Can fiber-optic distributed sensing be used to resolve temperature turbulence values over time and space?

Turbulence is essential for land atmosphere interactions; however, it is difficult to quantify because of its statistical nature. Typically, turbulence is determined using time series data, on which Taylor’s hypothesis is applied to obtain turbulent data over a length scale. Taylor’s frozen turbulence hypothesis is an assumption in the core of turbulence research, however currently turbulence measurements are limited to either time series (e.g., sonic anemometers) or integrated spatial measurements (e.g., scintillometers). Therefore, the spatiotemporal nature of turbulence cannot be independently assessed. In this study we use fiber-optic distributed sensing (FODS) to measure turbulence over both time and space.

The turbulence parameter used is the structure parameter of temperature, C_T², which quantifies the intensity of temperature fluctuations over a certain scale. The structure parameter can be determined using temperature series directly, using its definition. Alternatively, the inertial range of the turbulent temperature spectrum can be used to obtain structure parameter through the Kolmogorov -5/3 power law.

A FODS experiment was conducted in the LIAISE (Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment) field campaign during 15-30 July 2021 in the north-east of Spain. A set-up was installed with a horizontal extent of 70 m, measuring at four heights between 0.40 m and 2.05 m. A thin 0.5 mm cable was used in an effort to obtain the fastest possible time response. Measurements were made at 1 Hz and 12.7 cm resolution, however the actual sampling frequency appeared to be 0.15 Hz in the temperature spectrum, likely because of the long response time of the cable.               

Despite the limited 0.15 Hz sampling rate it was possible to obtain turbulence information through the use of the structure parameter of temperature. This parameter indicates the intensity of temperature fluctuations and was calculated over time, as is conventional. In a novel approach, it was also calculated over space. The spatial structure parameter obtained through the definition method was found to have the best correlation with a sonic anemometer reference, with a correlation coefficient of 0.88.

The temporal structure parameter lacks the structure that is shown in the spatial method, which is likely due to the use of 30-min averaged data for horizontal wind speed from the sonic anemometer or to Taylor's frozen turbulence hypothesis not being a suitable assumption within the dimensions of this research. Determining structure parameters through the turbulent spectrum was successful for limited data points for the time seriess and is currently inconclusive for the spatial series. This work provides a first step towards using FODS in capturing turbulent information along spatial temperature series.