On the opportunistic use of geostationary satellite signals to estimate rain rate in the purpose of radar calibration

International audienceRain gauge networks are often used for radar calibration. However, the deployment of such networks is relatively complex and expensive. The present study deals with the development of a new low cost microwave device devoted to radar calibration. The principle of this device is to use rain atmospheric attenuation along microwave links to deduce the path averaged rain rate. In order to perform a feasibility study of such a device, a measurement campaign is performed for two years near Paris. Measurements of atmospheric attenuation over an earth-space link have been carried out by receiving TV channels from different geostationary satellites in Ku-band. These links are characterized by an aperture angle of 2° and a 30° elevation angle corresponding more or less to a 6 km path length through troposphere. The aim of this paper is to propose an algorithm to retrieve rain rate from the measured signal and to quantify the expected averaged rain rate accuracy along the earth-space link. In practice, the received signal is sensitive to rain as well as to many other fluctuations due to atmospheric scintillations, clouds, water vapor, small changes in the satellite orbit, power fluctuations

Opportunistic rain rate estimation from measurements of satellite downlink attenuation: A survey

Recent years have witnessed a growing interest in techniques and systems for rainfall surveillance on regional scale, with increasingly stringent requirements in terms of the following: (i) accuracy of rainfall rate measurements, (ii) adequate density of sensors over the territory, (iii) space‐time continuity and completeness of data and (iv) capability to elaborate rainfall maps in near real time. The devices deployed to monitor the precipitation fields are traditionally networks of rain gauges distributed throughout the territory, along with weather radars and satellite remote sensors operating in the optical or infrared band, none of which, however, are suitable for full compliance to all of the requirements cited above. More recently, a different approach to rain rate estimation techniques has been proposed and investigated, based on the measurement of the attenuation induced by rain on signals of pre‐existing radio networks either in terrestrial links, e.g., the backhaul connections in cellular networks, or in satellite‐to‐earth links and, among the latter, notably those between geostationary broadcast satellites and domestic subscriber terminals in the Ku and Ka bands. Knowledge of the above rain‐induced attenuation permits the retrieval of the corresponding rain intensity provided that a number of meteorological and geometric parameters are known and ultimately permits estimating the rain rate locally at the receiver site. In this survey paper, we specifically focus on such a type of “opportunistic” systems for rain field monitoring, which appear very promising in view of the wide diffusion over the territory of low‐cost domestic terminals for the reception of satellite signals, prospectively allowing for a considerable geographical capillarity in the distribution of sensors, at least in more densely populated areas. The purpose of the paper is to present a broad albeit synthetic overview of the numerous issues inherent in the above rain monitoring approach, along with a number of solutions and algorithms proposed in the literature in recent years, and ultimately to provide an exhaustive account of the current state of the art. Initially, the main relevant aspects of the satellite link are reviewed, including those related to satellite dynamics, frequency bands, signal formats, propagation channel and radio link geometry, all of which have a role in rainfall rate estimation algorithms. We discuss the impact of all these factors on rain estimation accuracy while also highlighting the substantial differences inherent in this approach in comparison with traditional rain monitoring techniques. We also review the basic formulas relating rain rate intensity to a variation of the received signal level or of the signal‐to-noise ratio. Furthermore, we present a comprehensive literature survey of the main research issues for the aforementioned scenario and provide a brief outline of the algorithms proposed for their solution, highlighting their points of strength and weakness. The paper includes an extensive list of bibliographic references from which the material presented herein was taken

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

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\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

Variability of the Rain Drop Size Distribution:Stochastic Simulation and Application to Telecommunication Microwave Links

Precipitation is an important component of the Earth' water cycle and needs to be carefully monitored. Its large variability over a wide range of spatial and temporal scales must be taken into account. For example, hydrological models require accurate rainfall estimates at high spatial and temporal resolutions (e.g., 1 km and 5 min or higher). Obtaining accurate rainfall estimates at these scales is known to be difficult. So far, the only instruments capable of measuring rainfall over extended domains at such resolutions are weather radars. Their estimates are, however, affected by large errors and uncertainties partly due to the spatial and temporal variability of the drop size distribution (DSD). Major progress in the field is slowed down by the lack of knowledge about the spatial and temporal variability of DSD at scales that are relevant in remote sensing. This lack of reference data can be addressed through two different methods : (1) experimental investigations and (2) stochastic simulation. In this thesis, a comprehensive framework for the stochastic simulation of DSD fields at high spatial and temporal resolutions is proposed. The method is based on Geostatistics and uses variograms to describe the spatial and temporal structures of the DSD. The simulator' ability to generate large numbers of DSD fields sharing the same statistical properties provides a very useful theoretical framework that nicely complements experimental approaches based on large networks of weather sensors. To illustrate its potential, the simulator is applied to different rain events and validated using data from a network of disdrometers at EPFL. The results show that the simulator is able to reproduce realistic spatial and temporal structures that are in adequacy with ground measurements. The second part of this thesis focuses on the simulation and parametrization of intermittency (i.e., the alternating between dry/rainy periods). Simple scaling functions that can be used to downscale/upscale intermittency at different spatial and temporal resolutions are proposed and used to parametrize a new disaggregation method that includes the DSD as an output. Finally, different methods to identify dry and rainy periods and to quantify rainfall intermittency using telecommunication microwave links are proposed. The false dry/wet classification error rates of each method are estimated using data from a new and innovative experimental set-up located in Dübendorf, Switzerland. The results show that the dry/wet classification is significantly improved when data from multiple channels are used

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

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

Transboundary Rainfall Estimation Using Commercial Microwave Links

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