ARM TR-038

> Raman Lidar (RL) Handbook
>
> November 2004
>
> D. D. Turner
>
> Work supported by the U.S. Department of Energy,
>
> Office of Science, Office of Biological and Environmental Research

# Contents {#contents .Heading-Front}

[1. General Overview 1](#general-overview)

[2. Contacts 1](#contacts)

[3. Deployment Locations and History
1](#deployment-locations-and-history)

[4. Near-Real-Time Data Plots 1](#near-real-time-data-plots)

[5. Data Description and Examples 2](#data-description-and-examples)

[6. Data Quality 5](#data-quality)

[7. Instrument Details 5](#instrument-details)

**Figures**

[1. 2](#__RefHeading___Toc88376691)

[2. 6](#__RefHeading___Toc88376692)

[3. 7](#__RefHeading___Toc88376693)

[4. 8](#__RefHeading___Toc88376694)

[5. 8](#__RefHeading___Toc88376695)

# General Overview

The Raman Lidar (RL) is an active, ground-based laser remote sensing
instrument that measures vertical profiles of water-vapor mixing ratio
and several cloud- and aerosol-related quantities. Lidar (light
detection and ranging) is the optical analog of radar, using pulses of
laser radiation to probe the atmosphere. This system is fully computer
automated, and will run unattended for many days following a brief
(\~5-minute) startup period. The self-contained system (requiring only
external electrical power) is housed in a climate-controlled
8\'x8\'x20\' standard shipping container.

# Contacts

## Mentor

Tim Tooman\
Sandia National Laboratories\
P.O. Box 969, MS 9056\
Livermore, CA 94551-0969\
Phone: 510-294-2752

Fax: 510-294-1559\
<tooman@sandia.gov>

John Goldsmith (engineering)\
Sandia National Laboratories\
Phone: 925-294-2432\
<jgold@sandia.gov>

## Instrument Developer

John Goldsmith\
Sandia National Laboratories\
P.O. Box 969, MS 9409\
Livermore, CA 94551-0969\
Phone: 510-294-2432

Fax: 510-294-3870\
<jgold@sandia.gov>

# Deployment Locations and History 

The Raman lidar is currently operational at the Southern Great Plains
CART site. The system generally is not operated during periods of low
cloud base or precipitation. Pending installation of a hail shield, it
is only operated when personnel are present at the site (continuously
during intensive observational periods, normal week-day working hours
otherwise).

# Near-Real-Time Data Plots

[Near realtime
quicklooks](http://www.nsdl.arm.gov/Visualization/rl/frame.htm).

# Data Description and Examples

![](media/image1.jpeg){width="2.0555555555555554in"
height="2.0555555555555554in"}![](media/image2.jpeg){width="2.126388888888889in"
height="2.126388888888889in"}![](media/image3.jpeg){width="2.129166666666667in"
height="2.129166666666667in"}

[]{#__RefHeading___Toc88376691 .anchor}Figure 1.

Additional examples of measurements taken during the September 1996
Water Vapor Intensive Observational Period are presented in Figure 1.
These examples are PRELIMINARY, pending final analysis using calibration
constants determined from the final analysis of all IOP data.

**Nighttime Water Vapor Image**

The performance of Raman lidar systems is best during nighttime, in the
absence of the daytime solar background. The nighttime false-color image
[(PDF
document](http://www.arm.gov/instruments/images/rl/h20_nt_im_960928.pdf)
or [gif
image)](http://www.arm.gov/instruments/images/rl/h20_nt_im_960928.gif)
illustrates the temporal and spatial resolution provided by Raman lidar.
The time axis is in \"universal time\" (GMT); the local time for the
measurement started at 7:00 pm on September 27, through approximately
8:30 am on September 28. The altitude axis is km AGL (above ground
level). This image consists of \"pixels\" 39 m \"high\" by 2 minutes
\"wide,\" with color representing the water-vapor mixing ratio. The
vertical stripes at \~1:00 UT are caused by clouds at \~2 km AGL (the
laser beam cannot penetrate optically thick clouds; hence the ratio of
water vapor to nitrogen signal is \~0/0, producing \"noise\"). The
speckled appearance after \~12:30 UT is due to the increasing solar
background as the sun rises.

**Nighttime Raman Lidar - Radiosonde Comparison**

One method for examining the performance of Raman lidar systems is to
compare water-vapor profiles with measurements from collocated
radiosondes; many such measurements were made during the water-vapor
IOP. One nighttime example is shown in this plot [(PDF
document](http://www.arm.gov/instruments/images/rl/nt_l_s_960928.pdf) or
[gif image)](http://www.arm.gov/instruments/images/rl/nt_l_s_960928.gif)
(11:28 UT on September 28, or 6:28 am local time). This plot shows a
5-minute average of the Raman lidar data, with vertical resolution
varying from 39 m at lower altitudes to 312 m at higher altitudes (the
variation in vertical resolution with altitude can be determined by the
vertical spacing of the error bars). This plot is displayed with a
logarithmic x-axis to illustrate the nighttime capabilities of the Raman
lidar for measuring upper-tropospheric water vapor; longer temporal
averages and decreased vertical resolution can reduce the statistical
errors still further. Discrepancies between the two profiles may be due
to measurement errors, but are more likely due to the fact that the
radiosonde blows downwind as it rises, and thus the vertical-pointing
lidar and the radiosonde do not measure the same air parcels.

**Daytime Water Vapor Image**

The CART Raman lidar has state-of-the-art daytime capabilities also. A
sample daytime false-color image [(PDF
document](http://www.arm.gov/instruments/images/rl/h20_day_im_960921.pdf)
or [gif
image)](http://www.arm.gov/instruments/images/rl/h20_day_im_960921.gif)
illustrates this capability. In this image, the \"pixels\" are 10
minutes \"wide,\" with \"height\" varying from 78 m near the ground to
312 m at higher altitudes. This image does not have the resolution or
altitude range of the nighttime image simply due to the difficulty of
extracting the relatively weak Raman lidar signal from the large solar
background.

**Daytime Raman Lidar - Radiosonde Comparison**

A daytime comparison with a radiosonde is shown in this plot [(PDF
document](http://www.arm.gov/instruments/images/rl/day_l_s_960921.pdf)
or [gif
image)](http://www.arm.gov/instruments/images/rl/day_l_s_960921.gif)
(17:30 UT on September 21, 12:30 pm local time). The Raman lidar
measurement represents a 10-minute average, with vertical resolution
varying from 78 m near the ground to 312 m at higher altitudes. Again,
the daytime performance cannot match the nighttime performance, but this
figure illustrates the capabilities of the system for measuring the
majority of tropospheric moisture even during mid-day.

**24-Hour Water Vapor Image**

The Raman lidar automatically switches between \"daytime\" and
\"nighttime\" operation simply by adding or removing neutral density
filters in the water-vapor detection channels. The system thus provides
continuous, uninterrupted multiple-day coverage; a Raman lidar
capability that we believe is unique to this instrument. A false-color
image [(PDF
document](http://www.arm.gov/instruments/images/rl/h2o_24_hr_960910.pdf)
or [gif
image)](http://www.arm.gov/instruments/images/rl/h2o_24_hr_960910.gif)
from measurements recorded on September 10 illustrates this 24-hour
capability.

**Weather System (Water Vapor, Aerosol, and Depolarization Images)**

An interesting weather system passed over the CART site on September 20,
with a variety of cloud types and associated virga. Water-vapor [PDF
document](http://www.arm.gov/instruments/images/rl/h20_day_im_960920.pdf)
or [gif
image](http://www.arm.gov/instruments/images/rl/h20_day_im_960920.gif)),
aerosol scattering ratio ([PDF
document](http://www.arm.gov/instruments/images/rl/aer_day_im_960920.pdf)
or [gif
image](http://www.arm.gov/instruments/images/rl/aer_day_im_960920.gif)),
and depolarization ( [PDF
document](http://www.arm.gov/instruments/images/rl/dep_day_im_960920.pdf)
or [gif
image](http://www.arm.gov/instruments/images/rl/dep_day_im_960920.gif))
images are shown for the period 17:00-24:00 UT (12:00 pm - 7:00 pm local
time). In the water-vapor image, the white regions above \~3.5 km are
\"noise\" caused by the laser beam being block by clouds. Comparing the
aerosol scattering and depolarization images, it is interesting to note
the very low (\~0%) depolarization values below \~3.5 km beneath several
of the clouds (the background depolarization value of \~0.7% is caused
by slight depolarization from Rayleight and rotational Raman
scattering). This decrease in depolarization is caused by melting of the
cloud ice particles as they descend; the resulting liquid droplets are
spherical, producing significant scattering (evident in the aerosol
scattering image) with essentially no depolarization.

**Narrow and Wide Field-of-View Channel Comparison**

The narrow and wide field-of-view channels provide two nearly
independent detection systems for exploring potential systematic effects
in the lidar system (the common telescope being the only common
element). Water-vapor \"images\" produced with the two can be used to
visually compare measurements made with the two channels. The figure
[(PDF
document](http://www.arm.gov/instruments/images/rl/h20_hi_lo_961005.pdf)
or [gif
image)](http://www.arm.gov/instruments/images/rl/h20_hi_lo_961005.gif)
displays such a comparison for the nighttime period \~1:30-13:30 UT on
October 5 (\~8:30 pm October 4 to \~8:30 am October 5, local time), just
after the end of the water-vapor IOP. The similarity of the two images
is striking. Close examination illustrates the better performance of the
\"low channel\" at lower altitudes and the \"high channel\" at higher
altitudes, the reason for having the two channels in the first place.

## Data File Contents

### Primary Variables and Expected Uncertainty

The Raman Lidar has independent measurement channels that record
range-resolved backscatter signals from molecular water vapor, molecular
nitrogen, and combined Rayleigh and aerosol contributions (the latter at
polarizations parallel and perpendicular to the polarization of the
laser beam). Primary quantities obtained from these backscatter signals
are range-resolved vertical profiles of water-vapor mixing ratio (g/kg),
aerosol scattering ratio (unitless), and backscatter depolarization
ratio (percent). Additional cloud- and aerosol-related measurements can
also be derived from the backscatter signals.

#### Definition of Uncertainty

All \"raw\" signals are simply photon counts for the specified averaging
interval (in time and range). Poisson statistics can therefore be used
to calculate statistical uncertainties (error bars) for derived
quantities. Potential systematic errors appear to be smaller than
statistical errors, but are being explored carefully.

### Secondary/Underlying Variables

This section is not applicable to this instrument.

### Diagnostic Variables

This section is not applicable to this instrument.

### Data Quality Flags

[RL Data Object Design
Changes](http://science.arm.gov/tool/dod/showdod.php?Inst=rl) for ARM
netCDF file header descriptions.

### Dimension Variables

This section is not applicable to this instrument.

## Annotated Examples

This section is not applicable to this instrument.

## User Notes and Known Problems

This section is not applicable to this instrument.

## Frequently Asked Questions

This section is not applicable to this instrument.

# Data Quality

## Data Quality Health and Status

The following links go to current data quality health and status
results.

-   [DQ HandS](http://dq.arm.gov/) (Data Quality Health and Status)

-   [NCVweb](http://dq.arm.gov/ncvweb/ncvweb.cgi) for interactive data
    plotting using.

The tables and graphs shown contain the techniques used by ARM\'s data
quality analysts, instrument mentors, and site scientists to monitor and
diagnose data quality.

## Data Reviews by Instrument Mentor

Some QC products for the Raman Lidar have been developed by Dave Turner.
Among other things, they compare the output to other relevant
instruments, such as sondes. These products appear to be sufficient for
the moment, at least until we have more experience with the instrument.

## Data Assessments by Site Scientist/Data Quality Office

All DQ Office and most Site Scientist techniques for checking have been
incorporated within [DQ HandS](http://dq.arm.gov/) and can be viewed
there.

## Value-Added Procedures and Quality Measurement Experiments

Many of the scientific needs of the ARM Program are met through the
analysis and processing of existing data products into \"value-added\"
products or VAPs. Despite extensive instrumentation deployed at the ARM
CART sites, there will always be quantities of interest that are either
impractical or impossible to measure directly or routinely. Physical
models using ARM instrument data as inputs are implemented as VAPs and
can help fill some of the unmet measurement needs of the program.
Conversely, ARM produces some VAPs not in order to fill unmet
measurement needs, but instead to improve the quality of existing
measurements. In addition, when more than one measurement is available,
ARM also produces \"best estimate\" VAPs. A special class of VAP called
a Quality Measurement Experiment (QME) does not output geophysical
parameters of scientific interest. Rather, a QME adds value to the input
datastreams by providing for continuous assessment of the quality of the
input data based on internal consistency checks, comparisons between
independent similar measurements, or comparisons between measurement
with modeled results, and so forth. For more information, see the [VAPs
and QMEs](http://www.arm.gov/data/vaps.stm) web page.

# Instrument Details

## Detailed Description

### List of Components

![](media/image4.png){width="4.364583333333333in" height="2.59375in"}

[]{#__RefHeading___Toc88376692 .anchor}Figure 2.

The CART Raman Lidar is a custom instrument developed for the ARM
program by Sandia National Laboratories. This diagram in Figure 2 shows
the layout of the system inside the shipping container; a diagram of the
receiver optics appears in the next section of this page. Major
commercially supplied subcomponents include:

-   System enclosure (including optical mounting system, window/hatch
    assembly, climate control, and utilities): produced by Orca Photonic
    Systems, Inc.

-   Laser: Continuum Model 9030 frequency-tripled Nd:YAG laser (30 Hz,
    400 mJ pulses at 355 nm)

-   Laser beam expander: 15x unit produced by CVI, Inc.

-   Receiving telescope: 61 cm diameter f/9.3 Dall-Kirkham system
    produced by Optical Guidance Systems

-   Dichroic beam splitters and narrow band interference filters:
    produced by Barr Associates

-   Computer-controlled filter wheels: produced by CVI, Inc.

-   Dichroic beam splitters and narrow band interference filters:
    produced by Barr Associates

-   Photo multipliers: Thorn EMI Electron Tubes Model 9954B

-   Amplifiers: Phillips Scientific Model 6950

-   Discriminators: Phillips Scientific Model 6904

-   Multichannel scalers: Santa Fe Energy Research Model MCS-100

-   Data acquisition environment: National Instruments LabVIEW 3.1.

### System Configuration and Measurement Methods

![](media/image5.png){width="5.03125in" height="3.8333333333333335in"}

[]{#__RefHeading___Toc88376693 .anchor}Figure 3.

The system\'s \"receiver optics\" that follow the telescope in the
optical path are shown in Figure 3 (detailed characteristics appear in
the table in the \"Description of Observational Specifications\" section
below). Light collected by the telescope is first split into two pieces
by a wedged beam splitter, with 10% directed into the \"short-range\"
wide-field-of-view channels, and 90% into the \"long-range\"
narrow-field-of-view channels. For each set of channels, the light first
encounters an aperture (field stop), and is then collimated by field and
collimating lenses. Dichroic beam splitters separate the light by
wavelength into signals produced by backscatter from water vapor (408
nm), nitrogen (387 nm), and aerosol/Rayleigh scattering (355 nm, the
laser wavelength), followed by narrow band interference filters, imaging
lenses, and photo multipliers for each wavelength. In the \"long-range\"
optics, a polarizing beam splitter is used to separately analyze the
parallel and perpendicular polarization (relative to the laser beam) of
the aerosol/Rayleigh scattering.

For system calibration purposes, computer-controlled filter wheels are
used to replace the water-vapor and aerosol/Rayleigh filters by nitrogen
filters. Signals recorded in this configuration can by used to determine
any differences in the range dependence of the signals in the various
channels, and to track the long-term relative sensitivity of the
channels. During the daytime, the filter wheels add neutral-density
filters to the water-vapor channels to prevent these high-sensitivity
channels from being \"swamped\" by the large solar background.
Neutral-density filters can also be added to the optical path for use in
determining resolving time corrections (pulse-pileup effects) for the
electronics in the photon-counting multichannel scaler electronics.

### Specifications

![](media/image6.png){width="4.229166666666667in"
height="2.1354166666666665in"}

[]{#__RefHeading___Toc88376694 .anchor}Figure 4.

The table in Figure 4 lists the system specifications. The finest
vertical resolution is 39 m, determined by the minimum bin time of the
photon-counting electronics. The nominal temporal resolution is one
minute, although measurement periods as short as 10-30 seconds are
possible. Because the system literally counts photons in equal-sized
spatial and temporal bins, information is always recorded with 39-m
vertical and (nominal) one-minute temporal resolution. However, during
post-acquisition signal processing, additional averaging can be
performed to trade off spatial and/or temporal resolution for improved
sensitivity. In particular, this makes it possible to take advantage of
the slower (in time and in space) variation of water vapor at higher
altitudes to obtain higher sensitivity by post-acquisition averaging,
while still having the high-resolution measurements at lower altitude.

## Theory of Operation

![](media/image7.png){width="2.4611111111111112in"
height="2.9479166666666665in"}

[]{#__RefHeading___Toc88376695 .anchor}Figure 5.

Raman lidar systems detect selected species by monitoring the
wavelength-shifted molecular return produced by vibrational Raman
scattering from the chosen molecule or molecules, as illustrated in
Figure 5. Narrow band, narrow field-of-view operation provides good
daytime performance (discrimination of the weak Raman backscatter signal
above the background daylight) without sacrificing nighttime
performance. The system has been implemented as a dual field-of-view
instrument because narrow field-of-view operation provides very weak
short-range signals. A set of \"wide\" field-of-view channels provides
better results for short-range signals (out to \~500 m for water vapor).

For each channel \"i,\" the signal as a function of range z is inversely
proportional to the range squared, and proportional to the product of
constant k, overlap function O(z), Raman cross-section sigma(180 degree
backscatter), number density n(z), attenuation of the laser beam
traveling to the region of interest q(laser wavelength, z), and
attenuation of the backscattered radiation q(backscatter wavelength, z).

By taking the ratio of the signal at the water-vapor wavelength to the
signal at the nitrogen wavelength, most of the range-dependent terms
drop out, and one is left with a quantity that is almost directly
proportional to the water-vapor mixing ratio expressed as grams of water
vapor per kilogram of dry air (a small correction for the wavelength
dependence of the second attenuation term is easily taken into account).
Similarly, by taking the ratio of the signal at the laser wavelength to
the signal at the nitrogen wavelength, one is left with the aerosol
backscatter ratio; this ratio is normalized such that it is unity in
\"clean air\" (laser-wavelength scatter caused only by Rayleigh
scattering), and is in excess of unity for scattering by parcels of air
that contain aerosols (including cloud droplets/particles). Finally,
analysis of the polarization dependence of the backscatter signal at the
laser wavelength provides information on particle shape (phase);
spherical particles (cloud droplets) do not depolarize the laser
backscatter, whereas nonspherical particles (such as ice crystals in
cirrus clouds) can significantly depolarize the laser backscatter.

## Calibration

### Theory

The primary calibration issue for the Raman lidar is determining the
\"proportionality constants\" for the bottom two equations in the figure
at the top of the Theory of Operations\" section (and a similar one for
depolarization). For each observable, a single constant must be
determined for use over the entire vertical profile. For water vapor,
three general approaches can be taken: 1) match entire profiles to other
instruments that measure vertical profiles (e.g., radiosondes); 2) match
profiles at a single altitude using a fixed-height in-situ sensor (e.g.,
a tower sensor); or 3) integrate profiles and match the precipitable
water vapor with values derived from other instruments (e.g., microwave
radiometer or GPS instrument). All three approaches are being used for
the CART Raman lidar. The other observables are more straightforward.
For the aerosol backscatter ratio, the calibration can be accomplished
by examining a variety of profiles and finding a region of \"clean\"
(aerosol-free) air, and then choosing the constant that makes the ratio
unity in that region. For depolarization, daylight backgrounds from
overcast days provide unpolarized light for calibration purposes.

### Procedures

This section is not applicable to this instrument.

### History

The initial instrument calibration was performed using the dataset from
the first Water Vapor Intensive Observational Period (September 1996). A
QME that compares Raman lidar water-vapor profiles to measurements
obtained using other CART instruments is being used to investigate the
stability of the calibration over time.

## Operation and Maintenance

### User Manual

This section is not applicable to this instrument.

### Routine and Corrective Maintenance Documentation

This section is not applicable to this instrument.

### Software Documentation

ARM netCDF file header descriptions may be found at [RL Data Object
Design Changes](http://science.arm.gov/tool/dod/showdod.php?Inst=rl).

### Additional Documentation

This section is not applicable to this instrument.

## Glossary

See the [ARM Glossary](http://www.arm.gov/about/glossary.stm).

## Acronyms

CART: Cloud and Radiation Testbed\
Lidar: Light Detection and Ranging\
IOP: Intensive Observational Period\
PDF: Portable Document Format\
\
Also see the [ARM Acronyms and
Abbreviations](http://www.arm.gov/about/acronyms.stm).

## Citable References

Goldsmith, J.E.M., F.H. Blair, S.E. Bisson, and D.D. Turner. 1998.
Turn-key Raman lidar for profiling atmospheric water vapor, clouds, and
aerosols. *Applied Optics*, Volume 37, Number 21, pages 4979-4990.

For additional information on the Raman lidar technique, see Whiteman,
D.N., S.H. Melfi, and R.A. Ferrare. 1992. Raman lidar system for the
measurement of water vapor and aerosols in the Earth\'s atmosphere.
*Applied Optics*, Volume 31, Number 16, pages 3068-3082.

Turner, D.D., and J.E.M. Goldsmith. 1999. Twenty-four-hour Raman lidar
water vapor measurements during the Atmospheric Radiation Measurement
Program\'s 1996 and 1997 water vapor intensive observation periods. *J.
Atmos. Oceanic Technol.*, 16, 1062-1076.

Turner, D.D., W.F. Feltz, and R.A. Ferrare. 2000. Continuous water vapor
profiles from operational active and passive remote sensors. *Bull.
Amer. Meteor. Soc.*, 81, 1301-1317.