2019, Vierinen, J., Chau, J.L., Charuvil, H., Urco, J.M., Clahsen, M., Avsarkisov, V., Marino, R., Volz, R.

There are few observational techniques for measuring the distribution of kinetic energy within the mesosphere with a wide range of spatial and temporal scales. This study describes a method for estimating the three-dimensional mesospheric wind field correlation function from specular meteor trail echoes. Each radar echo provides a measurement of a one-dimensional projection of the wind velocity vector at a randomly sampled point in space and time. The method relies on using pairs of such measurements to estimate the correlation function of the wind with different spatial and temporal lags. The method is demonstrated using a multistatic meteor radar data set that includes ≈105 meteor echoes observed during a 24-hr time period. The new method is found to be in good agreement with the well-established technique for estimating horizontal mean winds. High-resolution correlation functions with temporal, horizontal, and vertical lags are also estimated from the data. The temporal correlation function is used to retrieve the kinetic energy spectrum, which includes the semidiurnal mode and a 3-hr period wave. The horizontal and vertical correlation functions of the wind are then used to derive second-order structure functions, which are found to be compatible with the Kolmogorov prediction for spectral distribution of kinetic energy in the turbulent inertial range. The presented method can be used to extend the capabilities of specular meteor radars. It is relatively flexible and has a multitude of applications beyond what has been shown in this study.

2018, Oppermann, Frank, Günther, Thomas

We present a new versatile datalogger that can be used for a wide range of possible applications in geosciences. It is adjustable in signal strength and sampling frequency, battery saving and can remotely be controlled over a Global System for Mobile Communication (GSM) connection so that it saves running costs, particularly in monitoring experiments. The internet connection allows for checking functionality, controlling schedules and optimizing pre-amplification. We mainly use it for large-scale electrical resistivity tomography (ERT), where it independently registers voltage time series on three channels, while a square-wave current is injected. For the analysis of this time series we present a new approach that is based on the lock-in (LI) method, mainly known from electronic circuits. The method searches the working point (phase) using three different functions based on a mask signal, and determines the amplitude using a direct current (DC) correlation function. We use synthetic data with different types of noise to compare the new method with existing approaches, i.e. selective stacking and a modified fast Fourier transformation (FFT)-based approach that assumes a 1∕f noise characteristics. All methods give comparable results, but the LI is better than the well-established stacking method. The FFT approach can be even better but only if the noise strictly follows the assumed characteristics. If overshoots are present in the data, which is typical in the field, FFT performs worse even with good data, which is why we conclude that the new LI approach is the most robust solution. This is also proved by a field data set from a long 2-D ERT profile.

2015, Guchhait, Biswajit, Liu, Yingliang, Siebert, Torsten, Elsaesser, Thomas

DNA oligomers are studied at 0% and 92% relative humidity, corresponding to N < 2 and N > 20 water molecules per base pair. Two-dimensional (2D) infrared spectroscopy of DNA backbone modes between 920 and 1120 cm(-1) maps fluctuating interactions at the DNA surface. At both hydration levels, a frequency fluctuation correlation function with a 300 fs decay and a slow decay beyond 10 ps is derived from the 2D lineshapes. The fast component reflects motions of DNA helix, counterions, and water shell. Its higher amplitude at high hydration level reveals a significant contribution of water to the fluctuating forces. The slow component reflects disorder-induced inhomogeneous broadening.