Statistical Characterisation of Water Vapour Variability in the Troposphere

Kurzfassung

Tropospheric water vapour plays an important role in thermodynamic and radiative processes which have an immediate impact on the weather and climate system. However, the processes that determine the distribution of water vapour remain poorly understood. The complexity arises out of a range of source and sink processes from convective clouds on the kilometre scale to cloud systems associated with motions on scales of a thousand or more kilometres, as well as advection of water vapour as a passive tracer outside of clouds. While large-scale advection of water vapour is well represented in general circulation models, the simulation of small-scale moist processes that are of central importance to the representation of clouds are heavily dependent on parameterisations. However, observations as well as processes that determine the distribution of the water vapour field are insufficiently explored, leading to constrained parameterisations and therefore contributing significantly to the uncertainty of numerical weather and climate predictions. Hence, a more accurate description of the inhomogeneous water vapour field based on high-resolution observations is required. This thesis investigates a comprehensive data set of two-dimensional airborne water vapour observations in the free troposphere collected by a Differential Absorption Lidar (DIAL) in order to gain a height-resolved statistical characterisation of the inhomogeneous water vapour field. Structure functions, i.e., statistical moments up to the fifth order of absolute increments over a range of scales, are investigated and power-law behaviour or scale dependence is identified over horizontal distances from about 5 km to 100 km. The slope of the power-law fit, the so-called scaling exponent, is found to take different values, depending on whether or not the observations were taken in an air mass where convective clouds were present. These results are consistent with a non-convective regime that is dominated by large-scale advective processes, leading to monofractal scaling, but strong localised input of small-scale variability by convective circulations leading to intermittent fields. Further, the observed power-law statistics are used to evaluate the high-resolution numerical weather prediction model COSMO-DE of the German weather service with regard to the small-scale water vapour variability. The results of the scaling exponent analysis of cloud-free and partly cloudy scenes suggest, that the smallscale variance is modeled quite well in comparison with the lidar observations. By using the advantage of the model simulation where data is not limited to a specific flight path, the influence of sampling limitation is estimated and is found to be not significant. Further, the simulation provides humidity data in and beneath clouds which allows for an estimation of the uncertainty of data gaps in the lidar observations due to optically thick clouds. The error is identified to be in a range of only few percents. This thesis demonstrates that airborne DIAL observations are useful to build up a height-resolved statistical characterisation of tropospheric water vapour variability that allows to distinguish physical mechansims that are responsible for the water vapour distribution, to get new insights into stochastic parameterisations and further to use the structure function method as a suitable reality check of the numerical weather model COSMO-DE.