Christian Chwala

The SCALEX Campaign: Scale-Crossing Land Surface and Boundary Layer Processes in the TERENO-preAlpine Observatory

AbstractScaleX is a collaborative measurement campaign, collocated with a long-term environmental observatory of the German Terrestrial Environmental Observatories (TERENO) network in the mountainous terrain of the Bavarian Prealps, Germany. The aims of both TERENO and ScaleX include the measurement and modeling of land surface–atmosphere interactions of energy, water, and greenhouse gases. ScaleX is motivated by the recognition that long-term intensive observational research over years or decades must be based on well-proven, mostly automated measurement systems, concentrated in a small number of locations. In contrast, short-term intensive campaigns offer the opportunity to assess spatial distributions and gradients by concentrated instrument deployments, and by mobile sensors (ground and/or airborne) to obtain transects and three-dimensional patterns of atmospheric, surface, or soil variables and processes. Moreover, intensive campaigns are ideal proving grounds for innovative instruments, methods, and...

Precipitation observation using commercial microwave communication links

Rain rate observation over complex terrain is still afflicted with large uncertainties introduced by system inherent drawbacks of radar and gauge measurements. We use a new method for near surface rain rate estimation exploiting attenuation data from commercial microwave backhaul links. Our test region is the pre-alpine and alpine region of Southern Germany where we record received signal level (RSL) data with minute resolution directly at the communication towers using small data loggers. To dynamically set the RSL baseline from which the attenuation is calculated, a wet/dry classification based on spectral time series analysis is employed. This algorithm is applied to continuous minute resolution RSL data from July 2010 till October 2010 for five microwave backhaul links. The derived rain rates are compared to weather radar and gauge data yielding good correlations of up to R2 = 0.84.

Stochastic Reconstruction and Interpolation of Precipitation Fields Using Combined Information of Commercial Microwave Links and Rain Gauges

For the reconstruction and interpolation of precipitation fields, we present the application of a stochastic approach called Random Mixing. Generated fields are based on a data set consisting of rain gauge observations and path-averaged rain rates estimated using Commercial Microwave Link (CML) derived information. Precipitation fields are received as linear combination of unconditional spatial random fields, where the spatial dependence structure is described by copulas. The weights of the linear combination are optimized such that the observations and the spatial structure of the precipitation observations are reproduced. The innovation of the approach is that this strategy enables the simulation of ensembles of precipitation fields of any size. Each ensemble member is in concordance with the observed path-averaged CML derived rain rates and additionally reflects the observed rainfall variability along the CML paths. The ensemble spread allows additionally an estimation of the uncertainty of the reconstructed precipitation fields. The method is demonstrated both for a synthetic data set and a real-world data set in South Germany. While the synthetic example allows an evaluation against a known reference, the second example demonstrates the applicability for real-world observations. Generated precipitation fields of both examples reproduce the spatial precipitation pattern in good quality. A performance evaluation of Random Mixing compared to Ordinary Kriging demonstrates an improvement of the reconstruction of the observed spatial variability. Random Mixing is concluded to be a beneficial new approach for the provision of precipitation fields and ensembles of them, in particular when different measurement types are combined.

ANN applications in detection of precipitation based on the received signal level of commercial microwave links

Detection of precipitation based on the received signal level of commercial microwave links has been increasingly used in the mountain areas where meteorological radars have limited ranges, and placing rain gauges is impossible due to terrain morphology. In this paper, focused time-delay neural networks were trained and tested, to detect the appearance of precipitation based on the data of the link received signal level. For training and testing the networks the results of the detection of precipitation using one of the previously proposed methods have been used. After choosing the network with the best characteristics for the final model, the detailed testing was done with the data obtained on the same link, which were not used for model development. The results show that the proposed method based on neural networks can be efficiently used instead of the previously proposed method (significantly shorter time of the data processing was achieved by using a neural networks).

Modelling of electromagnetic transmission through rain fields based on drop-scale scattering

This article presents extensions of a numerical technique to model electromagnetic propagation through rain. Embedded in the Helmholtz Virtual Institute “Regional Precipitation Observation by Cellular Network Microwave Attenuation and Application to Water Resources Management” (PROCEMA) the method serves as theoretical basis for a novel method to measure ground-based precipitation. Indeed Messer et al. [1] propose to utilize attenuation of commercial point-to-point links for obtaining precipitation information. This theoretical framework models a rain field and its influence on electromagnetic propagation taking into account drop scale scattering as well as rain drop movement. In the virtual rain model the rain drops populate a volume and scatter the incident electromagnetic wave by each drop individually. A derivation of the attenuation of the incident field by the global rain field extends the original algorithm. Additionally, a table of the scattering coefficients further accelerates numerical computations within the model.