Summary: | Free tropospheric water vapor variability, measured by long-range airborne differential-absorption lidar, has been analyzed by using structure functions of different orders at altitudes from 2 to 10 km. It is shown that the water vapor field exhibits scale invariance at spatial scales ranging from 5km to 100km, where scaling behavior is defined as a power law dependence of structure functions on length scale. In contrast to one-dimensional in situ measurements, two-dimensional water vapor lidar observations allow height-resolved analysis of scaling exponents with a vertical resolution of 200m. Using this data a clear distinction was found between scaling properties above and below an air-mass boundary. Data has been analysed from three campaigns, COPS/ETReC (2007) collected during summertime in middle and south Europe, T-PARC (2008) collected during late summer around Japan mostly over sea and T-IPY (2008) collected during winter around Spitsbergen mostly over sea. After discarding flights with low lidar signals or large data gaps, and after horizontal averaging to a resolution of 1-5km to obtain a high signal to noise ratio, structure functions were computed for 20 flights at various heights with a total length of more than 300,000 km. Scaling exponents were obtained for structure functions up to fifth order, and results will be presented for first and second order structure functions and for intermittency (variation of the scaling exponent with increasing order). The scaling exponents show no significant latitudinal, seasonal and land/sea dependence, but show significantly different behavior depending on whether the time series occured in an air mass influenced by cumulus convection or not. A classification of the time series into two groups according to whether the series occurred above or below the level of nearby convective cloud tops was performed by detecting the cloud height from the lidar backscatter signal of the corresponding flight. It was found that exponents can be divided into two groups depending ...
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