The Soil Moisture-Atmosphere Coupling Experiment (SMACEX) was conducted in the Walnut Creek watershed near Ames, Iowa, over the period from 15 June to 11 July 2002. A main focus of SMACEX is the investigation of the interactions between the atmospheric boundary layer, surface moisture, and canopy. A vertically staring elastic lidar was used to provide a high-time-resolution continuous record of the boundary layer height at the edge between a soybean and cornfield. The height and thickness of the entrainment zone are used to estimate the surface sensible heat flux using the Batchvarova-Gryning boundary layer model. Flux estimates made over 6 days are compared to conventional eddy correlation measurements. The calculated values of the sensible heat flux were found to be well correlated (R-2 = 0.79, with a slope of 0.95) when compared to eddy correlation measurements in the area. The standard error of the flux estimates was 21.4 W m(-2) (31% rms difference between this method and surface measurements), which is somewhat higher than a predicted uncertainty of 16%. The major sources of error were from the estimates of the vertical potential temperature gradient and an assumption that the entrainment parameter A was equal to the ratio of the entrainment flux and the surface heat flux.

The Los Alamos Raman lidar has been used to make high resolution (25m) estimates of the evapotranspiration rate over adjacent corn and soybean canopies. The lidar makes three-dimensional measurements of the water vapor content of the atmosphere directly above the canopy that are inverted using Monin–Obukhov similarity theory. This may be used to examine the relationship between evapotranspiration and surface moisture/soil type. Lidar estimates of evapotranspiration reveal a high degree of spatial variability over corn and soybean fields that may be associated with small elevation changes in the area. The spatial structure of the variability is characterized using a structure function and correlation function approach. The power law relationship found by other investigators for soil moisture is not clear in the data for evapotranspiration, nor is the data a straight line over the measured lags. The magnitude of the structure function and the slope changes with time of day, with a probable connection to the amount of evapotranspiration and the spatial variability of the water vapor source. The data used was taken during the soil moisture–atmosphere coupling experiment (SMACEX) conducted in the Walnut Creek Watershed near Ames, Iowa in June and July 2002.

Abstract A network of eddy covariance (EC) and micrometeorological flux (METFLUX) stations over corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] canopies was established as part of the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) in central Iowa during the summer of 2002 to measure fluxes of heat, water vapor, and carbon dioxide (CO2) during the growing season. Additionally, EC measurements of water vapor and CO2 fluxes from an aircraft platform complemented the tower-based measurements. Sensible heat, water vapor, and CO2 fluxes showed the greatest spatial and temporal variability during the early crop growth stage. Differences in all of the energy balance components were detectable between corn and soybean as well as within similar crops throughout the study period. Tower network–averaged fluxes of sensible heat, water vapor, and CO2 were observed to be in good agreement with area-averaged aircraft flux measurements.

Analysis of data collected by four disdrometers deployed in a 1-km(2) area is presented with the intent of quantifying the spatial variability of radar reflectivity at small spatial scales. Spatial variability of radar reflectivity within the radar beam is a key source of error in radar-rainfall estimation because of the assumption that drops are uniformly distributed within the radar-sensing volume. Common experience tells one that, in fact, drops are not uniformly distributed, and, although some work has been done to examine the small-scale spatial variability of rain rates, little experimental work has been done to explore the variability of radar reflectivity. The four disdrometers used for this study include a two-dimensional video disdrometer, an X-band radar-based disdrometer, an impact-type disdrometer, and an optical spectropluviometer. Although instrumental differences were expected, the magnitude of these differences clouds the natural variability of interest. An algorithm is applied to mitigate these instrumental effects, and the variability remains high, even as the observations are integrated in time. Although one cannot explicitly quantify the spatial variability from this experiment, the results clearly show that the spatial variability of reflectivity is very large.

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 75m 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.

High resolution, airborne multispectral imagery of a riparian system dominated by salt cedar (Tamarix spp) along the Rio Grande in New Mexico, USA, was used to determine the instantaneous evapotranspiration rates and spatially distributed energy balance components over the system. Comparisons of instantaneous spatially distributed upwind fluxes with values of ground-based measured fluxes using eddy correlation techniques and other micrometeorological instruments, were conducted for two different dates. Results show considerable differences between the fluxes that can be attributed to advection, canopy heat storage and wind variability. A careful footprint analysis will need to be conducted in the future to better match the ground-based and aircraft measurements.

Quantification of evapotranspiration (ET) continues to be challenging especially when attempting to evaluate ET at basin scales. Extending approaches to whole basins or larger regions is difficult due to issues related to landscape heterogeneity and scale. Reliable areal estimates of ET are essential for accurate modelling of the hydrological cycle and for assessing water-use of different ecosystems. A riparian corridor along the Rio Grande (USA) dominated by Tamarisk (salt cedar) is being studied to determine daily and total seasonal water-use. Local estimates of ET in the Tamarisk were made using eddy covariance instrumentation mounted on two towers. Radiometric temperature at the top of the canopy was also measured using a fixed-head infrared thermometer. The combination of these data will be used to evaluate evapotranspiration estimates along the corridor using data remotely sensed from an aircraft platform. that have been acquired periodically for a large extent of the riparian zone.

Field measurements are carried out to study statistical properties of the subgrid-scale (SGS) heat fluxes and SGS dissipation of temperature variance in the atmospheric surface layer, and to evaluate the ability of several SGS models to reproduce these properties. The models considered are the traditional eddy-diffusion model, the nonlinear (gradient) model, and a mixed model that is a linear combination of the other two. High-resolution wind velocity and temperature fields are obtained from arrays of 3D sonic anemometers placed in the surface layer. The basic setup consists of two horizontal parallel arrays (seven sensors in the lower array and five sensors in the upper array) at different heights (2.4 and 2.9 m, respectively). Data from this setup are used to compute the SGS heat flux and dissipation of temperature variance by means of 2D filtering in horizontal planes, invoking Taylor's hypothesis. Model coefficients are measured from the data by requiring the real and modeled time-averaged dissipation rates to match. Various other experimental setups that differ mainly in the separation between the sensors are utilized to show that filter size has a considerable effect on the various model coefficients near the ground. For the basic setup, conditional averaging is used to study the relation between large-scale coherent structures (sweeps and ejections) and the SGS quantities. It is found that under unstable conditions, negative SGS dissipation, indicative of backscatter of temperature variance from the subgrid scales to the resolved field, is most important during the onset of ejections transporting relatively warm air upward. Large positive SGS dissipation of temperature variance is associated with the end of ejections (and/or the onset of sweeps) characterized by strong drops in temperature and vertical velocity under unstable conditions. These results are also supported by conditionally sampled 2D (streamwise and vertical) velocity and temperature distributions, obtained using an additional setup consisting of the 12 anemometers placed in a vertical array. The nonlinear and mixed model reproduce the observations better than the eddy-diffusion model.

Lidar technology provides fast data collection at a resolution of meters in a three‐dimensional atmospheric volume. A modeling counterpart of this lidar capability can greatly enhance our understanding of near‐surface atmospheric turbulence. This paper describes an integrated research capability on the basis of data from a scanning water vapor lidar and a high‐resolution hydrodynamic model (HIGRAD) equipped with a visualization routine (VIEWER) which simulates the lidar scanning. The purpose is to better understand the degree to which the lidar measurements represent faithfully the spatial and temporal features of the atmospheric boundary layer and to extend the utility of the measurements in studying turbulent fields in this layer. Raman lidar water vapor data collected over the Pacific warm pool and the HIGRAD simulations thereof are first compared with each other. The results are then used to identify the potential aliasing effects of lidar measurements due to the relatively long duration of the lidar scanning. This integrated lidar‐model capability also helps improve the trade‐off between the spatial and the temporal resolution of the lidar measurements on the one hand and their coverage on the other.

An integrated tool that consists of a volume scanning high-resolution Raman water vapor LIDAR and a turbulence-resolving hydrodynamic model, called HIGRAD, is used to support the semi-arid land-surface-atmosphere (SALSA) program. The water vapor measurements collected during SALSA have been simulated by the HIGRAD code with a resolution comparable with that of the LIDAR data. The LIDAR provides the required "ground truth" of coherent water vapor eddies and the model allows for interpretation of the underlying physics of such measurements and characterizes the relationships between surface conditions, boundary layer dynamics, and measured quantities. The model results compare well with the measurements, including the overall structure and evolution of water vapor plumes, the contrast of plume variabilities over the cottonwoods and the grass land, and the mid-day suppression of turbulent activities over the canopy. The current study demonstrates an example that such an integration between modeling and LIDAR measurements can advance our understanding of the structure of fine-scale turbulent motions that govern evaporative exchange above a heterogeneous surface. (C) 2000 Elsevier Science B.V. All rights reserved.

A scanning, volume-imaging Raman lidar was used in August 1997 to map the water vapor and latent energy flux fields in southern Arizona in support of the (Semi-Arid Land Surface Atmosphere) SALSA program. The SALSA experiment was designed to estimate evapotranspiration over a cottonwood riparian corridor and the adjacent mesquite-grass community. The lidar derived water vapor images showed microscale convective structures with a resolution of 1.5 m, and mapped fluxes with 75 m spatial resolution.Comparisons of water vapor means over cottonwoods and adjacent grasses show similar values over both surfaces, but the spatial Variability over the cottonwoods was substantially higher than over the grasses. Lidar images support the idea that the enhanced variability over the cottonwoods is reflected in the presence of spatially coherent microscale structures. Interestingly, these microscale structures appear to weaken during midday, suggesting possible evidence for stomatal closure. Spatially resolved latent energy fluxes were estimated from the lidar using Monin-Obukhov gradient technique. The technique was validated from sap-flow flux estimates of transpiration, and statistical analysis indicates very good agreement (within +/-15%) with coincident lidar flux estimates. Lidar derived latent energy maps showed that the riparian zone tended to have the highest fluxes over the site. In addition, the spatial variability of 30 min average fluxes were almost as large as the mean values. Geostatistical techniques where used to compute the spatial lag lengths, they were found to be between 100 and 200 m.Determination of such spatially continuous evapotranspiration from such a complex site presents watershed managers with an additional tool to quantify the water budgets of riparian plant communities with spatial resolution and flux accuracy that is compatible with existing hydrologic management tools. (C) 2000 Elsevier Science B.V. All rights reserved.

Field measurements are undertaken with the specific purpose of addressing open issues in subgrid-scale (SGS) modeling of turbulence for large eddy simulation. Wind velocity and temperature signals are obtained using a horizontal linear array of six three-dimensional sonic anemometers placed at a height of 2.15 m in the surface layer over a grass field. From these data, the SGS heat flux and a two-dimensional surrogate of the SGS dissipation of temperature variance (chi) are computed by means of two-dimensional horizontal filtering and by invoking Taylor's hypothesis. Conditional averaging is used to isolate the effects of large-scale structures (sweeps and ejections) of the flow on the SGS dissipation under different stability conditions. During flow events associated with strong increments of vertical velocity (possibly associated with the onset of ejection events), negative Values of chi, indicative of transfer of temperature variance from the small scales to the resolved field (backscatter), have an important relative contribution regardless of atmospheric stability. Strong drops in the vertical velocity (possibly associated with the onset of sweeps) are accompanied by large positive values of the SGS dissipation. The two-dimensional SGS dissipation is compared with a one-dimensional surrogate based on a single sensor used in earlier work. The one- and two-dimensional results show qualitatively the same trends. Quantitative differences underscore the advantages of a two-dimensional approach based on the sensor array used in this work.

An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometeorological humidity data were used to calibrate the ratio of the lidar gains of the H(2)O and the N(2) photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem.

A scanning, ultraviolet, Raman water vapor lidar designed primarily for boundary layer measurements has been built and operated by the Los Alamos National Laboratory Ground-Based Earth Observing Network team. The system provides high temporal and spatial resolution measurements of the atmosphere within and above the atmospheric boundary layer (ABL). Several examples of the types of data collected and the techniques for processing the data are presented. The typical horizontal range for the lidar is approximately 700 m when scanning, while the vertical range with photon counting can be up to 12 km with corresponding spatial resolutions of 1.5 m in the near field to 75 m in the far field. The uncertainty in the water vapor mixing ratio was found to be +/-0.34 g kg(-1). The development of the scanning Raman lidar is directed at questions about the behavior of the surface atmosphere interface. These questions address the nature of spatial variability and intermittent microscale convective transport in the ABL and lower troposphere.

Static pressure fluctuations measured in the atmospheric surface layer over a grass covered forest clearing are studied in the context of Townsend’s 1961 hypothesis regarding the effect of the outer region on the inner region. It is shown that large-scale pressure features are actively straining the inertial-scale pressure fluctuations, thus invalidating the direct extension of Kolmogorov’s 1941 hypothesis to the spectral scaling of pressure within the inertial subrange. A parameter describing the large scale pressure fluctuations is added to the set of variables responsible for inertial-range pressure differences and dimensional analysis is employed to derive an improved scaling law for pressure spectra which more closely matches these and previous experimental results. An examination of the Poisson equation for pressure is conducted and found to support the dimensional and experimental results.

PM10 emissions from nonpoint sources need to be quantified in order to effectively meet air quality standards. In California's Central Valley, agricultural operations are highly complex but significant sources of PM10 that are difficult to quantify using point sampling arrays. A remote sensing technique, light detection and ranging (lidar), using a small field portable, fast-scanning lidar shows great potential for measuring PM10 emissions from agricultural nonpoint sources. The qualitative capabilities of the lidar instrument are demonstrated for land preparation operations at a wheat field. The range (>5 km), spatial resolution (2.5 m) and fast response times (s) of the lidar allow the following: (i) plume dynamics to be described in detail and eventually to be modeled as a function of source fluctuations and environmental conditions, (ii) measurements of average wind speed and direction over 50-100 m scales, (iii) quantitative determination of the fraction of dust missed by point sampling arrays, and (iv) currently provide unparalleled information on non point source emission variability, both temporally and spatially. The lidar data indicate the line source nature of plumes from tractor operations and suggest that fast lidar 2D vertical scans downwind of nonpoint sources will provide the best PM10 emission factor measurements. Widespread use of lidar for direct quantitative emission factor measurement depends on careful determination of particulate matter backscatter-mass calibration relationships.

D. C. Goodrich (1)*, A. Chehbouni (2), B. Goff (1), B. MacNish (3), T. Maddock (3), S. Moran (4), W.J. Shuttleworth (3), D.G. Williams (3), J.J. Toth (1), C. Watts (12), L.H. Hipps (5), D.I. Cooper (6), J. Schieldge(14), Y.H. Kerr (7), H. Arias (12), M. Kirkland (6), R. Carlos (6), W. Kepner (17), B. Jones (17), R. Avissar (19), A. Begue (8), G. Boulet (2), B. Branan (18), I. Braud (15), J.P. Brunel (2), L.C. Chen (9), T. Clarke (4), M.R. Davis (13), J. Dauzat (8), H. DeBruin (10), G. Dedieu (7), W.E. Eichinger (9), E. Elguero (2), J. Everitt (13), J. Garatuza-Payan (3), A. Garibay (12), V.L. Gempko (3), H. Gupta (3), C. Harlow (3), O. Hartogensis (10), M. Helfert (4), C. Holifield (4), D. Hymer (2), A. Kahle (14), T. Keefer (1), S. Krishnamoorthy (9), J-P. Lhomme (2), D. Lo Seen (8), D. Luquet (8), R. Marsett (1), B. Monteny (2), W. Ni (4), Y. Nouvellon (8), R. Pinker (20), C. Peters (3), D. Pool (16), J. Qi (4), S. Rambal (11), H. Rey (8), J. Rodriguez (12), E. Sano (3), S.M. Schaeffer (3), M. Schulte (3), R. Scott (3), X. Shao (6), K.A. Snyder (3), S. Sorooshian (3), C.L. Unkrich (1), M. Whitaker (3), I. Yucel (3)

We analyze the scale distribution of coherent water vapor structures in the marine atmospheric boundary layer as measured by a shipboard Raman lidar during the Combined Sensor Program (March 1996) using a two‐dimensional continuous wavelet transform. Coherent structures in the lidar measured water vapor concentration field correspond to locations where covariance with the wavelet is a local extremum. Scales of the significant structures are identified using a filtered wavelet variance (detection density) derived from 24 “images” in a horizontal plane. A dominant radius of 14 m is identified using complimentary approaches to the analysis.