A mechanistic understanding of the global carbon cycle requires quantification of terrestrial ecosystem CO2 fluxes at regional scales. In this paper, we analyze the potential of a Doppler DIAL system to make flux measurements of atmospheric CO2 using the eddy-covariance and boundary layer budget methods and present results from a ground based experiment. The goal of this study is to put CO2 flux point measurements in a mesoscale context. In June 2007, a field experiment combining a 2-m Doppler Heterodyne Differential Absorption Lidar (HDIAL) and in-situ sensors of a 447-m tall tower (WLEF) took place in Wisconsin. The HDIAL measures simultaneously: 1) CO2 mixing ratio, 2) atmosphere structure via aerosol backscatter and 3) radial velocity. We demonstrate how to synthesize these data into regional flux estimates. Lidar-inferred fluxes are compared with eddy-covariance fluxes obtained in-situ at 396m AGL from the tower. In cases where the lidar was not yet able to measure the fluxes with acceptable precision, we discuss possible modifications to improve system performance

Andrews, Arlyn

Beyon, Jeffrey Y.

Davis, Kenneth J.

Gibert, Fabien

Hilton, T.

Ismail, Syed

Koch, Grady J.

Singh, Upendra N.

NASA Technical Reports Server

A preliminary study of CO2 flux measurements by lidar 
 
 
F. Gibert(1), G.J. Koch(2), J.Y. Beyon(3), T. Hilton(1), K.J. Davis(1), A. Andrews(4), S. Ismail(2), U.N. Singh(2) 
(1) The Pennsylvania State University, Department of Meteorology, PA, USA, gibert@lmd.polytechnique.fr 
(2) NASA langley research Center, Hampton, VA, USA 
(3)California State university, Los Angeles, CA, USA 
(4)NOAA Earth System Research Laboratory, Boulder, CO, USA 
 
 
 
Abstract 
 
A mechanistic understanding of the global carbon 
cycle requires quantification of terrestrial 
ecosystem CO2 fluxes at regional scales. In this 
paper, we analyze the potential of a Doppler DIAL 
system to make flux measurements of atmospheric 
CO2 using the eddy-covariance and boundary layer 
budget methods and present results from a ground 
based experiment. The goal of this study is to put 
CO2 flux point measurements in a mesoscale 
context. In June 2007, a field experiment 
combining a 2-µm Doppler Heterodyne Differential 
Absorption Lidar (HDIAL) and in-situ sensors of a 
447-m tall tower (WLEF) took place in Wisconsin. 
The HDIAL measures simultaneously: 1) CO2 
mixing ratio, 2) atmosphere structure via aerosol 
backscatter and 3) radial velocity. We demonstrate 
how to synthesize these data into regional flux 
estimates. Lidar-inferred fluxes are compared with 
eddy-covariance fluxes obtained in-situ at 396m 
AGL from the tower. In cases where the lidar was 
not yet able to measure the fluxes with acceptable 
precision, we discuss possible modifications to 
improve system performance.  
 
1. Introduction 
 
A mechanistic understanding of the global carbon 
cycle requires quantification of terrestrial 
ecosystem CO2 fluxes at regional scales. Flux 
towers measurements above different type of forest 
or cultures are usually used to characterize the CO2 
exchanges of a specific surface [Desai et al. 05]. 
These ‘in-situ studies’ are then use to build a 
coupled model of land surface physics, carbon 
exchange and atmosphere to infer CO2 fluxes and 
concentrations at larger scale in a ‘bottom-up’ view 
[Wang et al. 07].  CO2 flux measurements from 
very tall towers have also been used in synergism 
with footprint and ecosystem models to directly 
infer the CO2 fluxes of different type of vegetation 
[Wang et al. 06]. In the ‘top-down’ view, CO2 
concentrations in synergism with a Lagrangian 
transport model are used to retrieve CO2 surface 
fluxes. However, these different attempts to infer 
CO2 fluxes from the small (0.1 – 1 km) to larger 
scales (10 – 100 km) need a real experimental 
verification. 
An airborne Doppler DIAL system has the ability to 
make direct range resolved flux measurements 
using an eddy-covariance method and to address the 
issue of CO2 surface flux variability in link with the 
spatial heterogeneity of the surface (soil, water 
vegetation) [Giez et al. 99, Kiemle et al., 2007]. 
In this paper, we present a preliminary study of CO2 
flux measurements by lidar using the different data 
(backscatter signal, radial velocity, CO2 mixing 
ratio) collected during a field experiment in 
Wisconsin, in June 2007, nearby an instrumented 
447-m tall tower (WLEF). 
 
2. Study site, instrumentation and method for 
simultaneous Lidar CO2 and velocity 
measurements 
  
The field experiment took place in June 23, 2007 at 
the WLEF tall tower site in the Chequamegon 
National Forest in northern Wisconsin (45.95°N, 
90.27°W, 472 m above sea level).  The region is a 
heavily forested zone of low relief. The tower is a 
447 m tall television transmitter. Two minute mean 
CO2 mixing ratios are sampled at six levels (11, 30, 
76, 122, 244, and 396 m) by two infrared gas 
analyzers (IRGA) (LiCor Model Li-6251) to give 
CO2 profiles. Turbulent winds, virtual potential 
temperature and H2O mixing ratio are also 
measured by three sonic anemometers and other 
IRGAs at 30, 122 and 396 m above the ground. A 
ground based meteorological station provides also 
others observations such as net radiation and 
surface pressure, temperature and moisture. 
The NASA Langley 2-µm Doppler DIAL was 
positioned underneath the tower, approximately 40-
m away from the tower’s centreline. The 2-µm lidar 
transmitter is a 90-mJ, 140-ns, 5-Hz pulsed 
Ho,Tm:YLF oscillator injection seeded by 
continuous-wave (CW) lasers. The pulsed laser has 
been described in detail in other publications [Koch 
et al., 2004, 2007]; its relevant parameters are 
summarized in Table 1.  
 
 
 
 
https://ntrs.nasa.gov/search.jsp?R=20080023465 2019-08-30T04:36:07+00:00Z
Tab.1.Experimental set up 
 
The On-line wavelength of the transmitter is locked 
onto the side of the R22 CO2 absorption line at 
2053.204 nm (within ±1.9 MHz) whereas the Off-
line is the non-absorbed wavelength positioned 0.25 
nm away. The atmospheric heterodyne signal 
consists in AC radio frequency (RF) voltage for 
On- and Off-wavelength (index i):  
 ( )[ ]iH,iiii tπνj(t)Pγ(t)S ϕ+∝ 2exp    (1) 
where iγ  is the heterodyne efficiency 
( 10 ≤≤ iγ ) and iP  is the atmospheric scattered 
power collected by the receiver telescope.  
Here, the RF frequency iH ,ν  is the difference 
between the return signal frequency (including a 
Doppler frequency shift (± ΔνD ) due to aerosol 
particles in motion) and the heterodyne reference 
frequency i.e. DirefiH ννν Δ±= ,, :  
 λν /2 rD V−=Δ  (2) 
where rV is the radial velocity of the scatterers 
along the line of sight (LOS).  
In our experiment, the signals are digitized with 8 
bits at a 500 MHz sampling frequency and then 
stored on a PC. Later processing is made using 
MATLAB software and Squarer and Levin-like 
estimators for both signal power and radial velocity 
estimates. The atmospheric signals are accumulated 
in range gate of 112.5 m (Δz) or 0.75 µs duration 
(Δt = 2Δz/c, c being the light velocity) along the 
line-of-sight that result in mean scattered power at 
the two frequencies at range R:  
( ) ( ) 2tSzP OffOff = and ( ) ( ) 2tSzP OnOn = . The 
signal strength difference between the On- and Off-
signals is due to the CO2 differential absorption 
(α ). For two On- and Off-frequencies close 
enough that the residual term in backscatter and 
attenuation different from CO2 absorption is 
negligible it comes at range R:  
 ( ) ( ) ⎟⎠⎞⎜⎝⎛= ∫ zOn
Off drrz
P
P
0
2exp α  (3) 
Now, the CO2 optical depth (OD) between ranges 0 
and R, ( )∫= R drr0ατ , can be written as:  
 ⎟⎟⎠
⎞
⎜⎜⎝
⎛=
On
Off
P
P
Ln
2
1τ  (4) 
A mean differential CO2 absorption coefficient can 
be retrieved from measurements as:  
 ⎥⎦
⎤⎢⎣
⎡=
dz
dτα  (5) 
The “slope method” considers a plot of increasing 
OD as a function of increasing altitude [Gibert et al. 
06]. A least square fit of the data accounting for 
standard deviation enables derivation of the 
slopeα  . For normally distributed measurement 
noise this corresponds to a maximum likelihood 
estimate. The accuracy on the slope α  depends on 
1) the maximum range (i.e. more points for linear 
regression whereas range is limited by 
propagation/attenuation properties) and instrument 
parameters (i.e. pulse energy, telescope size and 
detection efficiency), and 2) the standard deviation 
for one observation that depends on signal to noise 
ratio. In order to express the dependence on CO2 
mixing ratio
2CO
ρ , the OD is written as:  
 ( ) ( )drrWFrz CO∫=
0
2
ρτ  (6) 
where ( ) ( ) ( )TprTprnrWF a ,,~,, σΔ=  is a 
weighting function at range r , p  and T  are the 
atmospheric pressure and temperature, σ~Δ  is the 
effective differential absorption cross section 
between the On- and Off wavelengths, ( ) ( )[ ]wa kTprn ρ+= 1/  is the dry-air density, 
wρ  the water-vapor mixing ratio and k the 
Boltzmann constant. 
The weighting function is computed using new 
spectroscopic data [Toth et al., 06, 07] and WLEF 
in-situ sensors measurements of temperature, 
pressure and specific humidity. We extrapolate the 
meteorological sensors measurements up to 3 km 
assuming a linear decrease of temperature and an 
exponential decrease of the surface pressure. The 
specific humidity is assumed to be constant in the 
ABL and negligible in the free troposphere. Then, a 
mean CO2 mixing ratio in the HDIAL line of sight 
is obtained by:  
 
WFCO
αρ =
2
 (7) 
 
Pulse energy  90 mJ 
Pulse repetition rate for a 
wavelength pair On-Off 
2.5 Hz 
Pulse width/ Line width 140 ns/ 3 MHz 
Off-line/ On-line 
Spectral locking (On-line) 
2053.410/ 2053.242 nm 
±1.9 MHz 
Telescope aperture 100 mm 
Detection heterodyne
  
Dual balanced InGaAs 
photodiodes  
Signal digitization 8 bits/ 500 MHz 
Signal processing Estimators - Squarer (power) 
- Levin-like (power, 
velocity) 
3. Methods to infer CO2 flux estimates from 
Lidar measurements 
 
Knowing the vertical gradient of CO2 concentration 
and the height of the ABL, we are able to infer CO2 
surface flux measurements using a boundary-layer 
mass budget [Gibert et al. 07]. The Doppler DIAL 
system, providing both vertical velocities and range 
resolved CO2 mixing ratio measurements, can also 
be used to make direct eddy-covariance (EC) range-
resolved flux measurements. We address these two 
different methods in the two following parts. 
 
 Boundary-layer budget and comparison with 
WLEF EC flux measurements 
 
A mass balance approach shows that the rate of 
change in the mean CO2 mixing ratio in the ABL is 
driven by the sum of the fluxes across boundaries 
of an air column extending from the ground to the 
top of the layer considered: 
( )
22
2
, COCO
CO
a dt
dhNEE
dt
d
hn ρρρρ −+= +  (8) 
where an is the mean air density, h is the ABL 
height, ( )tzCO ,2ρ  is the mixing ratio profile of 
CO2 in the ABL, NEE is the mean net ecosystem 
exchange, +,2COρ is the CO2 mixing ratio in the 
layer above the ABL (residual layer or free 
troposphere). 
2CO
ρ is the mean CO2 mixing ratio 
in the ABL. 
 
Lidar reflectivity
WLEF 
CO2 profile
Max(v)
d2v/dt2
 
 
Fig. 1: Top: ABL height calculated using lidar reflectivity 
during the day (black solid line) and wind properties 
during the night using either lidar radial velocities (red 
and purple solid lines) or in-situ CO2 mixing ratio 
profiles (WLEF tower) (colour map and black markers). 
Bottom: in-situ eddy-covariance flux measurements at the 
three levels of the tower (30, 122 and 396 m) and NEE 
estimated by the boundary-layer budget and lidar 
measurements.  
 
The height and the structure of the ABL are directly 
inferred by lidar measurements. During the day, the 
height of the ABL is estimated using the second 
derivative of lidar backscatter signal. During the 
night, we use slant measurements of the lidar in two 
directions to get a vertical profile of wind speed and 
direction. We inferred like that a nocturnal layer 
height that we compared with the height calculated 
from the second derivative of in-situ CO2 profile 
from the tower (Fig. 1, top). Using both in-situ and 
lidar measurements we retrieved the NEE (Fig. 1, 
bottom).  
During the day time, in-situ and ABL-budget fluxes 
are in good agreement with a slight underestimation 
between 8 and 14 h and more fluctuations at the end 
of the afternoon though. During the night the ABL 
method over estimate the eddy-covariance fluxes 
measurements as expected. Indeed, the storage of 
CO2 is not negligible during the night as we have a 
strong vertical gradient of CO2 from the ground to 
the top of the nocturnal layer. Then to compare the 
NEE from the ABL budget and eddy-covariance 
measurements we have to use the following 
equation:  
∫ ∂∂
∂+= z COCO ztwNEE 0
2
2
''
ρρ   (9) 
 
3.2 Eddy-covariance method 
 
To infer a CO2 flux estimate using the eddy-
covariance method we need (as for in-situ data) 
high frequency measurements of CO2 and 
velocities. We are looking for a correlation between 
the fluctuations of CO2 mixing ratio and vertical 
velocities due to turbulence only. The CO2 EC flux 
is given by: 
'.'
22 COCO
wF ρ=     (10) 
where and are respectively for  the vertical 
and time resolution of lidar CO2 and velocities 
measurements.  
As a first step, we estimated the error on EC flux 
estimates knowing the error on CO2 mixing ratio 
and velocities measurements from the lidar. The 
reference for a zero error on the flux calculations is 
taken when the fluctuations in CO2 and velocities 
are due to turbulence only. The theoretical 
turbulence variance of CO2 is calculated using 
Moeng and Wyngaard, 1984 and WLEF tower 
surface data: 
( )
2
*
2
0,2 ''/ 2
2 w
wzzf COi
CO
ρρ =    (11) 
where '' 0,2COw ρ is the in-situ co2 surface flux, 
iz is the top of the ABL and ( ) 2/ ≈izzf  for 
2/izz = . 
 
The vertical and time resolution of lidar 
measurements are respectively 1 km and 2 min 30 
s. For such conditions, the vertical velocity and CO2 
mixing ratio standard deviations were 0.3 m.s-1 and 
30 ppm (Fig. 2). Using the lidar time series we can 
only infer a 1-h flux measurement with a precision 
of ~ 120 %. For comparison, we also show the in-
situ sensors parameters in Figure 2. With a typical 
1.6 ppm of standard deviation, the in-situ sensor 
provides a 1-h flux measurement with ~ 15 % of 
precision. However, EC flux estimates by lidar 
seem to be still achievable if we average the flux 
measurement over several hours.  
EC WLEF flux
EC Lidar flux
± 0.3 m.s-1
± 30 ppm±1.6 ppm±0.3 ppm
Turbulence 
Moeng and Wyngaard, 1989
 
Fig. 2: Relative error on mean 1h-EC flux 
calculations as a function of vertical velocity and 
co2 mixing ratio standard deviation. 
 
4. Conclusion  
In this paper, we analyzed the potential of a 2-µm 
Doppler DIAL system to address the issue of 
representativity of CO2 in-situ flux measurements. 
Using simultaneous measurements of reflectivity, 
radial velocity and CO2 mixing ratio, we showed 
two different methods to make flux measurements. 
The first one, the boundary-layer budget method 
uses accurate mean CO2 mixing ratio measurement 
and knowledge of the ABL structure and gradient. 
Using both in-situ and lidar measurements we 
showed a good agreement of daytime in-situ and 
ABL-budget fluxes. We are currently analyzing the 
night time discrepancies. The second one, the eddy-
covariance method uses high frequency 
measurements of CO2 mixing ratio and vertical 
velocity with a moderate precision. A preliminary 
study of this method showed that due to the 
instrument limitations, only mean daytime flux 
measurements could be estimated with a good 
precision (< 50 %). New results will be presented at 
the conference. 
 
References 
 
Desai A.R., P.V. Bolstad, B.D. Cook, K.J. Davis, E.V. 
Carey, 2005: Comparing net ecosystem exchange of 
carbon dioxide between old-growth and mature forest 
in the upper Midwest, USA. Agri. Forest Meteor., 
128, 33-55. 
Koch, G.J., B.W. Barnes, M. petros, J.Y. beyond, F. 
Amzajerdian, J. Yu, R.E. davis, S. ismail, S. Vay, 
M.J. Kavaya and U.N. singh, 2004: Coherent 
differential absorption lidar measurements of CO2. 
Appl. Opt., 43, 5092-5099.  
Koch, G.J., J.Y. Beyon, B.W. Barnes, M. Petros, J. Yu, F. 
Amzajerdian, M.J. Kavaya and U.N. Singh, 2007: 
High-Energy 2-μm Doppler Lidar for Wind 
Measurements. Opt. Eng. 46. 
Gibert F., P.H. Flamant, D. Bruneau and C. Loth, 2006: 
Two-micrometer heterodyne differential absorption 
lidar measurements of the atmospheric CO2 mixing 
ratio in the boundary layer. Appl. Opt., 45, 4448-
4458.  
Gibert F., M. Schmidt, J. Cuesta, E. Larmanou, M. 
Ramonet, P.H. Flamant, I. Xueref, P. Ciais, 2007: 
Retrieval of average CO2 fluxes by combining in-situ 
CO2 measurements and backscatter Lidar 
information. J. Geophys. Res., 112, D10301, 
doi:10.1029/2006JD008190. 
Giez A., G. Ehret, R. L. Schwiesow, K.J. Davis, D.H. 
Lenschow, 1999: Water vapor flux measurements 
from ground-based vertically pointed water 
differential absorption and Doppler lidars. J. Ocean. 
Atmos. Tech., 16, 237-250. 
Kiemle C., W.A. Brewer, G. Ehret, R.M. Hardesty, A. 
Fix, C. Senff, M. Wirth, G. Poberaj and M.A. 
LeMone, 2007: Latent heat flux profiles from 
collocated airborne water vapour and wind lidars 
during IHOP 2002, J. Ocean. Atmos. Tech., 24, 627-
639. 
Moeng, C.-H. and J.C. Wyngaard, 1984: Statistics of 
conservative scalars in the convective boundary layer. 
J. Atmos. Sci., 41, 3161-3169. 
Wang W. K.J. davis, B.D. cook, M.P. Butler and D.M. 
Ricciuto, 2006: Decomposing CO2 fuxes measured 
over a mixed ecosystem at a tall tower and extending 
to a region: a case study. J. Geophys. Res., 111, 
G02005, doi:10.1029/2005JG000093. 
Wang J.-W., A.S. Denning, L. Lu, I.T. Baker, K.D. 
Corbin, and K.J. Davis, 2007: Observations and 
simulations of synoptic, regional, and local variations 
in atmospheric CO2., J. Geophys. Res., 112, D04108, 
doi:10.1029/2006JD007410. 
 
 
 


A Preliminary Study of CO2 Flux Measurements by Lidar

A Preliminary Study of CO2 Flux Measurements by Lidar

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