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

Remote sensors are useful tools for making measurements that are not possible with conventional point instruments. We developed methods to obtain spatially resolved latent energy fluxes from Raman water vapor data and the regional virtual potential heat flux from elastic lidar data. The evaporation method is based on Monin-Obukhov similarity theory applied to spatially and temporally averaged data. Latent heat flux estimates were found to be well correlated (R^2 = 0.84, slope = 0.98) compared with eddy correlation measurements. The standard error of the flux estimates was 36.5 W m^-2 (a 14% root mean square [RMS] difference), which is close to the predicted uncertainty of 15%. A vertically staring elastic lidar was used to obtain a continuous record of the boundary layer height and thickness of the entrainment zone between a soybean [Glycine max (L.) Merr.] and a corn (Zea mays L.) field. The surface heat flux was calculated using the Batchvarova-Gryning boundary layer model. The virtual potential heat flux estimates were found to be well correlated (R^2 = 0.79, slope = 0.95) compared with eddy correlation measurements. The standard error of the flux estimates was 21.4 Wm^-2 (31% RMS difference between estimates and surface measurements), higher than the predicted uncertainty of 16%. Other parameters such as the Monin-Obukhov length, the stability correction functions, and integral scale can be obtained from lidar data.