Rain event detection in commercial microwave link attenuation data using convolutional neural networks

Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. This processing step is particularly challenging, because even when there is no rain, the signal level can show large fluctuations similar to that during rainy periods. False classifications can have a high impact on falsely estimated rainfall amounts. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall-specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4×105 trainable parameters. With a structure inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time series. We test the CNN's ability to recognize attenuation patterns from CMLs and time periods outside the training data. Our CNN is trained on 4 months of data from 800 randomly selected CMLs and validated on 2 different months of data, once for all CMLs and once for the 3104 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set, we use the gauge-adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a state-of-the-art reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 76 % of all rainy and 97 % of all nonrainy periods. From all periods with a reference rain rate larger than 0.6 mm h−1, more than 90 % was detected. We also show that the improved event detection leads to a significant reduction of falsely estimated rainfall by up to 51 %. At the same time, the quality of the correctly estimated rainfall is kept at the same level in regards to the Pearson correlation with the radar rainfall. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall-induced attenuation patterns in CML signal levels from a large CML data set covering all of Germany

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

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions on how to capitalize on state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world

Atmospheric remote sensing and radiopropagation: from numerical modeling to spaceborne and terrestrial applications

The remote sensing of electromagnetic wave properties is probably the most viable and fascinating way to observe and study physical media, comprising our planet and its atmosphere, at the same time ensuring a proper continuity in the observations. Applications are manifold and the scientific community has been importantly studying and investing on new technologies, which would let us widen our knowledge of what surrounds us. This thesis aims at showing some novel techniques and corresponding applications in the field of the atmospheric remote sensing and radio-propagation, at both microwave and optical wavelengths. The novel Sun-tracking microwave radiometry technique is shown. The antenna noise temperature of a ground-based microwave radiometer is measured by alternately pointing toward-the-Sun and off-the-Sun while tracking it along its diurnal ecliptic. During clear sky the brightness temperature of the Sun disk emission at K and Ka frequency bands and in the under-explored millimeter-wave V and W bands can be estimated by adopting different techniques. Parametric prediction models for retrieving all-weather atmospheric extinction from ground-based microwave radiometers are tested and their accuracy evaluated. Moreover, a characterization of suspended clouds in terms of atmospheric path attenuation is presented, by exploiting a stochastic approach used to model the time evolution of the cloud contribution. A model chain for the prediction of the tropospheric channel for the downlink of interplanetary missions operating above Ku band is proposed. On top of a detailed description of the approach, the chapter presents the validation results and examples of the model-chain online operation. Online operation has already been tested within a feasibility study applied to the BepiColombo mission to Mercury operated by the European Space Agency (ESA) and by exploiting the Hayabusa-2 mission Ka-band data by the Japan Aerospace Exploration Agency (JAXA), thanks to the ESA cross-support service. A preliminary (and successful) validation of the model-chain has been carried out by comparing the simulated signal-to-noise ratio with the one received from Hayabusa-2. At the next ITU World Radiocommunication Conference 2019, Agenda Item 1.13 will address the identification and the possible additional allocation of radio-frequency spectrum to serve the future development of systems supporting the fifth generation of cellular mobile communications (5G). The potential impact of International Mobile Telecommunications (IMT) deployments is shown in terms of received radio frequency interference by ESA’s telecommunication links. Received interference can derive from several radio-propagation mechanisms, which strongly depend on atmospheric conditions, radio frequency, link availability, distance and path topography; at any time a single mechanism, or more than one may be present. Results are shown in terms of required separation distances, i.e. the minimum distance between the earth station and the IMT station ensuring that the protection criteria for the earth station are met

Disaster warning system: Satellite feasibility and comparison with terrestrial systems. Volume 2: Final report

For abstract, see Vol. 1