Agricultural and Forest Meteorology 114 2003 213–234
Spatial source-area analysis of three-dimensional moisture fields from lidar, eddy covariance, and a footprint model
D.I. Cooper
a ,
∗
, W.E. Eichinger
b
, J. Archuleta
a
, L. Hipps
c
, J. Kao
a
, M.Y. Leclerc
d
, C.M. Neale
c
, J. Prueger
e
a
Los Alamos National Laboratory, Los Alamos, NM 87545, USA
b
University of Iowa, Iowa City, Iowa, IA 52242, USA
c
Utah State University, Logan, UT 84322, USA
d
University of Georgia, Griffin, GA 30223, USA
e
National Soil Tilth Laboratory, Ames, IA 50011, USA Received 1 May 2001; received in revised form 23 August 2002; accepted 4 September 2002
Abstract
The Los Alamos National Laboratory scanning Raman lidar was used to measure the three-dimensional moisture field over a salt cedar canopy. A critical question concerning these measurements is; what are the spatial properties of the source region
that contributes to the observed three-dimensional moisture field? Traditional methods used to address footprint properties rely on point sensor time-series data and the assumption of Taylor’s hypothesis to transform temporal data into the spatial domain.
In this paper, the analysis of horizontal source-area size is addressed from direct lidar-based spatial analysis of the moisture field, eddy covariance co-spectra, and a dedicated footprint model. The results of these analysis techniques converged on the
microscale average source region of between 25 and 75 m under ideal conditions. This work supports the concept that the scanning lidar can be used to map small scale boundary layer processes, including riparian zone moisture fields and fluxes.
Published by Elsevier Science B.V.
Keywords: Spatial source-area analysis; Three-dimensional moisture; Latent energy flux
1. Introduction
The Los Alamos National laboratory LANL scanning Raman lidar has been used to measure mul-
tidimensional water vapor fields for nearly a decade Cooper et al., 1992
. More recently, it has been used to estimate spatially resolved latent energy flux
LE using a scalar gradient-based similarity ap- proach
Cooper et al., 2000; Eichinger et al., 2000 .
Three-dimensional fields of water vapor can be trans-
∗
Corresponding author. Tel.: +1-505-665-6501; fax: +1-505-667-7460.
E-mail address: dcooperlanl.gov D.I. Cooper.
lated into spatial estimates of surface fluxes if a known surface emitting region can be connected to the wa-
ter vapor concentration at any particular height. This paper addresses the surface water vapor source-area
sampling criteria and the extent of the flux footprint for application to remote sensing observations in
the boundary layer. The sampling size depends on surface–atmosphere coupling and data acquisition
properties, including the time required to scan over an area of interest. This study attempts to charac-
terize lidar footprints and compare them with eddy covariance-derived co-spectra and a footprint model.
The “footprint issue” has been studied by inves- tigators for the past decade
Leclerc et al., 1997,
0168-192302 – see front matter. Published by Elsevier Science B.V. PII: S 0 1 6 8 - 1 9 2 3 0 2 0 0 1 7 5 - 2
214 D.I. Cooper et al. Agricultural and Forest Meteorology 114 2003 213–234
2003; Schuepp et al., 1990; Finn et al., 1996 , and
is a continuing topic of research. A number of dif- ferent approaches have been developed for both
neutral and convective boundary layers using a: La- grangian model, large eddy simulation model, and
analytical solutions to the advection–diffusion equa- tion
Leclerc et al., 1997 . However, verification of
the models with data has previously been limited to point sensors
Finn et al., 1996; Leclerc et al., 2003; Warland and Thurtell, 2000
or airborne line obser- vations
Ogunjemiyo et al., 1997 . Since footprints
and source-area concepts are inherently spatially de- pendent processes, multidimensional remotely sensed
atmospheric data offer a unique opportunity to verify these models. Unlike other remote sensing systems,
the scanning Raman lidar can spatially resolve atmo- spheric phenomena, especially within the boundary
layer, and can thus be used to improve and perhaps verify boundary layer models
Kao et al., 2000 . In
turn, the footprint model results enhance the inter- pretation of lidar data and may help to improve lidar
techniques in the field. The primary objective of this paper is to evaluate the optimum spatial sampling size
for lidar derived variables such as latent energy flux.
In this paper, we first discuss the experimental site, the tower-based instruments used, and the scanning
Raman lidar Section 2
. In next section Section 3
, we describe the Monin–Obukhov method for estimat-
ing latent energy from lidar measured water vapor profiles and independent friction velocity measure-
ments, as well as the dimensionless moisture flux, q
∗
. Section 4
describes three methods for estimating lidar average horizontal sampling size and point-source-
area footprints by A combining lidar-eddy covari- ance, B footprint modeling analysis, C co-spectra
of the vertical wind w and water vapor q time se- ries and D integral length scales. We finally discuss
results and summarize in
Section 5 .
2. Site, instrument, and lidar overview
