Predicting near-surface temperature profiles is an essential, yet often challenging aspect of modeling boundary layer meteorology. The surface temperature is commonly inferred from similarity relationships. These predict the vertical profiles of both wind and temperature at some height above a surface with roughness elements, such as grass. These profiles have a logarithmic shape along the vertical. Due to experimental limitations, there are very few observations in the region close to the surface where roughness elements are present. As a consequence, the logarithmic profiles are commonly extrapolated down to the surface. However, this approach is physically inconsistent at its core. Temperature gradients become infinite as the surface is approached, as a result of the logarithmic properties of the similarity profiles. One consequence is that these profiles are extremely sensitive to small perturbations close to the surface, which is a major source of uncertainty when extrapolating temperatures. To combat this, new physical models are being investigated in an attempt to describe these profiles in a more accurate and physically rigorous manner.
A broader goal in the field of atmospheric science is to study a way to unify internal canopy dynamics with the dynamics above the canopy to yield temperature profiles that are valid from the surface, through the canopy, up into the atmosphere. In working towards these goals, a key element still lacks; precise, high resolution temperature measurements through the canopy-atmosphere interface. Novel measurement techniques such as distributed temperature sensing (DTS) have advanced the quality of datasets significantly, yielding temperature profiles with a resolution and accuracy on the order of centimeters. However, it has thus far not yielded sufficient accuracy for conclusive model comparison and for studying internal canopy temperature profiles. Therefore, there is a need for a more accurate, high-resolution dataset.
To this end, an experiment was designed to gain detailed insight into these regimes. A helical frame structure was designed, built and combined with the method of distributed temperature sensing (DTS) to attain a high resolution temperature profile along the vertical. The setup was installed at Cabauw where several weeks of data were acquired. Preliminary data analysis shows that the resulting data is well suited for the aforementioned purposes. Strong gradients near the surface can be identified as a result of the high measurement resolution on the millimeter scale. Furthermore, close to 80 data points are located within the 10cm canopy, allowing for the investigation of internal canopy transport dynamics. Finally, the data may be used in the future to validate any future models that aim to combine the canopy and atmospheric regimes.