6,477 research outputs found

    Monthly and Diurnal Variability of Rain Rate and Rain Attenuation during the Monsoon Period in Malaysia

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    Rain is the major source of attenuation for microwave propagation above 10 GHz. In tropical and equatorial regions where the rain intensity is higher, designing a terrestrial and earth-to-satellite microwave links is very critical and challenging at these frequencies. This paper presents the preliminary results of rain effects in a 23 GHz terrestrial point-to-point communication link 1.3km long. The experimental test bed had been set up at Skudai, Johor Bahru, Malaysia. In this area, a monsoon equatorial climate prevails and the rainfall rate can reach values well above 100mm/h with significant monthly and diurnal variability. Hence, it is necessary to implement a mitigation technique for maintaining an adequate radio link performance for the action of very heavy rain. Since we now know that the ULPC (Up Link Power Control) cannot guarantee the desired performance, a solution based on frequency band diversity is proposed in this paper. Here, a secondary radio link operating in a frequency not affected by rain (C band for instance) is placed parallel with the main link. Under no rain or light rain conditions, the secondary link carries without priority radio signals. When there is an outage of the main link due to rain, the secondary link assumes the priority traffic. The outcome of the research shows a solution for higher operating frequencies during rainy events

    Wavelet Transforms for Rain and Snow Classification with Commercial Microwave Links: Evaluation Using Real-World Data

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    The need for improved precipitation estimations has prompted the exploration of opportunistic alternatives such as utilizing commercial microwave links (CML), particularly in areas with poor coverage of weather radars and rain gauges. It has been known that rainfall-induced attenuation in the microwave signal can be used to determine rainfall intensity accurately. However, detecting other types of precipitation, such as dry snow, remains a challenge. This study evaluates the feasibility of using wavelet transform combined with a random forest classifier to identify rain and snow events. Real-world signal attenuation data from telecommunication operators and precipitation data from nearby disdrometers in Norway were used to develop the classification methods proposed in this study. The rain classifier was based on data from June 2022, while the snow classifier was evaluated using data from December 2021. The operating frequency of the CMLs used in this study was between 30-40 GHz. The algorithm for rain detection performed similarly to other wet-dry classification methods, with a mean Matthews correlation coefficient (MCC) of 36 % among 52 CMLs. The snow detection algorithm, however, showed no correlation between signal attenuation from 41 CMLs and dry snowfall. In conclusion, the wavelet transforms effectively extract useful information from signal attenuation for rain classification but are unsuitable for detecting snow. Moreover, the study recommends testing commercial microwave links with higher operating frequencies than those used in this study, combined with temperature data, to improve the possibilities of dry snow detection

    Commercial microwave links as a tool for operational rainfall monitoring in Northern Italy

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    open4noThere is a growing interest in emerging opportunistic sensors for precipitation, motivated by the need to improve its quantitative estimates at the ground. The scope of this work is to present a preliminary assessment of the accuracy of commercial microwave link (CML) retrieved rainfall rates in Northern Italy. The CML product, obtained by the open-source RAINLINK software package, is evaluated on different scales (single link, 5kmx5km grid, river basin) against the precipitation products operationally used at Arpae-SIMC, the regional weather service of Emilia-Romagna, in Northern Italy. The results of the 15 min singlelink validation with nearby rain gauges show high variability, which can be caused by the complex physiography and precipitation patterns. Known sources of errors (e.g. the attenuation caused by the wetting of the antennas or random fluctuations in the baseline) are particularly hard to mitigate in these conditions without a specific calibration, which has not been implemented. However, hourly cumulated spatially interpolated CML rainfall maps, validated with respect to the established regional gauge-based reference, show similar performance (R2 of 0.46 and coefficient of variation, CV, of 0.78) to adjusted radar-based precipitation gridded products and better performance than satellite-based ones. Performance improves when basin-scale total precipitation amounts are considered (R2 of 0.83 and CV of 0.48). Avoiding regional-specific calibration therefore does not preclude the algorithm from working but has some limitations in probability of detection (POD) and accuracy. A widespread underestimation is evident at both the grid box scale (mean error of 0:26) and the basin scale (multiplicative bias of 0.7), while the number of false alarms is generally low and becomes even lower as link coverage increases. Also taking into account delays in the availability of the data (latency of 0.33 h for CML against 1 h for the adjusted radar and 24 h for the quality-controlled rain gauges), CML appears as a valuable data source in particular from a local operational framework perspective. Finally, results show complementary strengths for CMLs and radars, encouraging joint exploitation.openGiacomo Roversi, Pier Paolo Alberoni, Anna Fornasiero, Federico PorcùGiacomo Roversi, Pier Paolo Alberoni, Anna Fornasiero, Federico Porc

    The future of Earth observation in hydrology

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    In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

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

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    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

    A monostatic microwave transmission experiment for line integrated precipitation and humidity remote sensing

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    AbstractNear-surface water vapor and precipitation are central hydrometeorological observables which are still difficult to quantify accurately above the point scale. Both play an important role in modeling and remote sensing of the hydrologic cycle. We present details on the development of a new microwave transmission experiment that is capable of providing line integrated estimates of both humidity and precipitation near the surface. The system is located at a hydrometeorological test site (TERENO-prealpine) in Southern Germany. Path length is kept short at 660m to minimize the likelihood of different precipitation types and intensities along the path. It uses a monostatic configuration with a combined transmitter/receiver unit and a 70cm trihedral reflector. The transmitter/receiver unit simultaneously operates at 22.235GHz and 34.8GHz with a pulse repetition rate of 25kHz and alternating horizontal and vertical polarization, which enable the analysis of the impact of the changing drop size distribution on the rain rate retrieval. Due to the coherence and the high phase stability of the system, it allows for a sensitive observation of the propagation phase delay. Thereof, time series of line integrated refractivity can be determined. This proxy is then post-processed to absolute humidity and compared to station observations. We present the design of the system and show an analysis of selected periods for both, precipitation and humidity observations. The theoretically expected dependence of attenuation and differential attenuation on the DSD was reproduced with experimental data. A decreased performance was observed when using a fixed A–R power law. Humidity data derived from the phase delay measurement showed good agreement with in situ measurements

    Assessing the utility of geospatial technologies to investigate environmental change within lake systems

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    Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

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

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    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

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

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    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\u27s 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
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