Ozone (O-3) flux (F-o) was measured over a wheat field in Yucheng, China, using the eddy covariance technique. A dry chemiluminescence fast-response O-3 analyzer and a UV-absorption based slow-response O-3 analyzer were used for measuring fast O-3 concentration fluctuations and absolute concentrations, respectively. The main objective of this study was to determine a group of suitable methods for calculating F-o based on the performance of the two O-3 analyzers in the field. We evaluated the effects of three calibration methods on F-o. These calibration methods include the ratio method (RM, it assumes that the fast-response analyzer's signal-output is directly proportional to absolute ambient O-3 concentration within a 30-min interval), the ratio offset method (ROM, it is based on the ratio method with analyzer's offset modification), and the ratio variation method (RVM, it is based on the ratio method with the fast-response analyzer's gain factor variation within a 30-min interval). Three frequency response corrections (analytic, revised analytic, and in-situ) for the estimation of F-o were also evaluated. Our results show that: (1) Compared to the O-3 flux with the commonly-used method (RM), on average, the fluxes with ROM and RVM were decreased by about 9% and 7%, respectively. (2) Based on the spectral analysis, the fast-response 03 analyzer's noise could cause random errors, but did not result in a systematic error in F-o. Tubing attenuation in O-3 concentration fluctuations can cause up to 20% loss in F-o. (3) Frequency response corrections for F-o with the original analytic method, the in-situ method, and the revised analytic method were 34.6%, 23.8% and 25.4% on average, respectively. (C) 2015 Elsevier B.V. All rights reserved

The planning and realization of wind energy projects requires an as accurate and precise wind resource estimation as possible. Standard procedures combine shorter on-site measurements with the application of numerical models. The uncertainties of the numerical data generated from these models are, particularly in complex onshore terrain, not just rather high but typically not well quantified. In this article we propose a methodology for using a single scanning Doppler wind lidar device to calibrate the output data of a numerical flow model and with this not just quantify but potentially also reduce the uncertainties of the final wind resource estimate. The scanning lidar is configured to perform Plan Position Indicator (PPI) scans and the numerical flow data are projected onto this geometry. Deviations of the derived from the recorded line-of-sight wind speeds are used to identify deficiencies of the model and as starting point for an improvement and tuning. The developed methodology is demonstrated based on a study for a site in moderately complex terrain in central Germany and using two rather different types of numerical flow models. The findings suggest that the use of the methodology and the introduced scanning wind lidar technology offers a promising opportunity to control the uncertainty of the applied flow models, which can otherwise only be estimated very roughly.