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  • 1
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    Transboundary Rainfall Estimation Using Commercial Microwave Links (2023)
    Details
    Publication Date: 2023-12-05
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Unlike actual rainfall, the spatial extent of rainfall maps is often determined by administrative and political boundaries. Similarly, data from commercial microwave links (CMLs) is usually acquired on a national basis and exchange among countries is limited. Up to now, this has prohibited the generation of transboundary CML‐based rainfall maps despite the great extension of networks across the world. We present CML based transboundary rainfall maps for the first time, using independent CML data sets from Germany and the Czech Republic. We show that straightforward algorithms used for quality control strongly reduce anomalies in the results. We find that, after quality control, CML‐based rainfall maps can be generated via joint and consistent processing, and that these maps allow to seamlessly visualize rainfall events traversing the German‐Czech border. This demonstrates that quality control represents a crucial step for large‐scale (e.g., continental) CML‐based rainfall estimation.〈/p〉
    Description: Plain Language Summary: Rainfall maps are usually based on gauge observations on the ground or radar. They are crucial for predicting or reconstructing flooding events. Commercial microwave links are special kinds of rainfall sensors. Their actual purpose is the signal propagation within a cellular network. However, since the signal is attenuated when it rains, they can also be exploited for rainfall estimation. To estimate rainfall from the observed attenuation requires careful data processing. Algorithms for that are usually adjusted to national data sets with their specific characteristics. In this study, we use, for the first time, two independent data sets of commercial microwave links (from Germany and the Czech Republic) with the aim of generating transboundary rainfall maps. As the data sets vary in many respects, several algorithms need to be adjusted and extended to allow processing them consistently. We show that it is possible to create meaningful rainfall maps of rain events that traverse the border between Germany and the Czech Republic. This can be considered a major step toward seamless rainfall maps on even larger, that is, continental scale.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉Transboundary rainfall maps based on independent networks of commercial microwave links (CMLs) are generated for the first time〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉German and Czech data sets of CMLs differ significantly with respect to network characteristics〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉Quality control is important for heterogeneous data of CMLs〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: German Research Foundation
    Description: Czech Science Foundation
    Description: https://doi.org/10.5281/zenodo.4810169
    Description: https://doi.org/10.5281/zenodo.7973736
    Description: https://opendata.dwd.de/climate_environment/CDC
    Keywords: ddc:551.6 ; transboundary rainfall maps ; commercial microwave links ; quantitative precipitation estimation ; data quality control
    Language: English
    Type: doc-type:article
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    CITATION GEO-LEO
  • 2
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    Missing Rainfall Extremes in Commercial Microwave Link Data Due To Complete Loss of Signal (2023)
    Details
    Publication Date: 2023-12-05
    Description: An important aspect of rainfall estimation is to accurately capture extreme events. Commercial microwave links (CMLs) can complement weather radar and rain gauge data by estimating path‐averaged rainfall intensities near ground. Our aim with this paper was to investigate attenuation induced complete loss of signal (blackout) in the CML data. This effect can occur during heavy rain events and leads to missing extreme values. We analyzed 3 years of attenuation data from 4,000 CMLs in Germany and compared it to a weather radar derived attenuation climatology covering 20 years. We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for longer CMLs (e.g., 〉10 km) despite their increased dynamic range. Therefore, both the hydrometeorological community and network providers can consider our analysis to develop mitigation measures.
    Description: Plain Language Summary: Commercial microwave links (CMLs) are used to transmit information between towers of cellphone networks. If there is rainfall along the transmission path, the signal level is attenuated. By comparing the transmitted and received signal levels, the average rainfall intensity along the path can be estimated. If the attenuation is too strong, no signal is received, no information can be transmitted and no rainfall estimate is available. This is unfavorable both for network stability and rainfall estimation. In this study, we investigated the frequency of such blackouts in Germany. How many blackouts per year are observed in a 3 year CML data set covering around 4,000 link paths and how many are expected from 20 years of weather radar data? We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for long CMLs, which was an unexpected finding. While only one percent of the annual rainfall amount is missed during blackouts, the probability that a blackout occurs was very high for high rain rates. Both, the hydrometeorological community and network providers can consider our analysis to develop mitigation measures.
    Description: Key Points: Complete loss of commercial microwave link (CML) signals during heavy rain leads to missing rainfall extremes. Magnitude of observed blackouts exceeds climatologically expected values. Unexpectedly, longer CMLs experience more blackouts.
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: Helmholtz Association http://dx.doi.org/10.13039/501100009318
    Description: Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347
    Description: Karlsruhe Institute of Technology http://dx.doi.org/10.13039/100009133
    Description: https://doi.org/10.5281/zenodo.7245440
    Description: https://github.com/pycomlink/pycomlink/blob/12fc302539851b19f7656cf7e2438c0ddbaa48bf/notebooks/Blackout%20gap%20detection%20examples.ipynb
    Description: https://doi.org/10.5281/zenodo.6337557
    Description: https://doi.org/10.5676/DWD/RADKLIM_YW_V2017.002
    Keywords: ddc:551.6 ; commercial microwave links ; rainfall ; opportunistic sensing ; weather radar ; rainfall extremes ; precipitation
    Language: English
    Type: doc-type:article
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  • 3
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    Combining Commercial Microwave Link and Rain Gauge Observations to Estimate Countrywide Precipitation: A Stochastic Reconstruction and Pattern Analysis Approach (2022)
    Details
    Publication Date: 2024-02-28
    Description: Accurate spatiotemporal precipitation quantification is a crucial prerequisite for hydrological analyses. The optimal reconstruction of the spatial distribution, that is, the rainfall patterns, is particularly challenging. In this study, we reconstructed spatial rainfall on a countrywide scale for Germany by combining commercial microwave link and rain gauge observations for a better representation of the variability and spatial structure of rainfall. We further developed and applied the Random‐Mixing‐Whittaker‐Shannon method, enabling the stochastic reconstruction of ensembles of spatial fields via linear combinations of unconditional random fields. The pattern of rainfall objects is evaluated by three performance characteristics, that is, ensemble Structure‐, Amplitude‐, and Location‐error. Precipitation estimates obtained are in good agreement with the gauge‐adjusted weather radar product RADOLAN‐RW of the German Weather Service (DWD) which was used as a reference. Compared to reconstructions by Ordinary Kriging, Random Mixing showed clear advantages in the pattern representation via a five times smaller median structure error.
    Description: Plain Language Summary: Rainfall is commonly measured by dedicated sensors such as rain gauges or weather radars. Commercial microwave links (CMLs), which have the primary purpose of signal forwarding within cellular networks, can be used for rainfall measurements too. The signal, which is transmitted from one antenna to another, is being attenuated if it rains along the path. From the amount of attenuation an average rain rate can be retrieved. For many hydrological applications, it is of major interest to estimate area‐wide rainfall (i.e., rainfall maps) while observations provide only scattered information. In this study, we used the local information from almost 1,000 rain gauges and the information along the paths of 3,900 CMLs distributed over Germany to reconstruct rainfall maps. We did this by applying a method of stochastic simulation (called Random Mixing) which we compared to a more common method of estimation (Ordinary Kriging). To evaluate the quality of the obtained maps, we compared them to rainfall information from weather radars. We found that the general agreement is high, and that maps reconstructed by Random Mixing have particular advantages in representing the spatial structure, that is, the shape of rainfall cells.
    Description: Key Points: Geostatistical Random Mixing simulation now capable of countrywide spatial rainfall interpolation. Variability assessment via commercial microwave link path consideration and ensemble estimation. Realistic rainfall pattern representation quantified by ensemble Structure‐, Amplitude‐, and Location‐error metrics.
    Description: German Research Foundation
    Description: Federal Ministry of Education and Research
    Description: https://doi.org/10.5281/zenodo.4810169
    Description: https://opendata.dwd.de/climate_environment/CDC
    Description: https://maps.dwd.de/geoserver/web/wicket/bookmarkable/org.geoserver.web.demo.SRSDescriptionPage?10 26code=EPSG:1000001
    Description: https://doi.org/10.5281/zenodo.5380342
    Description: https://doi.org/10.5281/zenodo.7048941
    Description: https://doi.org/10.5281/zenodo.7049826
    Description: https://doi.org/10.5281/zenodo.7049846
    Keywords: ddc:551.5 ; precipitation estimation ; geostatistical simulation ; spatial pattern analysis ; commercial microwave links ; rain gauges ; random mixing
    Language: English
    Type: doc-type:article
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  • 4
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    Rain event detection in commercial microwave link attenuation data using convolutional neural networks (2020)
    Copernicus Publications (EGU)
    In:  Atmospheric Measurement Techniques, 13 (7). pp. 3835-3853.
    Details
    Publication Date: 2021-01-08
    Description: 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.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.5194/amt-13-3835-2020
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 5
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    Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data (2020)
    Copernicus Publications (EGU)
    In:  Hydrology and Earth System Sciences, 24 (6). pp. 2931-2950.
    Details
    Publication Date: 2021-01-08
    Description: Rainfall is one of the most important environmental variables. However, it is a challenge to measure it accurately over space and time. During the last decade, commercial microwave links (CMLs), operated by mobile network providers, have proven to be an additional source of rainfall information to complement traditional rainfall measurements. In this study, we present the processing and evaluation of a German-wide data set of CMLs. This data set was acquired from around 4000 CMLs distributed across Germany with a temporal resolution of 1 min. The analysis period of 1 year spans from September 2017 to August 2018. We compare and adjust existing processing schemes on this large CML data set. For the crucial step of detecting rain events in the raw attenuation time series, we are able to reduce the amount of misclassification. This was achieved by using a new approach to determine the threshold, which separates a rolling window standard deviation of the CMLs' signal into wet and dry periods. For the compensation for wet antenna attenuation, we compare a time-dependent model with a rain-rate-dependent model and show that the rain-rate-dependent model performs better for our data set. We use RADOLAN-RW, a gridded gauge-adjusted hourly radar product from the German Meteorological Service (DWD) as a precipitation reference, from which we derive the path-averaged rain rates along each CML path. Our data processing is able to handle CML data across different landscapes and seasons very well. For hourly, monthly, and seasonal rainfall sums, we found good agreement between CML-derived rainfall and the reference, except for the winter season due to non-liquid precipitation. We discuss performance measures for different subset criteria, and we show that CML-derived rainfall maps are comparable to the reference. This analysis shows that opportunistic sensing with CMLs yields rainfall information with good agreement with gauge-adjusted radar data during periods without non-liquid precipitation.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.5194/hess-24-2931-2020
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    OceanRep: Article in a Scientific Journal - peer-reviewed
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