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An Advanced Method for Deriving Latent Energy Flux from a Scanning Raman Lidar

^a Los Alamos National Lab., MS J577, Los Alamos, NM 87545
^b Iowa Inst. for Hydraulic Research, Univ. of Iowa, Iowa City, IA 52242
^c Dep. of Plants, Soils and Biometeorology, Utah State Univ., Logan UT 84322
^d Dep. of Biological and Irrigation Engineering, Utah State Univ., Logan UT 84322
^e USDA National Soil Tilth Lab., Ames IA 50011

View larger version (73K): [in this window] [in a new window]   Fig. 1. (A) Site map showing the location of the Bosque relative to the state of New Mexico, and (B) an infrared aerial photograph of the Bosque indicating the location of the lidar and the lidar scanning pattern used to generate horizontal spatial series.

View larger version (45K): [in this window] [in a new window]   Fig. 2. The processing of a horizontal spatial series from 9 Sept. 1998 at 1208 h illustrating: (A) "raw data," (B) conditional water vapor mixing ratio (q) series, (C) smoothed conditional data, and (D) autocorrelation function for the spatial series. o is the first zero crossing of the autocorrelation function.

View larger version (19K): [in this window] [in a new window]   Fig. 3. Comparison of coincident autocorrelation functions from (A) the tower-mounted krypton hygrometer and (B) lidar on 12 Sept. 1998 at 1440 h. The plots show the zero-crossing lag (o) for each function and their corresponding integral scales.

View larger version (24K): [in this window] [in a new window]   Fig. 4. Relationship between lidar and eddy covariance derived integral spatial scales and the Obukhov length estimated from the Bosque experiments. The similarity model based on a stability function is shown as a dotted line and the ±10% uncertainty functions are shown as dashed lines. Inset A shows statistical analysis of eddy covariance sonic anemometer measured Obukhov length (L) versus lidar-derived values (points), where the solid line is the unity scale and the least-squares regression line (dotted line with equation) is shown with ±95% confidence intervals.

View larger version (16K): [in this window] [in a new window]   Fig. 5. Scatter plot showing the 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 (34K): [in this window] [in a new window]   Fig. 6. Comparison of latent energy flux maps derived by tower-independent method (integral scale similarity approach, ISSA) and tower-lidar method (micrometeorological integrated similarity approach, MISA) compiled from data acquired at the Soil Moisture Experiment on 1 July 2002 at 1045 h. The dotted lines on the flux maps show the location of the fence line separating the corn from the soybean. Also shown in the figure are histograms of the spatial distribution of the fluxes from the corn and soybean flux maps, and the latent energy (LE) flux measured by eddy covariance instruments mounted on 3-m towers in the corn and soybean fields. The two eddy covariance points on each histogram refer to the LE flux and the closure-corrected flux (LECC). The uncertainty bars on the eddy covariance measurements are ±10%.