Taylor's frozen turbulence hypothesis is the central assumption invoked in most experiments designed to investigate turbulence physics with time resolving sensors. It is also frequently used in theoretical discussions when linking Lagrangian to Eulerian flow formalisms. In this work we seek to quantify the effectiveness of Taylor's hypothesis on the field scale using water vapour as a passive tracer. A horizontally orientated Raman lidar is used to capture the humidity field in space and time above an agricultural region in Switzerland. High resolution wind speed and direction measurements are conducted simultaneously allowing for a direct test of Taylor's hypothesis at the field scale. Through a wavelet decomposition of the lidar humidity measurements we show that the scale of turbulent motions has a strong influence on the applicability of Taylor's hypothesis. This dependency on scale is explained through the use of dimensional analysis. We identify a 'persistency scale' that can be used to quantify the effectiveness of Taylor's hypothesis, and present the accuracy of the hypothesis as a function of this non-dimensional length scale. These results are further investigated and verified through the use of large-eddy simulations.

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The flux of water vapor due to advection is measured using high-resolution Raman lidar that was orientated horizontally across a land-lake transition. At the same time, a full surface energy balance is performed to assess the impact of scalar advection on energy budget closure. The flux of water vapor due to advection is then estimated with analytical solutions to the humidity transport equation that show excellent agreement with the field measurements. Although the magnitude of the advection was not sufficient to account for the total energy deficit for this field site, the analytical approach is used to explore situations where advection would be the dominant transport mechanism. The authors find that advection is at maximum when the measurement height is 0.036 times the distance to a land surface transition. The framework proposed in this paper can be used to predict the potential impact of advection on surface flux measurements prior to field deployment and can be used as a data analysis algorithm to calculate the flux of water vapor due to advection from field measurements.

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The transposition of atmospheric turbulence statistics from the time domain, as conventionally sampled in field experiments, is explained by the so‐called ergodic hypothesis. In micrometeorology, this hypothesis assumes that the time average of a measured flow variable represents an ensemble of independent realizations from similar meteorological states and boundary conditions. That is, the averaging duration must be sufficiently long to include a large number of independent realizations of the sampled flow variable so as to represent the ensemble. While the validity of the ergodic hypothesis for turbulence has been confirmed in laboratory experiments, and numerical simulations for idealized conditions, evidence for its validity in the atmospheric surface layer (ASL), especially for nonideal conditions, continues to defy experimental efforts. There is some urgency to make progress on this problem given the proliferation of tall tower scalar concentration networks aimed at constraining climate models yet are impacted by nonideal conditions at the land surface. Recent advancements in water vapor concentration lidar measurements that simultaneously sample spatial and temporal series in the ASL are used to investigate the validity of the ergodic hypothesis for the first time. It is shown that ergodicity is valid in a strict sense above uniform surfaces away from abrupt surface transitions. Surprisingly, ergodicity may be used to infer the ensemble concentration statistics of a composite grass‐lake system using only water vapor concentration measurements collected above the sharp transition delineating the lake from the grass surface.