. This was an effort to see if more 

simultaneous scale information could be obtained more efficiently. The tower configuration was 

unchanged for this phase.
9

Figure 6. Horizontal array layout for Phase 2B (asymmetric placement).

Figure 7. Tower layout for Phases 2A and 2B. Side and plan views of tower and 

surroundings are shown.
10

Figure 8. Photograph of the Phase 2A setup, 31 July 2007, looking approximately south.

3. Data Collection and Reduction

3.1 Data Collection Hardware and Methodology

The high speed sensors that were used in this measurement series included three Li-Cor model 

LI-7500 hygrometer sensor systems and eight R. M. Young model 81000 ultrasonic 

anemometers (hereafter referred to as “sonics”). These instruments were set to sample at 

20 hertz (Hz), to provide high resolution data for water vapor concentration, temperature, and 

wind component fluctuations. A lower sample rate of 1 Hz was used for the Kipp and Zonen 

Model CNR1 shortwave and longwave infrared (IR) upward and downward flux sensor and for 

six model T107 temperature probes (which were mounted in louvered radiation shields). These 

last devices were used to check the accuracy of (and to normalize) the high speed temperature 

data measured by the sonic probes.

Three laptop computers were used to log data streams from the horizontal array and tower array 

sensor groupings. One laptop was located in a protective outdoor enclosure beside the tower, 

and archived serial (RS-232) data directly from the sonic wind sensors. This laptop also 

archived serial data from a Campbell CR-5000 micrologger that was connected to the T107 

temperature probes and the CNR1 radiative flux sensor. A second laptop (also in a protective 

enclosure) located next to the horizontal sensor array archived serial data from the three Li-Cor 

water vapor sensors and three sonics that populated this array. The two outdoor laptops were 

connected by Ethernet links to a central hub, which was located in the horizontal array enclosure. 

A third laptop was then networked to the two data acquisition machines and used to examine 
11

data and download completed hourly data files. The data link was also used to support a 

Network Time Protocol (NTP) synchronization of the laptop system clocks.

3.2 Auditing Data Quality

We used various procedures to ensure that the collected data was free of pathologies, and could 

be used for a reasonable analysis of trends and phenomena. The sonic data records ended with 

an error flag field that allowed for some degree of rejection of bad data. This particular 

automated check was embedded in the reduction programs that we wrote in support of this effort. 

Another check is one that flags fixed-length data records that are either too short or too long. 

Automated techniques could not be used (at least at this point) in cases where the sensor doesn’t 

flag questionable records or if the environment itself is causing physically incorrect sensor 

outputs. An instance of this is rain-induced malfunctions of the Li-Cor sensor readings. As an 

example, figure 9 shows data collected during a rain event, illustrating the importance of vetting 

data through graphical means. This example was from the evening of 25 May 2007, when a 

cluster of thunderstorms developed in the WSMR/Las Cruces, NM, region. 

Figure 9. Li-Cor humidity sensor records for 25 May 2007, beginning at 2000 Mountain Daylight 

Time (MDT). Sensors were in a horizontal linear array, with a 2 m separation (Phase 1A 

configuration). Sensor 1 was the northernmost, sensor 3 was the southernmost.

Figure 10 shows the progress of the thunderstorm complex on local Doppler radar plots. The 

daily plot of water vapor concentration from one of the Li-Cor sensors appears normal until 

about 2030 MDT, when the range of fluctuations suddenly widens markedly. The plots in figure 

10 indicate that the line of thunderstorms passed over the observation point sometime between 
12

2000 and 2100 MDT, consistent with the timing of the hygrometer anomalies. The wild 

excursions of the humidity readings were probably caused by liquid rain water coating the 

windows of the transmissometer sensor. Although we set the Li-Cor Sensors up using the 

manufacturer’s recommended tilt of the optical window relative to horizontal to permit rain 

water to shed, a persistent rain event appears to still seriously degrade accuracy until the 

windows are finally dry. It would perhaps be possible to provide a rain shield above the sensors, 

but that tactic would perturb the measurement results. It would also be possible that misting, 

wind driven rains and strong winds that frequently accompany thunderstorms could still easily 

erode the accuracy of the measurements or worse, damage the equipment setup.

Figure 10. El Paso (EPZ) Doppler radar base reflectivity maps for 2000, 2100, and 2200 MDT, 25 May 

2007. White diamond symbol on 2000 MDT plot depicts approximate location of observing 

site.

3.3 Daily Meteorological Summary Specifications

In our final data summary and analysis report, we will include archived daily summaries of 

conventional meteorological parameters observed for the WSMR, NM, region. These will 

include the following data, which will generally be presented in graphical form:

• Rawinsonde soundings for El Paso, TX, and WSMR, NM (when available)

• Air temperature 
13

• Air pressure

• Dew point

• Wind speed

• Wind direction

• Surface weather summary (frontal position plots and synoptic discussion, when available)

• Precipitation radar (when rain events were seen in the area)

• Satellite: visible, IR, and water vapor imagery (when appropriate)

These parameters will serve to complement and check the temperature, pressure, wind 

component, and absolute humidity data that our high and low speed sensors collect in our test 

setup.

4. Data Analysis Products

4.1 Water Vapor Spatial Power Spectra

The water vapor molar density (milli-moles [mmol] m-3

) at each sampling point was converted to 

an absolute humidity (g m–3

) and a running mean was computed. The value of the absolute 

humidity fluctuation about this running mean was then computed at each sampling point. The 

high speed (20 Hz sampling rate) temporal data for the UVW wind velocity components were 

reduced to a spatial data set by computing 1 second sliding window averages of wind speed at 

each data point. This, multiplied by the sampling interval time, yielded a wind run position for 

the given point. Because the wind speed varied from point to point, the resulting spatial onedimensional grid of data points was non-uniformly spaced. To better accommodate the analysis 

of the spectrum, which uses a fast Fourier transform (FFT) that expects uniformly-spaced points, 

the reduction software performed an interpolation of all of the points onto a uniform grid. The 

grid interval simply equals the total wind run divided by (N-1), where N is the total number of 

fluctuation data points.

The resulting regridded absolute humidity fluctuation data were windowed with a Hanning 

function and then transformed via an FFT; the power spectrum was then constructed from this 

result. Example spectra for Li-Cor sensor 1 data between 1700 and 1800 MDT on 25 May 2007, 

1800–1900 MDT on 29 May 2007, and 0900–1000 MDT on 1 Aug 2007 are shown in figures 

11–13. For the most part, this spectrum follows a Kolmogorov power law behavior (a straight 

line of –5/3 slope for log-log axes), although the spectral slope becomes significantly less 

negative at lower spatial frequencies (below about 0.1 m–1

). A sharp dip in the spectrum around 

2×10-3 m–1 is apparent in figure 11 and to a lesser degree in the other plots. This dip is possibly 
14

due to our selection of 500 m as the exponential weighting constant for computation of the 

running mean. At higher spatial frequencies (above about 1 m–1), the spectral slope becomes 

more negative than the nominal –5/3 value. Another interesting difference between figure 11 

and the other plots is the relatively high spectral power at the lowest spatial frequencies for the 

25 May 2007 results. It is also evident that the point spectra shown here will not provide clearcut estimates for the location of inner and outer scale humidity turbulence features. We will 

need to resort to cross-correlation and covariance methods to investigate departures from the 

inertial subrange and the structure of large scale humidity features.

Figure 11. Example spatial power spectrum for 1700–1800 MDT on 25 May 2007. The red curve 

represents the full spectrum of about 72000 points, with the green curve representing a 

5-point running mean.
15

Figure 12. Spatial power spectrum for absolute humidity fluctuations for 1800–1900 MDT on 29 

May 2007. Green line is –5/3 power law curve that intersects average near spatial 

frequency of 1 m-1

.

Figure 13. Spatial power spectrum for absolute humidity fluctuations for 0900–1000 MDT on 

1 Aug 2007. Green line is –5/3 power law curve that intersects average near spatial 

frequency of 1 m-1

.
16

4.2 Cross-Correlation Temporal Results for Horizontal Array

One possible way to examine the existence and strength of large scale humidity fluctuations is 

through the calculation of the cross-correlation function between the absolute humidity ρ

measured by sensors i and j:

∫

∞

−∞

Γ t = t′ t′ + t dt′ i j ( ) ρ ( ) ρ ( ) ρ . (1)

This relation has to be numerically evaluated over the finite sampling domain by either direct 

numeric means or by application of the convolution theorem for Fourier transforms. We 

implemented both approaches and obtained good agreement in the results. The FFT approach 

will be used operationally, because it is much faster, and the data sets are extensive enough to 

warrant its use. Figure 14 shows the 25 May 2007 1500–1600 MDT results of the non-FFT 

calculation of cross-correlation between sensor sets 1–2, 2–3, and 1–3, which all have the same 

lag direction relative to the mean wind for the observing period. This consistent lag is reflected 

by the shift of the cross-correlation peak to negative values. The 1–3 correlation lag value (4 m 

baseline) is also roughly twice as large as the 1–2 or 2–3 values (2 m baseline), as expected. The 

maximum for the 1–3 correlation function is also lower than the 1–2 or 2–3 maxima, indicating 

the weaker intensity of larger humidity structures.

Figure 14. Example unnormalized cross-correlation for absolute humidity fluctuations for 

1500–1600 MDT on 25 May 2007. 
17

4.3 Heat and Momentum Fluxes

Other analysis products from the BLT experiment data are the sensible heat fluxes, latent heat 

fluxes and momentum fluxes that are derived from “instantaneous” fluctuations about running 

means for wind stream speed u, temperature T, vertical wind speed w, and absolute humidity ρw

at time t:

u t u

′ = u(t)− < > , (2)

T t T′ = T(t)− < > , (3)

w t w′ = w(t)− < > , (4)

w w w t ρ′ = ρ (t)− < ρ > , (5)

where primes indicate the fluctuation value, and the running mean <q>t for quantity q is 

constructed using the iteration

/ / (1 ) ( ) t t

t t t q e qt e q −∆ τ τ −∆ < >= − + < > −∆ , (6)

where Δt is the time interval between data points (0.05 seconds in this case), and τ is the decay 

time constant for the running mean (60 seconds was used for this value in what follows). The 

vertical fluxes due to turbulent eddy transport are derived from standard developments (Stull, 

1988):

H C wT S p =<> ρ ′ ′ , (7)

m τ ρ =< > u w′ ′ , (8)

HL = LV < w′ρ w

′ > , (9)

where HS is the sensible heat flux, τm is the momentum flux, HL is the latent heat flux, ρ is the 

moist air density, Cp is the specific heat at constant pressure, and LV is the latent heat of 

vaporization. A negative value of a quantity given by equations (7)–(9) indicates a net 

downward transport of that quantity. The sensible and latent heat fluxes given by equations 

(7) and (9) were computed for each wind-humidity sensor pair in the horizontal array. Typical

2 m AGL flux results for a relatively dry day (28 Jul 2007, preceded by a day with little 

precipitation) are shown in figure 15. Very little vertical moisture flux is apparent, but those 

fluctuations that do occur show mostly positive correlations with variations in the sensible heat 

flux during the daylight hours and negative correlations at night. With a few exceptions, the 

three sensor pairs also produce flux time series that correlate very well over periods down to 

5-min scales. Figure 16 compares the sensor pair 3 flux results from figure 15 with the diurnal 

wind speed (in m sec–1

, divide by 20 to scale from plot axis) and absolute humidity (in 

g m–3

, divide by 10 to scale from axis) time series. 
18

Figure 15. Comparison of diurnal variation of sensible and latent heat fluxes for the three sonic 

anemometer-hygrometer horizontal array sensor pairs for a typical dry day.

Figure 16. Comparison of sensible and latent heat flux time series with wind speed 

and absolute humidity for sensor pair 3 in the horizontal array.
19

There are two large afternoon dips in the sensible heat flux that are apparent on this plot which 

are likely due to cloud cover. At the trailing edge of the second sensible heat trough, between 

1700 and 1800 MDT, there is a noticeable concurrent spike in wind speed, absolute humidity, 

and latent heat flux. 

The cause of these concurrent spikes is probably due to downdraft gust outflow from a compact, 

fast-moving thunderstorm cell that passed just to the south of the observation site, moving from 

the northeast to the southwest. The base reflectivity radar archive maps for this date for 1700 

and 1800 MDT are shown in figure 17.

Figure 17. Radar base reflectivity maps for 28 Jul 2007 showing progress of compact storm cell near 

observation site. Approximate observation site location is indicated by white triangle on 1700 

MDT plot, with approximate cell motion indicated by arrow.

Figure 18 compares the sensible and latent heat flux time series from a more moist day (28 May 

2007) in the atypical year of 2007. The latent heat flux is much larger due to the moist soil that 

resulted from the unusually heavy rains in May. The daytime positive and nighttime negative 

correlations between the sensible and latent fluxes are more clearly seen than in the 28 Jul 2007 

example shown above.
20

Figure 18. Comparison of sensible and latent heat fluxes for 28 May 2007.

Figure 19 depicts the evolution of an interesting anomaly that occurred during the night of 29–30 

May 2007. The sensible heat flux went strongly negative (indicating unusually pronounced 

downward transport of thermal energy) after about 2000 MDT and stayed that way until shortly 

before 0400 the following morning. Sensor pair 1 was used to generate this panel of diurnal 

plots. Wind speed and absolute humidity are scaled as in figure 16. The source of this anomaly 

can be readily discerned by the behavior of the wind. The wind speed is seen to increase after 

2100 MDT, creating a situation where heat is mixed down to the observation height of 2 m AGL. 

The wind speed begins to decrease after midnight and the sensible heat flux restores to a near 

zero value in the predawn hours. The absolute humidity varies over only a narrow range over 

this nighttime period, and the near-zero latent heat flux reflects this behavior.
21

Figure 19. Sensible heat flux event during the night of 29–30 May 2007.

5. Discussion

These initial results indicate that many interesting daytime and nighttime boundary layer 

phenomena will be uncovered as we perform additional detailed spectral, cross correlation, and 

covariance analyses. Data auditing software has been developed to uncover problems with 

instruments and atmospheric conditions that might complicate or invalidate results from analysis 

software further down the reduction chain. We will continue to refine our reduction and analysis 

software to capture the temporal and spatial scales that can be observed for humidity 

fluctuations, and compare these with what might be expected from theory.

The advantage of having a measurement site that is easily secured, maintained and run on a 

continuous basis has been translated into a very useful database. The data sets span large periods 

of time and, because the sensors were allowed to collect data without interruption, a very 

valuable climatological reference data set for a desert environment will be available for future 

analysis. The availability of this resource is not a trivial consideration, because future 

deployments of THz or mid-wave infrared (MWIR) systems in desert environments are fairly 

likely. As our analysis proceeds, phenomena will probably be identified that will merit 

reconfiguration of the sensor layouts and combinations. These factors will be the subject of 

future discussion in subsequent analysis documents and open literature publications.
22

6. References

Deardorff, J. W. Efficient Prediction of Ground Surface Temperature and Moisture, With 

Inclusion of a Layer of Vegetation. J. Geophys. Res. 1978, 83, 1889–1903.

O’Brien, S. G.; Tofsted, D. H. Development of a Terahertz Short Range Imaging Model, 

Proceeding of the SPIE Defense and Security Conference, Vol. 6239. Orlando, FL, 2006.

O’Brien, S. G.; Tofsted, D. H. Terahertz Target Fluctuation Estimates Derived from Field 

Measurements of Atmospheric Water Vapor, Proceeding of the SPIE Defense and 

Security Conference, Vol. 6949. Orlando, FL, 2008.

O’Brien, S. G.; Tofsted, D. H.; Yarbrough, J.; Scott Elliott, D.; Quintis, D. Analysis of Boundary 

Layer Meteorological Data Collected at the White Sands Missile Range, International 

Test and Evaluation Association (ITEA) Modeling and Simulation Conference, Las 

Cruces, NM, December 2007.

Oke, T. R. Boundary Layer Climates; Methuen & Co.: London, 1978.

Stull, R. B. An Introduction to Boundary Layer Meteorology; Kluwer Academic Publishers:

Dordrect, The Netherlands, 1988.
23

List of Symbols, Abbreviations, and Acronyms 

3D three-dimensional

AGL above ground level

ARL U.S. Army Research Laboratory

BLT Boundary Layer Turbulence

DARPA Defense Advanced Research Projects Agency

EPZ El Paso Doppler radar site code

FFT fast Fourier transform

h hour

Hz hertz

IR infrared

LIDAR Light Detection and Ranging

MDT Mountain Daylight Time

mmol milli-mole

MWIR mid-wave infrared

NTP Network Time Protocol

NVESD U.S. Army Night Vision and Electronic Sensors Directorate

S south

THz terahertz

TIFT Terahertz Imaging Focal-plane-array Technology

W west

WSMR White Sands Missile Range
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