FEDERAL COORDINATOR

FOR

METEOROLOGICAL SERVICES AND SUPPORTING RESEARCH

8455 Colesville Road, Suite 1500

Silver Spring, Maryland 20910

U.S. WIND PROFILERS:

A REVIEW

FCM-R14-1998

Washington, DC

March 1998
iii

FOREWORD

This report was prepared by the Working Group for Profiler Systems as a result of the

Profiler Signal Processing Workshop held in Boulder, CO in April 1997. An objective which

came from that workshop was to improve the quality and reliability of products produced by wind

profiler systems. To accomplish this objective, the Working Group for Profiler Systems was

asked to prepare a report on the state-of-the-technology with profiler systems and then to develop

a plan to meet the quality objective. This document satisfies the first item and provides an

excellent summary of the current state of profilers in the United States, of operational and

research applications of profiler systems, and of challenges which remain in the processing of

profiler signals. 

As the report points out, the NOAA Profiler Network has demonstrated that profilers can

have an operational impact but issues remain in developing an integrated observing system which

takes observations from many sources and assimilates them into the end-to-end forecast system. 

These issues are being addressed by programs such as the North American Atmospheric

Observing System (NAOS). 

The editors are to be congratulated for a job well done. It remains for us, the readers of

this report from both the research community and the operational community, to move forward

and solve the technical problems and make optimum use of the information available from these

systems. 

Julian M. Wright, Jr.

Federal Coordinator for

 Meteorological Services and 

 Supporting Research
v

EXECUTIVE SUMMARY

Over the past two decades, wind profiling technology has proved valuable in both research

and operational applications, including severe weather analysis and forecasting, numerical

modeling, pollution monitoring and space launch support. Individual profilers and networks of

profilers provide continuous measurements, economically and automatically, with high space and

time resolutions which are necessary in defining smaller scale as well as synoptic scale

phenomena. Following a discussion of the theory of operation and history of wind profiler

development, this paper examines the state-of-the-art of wind profiling in the United States. 

Many examples of the profiler’s widespread use are described along with new avenues of research

that are now possible because of profilers. Strengths and limitations of the technology as it is

currently implemented are examined. Ongoing efforts to improve the technology are discussed.
vii

ACKNOWLEDGEMENTS

The Working Group for Profiler Systems is a subcommittee of the Committee for Observing

Systems under the Office of the Federal Coordinator's Interdepartmental Committee for

Meteorological Services and Supporting Research. This Working Group was charged in April

1997 with preparing a review of U.S. wind profiler activity. The contributions of the following

scientists and engineers are gratefully acknowledged: 

Margot Ackley, National Oceanic and Atmospheric Administration (NOAA)/

Forecast Systems Laboratory (FSL)

Russ Chadwick, NOAA/FSL

James Cogan, Army Research Laboratory, White Sands Missile Range (WSMR)

Christy Crosiar, U.S. Air Force, Space Command

Frank Eaton, U.S. Air Force, Phillips Laboratory

Ken Gage, NOAA/Aeronomy Laboratory 

Earl Gossard, Cooperative Institute for Research in Environmental Sciences (CIRES),

University of Colorado/NOAA, Environmental Technology Laboratory (ETL)

Rick Lucci, U.S. Air Force, Space Command

Francis Merceret, National Aeronautics and Space Administration (NASA)/

Kennedy Space Center (KSC)

William Neff, NOAA/ETL

Martin Ralph, NOAA/ETL

Richard Strauch, CIRES, University of Colorado/NOAA, ETL

Douglas van de Kamp, NOAA/FSL

Bob Weber, NOAA/ETL

Allen White, CIRES, University of Colorado/NOAA, ETL

The document was co-edited by Donald W. Beran, NOAA/ETL, and Timothy L. Wilfong, *

Science and Technology Corporation (STC)/NOAA, ETL.

_______________

Chairman, Working Group for Profiler Systems. *
ix

TABLE OF CONTENTS

FOREWORD iii

EXECUTIVE SUMMARY v

ACKNOWLEGEMENTS vii

TABLE OF CONTENTS ix

LIST OF FIGURES xi

Chapter 1. INTRODUCTION 1

Chapter 2. WIND PROFILER THEORY AND TECHNOLOGY 3

2.1. Theory 3

2.2. Description of the Technology 4

2.3. Strengths and Potential 5

2.4. Limitations 5

2.5. Performance 7

2.6. Future Enhancements 8

Chapter 3. APPLICATIONS 15

3.1. Boundary Layer Profilers 15

3.2. Lower Tropospheric Profilers 16

3.3. Tropospheric Profilers 16

3.4. NOAA Profiler Network 16

3.5. Space Launch and Test Range Activities 17

3.5.1. The Eastern Space Launch Range 17

3.5.2. The Western Space Launch Range 18

3.5.3. The U.S. Army Atmospheric Profiler Research Facility 19

3.6. Mobile Profiler Systems 20

Chapter 4. RESEARCH USES OF PROFILERS 25

4.1. Fronts, Jets, and Baroclinic Waves 26

4.2. Gravity Waves 26

4.3. Convective Storms 27

4.4. Coastal Weather 27

4.5. Air Quality Monitoring 27

4.6. Global Climate Research 28

4.7. Turbulence Measurements 30

4.8. Moisture Profiling 32

Chapter 5. SUMMARY 37

APPENDIX A. REFERENCES A-1

APPENDIX B. ABBREVIATIONS B-1
C

2

n C

2

w

xi

LIST OF FIGURES 

Figure Page

2-1. Typical wind profiler beam configuration consisting of three to five beams. 10

2-2. Percentage of time hourly winds were derived and passed quality control, as 

a function of height. 11

2-3. Signal processing steps being developed for the next-generation wind profilers. 12

2-4. Wind profiler data from a 915 MHz boundary layer profiler. 13

3-1. Locations of wind profilers in the NOAA Profiler Network. 21

3-2. Data distribution from the NOAA Profiler Network. 22

3-3. Wind profiler locations at Cape Canaveral Air Force Station and Kennedy 

Space Center, Florida. 23

3-4. Data sample from the Vandenberg AFB 449-MHz wind profiler 

for 23 August 1997. 24

4-1. Time-height cross section of hourly averaged horizontal winds observed by 

 the Hillsboro, Kansas, profiler on 14 May 1992. 33

4-2. The Trans-Pacific Profiler Network (TPPN) completed just prior to the end 

of TOGA. 34

4-3. Time-height profiles of vertical velocity (top), (middle), and (bottom) 

 depicting a rapidly evolving CBL. 35
1

CHAPTER 1

INTRODUCTION

Measurements of atmospheric parameters are a first step toward understanding atmospheric

dynamics, and the ability to make weather and climate predictions. Introduction of the radiosonde

and subsequent improvements in our weather forecasts testify to the validity of this observation. With

a network of nearly 800 radiosonde stations worldwide, the twice-per-day manually launched

radiosonde has become the standard source for upper air data. This network's temporal resolution

is sufficient for large scales; however, our increased awareness of the importance of smaller scales

and our desire for greater resolutions has led to the need for continuous data from automated

systems. This issue is being addressed on many fronts, including new analysis techniques, automated

balloon launchers, in-flight aircraft data, and a variety of remote sensing techniques that can be

employed from the surface and on air- or space-based platforms. This review reports on one of these

techniques: the surface-based radar wind profiler. Where appropriate, we will also mention the

Radio Acoustic Sounding System (RASS) (May et al. 1989; Strauch et al. 1989; Moran et al. 1991;

Masuda and Nakamura 1994), which uses an acoustic source in conjunction with the wind profiler

to measure virtual temperature profiles.

This review covers wind profiling activity only in the United States. Progress in other

countries has been equally impressive, and should be referred to for a worldwide view of the field.

The review opens with a discussion of wind profiler theory, instrument characteristics and limitations,

and some ideas on possible improvements. This discussion is followed by a brief development

history, examples of the profiler's widespread use, and some ideas on new avenues of research that

are now possible using profiler technology. The intent is to provide an overview that highlights

important points, with ample references for the reader who desires more detail.
3

CHAPTER 2

WIND PROFILER THEORY AND TECHNOLOGY

2.1. Theory. Radar (RAdio Detection And Ranging) technology has undergone continuous

refinement since its introduction early this century. “Radar is an addition to man's sensory equipment

which genuinely affords new facilities.” So starts the Massachusetts Institute of Technology (MIT)

Radiation Laboratory Series, a set of 27 textbooks published in 1947, which thoroughly describes the

radar technology critical to the defeat of the Axis Powers in World War II. Theoretical studies in the

1950s indicated that radio waves are scattered by turbulence in the atmosphere in a predictable way

that might allow monitoring of atmospheric parameters. Conventional weather radars detect

reflections from objects in the air (e.g., hydrometeors), rather than the air itself. Wind profiling

radars, on the other hand, depend on the scattering of electromagnetic energy by minor irregularities

in the index of refraction, which is related to the speed at which electromagnetic energy propagates

through the atmosphere. When an electromagnetic wave encounters a refractive index irregularity,

a minute amount of energy is scattered in all directions. Backscattering, i.e., scattering of energy

toward its point of origin, occurs preferentially from irregularities of a size on the order of one-half

the wavelength of the incident wave. Because the refractive index fluctuations are carried by the

wind, they can be used as tracers. Also, because these irregularities exist in a size range of a few

centimeters to many meters, most wind profilers operate at frequencies well below those of

conventional weather radars. Experiments in the 1960s verified the theory and showed that

atmospheric structure from the surface up into the stratosphere could be detected and many

atmospheric processes studied (e.g., Hardy and Katz 1969). In the mid-1970s the National Oceanic

and Atmospheric Administration (NOAA) Aeronomy Laboratory began a research program that

showed for the first time that tropospheric winds could be measured by very-high-frequency (VHF)

(30–300 MHz) Doppler radar that used the Doppler frequency shift of signals scattered from

atmospheric turbulence to monitor wind profiles from near the surface to well into the stratosphere

(Ecklund et al. 1979).

The general principles of the wind profiler are detailed by, among others, Balsley and Gage

(1980) and Rottger and Larsen (1990). Here we primarily address a specific type of radar wind

profiler, the ultrahigh-frequency (UHF) (300–3000 MHz) Doppler system that is widely used in the

United States. Other radar frequencies, primarily VHF but also microwave, are mentioned where

applicable. A different method of wind measurement with numerous variations, called the spacedantenna (SA) method, may also be used to derive wind profiles. The SA method has not been widely

used in the United States, but Doviak et al. (1995) describe a 33-cm-wavelength SA system.
4

2.2. Description of the Technology. The UHF Doppler wind profiler produces vertical profiles of

the horizontal and vertical wind by measuring the radial velocity of the scatterers as a function of

range on three or five antenna beam positions (Fig. 2-1). The method of wind measurement is

described in detail by Strauch et al. (1984); the following is a brief summary. 

One antenna beam is pointed toward zenith, and the other two or four beams are pointed

about 15 degrees off-zenith with orthogonal azimuths (three-beam systems) or orthogonal and

opposite azimuths (five-beam systems). The beam-pointing sequence is typically repeated every

1–5 min. More than one range resolution mode may be used at each beam position. The Doppler

velocity spectrum is computed for each radar resolution cell during a dwell period; more than 105

radar pulses are commonly used to measure each Doppler spectrum. Useful radial velocity estimates

can be made with a per-pulse signal-to-noise ratio (SNR) below -40 dB. Signal processing involves

(1) coherent integration of the complex video signal, (2) spectral analysis, (3) incoherent integration

of Doppler spectra, (4) isolation of the signal spectrum from the signal-plus-noise spectrum,

(5) velocity calculation, (6) temporal averaging of the radial velocities for a number of beam position

sequences, and (7) the calculation of wind profiles. Nearly all UHF Doppler wind profilers operate

like this, with very few changes in the basic technique during the past 15 years. 

To measure wind profiles from velocity measurements made at three- or five-beam pointing

positions, we assume that the wind field has local horizontal uniformity. Three unknowns (u,v,w) can

be found from three radial velocities, or redundantly from a five-beam system. In some situations,

such as the convective boundary layer (CBL) and convective precipitation, local horizontal uniformity

cannot be assumed. When the wind field is not horizontally uniform over distances of the order of

the separation of the radar resolution cells (a distance that increases with altitude and is on the order

of 3 km at 10 km altitude), there are potentially two types of errors in the horizontal wind

measurement: (1) the horizontal wind measured at the resolution cell is in error because of horizontal

gradients of w, and (2) the horizontal wind above the profiler is not the same as that measured at the

resolution cells because of gradients of u or v. 

Although evaluation of the degree of local uniformity, i.e., horizontal homogeneity and

stationarity, is possible using systems with more than three beams, currently implemented signal

processing does not support these checks. Instead, it has traditionally been assumed either that

uniform conditions exist or that time averaging (typically over 1 hour) will significantly reduce errors

from these effects. Of course, neither assumption may be valid. Spatial variability of radial velocities

across different antenna beams (e.g., due to gravity waves, convection, or precipitation) may generate

meteorological noise in the wind component estimates. When high-time-resolution wind

measurements are required, an assessment of the contribution of meteorological noise should be

provided in the form of an error estimate based on separate evaluations of the temporal and spatial

variability of the wind. Temporal variability on each antenna beam can be established from time series

of measurements, whereas horizontal spatial variability across antenna beams requires four or more

antenna beam-pointing directions. 
5

Unfortunately, the radar signal is not always the result of scattering from refractive turbulence

in the radar resolution cell. The UHF Doppler method described earlier in this section performs quite

well for refractive index scattering (if the winds are locally horizontally uniform), but scattering from

other targets can introduce serious errors in wind measurements. Scattering from hydrometeors (rain,

snow, cloud droplets, ice crystals) can be much greater than that from refractive turbulence. This

effect is more pronounced at higher frequencies. When this occurs, the profiler cannot measure the

vertical wind; rather, it measures the mean fall speed of the hydrometeors. However, the profiler can

still measure the mean horizontal wind if there is local horizontal uniformity of the wind and of the

mean particle fall speed. Problems caused by other scatterers are discussed in section 2.4. 

2.3. Strengths and Potential. The wind profiler can measure vertical profiles of horizontal and

vertical wind in nearly all weather conditions with time resolution on the order of 10 min or longer

and vertical range resolution as small as a few tens of meters. The resulting quasi-continuous timeheight cross sections of the horizontal wind profiles provide interesting detail not seen with other

methods. The relative accuracy and precision of the wind data have been validated using a five-beam

profiler to measure simultaneous independent profiles (Strauch et al. 1987); the effects of

precipitation are discussed by Wuertz et al. (1988). Numerous comparisons of winds measured by

profilers and radiosondes (Larsen 1983) show results that are similar to radiosonde-radiosonde

comparisons (on the order of 1 m s ). When there are no interfering signals, the time-height wind -1

profiles are usually very impressive. 

2.4. Limitations. A decade of experience with a variety of UHF Doppler wind profilers is available

for judging their performance. A major limitation is the assumption of local horizontal uniformity,

mentioned in section 2.2. If this condition is met and the return signal is strong enough, then only

one cycle of the antenna beam pointing positions is needed to measure the wind. However, timeheight profiles of wind data show that local horizontal uniformity is rarely, if ever, satisfied. What

has been demonstrated by comparisons with radiosondes is that the profiler can measure mean wind

profiles when the radial velocities are averaged over a number of cycles of antenna pointing positions.

The averaged radial velocities are then representative of the actual mean radial winds, at least in most

meteorological conditions. If the mean wind is not horizontally uniform during the averaging time,

then the averaged radial velocities may not be representative. Meteorological conditions in which

short spatial and temporal scales of variability have amplitudes as large as the mean, such as the CBL

and severe storms, limit the use of profilers for measuring horizontal wind profiles. Note, however,

that even in these cases the radial velocities measured by the profiler may be very accurate even for

just one antenna cycle, and, as long as these radial velocity profiles are treated independently, the data

can portray the dynamics of the radial velocity field if the sampling interval is sufficiently short. 

Another condition that can cause the local horizontal wind uniformity assumption to be

invalid, even with temporal averaging, is the presence of gravity waves. The vertical velocity

measured by the zenith beam can be very different from the vertical velocity at the oblique resolution

volumes, and if, for example, the waves are standing waves, temporal averaging will not reduce the

difference. The gravity waves of most concern are those with spatial scales less than the resolution

volume separation and temporal scales longer than the profiler averaging time. The extent of
6

problems caused by gravity waves in profiler data is not known; however, gravity waves with

amplitudes large enough to cause errors are not uncommon (VanZandt 1982, 1985; Nastrom and

Gage 1984; Nastrom et al. 1990; see also section 4.2).

Profiler data can have problems caused by interfering signals, even with well-designed and

properly operating systems at relatively clutter-free sites. The primary sources of interfering signals

are

• ground and sea clutter,

• radio frequency interference (RFI),

• migrating songbirds, and 

• atmospheric echoes in radar sidelobes.

Not included in this list are transitory targets that may have very strong echoes, such as aircraft

or birds, but whose transitory nature allows conventional profiler data processing to operate

satisfactorily. 

When the desired atmospheric echo is separated in velocity and stronger than the interfering

signal, conventional processing is able to extract valid mean velocity estimates. A number of

techniques have been developed and tested to extract valid mean velocity estimates when the

atmospheric echo is separated in velocity but weaker than the interfering signal. The most difficult

problem arises when the atmospheric echo and the interfering signal have nearly the same mean

velocity. This problem is most prevalent in the lower altitude gates (especially on the vertical beam)

where ground clutter echoes are present. With present data processing, the clear-air vertical

velocities measured in the lowest few kilometers by the vertical beam of UHF Doppler profilers are

biased and generally useless if there is ground clutter. Jordan et al. (1997) and May and Strauch

(1998) describe methods for reducing the clutter power without affecting the desired signal even

when the velocities are not separated; however, these methods have not been implemented in

currently available processing. The inability of UHF profilers to measure vertical winds at low

altitudes because of ground clutter is particularly frustrating because the upper-level vertical winds

are found with such accuracy that they promise unique data for numerical models. Sea clutter is

another example of interference that has a distinctive spectral signature that can be used to identify

and remove it. Again, no techniques have been implemented in commercial profilers to do this, and

atmospheric spectra can overlay the sea clutter spectra, resulting in a bias in the wind velocity

estimates. 

RFI has not been a major issue in the past, but it is likely to become one soon as UHF systems

move from 404 MHz to 449 MHz, the recently approved operational profiler frequency. The

449-MHz profilers will see amateur radio repeaters. Other UHF profiler frequencies will be under

increasing pressure from all kinds of communication systems. In some cases it is possible to choose

operating parameters for the profiler that will mitigate the effects of RFI, which tends to be spread

only a few kilohertz.
(C

2

n

)

C

2

n

7

Problems caused by migrating birds have received considerable attention in the past few years

(e.g., Wilczak et al. 1995). Automated ways to recognize bird contamination in the wind data have

been developed. For example, Merritt (1995) describes a method that allows the winds to be

measured in the presence of bird or other contamination as long as the contamination is intermittent.

Strong signals in antenna sidelobes can be generated by thunderstorms, but can also occur if

there is a very strong horizontally stratified reflectivity layer. Layer reflectivity in a sidelobe usually

appears at higher altitude resolution volumes where the reflectivity is low. This type of interference

has not been identified as a major concern in UHF profilers. 

Much of the interference (except bird echoes) would be eliminated if profiler antennas had

better sidelobe performance. Given that the minimum detectable signal for wind measurement is of

the order of -150 dBm and the transmitted power is of the order of +60 dBm, it is unlikely that the

antenna can be improved enough to eliminate interference. Thus, improved data processing methods

are needed.

2.5. Performance. The performance of any wind profiler is limited by its sensitivity, which improves

with higher transmitted power levels and larger antennae. The returned signal strength is also a

function of the refractive index structure parameter , which tends to decrease with height and

is dependent on meteorological conditions. Thus if is small, returned power may not be strong

enough to make a meaningful measurement of the wind. An important indicator, then, is the

percentage of time wind measurements are reported. Figure 2-2 shows the percentage as a function

of height for a network of 29 wind profilers from June 1992 through May 1994 (Barth et al. 1994b).

Numerous studies have compared wind-profiler-measured winds with winds measured by

other types of instruments (Balsley and Farley 1976; Farley et al. 1979; Fukao et al. 1982; Larsen

1983; Lawrence et al. 1986). Weber and Wuertz (1990) made an extensive comparison of wind

measured with a UHF wind profiler and rawinsondes over a 2-year period at Stapleton Airport in

Denver, Colorado. Differences with a standard deviation of 2.5 m s were attributed mainly to

-1

natural variability in the wind fields. Strauch et al. (1987) used a five-beam UHF profiler to derive

independent near-simultaneous three-beam measurements of the horizontal wind during February

1986. They found a standard deviation of 1.3 m s for these clear-air observations. Wuertz et al. -1

(1988) repeated the experiment between May and August, when rain could be expected. When rain

drop fall speeds were properly included in the horizontal wind calculations, errors of 2–4 m s were -1

found.
8

2.6. Future Enhancements. Continued integration of wind profiling technology into operations and

research requires continued improvement in the reliability and accuracy in the derived meteorological

products. In particular, extracting measurements of meteorological quantities in the presence of

interfering signals and quantifying the error in the measurements introduced by nonhomogeneous and

other limiting meteorological conditions must be addressed. Certainly, improvements in profiler

hardware offer some advantages and must be pursued; however, these improvements will be

incremental. Significant improvements are possible through signal processing advances discussed in

this section. Ideal antennas would eliminate all the interfering signals listed in section 2.4 except for

migrating birds. However, the sensitivity of the profiler receiving system is such that a significant

improvement in antenna sidelobe performance may not dramatically reduce the interference problem.

Nevertheless, improving antenna sidelobe performance would help and should be the priority for

hardware developers.

Other hardware (i.e., better solid-state transmitters, digital receivers, and automated

reflectivity calibrations) would improve profiler performance, especially reliability. The problem of

saturation in precipitation could be addressed if the dynamic range of the typical linear receiver used

in UHF Doppler profilers were large enough to allow reflectivity measurements in heavy

precipitation. This could be done with some combination of more dynamic range in the analog-todigital converters (ADC), a separate logarithmic reflectivity channel, dual linear channels, and

dynamic automatic gain control (AGC). 

Current signal processing methods generally follow the techniques described in section 2.2

and by Barth et al. (1994a). A limiting assumption in the current algorithms is that the atmospheric

return is the only signal present. Contamination can obscure, or be mistaken for, clear-air return from

the atmosphere, resulting in erroneous or even meaningless measurements. While the consensus

average technique eliminates much contamination, it is sometimes ineffective, and it may restrict

temporal resolution. Methods such as postprocessing and quality control are not always effective

because important information may be lost during the early stages of signal processing. In some

cases, postprocessing may not detect contamination without other independent measurements.

Improvements in the timeliness and quality of wind profiler products will depend on improving

the signal processing. Figure 2-3 shows the steps involved in a more robust profiler signal processing

system now under development (Merritt et al. 1997; Wilfong et al. 1997). Signal processing begins

with time series acquisition. If potential interference of a known frequency is present, e.g., amateur

radio operations, the interpulse period can be chosen so that interference does not fall at a harmonic

of the sampling frequency. Low-pass filtering and compression may be done in either the time or the

frequency domain, or both. Producing a highly resolved spectrum requires very long, uninterrupted

time series. In addition to allowing the traditional boxcar average, one may use a more optimally

weighted filter or no filter at all. A long, optimized digital Fourier transform (DFT) may be used to

compute the spectrum, and radial velocities larger than, say, 10 to 40 m s are discarded. A long

-1

DFT does not compromise the benefits of time-domain averaging because both are coherent

processes. Thus, in addition to data compression, spectral clipping accomplishes bandpass filtering

in the spectral domain.
9

Traditionally, numerous spectra collected over a minute or so have been simply averaged

incoherently to reduce variability, and thereby increase signal detectability. When some of the spectra

are contaminated (e.g., by RFI, bird echoes), a simple average can produce a contaminated mean

spectrum in which the atmospheric signal is obscured. Intermittent contamination is reduced or

eliminated using other smoothing techniques such as a statistical averaging method (Merritt 1995).

Profiler spectra often contain multiple signals, none of which may be due to radar return from

the atmosphere. The next-generation signal processing should accommodate signal detection and

identification algorithms and techniques that (1) determine the presence of multiple signals in each

spectrum, (2) model data to estimate spectral moments of noise and signals, and (3) use quality

controls to identify signals associated with radar return from the atmosphere. Elimination of

nonatmospheric signals is the most difficult and error-prone part of signal processing. Consistency

over time and over space is the most general principle affecting confidence in signal identification and

can be used to identify and reject nonatmospheric signals. Further, if a five-beam configuration is

used, opposing beam signal consistency can be checked. 

Most of the meteorological products desired from wind profilers require the combination of

independent measurements made on antenna beams pointed in different directions. To compensate

for temporal and vertical sampling differences, the data from the different beams are interpolated to

a common time-height grid prior to being combined to form final wind profiler products. In addition

to the accumulated confidence estimates associated with signals prior to gridding, new confidence

values are introduced describing the fit of the data to the common grid. It is not possible to

compensate for the horizontal spatial separation of the measurements. With profilers using more than

three beams, additional confidence estimates can be derived that indicate the degree of horizontal

homogeneity present. Such confidence estimates are an essential part of the final products from wind

profilers.

New processing methods (Merritt et al. 1997; Wilfong et al. 1997) are being tested on a

variety of data. For example, moment data gathered during the 1997 Southern California Ozone

Study (SCOS97) have been processed using both conventional and new processing techniques that

employ spatial and temporal continuity. The dramatically increased coverage (see Fig. 2-4) shows

the value of the new processing method.
Wind Profiler

Radar Antenna

Equipment

Shelter

RASS Antennae

EAST

VERTICAL

NORTH

SOUTH WEST

Θ

High

Mode

Low

Mode

Radar Beams

10

Figure 2-1. Typical wind profiler beam configuration consisting of three to five beams: one

vertical, and two or four tilted near 15 degrees from the zenith in orthogonal directions. Many

profilers employ overlapping low and high modes where power and height resolution may change. 

The acoustic source for RASS are typically located around the radar antenna, as shown.
30 40 50 60 70 80 90 100 

Percent

0.50

2.00

3.50

5.00

6.50

8.00

9.75

11.00

12.50

14.00

15.50

Height AGL (Km)

HIGH

MODE

LOW

MODE

% Of Winds Reported

% Of Winds Reported At Least

Once In 3 Hours

 Figure 2-2. Percentage of time hourly winds were derived and passed quality control, as a function of height. Data were averaged

 over 29 of the national network profilers from June 1992 through May 1994. The top pair of curves is for the high mode, and the

 bottom pair is for the low mode (see Fig. 2-1). 
Acquisition & time domain filtering

 Flexible inter-pulse period

 Very long time series

 Weighted or no averaging

Digital Fourier transform

Multiple signal detection

Adaptable spectral filtering

 Bandpass -- clipping

 In band -- statistical averaging method (Merritt 1995)

Signal identification & moment calculation

 Pattern recognition

 Simple atmospheric models

Generation of meteorological products

12

Figure 2-3. Signal processing steps being developed for the next-generation wind profilers.
13

Figure 2-4. Top: Conventionally processed wind profiler data from a 915-MHz boundary layer

profiler located at Barstow, California, for the period 0000 UTC 23 July 1997 to 0000 UTC 24 July

1997. Wind profiles were produced by treating the moments from each beam (i.e., the signal power,

radial velocity, and spectral width) with a conventional consensus average technique. Bottom: The

same moment data, but treated with an analysis using temporal and spatial continuity (Wilfong et al.

1997).
15

CHAPTER 3

APPLICATIONS

UHF Doppler systems have a number of different uses; all rely on the basic technique

described in section 2.2 and differ mainly in their sensitivity and cost. Improved sensitivity is obtained

by higher average transmitted power (peak power is not important) and larger antenna effective

apertures, and sensitivity determines the altitude coverage and/or averaging time needed to measure

the radial velocity. Three general categories of profilers are described below and typical

characteristics are listed in Table 3-1. 

Table 3-1. Typical characteristics of UHF Doppler profilers.*

Boundary Layer Lower Tropospheric Tropospheric

Minimum altitude (m) 60–100 100–200 250–500

Maximum altitude (km) 1–3 5–8 12–16

Altitude resolution (m) 60–100 100–300 250–1000

Time resolution (min) 10–15 20–30 30–60

Average power (W) 10–50 100–500 1000–2000

Antenna aperture (m ) 3–10 10–25 100–200 2

Characteristics listed in this table are typical for midlatitude Continental United States (CONUS) operation. In *

 other regimes the maximum altitude can be quite different. For example, in the tropics the boundary layer systems

 will typically measure to 5–6 km altitude, whereas in the Arctic the minimum altitude may be less than 200 m. 

3.1. Boundary Layer Profilers. These systems, operating primarily at 915 MHz (Ecklund et al.

1988), are commonly used for air quality or urban-scale studies. They are also used to complement

profilers whose minimum observing altitude or range resolution cannot portray the boundary layer

winds. Their small size allows them to also be deployed on ships (e.g., Fairall et al. 1997), as part

of integrated sounding systems (ISSs) (e.g., Parsons et al. 1994) and mobile profiling systems (MPSs)

(Cogan 1995). Several universities are using them in research and in a teaching role. RASS, for

measuring temperature profiles, is a popular addition because it can provide high-temporal-resolution

temperature soundings with the same height resolution as for wind measurements. 
16

3.2. Lower Tropospheric Profilers. These systems, with more sensitivity and higher cost than the

boundary layer profilers, have not been widely used. They have a number of potential applications

including monitoring airport winds, and transport and diffusion of hazardous materials, and for wind

corrections for artillery. A 404-MHz system was used for several years to aid in the safe operation

of a tethered balloon (Moran et al. 1989). Although the profiler may provide valuable data for airport

operations, it should not be envisioned as a “wind-shear” warning device; the profiler will measure

vertical shear of the horizontal wind, but because of the inhomogeneity associated with convective

storms, profilers are not appropriate for detecting and warning of the wind shears from downdrafts

that lead to dangerous conditions for takeoff and landing of large aircraft. 

3.3. Tropospheric Profilers. The best example of tropospheric UHF Doppler wind profilers is the

instruments used in NOAA's Wind Profiler Demonstration Network (WPDN) (Chadwick 1986; Beran

1991). These profilers were installed in the early 1990s to provide data for weather forecasting, both

for local forecasts and for input to numerical models. The network data are available on the Web

(http://www-dd.fsl.noaa.gov). Another application for wind profilers is for tropospheric/lower

stratospheric height coverage for rocket launch support (Beran 1985; Beran and Kaimal 1989).

However, UHF half-wavelength turbulent scales that cause radar scattering may be damped by

viscosity in the stratosphere; this uncertainty argues for using longer wavelength systems. 

Longer wavelength (VHF) profilers, often referred to as stratospheric-tropospheric (ST)

profilers, were developed earlier than UHF profilers; the pioneering work at Poker Flat, Alaska (see

Balsley and Gage 1980), set the stage for the Eastern Range (ER) system and White Sands Missile

Range (WSMR) system described in sections 3.5.1 and 3.5.3, respectively. 

3.4. NOAA Profiler Network. In 1980, the NOAA Wave Propagation Laboratory (WPL), now the

Environmental Technology Laboratory (ETL), extended earlier profiler research in the NOAA

Aeronomy Laboratory by beginning a program that made wind profilers practical for routine

meteorological measurements. The Colorado wind profiler network, consisting of three VHF and

one UHF profiler (Strauch et al. 1984), demonstrated the ability of profiling systems to portray the

winds aloft over a large area with automated, unattended systems. This work was the precursor to

NOAA's initiative to build the current national wind profiler network. 

In the early 1990s, the WPDN, consisting of 32 commercially produced 404-MHz wind

profilers, was deployed in the central third of the United States (Fig. 3-1). This network, later

renamed the NOAA Profiler Network (NPN), has been operating since 1992. In 1994, the 404-MHz

experimental frequency was replaced by a permanent 449-MHz frequency allocation for wind

profilers. New systems must comply with this standard. In 1995, NOAA began a cooperative effort

with the Air Force to replace the 404-MHz profiler at Vandenberg Air Force Base (AFB), California,

with a 449-MHz system. NOAA is installing three 449-MHz profilers in Alaska and deactivating the

existing Alaskan 404-MHz system.
17

Details of the NPN operation are described by Barth et al. (1994a). Data from the network

profilers are gathered in near-real time at a hub in Boulder, Colorado. Data include both wind

measurements and details regarding the health and status of various components of the radars. After

quality control at the hub, the data are distributed within NOAA, to universities, and to other

government agencies and the general public (Fig. 3-2). The staff in the Profiler Control Center

(PCC), where the hub is located, are responsible for remotely monitoring the operation of each

profiler in the NPN. This includes responding to profiler reported faults, coordination of repair

logistics, communications monitoring, and the subjective monitoring of engineering and

meteorological data. 

The impact of the NPN has been studied by several authors (Weber et al. 1990). Both

theoretical studies and analyses of actual situations have demonstrated its value to numerical models

(Kuo and Guo 1989; Smith and Benjamin 1993). One important contribution has been the ability to

observe the role of the nocturnal low-level jet in the rapid transport of moisture-laden warm air

northward from the Gulf of Mexico (Shiyuan et al. 1996). NPN data have been key to predicting the

occurrence of nocturnal thunderstorms caused by this low-level jet (Leftwich and Beckman 1991;

Miller et al. 1993). In subjective evaluations, wind profiler data have been found to be useful in

numerous forecasting tasks including aviation forecasts, precipitation onset and termination, and

severe weather warnings and advisories. To date, the most comprehensive study of the NPN is

contained in the “Wind Profiler Assessment Report” edited by Schlatter and Zbar (1994). 

3.5. Space Launch and Test Range Activities. During the early 1990s, wind profilers were

introduced at national space launch and test range facilities. Today, they are being used to support

operations at the Eastern and Western Space Launch Ranges, and research at WSMR. 

3.5.1. The Eastern Space Launch Range. The use of wind profilers to directly support

space launch operations began in 1990 when the National Aeronautics and Space Administration

(NASA) installed a 50-MHz profiler at Kennedy Space Center (KSC) to evaluate the applicability of

the technology for assessing launch wind conditions (Wilfong et al. 1993). Like an identical 50-MHz

system at the WSMR, this profiler has an effective aperture of 13,500 m with a peak power of

2

250 kW and a maximum average power of 12 kW. Two modes of operation provide range

resolutions of 150 or 600 m for 110 gates. In practice only the 150-m range resolution mode is used,

with the lowest gate at 2 km and the highest gate at 18.5 km.

The standard consensus-average technique delivered with the system was judged inadequate

for launch support. To produce high-quality wind profiles in minimal time, NASA replaced the

conventional signal processing with one that uses a median filter to remove spurious echoes from the

averaged Doppler spectral data and constrains the search by a first guess (Wilfong et al. 1993;

Shumann et al. 1998). 

The KSC 50-MHz profiler is now integrated into the prelaunch wind evaluation process at

the ER. NASA uses the data to evaluate wind persistence for Shuttle launches, but does not use the

data to compute expected Shuttle loads during launch. The Titan IV program uses the profiler data
18

to compute expected loads, but continues to use balloon-derived winds as the primary data source.

This conservative approach is expected to continue until the use of profiler data in launch support is

fully validated. The 50-MHz system has recently been used to study the probability distribution of

short-period upper-air wind changes. Merceret (1997) found the distribution is lognormal, which

implies that large wind changes may occur much more frequently than if the distribution were

Gaussian, as is now assumed during the prelaunch vehicle risk assessment (Merceret 1998). 

The National Weather Service (NWS) Spaceflight Meteorology Group (SMG) at Johnson

Space Center, Houston, Texas, is required to provide wind profile forecasts from the surface to

80,000 feet (24,384 m) for Space Shuttle landings (Bellue et al. 1996). These forecasts are used to

calculate Space Shuttle vehicle performance during descent and landing at KSC. The 50-MHz data

are routinely transmitted from the Cape Canaveral Air Station to forecasters at Johnson Space Center.

These data are used in conjunction with Jimsphere and rawinsonde data, and numerical model output,

to produce the forecast wind profile. The 50-MHz profiler data are also used for analysis and

forecasts that support ground operations. 

In 1996, the ER completed installation of a network of five 915-MHz radars with RASS

(Heckman et al. 1996). This network is designed to provide three-dimensional wind direction and

speed estimates in the boundary layer from 120 m to 4 km AGL and virtual temperature (T )v

estimates from 120 m to 1.5 km AGL. It was installed to provide wind data with high spatial and

temporal resolution in the gap between the top of the local wind tower network (150 m) and the

lowest gate (2 km) of the 50-MHz wind profiler. The five profilers are arranged in a diamond pattern

with an average spacing of 10–15 km (see Fig. 3-3). Although the systems are capable of generating

five beams, they are operated in a three-beam mode to accommodate a 10-min wind measurement

cycle followed by a 5-min RASS temperature measurement cycle.

A primary launch-specific use for the 915-MHz profilers is for characterization of the wind

and temperature fields for toxic hazard assessment. When fully operational, the network will support

forecasts of low-level winds for launch analyses on all vehicles and for Space Shuttle landings. The

network will also enhance general forecasting capability for such problems as thunderstorms and high

winds.

3.5.2. The Western Space Launch Range. The Western Range (WR), located at

Vandenberg AFB, California, hosted one of the original NPN profilers until 1997. As mentioned in

section 3.4, this NPN 404-MHz system is being replaced with a new 449-MHz profiler. The new

profiler incorporates features such as (1) five beams, (2) four modes with range resolutions of 125 m,

250 m, 500 m, and 1 km, and (3) flexible operating modes. Its capability to retrieve high-resolution

low-altitude data is shown in Fig. 3-4.

The WR now has two mobile 915-MHz low-altitude profilers. One is collocated with the new

449-MHz radar; the second is located near the space launch complexes. Both profilers are currently

operating in dual mode on a 30-min cycle. Winds are sampled for 25 min and temperature is sampled

for 5 min. In addition, during the wind-sampling period, each radar operates in two different modes
(C

2

n

)

C

2

n

C

2

n

C

2

n

C

2

n

C

2

n

19

that provide two overlapping profiles for each site. One mode provides 100-m vertical resolution

from 120 m AGL to 2 km. The other mode provides 200-m vertical resolution from 320 m AGL to

about 3–4 km. Temperature profiles provide data at 60-m vertical resolution from 120 m AGL to

1 km. All three systems (the 449-MHz radar and the two 915-MHz radars) employ the same data

system and thus provide a common interface for data collection at the local Air Force weather station,

where the wind and temperature data can be viewed independently. 

3.5.3. The U.S. Army Atmospheric Profiler Research Facility. The U.S. Army Research

Laboratory's Battlefield Environment Directorate (ARL-BED) at WSMR, New Mexico, operates the

Atmospheric Profiler Research Facility (APRF). This facility uses remote sensors to measure highresolution vertical profiles of refractive index , wind speed, wind direction, and ambient

temperature. The measurements are continuous and provide high resolution from the surface up to

about ~19 km AGL. The APRF systems include (1) two high-performance clear-air atmospheric

profilers (50 MHz and 2900 MHz), (2) a specialized independent optical turbulence measurement

system, (3) a suite of three supporting standard atmospheric profilers operating at 404 and 924 MHz,

(4) RASS, (5) an array of specialized tower and surface-mounted point and integrated-path

instrumentation, and (6) tethered and free-flight balloon capability. 

The 50-MHz profiler provides high-resolution calibrated values, winds, and virtual

temperature. Specialized spectral processing converts the total received power into calibrated 

values at 150-m resolution. Calibrated values, based on a first-principles calibration approach

(Eaton et al. 1988), are obtained for both the 50- and 2900-MHz radar profilers. 

The 2900-MHz profiler obtains continuous measurements of radar power return with

ultra-high range resolution (1–2 m) from 50 to 2200 m AGL. When hydrometeor-type backscatter

is being observed, radar hardware gains can be adjusted to obtain similar resolution for Rayleigh

scattered precipitation. These frequency modulated-continuous wave (FM-CW) radar measurements

are used to study boundary layer dynamics, hydrometeors, radio wave propagation, insect

interference, imaging, and laser propagation. Slow-rate azimuth scans can be made for velocityazimuth display (VAD) wind profiling with lower space and time resolution, or the antennas can be

directed vertically for temperature profiling at lower height resolution or for backscatter profiling at

high resolution (McLaughlin 1992). The radar is used predominantly for high-height-resolution 

profiling. In this mode the antennas are directed vertically, and typically, the data are averaged over

6–10 s, with a 2.15-m resolution from 0 to 2200 m AGL. 

The APRF provides high-resolution (spatial/temporal) , wind speed, wind direction, and

ambient temperature measurements needed for micrometeorological, boundary layer, and upper

atmospheric research, optical sensor evaluations, transport and diffusion studies, scintillation studies,

satellite ground truth, and propagation studies. Features in the atmosphere, including insect

migrations in the New Mexico Tularosa basin and the different backscatter return signatures derived

from the different operating frequencies of the various radar profilers, have also stimulated new

research interests.
20

3.6. Mobile Profiler Systems. An MPS called the Profiler is being developed by the ARL

Information Science and Technology Directorate (ISTD), with the assistance of NOAA’s ETL. The

system is designed to provide complete meteorological soundings from the surface to more than

30 km as often as once every 3 to 5 min by integrating data from a number of sources, including

meteorological satellite sounders, a microwave radiometer, RASS, and a 924-MHz wind profiler.

The radar processing algorithms developed by ETL for the wind profiler (Wolfe et al. 1995) allow

for higher data quality at faster data rates than were previously possible. A more advanced prototype

Meteorological Measuring Set-Profiler (MMS-P) is expected to be ready for initial field testing in the

summer of 1998. A shelter on a standard pickup truck, or equivalent, plus a small trailer will contain

the MMS-P equipment. 

The Profiler has certain elements in common with fixed-site systems described by Parsons

et al. (1994) and Stokes and Schwartz (1994), but it has additional features such as a microwave

radiometer, a combined radar/RASS antenna, and software for processing and quality control of data

from the ground-based sensors and for combining satellite soundings with ground-based profiles in

near-real time. Wolfe et al. (1995) provide details on the Profiler as configured and operated during

the Los Angeles Free Radical Experiment (LAFRE) in Claremont, California, along with examples

of the various products. Cogan (1995) presents additional samples of output and gives preliminary

quantitative results. Wolfe et al. (1995) and Cogan (1995) briefly describe the method for merging

data from satellite and ground-based sensors. A more complex description of the merging algorithms,

and a fuller, quantitative presentation and discussion of test results, are given in Cogan et al. (1997).

The future MMS-P will have a variety of military and civilian applications, including timely

support for airfield operations, and near-real-time indications of potentially hazardous wind

conditions. Mesoscale models will have access to detailed, near-real-time, atmospheric soundings

within and somewhat above the boundary layer. Through access to data from environmental

satellites, and potentially from airborne sensors and dropsondes, the MMS-P could potentially obtain

meteorological data throughout the domain of a mesoscale model. 
Figure 3-1. Locations of wind profilers in the NOAA Profiler Network. There will be three sites in Alaska after 1998.
Government

Research

Community

University

Community

Private

Sector

WMO

Community

Archive

NCDC

Forecasts

NSSFC

WFOs

Models

NCEP

World

Wide

Web

ERL

Laboratories

Profiler

Control

Center

NWSTG

22

 Figure 3-2. Data distribution from the NOAA Profiler Network [World Meteorological

 Organization (WMO), Environmental Research Laboratories (ERL), National Centers for

 Environmental Prediction (NCEP), National Severe Storms Forecast Center (NSSFC), 

 Weather Forecast Office (WFO), and National Climatic Data Center (NCDC)]. 
 Mosquito Lagoon

 South Cape

 TI-CO

 10 km 

LC 39A

LC 39B

SLF

Merritt

Island

50 MHz

23

Figure 3-3. Wind profiler locations at Cape Canaveral Air Force Station and Kennedy Space

Center, Florida. The 915-MHz and 50-MHz profiler locations are indicated by solid squares. The

names of the locations are printed next to the sites, and a scale is provided in the upper right. 

Also shown are the Shuttle launch complexes (LC 39A,B), the Shuttle Landing Facility (SLF),

and Titusville Corporate Airport (TI-CO). 
24

Figure 3-4. Data sample from the Vandenberg AFB 449-MHz wind profiler for 23 August 1997.
25

CHAPTER 4

RESEARCH USES OF PROFILERS

Mesoscale meteorology, which focuses on spatial scales ranging from 2 to 2000 km and

temporal scales of a few minutes to several hours, has depended traditionally on remote sensing

techniques such as satellites and scanning weather radars, e.g., the Geostationary Operational

Environmental Satellite (GOES) and Weather Surveillance Radar 1988 Doppler (WSR-88D),

to provide the observations. These tools in turn depend mostly on the presence of clouds or

precipitation, leaving the extensive and important regions of clear air less well observed, and thus our

understanding of mesoscale phenomena less complete. Another limitation of these observing systems

has been their inability to directly observe vertical air motion, which is largely responsible for the

organization of precipitation. It is now well established that wind profilers provide the most direct

measurements of mesoscale vertical air motions in the free troposphere, even in the context of heavy

precipitation. Because profilers monitor winds above ground level, they complement the more

extensive surface network. This has helped to provide significant insight concerning issues related

to decoupling of the surface from the free troposphere in stable conditions, such as produced most

nights by the nocturnal boundary layer. In these, and other ways, wind profilers have helped fill

important observational gaps, and thus have contributed to our understanding of mesoscale

phenomena. 

Some key attributes of wind profilers that make them useful in mesoscale meteorological

research are

• frequent (hourly or better), continuous wind profiling under nearly all weather 

conditions (Shapiro et al. 1984; Strauch et al. 1984, 1987; Augustine and Zipser 

1987; Wuertz et al. 1988),

• ability to see atmospheric flow above stable boundary layers that otherwise mask

conditions aloft (Neiman et al. 1997),

• direct measurement of vertical air motion (Nastrom and Gage 1984; Ralph 1991;

Yoe et al. 1992; Moran and Strauch 1994; McAffe et al. 1994, 1995),

• simultaneous measurement of vertical profiles of horizontal wind and precipitation

(Fabry et al. 1993; Rogers et al. 1993; Ralph et al. 1995; Cifelli et al. 1996;

Neiman et al. 1997), and

• applicability in mesoscale networks (Zamora et al. 1987; Cram et al. 1991; Wilczak

et al. 1992; Bluestein and Speheger 1995; Spencer et al. 1996). 
26

Although these key attributes are shared by most types of profilers, not all types are suitable for all

problems; e.g., VHF profilers are best for distinguishing between vertical air motions and

hydrometeor fall velocities in heavy precipitation, and UHF profilers are best for studies of boundary

layer phenomena.

4.1. Fronts, Jets, and Baroclinic Waves. Fronts, jets, and baroclinic waves play crucial roles in

determining day-to-day weather in midlatitudes. Profilers provide uniquely complete measurements

of key features of these systems as they pass through a region. Features include not only changes in

the vertical vorticity, divergence, and baroclinicity, but also the vertical air motions forced by these

systems (Shapiro et al. 1984; Zamora et al. 1987; Neiman and Shapiro 1989; Crochet et al. 1990;

Neiman et al. 1992, 1997; Bluestein and Speheger 1995; Spencer et al. 1996). Identifying

precipitation from either the raw Doppler power spectra (e.g., Wakasugi et al. 1986, 1987; Gossard

1988; Rajopadhyaya et al. 1994) or just the spectral moments (Ralph 1995; Williams et al. 1995;

Ralph et al. 1995, 1996) has also permitted the important kinematic wind features to be related to the

distribution of precipitation (Fabry et al. 1993; Rogers et al. 1993; Ralph et al. 1995; Marwitz et al.

1997; Neiman et al. 1997). An example of using spectral moment data to identify precipitation is

shown in Fig. 4-1 from Ralph et al. (1995). Even a baroclinic zone aloft can be clearly revealed by

easily monitoring the height of the melting layer as a function of time (Neiman et al. 1995, 1997).

Among numerous other contributions, these data have led to a clearer view of the structure of fronts

extending from the surface to the tropopause, including both precipitating and clear regions. This

approach has yielded new understanding of the relationship between cold fronts and prefrontal squall

lines where the latent heating and cooling occurs fully on the warm side of a strong cold front

(Neiman et al. 1997). It has also provided clear evidence of strong and deep vertical motions even

in dry fronts (Ralph et al. 1993a). Results from studies of cold fronts aloft and observations of

multiple frontal zones and their mergers are presented by Neiman et al. (1997).

4.2. Gravity Waves. Atmospheric gravity waves are characterized by circulations containing

horizontal convergence/divergence and vertical motions. They can propagate horizontally if they are

ducted, or vertically. They contribute to vertical and horizontal fluxes of momentum, and can also

organize clouds and precipitation. Wind profilers have provided unique measurements of these

processes, including documentation of the spectrum of atmospheric motions (e.g., VanZandt 1982,

1985; Gage and Nastrom 1985; Fritts and VanZandt 1987; Fritts et al. 1988; Carter et al. 1989;

VanZandt et al. 1991). Most of these and other early profiler-based gravity wave studies are

summarized nicely by Gage (1990). More recent progress includes quantitative assessments of

vertical fluxes crucial to the atmospheric momentum balance on a global scale (Prichard and Thomas

1993; Worthington and Thomas 1996). For example, the first observations of the complete structure

of a gravity wave in a duct involving a critical layer were provided by the profiler’s vertical velocity

measurement capability, and by operation in both clear and precipitating conditions (Ralph et al.

1993b). Profilers helped identify mountains, fronts, upper-level jets, and convection as sources of

gravity waves, and quantified their relative importance (Nastrom et al. 1987, 1990; Ralph et al.

1993a; Jin et al. 1996). The production of turbulence in the clear air due to critical levels associated

with mountain waves has now been clearly demonstrated (Prichard et al. 1995). Changes in vertical

motions observed downstream of mountains have illustrated and quantified that mountain waves can
27

be highly nonstationary, even though theory has been concerned primarily with stationary conditions

(Ralph et al. 1992, 1997; Caccia et al. 1997). This has brought into question key earlier findings

concerning the impact of mountains on the global atmospheric momentum balance.

4.3. Convective Storms. Unlike conventional Doppler weather radars, wind profilers (especially

VHF profilers) have provided the best measurements of vertical air motion within convective storms

because the air motion and hydrometeor fall velocities can be distinguished from one another. This

and the horizontal wind information are used to explore the relationship between air motions within

the storm and the microphysics (Wakasugi et al. 1986, 1987; Augustine and Zipser 1987; Gossard

1988; Ralph et al. 1993b; Cifelli and Rutledge 1994; Rajopadhyaya et al. 1994; Williams et al. 1995;

Cifelli et al. 1996; May and Rajopadhyaya 1996). Profilers have also been used to document the

mesoscale environment associated with severe convection, including vorticity and divergence

calculated from a triangle of profilers (Wilczak et al. 1992), and the generation of the Denver

Cyclone, which is a key feature responsible for the organization of severe convection near Denver,

Colorado (Wilczak and Glendening 1988; Wilczak and Christian 1990). Hourly wind profiler data

have also been used to study helicity as a tornado forecast parameter (Davies-Jones et al. 1990;

Morris 1993). 

4.4. Coastal Weather. Despite the high population density along the west coast of the United

States, the region suffers from inadequate observations. Wind profilers along the coast have provided

crucial documentation of land-falling winter storms (Neiman et al. 1995) and of strong wind shifts

that propagate northward along the coast during summer (Ralph et al. 1998). These data also make

it possible to increase our understanding of the typical diurnal cycle by extending earlier studies that

could not continuously measure conditions above the surface, using techniques based on an earlier

boundary layer climatology over Colorado (May and Wilczak 1993). Several current sites on islands

offshore add greatly to the potential of this dataset. For example, profiler and RASS data helped

establish that the coastal surges during summer are best characterized by a three-layer system rather

than a two-layer system, because of the surprising observation that the marine boundary layer depth

did not change significantly during the initial passage of the disturbance (Ralph et al. 1998).

4.5. Air Quality Monitoring. Although Doppler sodars (acoustic sounders) have filled an essential

role in air quality observations, their limited vertical range has been a disappointment for applications

where information above the boundary layer is required. The boundary layer profiler developed by

Ecklund et al. (1988) has overcome these limitations. This instrument has been deployed extensively

in mesoscale air quality research programs (Neff 1994). Its low cost, and portability, have made it

the instrument of choice for low-altitude atmospheric research and monitoring of mean wind,

temperature, and mixing depth.

A limitation on the deployment of wind profilers in urban areas is their susceptibility to clutter

contamination. Recent advances in clutter-screen design, diffraction reduction, and signal processing

such as wavelet transforms applied to raw radar time series data (Jordan et al. 1997) portend

significant advances in the capability of wind profilers to operate over a much broader range of

environmental conditions.
28

The combination of RASS and wind profilers has proved valuable in the challenging

measurement of temperature and mixed-layer profiles in urban areas. RASS rarely provides sufficient

height coverage in deep, mixed layers (between 500 and 1500 m for 915-MHz-based systems), but

when it is combined with the radar reflectivity profile, good mixing-depth comparisons using airborne

aerosol lidar measurements are obtained (White et al. 1998). In these applications, it is necessary to

collocate the profiler with a laser ceilometer so as to identify cloudy conditions. A payoff in the

collocation of such instruments is the ability of the profiler to see into the cloud systems and

determine the potential for cloud-venting of pollutants. 

Measurements in the nocturnal boundary layer are important to characterize the deposition

of pollutants to the surface as well as to determine the degree of isolation of the surface layer from

the residual layer. Where the minimum range and coarse resolution (typically 150-m minimum range

and 60-m resolution) of radar wind profilers is inadequate, monostatic sodars can be used to help

interpret boundary layer processes (e.g., Beyrich and Gorsdorf 1995).

4.6. Global Climate Research. Profilers became widely used for climate observations, especially

during the Tropical Ocean Global Atmosphere (TOGA) decade (1985–1994). The TOGA Program

began in 1985 with an objective of improving observations of the coupled ocean-atmosphere system,

particularly those that relate to the El Niño/Southern Oscillation (ENSO) phenomenon (McPhaden

et al. 1997). Profilers have been used in numerous field campaigns in the last decade, most notably

as part of the ISSs specially designed for the TOGA Coupled Ocean-Atmosphere Response

Experiment (COARE) (Parsons et al. 1994). 

Profilers were recognized at the beginning of TOGA by NOAA's Office of Global Programs

as a cost-effective means to obtain wind information over the data-sparse equatorial Pacific Ocean.

Reliable wind measurements in real time from remote islands in the tropical Pacific were

demonstrated first at Christmas Island, Kiribati, using a 50-MHz wind profiler (Gage et al. 1988,

1994a) and later using a 915-MHz wind profiler (Carter et al. 1995). This profiler has provided

tropospheric wind observations nearly continuously for more than a decade. Because it is incapable

of measuring wind below 1.5 km, NOAA's Aeronomy Laboratory developed a UHF lower

tropospheric wind profiler (Ecklund et al. 1988; Carter et al. 1995) to fill the low-level gap.

An important part of the TOGA observing system is the Trans-Pacific Profiler Network

(TPPN) completed just prior to the end of TOGA. The TPPN extends from Indonesia on the west

to Peru on the west coast of South America, as shown in Fig. 4-2. The continued operation of the

TPPN relies upon the collaboration and cooperation of interested parties in the countries of Peru,

Ecuador, Kiribati, Nauru, Papua New Guinea, and Indonesia. The principal collaborators are

currently the University of Colorado/Cooperative Institute for Research in Environmental Sciences

(CIRES) and the NOAA Aeronomy Laboratory.

The Christmas Island profiler has been in operation long enough to provide a record of the

variability of winds over the central Pacific on the interannual time scale pertinent to ENSO. Gage

et al. (1996b) analyzed the zonal winds observed at Christmas Island and noted a robust annual cycle
29

modulated by the ENSO cycle. During the northern winter months, upper tropospheric westerlies

are dominant during non-El Niño years. These upper tropospheric westerlies are thought to be

related to the Walker circulation and are strongest during La Niña when convection is most active

over the warm-pool region of the western Pacific. During El Niño when the convection moves

eastward into the central Pacific, the upper tropospheric winds are replaced for the most part by

easterlies. The occurrence of upper tropospheric westerlies is of considerable importance in tropical

dynamics because they provide a duct for midtropospheric disturbances to propagate into the

equatorial zone (Webster and Holton 1982).

Profilers also provide direct measurements of vertical motions in the tropics (Balsley et al.

1988; Gage et al. 1991; Huaman and Balsley 1996). The direct, long-term, measurements of vertical

motions are unique. They help explain the heat balance in the tropical atmosphere because the

vertical motions give the component of adiabatic heating and cooling that largely balances diabatic

heating and cooling (Gage et al. 1991). Although Nastrom and VanZandt (1994) have shown that

at midlatitudes directly measured vertical motions are often biased by internal gravity waves, the level

of gravity waves is small enough in the tropical atmosphere to permit unbiased measurement of

vertical motions on the order of 0.01 m s (Huaman and Balsley 1996). -1

The hydrological cycle is a very important component of the earth-ocean-atmosphere climate

system that is only poorly observed and simulated in numerical models (Webster 1994). Profilers

provide the kind of observations needed to improve our ability to quantify the hydrological cycle.

For example, the moisture flux must be well measured to close the water budget. Good moisture flux

measurements require both humidity and wind measurements in the lower troposphere. UHF profilers

are proven tools for improving moisture flux measurements. Their contribution to water budget

calculations during the TOGA COARE Intensive Observing Period (IOP) is reported in Ciesielski

et al. (1997). 

Another crucial element of the hydrological cycle is precipitation. Atmospheric models do

not resolve the hydrological cycle very well, partly because they must parameterize convection as a

subgrid-scale process. Eventually, precipitation must be estimated on a global basis by satellites, but

the satellite observations themselves must be calibrated and validated. UHF profilers are excellent

tools for observing the vertical structure and evolution of precipitating cloud systems (Gage et al.

1994a,b, 1996a; Ecklund and Gage 1995) and can be used to provide vertically resolved estimates

of the mass flux of precipitation. Observations in the tropics have been used for classification of

precipitation into convective and stratiform components (Williams et al. 1995). Because profilers

observe continuously, they provide the needed data to derive the diurnal cycle of precipitation. 

Island- and ship-based profilers have recently been used in the Combined Sensor Program

(CSP) sponsored by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM)

Program to clarify the influence of islands on clouds and precipitation (Post et al. 1997). Profilers

continue to play a vital role in monitoring atmospheric winds in data-sparse regions of the tropical

Pacific and are an important part of the DOE/ARM tropical western Pacific Atmospheric Radiation

and Cloud Station (ARCS) currently installed at Manus Island, Papua New Guinea. A continental
C2

x

C2

n

C2

u

C2

u

C2

n

30

component of the DOE program is located at the center of the NPN in Oklahoma. This southern

Great Plains ARM Cloud and Radiation Testbed (CART) site employs three 915-MHz RASSequipped profilers and one 50-MHz profiler (Stokes and Schwartz 1994). 

4.7. Turbulence Measurements. While the mean winds measured by wind profilers are quite useful,

they are only part of the information required for many studies of the atmosphere. For example, air

pollution or dispersion models may require velocity variances, momentum and scalar fluxes, and

convective mixed-layer depth. The structure function parameters, , where x could be velocity (u,

v, or w), temperature (T), humidity (Q), or refractive index (n), are also useful statistics for describing

the turbulent structure of the atmosphere. The appropriate structure function parameters are related

to the turbulent kinetic energy (TKE) and scalar dissipation rates (Corrsin 1951). All these

turbulence variables have been measured, with varying degrees of success, using wind profilers. A

small portion of that research is highlighted here along with some of the problems facing the

researcher. A more complete review of the subject is given by Gage (1990). Figure 4-3 shows

examples of turbulence data taken in the CBL with the NOAA 915-MHz wind profiler.

The measurement of from radar backscatter is described formally in many standard texts

(e.g., Battan 1973; Gossard and Strauch 1983; Doviak and Zrníc 1984). To obtain quantitatively

accurate results, a wind profiler must be calibrated. The calibration depends on assumptions about

the transmitted beam and on properties of the radar hardware, including antenna efficiency and

receiver bandwidth. Methods to calculate from wind profiler measurements are described by

Hocking (1985), Gossard et al. (1990), and Cohn (1995). Part of the problem with validating these

techniques has been the lack of direct observations available for comparison. White (1996) found 

measurements in the CBL obtained from a sonic anemometer mounted on a 300-m tower and from

a 915-MHz wind profiler to be only moderately correlated (r = 0.7).

The depth of the CBL is a critical parameter for dispersion modeling, but it is difficult to

parameterize accurately. A comparison of different in situ and remote sensing techniques for

detecting the depth of the CBL was given by Kaimal et al. (1982). The remote sensors used in that

study consisted of two radars (X-band and FM-CW), a sodar, and a lidar. White (1993) and

Angevine et al. (1994) demonstrated a similar capability for the 915-MHz wind profilers. The

technique is based on experimental and theoretical evidence indicating that the profile of exhibits

a peak at the inversion capping the mixed layer.

Entrainment is an important but physically intractable process related to boundary layer

growth. The entrainment velocity is defined as the difference between the total time rate of change

of the boundary layer depth and the average vertical velocity at the top of the boundary layer.

Angevine et al. (1998) found reasonable rates of entrainment using a triangle of wind profilers. The

vertical velocity at the top of the boundary layer was found by vertically integrating the horizontal

wind divergence calculated from the hourly wind profiles measured at the corners of the triangle.

Radar backscatter data were used to determine the evolution of boundary layer height. White et al.

(1991) explored a different technique in which they used the structure function parameters measured

by a 404-MHz wind profiler in conjunction with the interfacial-layer model of Wyngaard and LeMone
31

(1980) to estimate the entrainment velocity. White et al. (1991) relied on rawinsondes to provide the

jumps in temperature and humidity in the interfacial layer and the lapse rate of temperature above the

inversion required by the model. However, a wind profiler equipped with RASS could provide the

necessary temperature measurements, and as discussed in section 4.8, the same wind profiler/RASS

combination can be used to estimate humidity gradients aloft.

Angevine et al. (1993a,b) examined the possibility of using a wind profiler with RASS to

measure profiles of virtual temperature flux and momentum flux. The temperature flux was

calculated by the method of Peters et al. (1985), which does not include correlating temperature with

vertical velocity at zero lag, because errors in the vertical velocity are directly correlated with errors

in the virtual temperatures retrieved from RASS. The momentum flux was computed by the variance

differencing technique of Vincent and Reid (1983). The wind profiler virtual temperature flux

estimates agreed more favorably with aircraft measurements than did the wind profiler momentum

flux estimates. For the latter, Angevine (1993b) concluded that additional research was needed to

establish the feasibility of the wind profiler technique.

It is indeed possible to take the time series of velocity components measured with a wind

profiler and calculate variances, covariances, and even higher-order moments. However, the finite

sampling volume imposes a low-pass filter on the spatial structure of turbulence measured. The

velocity fluctuations are also averaged over a finite averaging time, which imposes additional lowpass filtering on the fluctuations. White (1996) demonstrated the effect of these sampling filters by

comparing the vertical velocity fluctuations measured by a 915-MHz wind profiler and a sonic

anemometer. The high-frequency structure is recoverable, in theory, because lines in the Doppler

velocity spectrum are broadened by the unresolved frequencies. Unfortunately, additional factors

other than turbulence contribute to spectral broadening, which make it difficult to diagnose the

turbulence contribution. Corrections to account for these factors are given by Gossard (1990).

Some of the same problems that affect the performance of wind profilers for mean wind

profiling also pertain to turbulence applications, because both make use of the moments generated

from the Doppler velocity spectrum. The foremost problem is the interfering signals received from

nonatmospheric targets. The integrity of the spectral moments depends on the ability of the radar

signal processing algorithm to recognize and remove the contaminating signals. Other problems are

related to signal processing strategies, which traditionally have been designed to maximize the

performance for mean wind profiling rather than for turbulence applications. For example, spectral

averaging is often applied before the Doppler velocities are calculated. This process increases the

detectability of the signal and ultimately improves the height coverage of the mean wind profiles

produced by a consensus algorithm. However, this process also increases the dwell time, which in

turn increases the low-pass filtering of the turbulence. It is also common to clip the spectral energy

from the zero-velocity spectral bin in the Doppler velocity spectrum automatically to reduce the

impact of ground clutter. As demonstrated by White (1996), this procedure introduces a bias in the

mean vertical velocity calculated from wind profiler time series in the CBL. The problem is

exacerbated by using too few spectral points to resolve the Doppler velocity spectrum, further

evidence that new data processing techniques are needed to enhance the utility of profilers.
32

The results from previous studies are encouraging, suggesting that we should continue to

evaluate and validate techniques to measure turbulence variables with wind profilers. We must also

consider the limitations of wind profilers for turbulence applications, particularly the spatial and

temporal averaging inherent in the sampling. If these techniques prove to be viable, the turbulence

profiles measured by wind profilers could be used, for example, to evaluate the performance of the

boundary layer parameterizations contained in mesoscale numerical models (e.g., Burk and Thompson

1989) and the turbulence structure inferred from large-eddy simulation (e.g., Peltier and Wyngaard

1995). One reason for optimism is the current commitment by research institutions and commercial

vendors to improve wind profiler signal processing.

4.8. Moisture Profiling. Backscattered power from vertically pointing Doppler radars has long been

recognized as providing an excellent representation of profiles of the gradient of atmospheric

refractive index (e.g., Friend 1949; Saxton et al. 1964; Ottersten 1969; Richter 1969; Gossard et al.

1970; Chadwick and Gossard 1983; Gossard 1990). Recent experiments in southern California have

demonstrated very high correlation between the radar-measured refractive index structure parameter

and the square of the radiosonde-measured potential refractive index gradient (Gossard et al. 1997).

In the lower troposphere, refractive index fluctuations are due almost entirely to fluctuations in

specific humidity. Thus, profiles of specific humidity are very highly correlated to profiles of potential

refractive index in the lower atmosphere. The challenge is to generalize the empirical correlation with

an improved description of the physical relationships between radar-measured quantities and other

meteorological parameters. The formal calculation of the humidity gradient from radar-measured

parameters leads to a prediction of the square of the humidity gradient, and thus loses the sign of the

gradient. Therefore, the sign of the gradient must be determined by other constraints such as virtual

temperature profiles derived from RASS, total precipitable water derived from global positioning

system (GPS) measurements, and numerical models. 
Vr

Vr

Vr

 Figure 4-1. Time-height cross section of hourly averaged horizontal winds observed by the

 Hillsboro, Kansas, profiler on 14 May 1992. For clarity, only every other range gate is plotted.

 Regions of snow (0.7 m s < < 3.0 m s , light shading) and rain ( > 3.0 m s , dark -1 -1 -1

 shading), as determined from the spectral moment data, are highlighted. is fall velocity

 (vertical) of the hydrometeors. The arrow represents a convective updraft within the storm.

 [From Ralph et al. (1995).]
Biak, Irian Jaya,

Indonesia Tarawa, Kiribati

Christmas Island,

Kiribati

Republic of Nauru

Manus Island, Papua New Guinea

Galapagos, Equador

Piura,

Peru

 Figure 4-2. The Trans-Pacific Profiler Network (TPPN) completed just prior to the end of TOGA. The 

 TPPN extends from Indonesia on the west to Peru on the west coast of South America.
2

n

2

w

35

Figure 4-3. Time-height profiles of vertical velocity (top), (middle), and (bottom)

depicting a rapidly evolving CBL. These measurements were taken with the NOAA 915-MHz

wind profiler at Erie, Colorado, on 16 July 1993 from 1100 to 1400 MST. For this case, the wind

profiler was programmed to obtain vertical profiles only with 60-m vertical resolution and 15-s

temporal resolution.
37

CHAPTER 5

SUMMARY

Wind profilers have proved to be a valuable observing system for numerous applications.

These include research and operational weather and climate analyses and forecasts, numerical

modeling, pollution monitoring, measuring aviation winds, and test range support. Whether as stand

alone instruments or as networks, profilers economically and automatically provide continuous highresolution measurements that are not possible with other techniques. Because of these unique

capabilities, it is likely that the profiler's role in both research and operations will expand and become

even more important for mesoscale and smaller scale applications.

Whereas profilers have proved their worth in many areas, the pace of future expanded use is

somewhat dependent on the speed with which remaining limitations can be overcome. Profiler data

can be contaminated by returns from things other than the atmosphere. Progress has been made in

signal processing that can reduce the negative impact of this contamination, but much remains to be

done before all profilers are optimally equipped.

Profilers are not likely to achieve their full potential until we gain a better understanding of

how they fit with the overall observing system. The NPN has demonstrated that they have an

operational impact, but the issue of how profiler data merge with data from radiosondes, aircraft, and

satellites is only now being addressed by programs such as the North American Atmospheric

Observing System (NAOS). The research community is also going through this learning process and

has already produced such innovative techniques as RASS and instrument combinations that measure

moisture profiles. It is encouraging to see that there are few field experiments that do not now

include a complement of wind profilers along with a host of other sensors, and that the NPN is to be

used to help calibrate/validate measurements made by the spaceborne Doppler wind lidar (WDL)

planned for a Space Shuttle mission in 2000. This leadership from the research community should

pave the way for increasingly effective ways to combine datasets for operational weather services,

and to produce more complete understanding of the complex physical processes that contribute to

the creation of mesoscale weather phenomena.
APPENDIX A

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B-1

APPENDIX B

ABBREVIATIONS

ADC Analog-to-Digital Converter

AGC Automatic Gain Control

APRF Atmospheric Profiler Research Facility

ARCS Atmospheric Radiation and Cloud Station

ARM Atmospheric Radiation Measurement

CART Cloud and Radiation Testbed

CBL Convective Boundary Layer

CIRES Cooperative Institute for Research in Environmental Sciences

COARE Coupled Ocean-Atmosphere Response Experiment

CW Continuous Wave

DFT Digital Fourier Transform

ENSO El Nino/Southern Oscillation

ER Eastern Range

ETL Environmental Technology Laboratory

GOES Geostationary Operational Environmental Satellite

GPS Global Positioning System

IOP Intensive Observing Period

KSC Kennedy Space Center

LAFRE Los Angeles Free Radical Experiment

MMS-P Meteorological Measuring Set-Profiler

MPS Mobile Profiling System

NAOS North American Atmospheric Observing System

NPN NOAA Profiler Network

PCC Profiler Control Center

RASS Radio Acoustic Sounding System

RFI Radio Frequency Interference

SA Spaced Antenna
B-2

SCOS97 Southern California Ozone Study

SLF Shuttle Landing Facility

SMG Spaceflight Meteorology Group

SNG Signal-to-Noise Ratio

ST Stratospheric-Tropospheric

TOGA Tropical Ocean Global Atmosphere

TKE Turbulent Kinetic Energy

TPPN Trans-Pacific Profiler Network

VAD Velocity-Azimuth Display

WPL Wave Propagation Laboratory

WPDN Wind Profiler Demonstration Network

WR Western Range

WSMR White Sands Missile Range