Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales

Study region: The study region is Germany and two sub-regions in Germany, i.e. the state of Rhineland-Palatinate and the city of Reutlingen. Study focus: Opportunistic rainfall sensors, namely personal weather stations and commercial microwave links, together with rain gauge data from the German Weather Service, were used in different combinations to derive rainfall maps with a geostatistical interpolation framework for Germany. This kriging type framework considered the uncertainty of opportunistic sensors and the line structure of commercial microwave links. The resulting rainfall maps were compared to two gauge-adjusted radar products and evaluated to three reference gauge datasets in the respective study regions on both daily and hourly basis. New Hydrological Insights for the Region: The interpolated rainfall products from opportunistic sensors provided good agreement to the reference rain gauges. The dataset combinations including information from the opportunistic sensors performed best. The addition of rain gauges from the German Weather Service did not consistently lead to an improvement of the interpolated rainfall maps. On the country-wide, daily scale the interpolated rainfall maps performed well, but the gauge-adjusted radar products were closer to the reference. For the regional and local scale in Rhineland-Palatinate and Reutlingen with an hourly resolution, the interpolated rainfall maps outperformed the interpolated product from DWD rain gauges and showed a similar agreement to the reference as the radar products

Deep convolutional regression modelling for forest parameter retrieval

Accurate forest monitoring is crucial as forests are major global carbon sinks. Additionally, accurate prediction of forest parameters, such as forest biomass and stem volume (SV), has economic importance. Therefore, the development of regression models for forest parameter retrieval is essential. Existing forest parameter estimation methods use regression models that establish pixel-wise relationships between ground reference data and corresponding pixels in remote sensing (RS) images. However, these models often overlook spatial contextual relationships among neighbouring pixels, limiting the potential for improved forest monitoring. The emergence of deep convolutional neural networks (CNNs) provides opportunities for enhanced forest parameter retrieval through their convolutional filters that allow for contextual modelling. However, utilising deep CNNs for regression presents its challenges. One significant challenge is that the training of CNNs typically requires continuous data layers for both predictor and response variables. While RS data is continuous, the ground reference data is sparse and scattered across large areas due to the challenges and costs associated with in situ data collection. This thesis tackles challenges related to using CNNs for regression by introducing novel deep learning-based solutions across diverse forest types and parameters. To address the sparsity of available reference data, RS-derived prediction maps can be used as auxiliary data to train the CNN-based regression models. This is addressed through two different approaches. Although these prediction maps offer greater spatial coverage than the original ground reference data, they do not ensure spatially continuous prediction target data. This work proposes a novel methodology that enables CNN-based regression models to handle this diversity. Efficient CNN architectures for the regression task are developed by investigating relevant learning objectives, including a new frequency-aware one. To enable large-scale and cost-effective regression modelling of forests, this thesis suggests utilising C-band synthetic aperture radar SAR data as regressor input. Results demonstrate the substantial potential of C-band SAR-based convolutional regression models for forest parameter retrieval

1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

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