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Using Lidar Remote Sensing for Spatially Resolved Measurements of Evaporation and Other Meteorological Parameters

^a Univ. of Iowa, Iowa City, IA 52242
^b Los Alamos National Lab., Los Alamos, NM 87545

View larger version (18K): [in this window] [in a new window]   Fig. 1. A plot of the spectrum of light returning from a 248-nm Raman lidar showing the Raman scattering peaks from the major atmospheric constituents.

View larger version (18K): [in this window] [in a new window]   Fig. 2. A diagram showing the layout of the Los Alamos scanning Raman lidar. With the exception of the scanning mirror, the arrangement is typical of Raman lidars.

View larger version (109K): [in this window] [in a new window]   Fig. 3. The Los Alamos scanning Raman lidar.

View larger version (23K): [in this window] [in a new window]   Fig. 4. A conceptual drawing showing how different lines of sight from the lidar are combined to map the water vapor concentration in a vertical slice of the atmosphere. The water vapor concentration is determined every 1.5 m along each of the lines shown. The lines of sight in actual practice are 0.07 to 0.25° apart.

View larger version (19K): [in this window] [in a new window]   Fig. 5. A vertical scan showing the water vapor concentration in a vertical plane above the corn canopy during SMEX02. The day was strongly convective. Red colors represent areas of high water vapor concentration, while blue colors represent lower concentrations. The intense red color at the bottom is a result of the attenuation of the laser beam by the ground or canopy (in this case, corn). The change in the lidar signal as it reaches the canopy top enables determination of the shape and orientation of the surface.

View larger version (13K): [in this window] [in a new window]   Fig. 6. An example of a lidar fitted vertical profile (solid line) and the data from which it was calculated. The data are from a 25-m section from a vertical scan. The variability in the data about the fitted line is due to the presence of discrete structures. If a large enough area is averaged, the mean value at each elevation converges to a logarithmic profile.

View larger version (19K): [in this window] [in a new window]   Fig. 7. A conceptual drawing of a 25-m region and all of the lidar lines of sight within it. The location of a line approximating the surface is determined and the distance from each measured value to this line along a perpendicular to the line is calculated. All of the measured values of water vapor concentration are used to create vertical profiles similar to that in Fig. 6.

View larger version (28K): [in this window] [in a new window]   Fig. 8. Three evapotranspiration maps of the area around the lidar at various times on 27 June 2002 during SMEX02. Red indicates areas of higher evapotranspiration and blues are lowest. To show the variability of the fields, the color scales are different for each time. Soybean was planted to the south of the lidar and corn to the north. The dividing line is the fence that can be seen in the photograph just below the lidar location. Also shown is an aerial three-band false color composite of canopy reflectance (near infrared [red color], red band [green color] and green band [blue color]) of the site, at the same scale, for comparison. Red colors are indicative of greater amounts of biomass.

View larger version (51K): [in this window] [in a new window]   Fig. 9. A comparison of an aerial thermal infrared photograph of an area populated with salt cedar in the Bosque del Apache riparian area in New Mexico (right) and a lidar-generated evapotranspiration map of the same area (left). Red areas of the photograph indicate hot areas (which should have low evapotranspiration rates) while blue areas are cool areas, which should have high evapotranspiration rates.

View larger version (24K): [in this window] [in a new window]   Fig. 10. A comparison of eddy correlation evapotranspiration rates over a salt cedar canopy with lidar-estimated evapotranspiration rates made in the same 25-m lidar pixel. The agreement is generally good and along the 1:1 line. The six-point excursion was from an afternoon with exceptionally high winds in which the eddy correlation instruments may have been above the inner region.

View larger version (20K): [in this window] [in a new window]   Fig. 11. Relationship between lidar- and eddy-covariance-derived integral length scales and the Obukhov length (L) estimated from two field experiments. The similarity model based on a stability function (Eq. [13]) is shown as a dotted line and the ±10% uncertainty functions are shown as dashed lines; the equations used to compute the similarity based estimates based on z/L and friction velocity are also shown.

View larger version (15K): [in this window] [in a new window]   Fig. 12. Relationship between sonic-anemometer-measured and lidar-estimated friction velocity (u*). The dashed line is the least-squares regression fit, and the solid line is the 1:1 line. The dotted lines are the 95% confidence intervals for the regression line.

View larger version (120K): [in this window] [in a new window]   Fig. 13. The vertically staring elastic lidar. This system is highly compact and portable and requires no operator input once started.

View larger version (27K): [in this window] [in a new window]   Fig. 14. A traditional thermodynamic model of an unstable atmospheric boundary layer. A logarithmic layer near the surface blends into a constant-temperature mixed layer that extends to the top of the boundary layer. A stable atmosphere with a temperature inversion acts as a lid to the vertical motions of the air below. A lidar signal showing the height of the boundary layer with time is shown on the right. Reds are highest particulate concentrations and blues are lowest. The thermodynamic diagram is shown to the left, scaled to the lidar signal.

View larger version (28K): [in this window] [in a new window]   Fig. 15. A plot showing the values of the ratio of the entrainment of virtual potential heat flux (from the entrainment of warm air above the inversion into the boundary layer) to the surface virtual potential heat flux, A, determined for 25, 27, and 29 June as a function of the potential temperature gradient with height, . Note that there appears to be a relationship between the values of A and the gradient.

View larger version (13K): [in this window] [in a new window]   Fig. 16. A plot of the lidar data for 27 June from 900 through 1230 h. The data is shown as altitude vs. time, with color showing the relative aerosol content (reds are highest concentrations with blues being the lowest). The blue color above the boundary layer is the residual layer from the previous day. White areas are regions of low aerosol content, generally indicating air from the free troposphere.

View larger version (37K): [in this window] [in a new window]   Fig. 17. A comparison of virtual potential heat flux estimates from the lidar with virtual potential heat flux estimates from eddy correlation instruments. The data are from five mornings during the SMEX02 experiment. The crosses are the data for 27 June and correspond to the lidar data shown in Fig. 16.