Commercial Microwave Links Research Articles

Estimating the Parameters of the Spatial Autocorrelation of Rainfall Fields by Measurements From Commercial Microwave Links

The spatial structure of rain fields is important to the understanding of their effects on ground-level aspects, such as runoff generation, and is considered crucial information for the accurate reconstruction of these fields. It is commonly characterized by a simplified spatial autocorrelation function (ACF). The near-ground ACF, and—particularly—its decorrelation distance, is evaluated from point measurements (rain gauges and distrometers). However, the spatial representation of such measurements is limited and therefore rarely sufficient for reliable ACF estimation. The emerging use of commercial microwave links (CMLs) for near-ground rain retrieval, and their spatial abundance, suggests using them for ACF estimation. In this study, we propose a method for extracting spatial features of a rain field, and in particular its decorrelation distance, from CML measurements. When sampled by path integration, the rain measurements acquire a distortion as a result of the averaging of a once fluctuating signal, where extreme rain intensities are being smeared. When evaluating the AFC from CMLs’ measurements, this effect needs to be compensated for. We propose methods for retrieving the original parameters characterizing the AFC and validate them on semisynthetic simulated data, based on actual rain events. The error was found to be 5%.

Application of Machine Learning Methods on Climate Data and Commercial Microwave Link Attenuations for Estimating Meteorological Visibility in Dusty Condition

Accurately measuring meteorological visibility is an important factor in road, sea, rail, and air transportation safety, especially under visibility-reducing weather events. This paper deals with the application of Machine Learning methods to estimate meteorological visibility in dusty conditions, from the power levels of commercial microwave links and weather data including temperature, dew point, wind speed, wind direction, and atmospheric pressure. Three well-known Machine Learning methods are investigated: Decision Trees, Random Forest, and Support Vector Machines. The correlation coefficient and the mean square error, between the visibility distances estimated by Machine Learning methods and those provided by Burkina Faso weather services are computed. Except for the SVM method, all the other methods give a correlation coefficient greater than 0.90. The Random Forest method presents the best result both in terms of correlation coefficient (0.97) and means square error (0.60). For this last method, the best variables that explain the model are selected by evaluating the weight of each variable in the model. The best performance is obtained by considering the attenuation of the microwave signal and the dew point.

Rain Events Detection using Energy Variation of Commercial Microwave Links Attenuation

Rain Events Detection using Energy Variation of Commercial Microwave Links Attenuation

Near Real-Time Estimation of High Spatiotemporal Resolution Rainfall from Cloud Top Properties of the Msg Satellite and Commercial Microwave Link Rainfall Intensities

Near Real-Time Estimation of High Spatiotemporal Resolution Rainfall from Cloud Top Properties of the Msg Satellite and Commercial Microwave Link Rainfall Intensities

Retrieving Water Vapor From an E‐Band Microwave Link With an Empirical Model Not Requiring In Situ Calibration

AbstractAn empirical model estimating water vapor density from the attenuation of electromagnetic waves at E‐band frequencies is proposed and tested on seven months of 5‐min attenuation observations from a 4.87‐km‐long full‐duplex E‐band commercial microwave link (CML) operating at 73.5 and 83.5 GHz. The model does not require in situ calibration. Estimated water vapor density is compared to the observations from two meteorological stations located nearby the CML end nodes. Best performance is achieved for the 83.5 GHz sublink (RMSE = 1.46 g m−3, R2 = 0.85) when the actual temperature is used as a model input, in addition to the attenuation data. Use of temperature averaged over the whole evaluation period resulted in significantly worse performance. The study demonstrates the potential of E‐band CMLs for measuring near‐surface air humidity and the need for careful quality control when upscaling the water vapor monitoring to the entire network of E‐band CMLs.

Rainfall retrieval using commercial microwave links: Effect of sampling strategy on retrieval accuracy

This study presents the first evaluation of using commercial microwave link (CML) data for rainfall measurements in Australia, with the test site being the greater Melbourne Metropolitan area. More than 100 CMLs with microwave frequency ranging between 10 and 40 GHz have been used for the rainfall retrieval. The 15-minute received signal levels (RSLs) for each CML based on two sampling strategies (average and minimum/maximum) collected for 2 years provided a unique dataset to compare performances of rainfall retrievals. The open source algorithm RAINLINK was used for deriving rainfall from the 15-minute RSL data. From two years of data, a subset of 30 rainy days distributed across this period were used for calibrating the RAINLINK parameters, with the remaining data used for validation. For this study, only path-averaged rainfall intensities were validated based on a gauge-adjusted radar product serving as the reference. The result of the wet-dry classification showed that the minimum and maximum RSL data performed better, with lower probability of false detection and higher Matthews correlation coefficient than average RSL data. For the rainfall retrieval, both datasets showed similar correlation with the gauge adjusted radar product. However, based on other statistics (RMSE, bias and CV) minimum and maximum RSL data outperformed average for the rainfall retrieval. Overall, this study highlights the robust accuracy of commercial microwave links for rainfall retrieval while using only minimum and maximum RSL data.

A year of attenuation data from a commercial dual-polarized duplex microwave link with concurrent disdrometer, rain gauge, and weather observations

Abstract. Commercial microwave links (CMLs) in telecommunication networks can provide relevant information for remote sensing of precipitation and other environmental variables, such as path-averaged drop size distribution, evaporation, or humidity. The CoMMon field experiment (COmmercial Microwave links for urban rainfall MONitoring) mainly focused on the rainfall observations by monitoring a 38 GHz dual-polarized CML of 1.85 km path length at a high temporal resolution (4 s), as well as a co-located array of five disdrometers and three rain gauges over 1 year. The dataset is complemented with observations from five nearby weather stations. Raw and pre-processed data, which can be explored with a custom static HTML viewer, are available at https://doi.org/10.5281/zenodo.4923125 (Špačková et al., 2021). The data quality is generally satisfactory for further analysis, and potentially problematic measurements are flagged to help the analyst identify relevant periods for specific study purposes. Finally, we encourage potential applications and discuss open issues regarding future remote sensing with CMLs.

The MSG Technique: Improving Commercial Microwave Link Rainfall Intensity by Using Rain Area Detection from Meteosat Second Generation

Commercial microwave link (MWL) used by mobile telecom operators for data transmission can provide hydro-meteorologically valid rainfall estimates according to studies in the past decade. For the first time, this study investigated a new method, the MSG technique, that uses Meteosat Second Generation (MSG) satellite data to improve MWL rainfall estimates. The investigation, conducted during daytime, used MSG optical (VIS0.6) and near IR (NIR1.6) data to estimate rain areas along a 15 GHz, 9.88 km MWL for classifying the MWL signal into wet–dry periods and estimate the baseline level. Additionally, the MSG technique estimated a new parameter, wet path length, representing the length of the MWL that was wet during wet periods. Finally, MWL rainfall intensity estimates from this new MSG and conventional techniques were compared to rain gauge estimates. The results show that the MSG technique is robust and can estimate gauge comparable rainfall estimates. The evaluation scores every three hours of RMSD, relative bias, and r2 based on the entire evaluation period results of the MSG technique were 2.61 mm h−1, 0.47, and 0.81, compared to 2.09 mm h−1, 0.04, and 0.84 of the conventional technique, respectively. For convective rain events with high intensity spatially varying rainfall, the results show that the MSG technique may approximate the actual mean rainfall estimates better than the conventional technique.

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.

Improved Gridded Precipitation Data Derived from Microwave Link Attenuation

The motivation for improving gridded precipitation data lies in weather now-casting and flood forecasting. Therefore, over the past decade, Commercial Microwave Link (CML) attenuation data have been used to determine rain rates between microwave antennas, and to produce more accurate countrywide precipitation grids. CML networks offer a unique advantage for precipitation measurements due to their high density. However, these data experience uncertainty from several sources as reported in earlier research. This current work determines the reliability of rainfall measurements for each link by comparing CML-derived rain rates to adjusted weather radar rainfall at the link location, over three months. Dynamic Time Warping (DTW) is applied to the pair of CML/radar time-series data in two study areas, Israel and Netherlands. Based on the DTW amplitude and temporal distance, unreliable links are identified and flagged, and interpolated gridded precipitation data are derived in each country after filtering out those unreliable links. Correlations between CML-derived grids and rain observations from an independent set of gauges, tested over several rain events in both study areas, are higher for the reliable subset of CML than the full set. For certain storm events, the Kendall rank correlation for the set of reliable CML is almost double that of the complete set, demonstrating that improved gridded precipitation data can be obtained by removing unreliable links.

Tropical rainfall monitoring with commercial microwave links in Sri Lanka

Commercial microwave links (CMLs) from cellular telecommunication networks can provide a valuable ‘opportunistic’ source of high-resolution space-time rainfall information, complementing traditional in-situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Their greatest potential lies in areas with low gauge densities and lack of weather radars, often in developing countries with a subtropical or tropical climate and generally large spatial rainfall variability. Here, the open-source R package RAINLINK is employed to retrieve CML rainfall maps covering the majority of Sri Lanka for a 3.5 month period based on CML data from on average 1140 link paths. These are compared locally to hourly and daily rain gauge data, as well as to rainfall maps from the dual-frequency precipitation radar on board the global precipitation measurement core observatory satellite. The potential of CMLs for real-time tropical rainfall monitoring is demonstrated.

Rainfall measurement from Commercial microwave links for urban hydrology in Africa: a simulation framework for sensitivity analysis.

Abstract Urban floods due to intense precipitation are a major problem in many tropical regions as in Africa. Rainfall measurement using microwave links from cellular communication networks has been proposed as a cost effective solution to monitor rainfall in these areas where the gauge network is scarce. The method consists in retrieving rainfall from the attenuation estimated along the commercial microwave links (CMLs) thanks to the power levels provided by an operator. In urban areas where the network is dense, rainfall can be estimated and mapped for hydrological prediction. Rainfall estimation from CMLs is subject to uncertainties. This paper analyzes the advantages and limitations of this rainfall data for a distributed hydrological model applied to an urban area. The case study is in West Africa in Ouagadougou where a hydrological model has been set up. The analysis is based on numerical simulations, using high resolution rain maps from a weather radar to emulate synthetic microwave links. Two sources of uncertainty in the rain estimation and on the simulated discharge are analyzed by simulations: i) the precision of the raw information provided by the operator and ii) the density and geometry of the network. A coarse precision (1 dB) in the signal provided by the operator can lead to substantial underestimation of rainfall and discharge, especially for links operating at low frequency (below 10 GHz) or short (less than 1 km). The density of the current mobile networks in urban areas is appropriate to analyze hydrological impact of tropical convective rainfall.

Estimating Water Vapor Using Signals from Microwave Links below 25 GHz

Water vapor is a key element in both the greenhouse effect and the water cycle. However, water vapor has not been well studied due to the limitations of conventional monitoring instruments. Recently, estimating rain rate by the rain-induced attenuation of commercial microwave links (MLs) has been proven to be a feasible method. Similar to rainfall, water vapor also attenuates the energy of MLs. Thus, MLs also have the potential of estimating water vapor. This study proposes a method to estimate water vapor density by using the received signal level (RSL) of MLs at 15, 18, and 23 GHz, which is the first attempt to estimate water vapor by MLs below 20 GHz. This method trains a sensing model with prior RSL data and water vapor density by the support vector machine, and the model can directly estimate the water vapor density from the RSLs without preprocessing. The results show that the measurement resolution of the proposed method is less than 1 g/m3. The correlation coefficients between automatic weather stations and MLs range from 0.72 to 0.81, and the root mean square errors range from 1.57 to 2.31 g/m3. With the large availability of signal measurements from communications operators, this method has the potential of providing refined data on water vapor density, which can contribute to research on the atmospheric boundary layer and numerical weather forecasting.

Quantitative Analysis of the Performance of Spatial Interpolation Methods for Rainfall Estimation Using Commercial Microwave Links

AbstractUsing signal level measurements from commercial microwave links (CMLs) has proven to be a valuable tool for near-ground 2D rain mapping. Such mapping is commonly based on spatial interpolation methods, where each CML is considered as a point measurement instrument located at its center. The validity of the resulted maps is tested against radar observations. However, since radar has limitations, accuracy of CML-based reconstructed rain maps remains unclear. Here we provide a quantitative comparison of the performance of CML-based spatial interpolation methods for rain mapping by conducting a systematic analysis: first by quantifying the performance of maps generated from semisynthetic CML data, and thereafter turning to real-data analysis of the same rain events. A radar product of the German Weather Service serves as ground truth for generating semisynthetic data, in which several temporal aggregations of the radar rainfall fields are used to create different decorrelation distances. The study was done over an area of 225 × 245 km2 in southern Germany, with 808 CMLs. We compare the performance of two spatial interpolation methods—inverse distance weighting and ordinary kriging—in two cases: where each CML is represented as a single point, and where three points are used. The points’ measurements values in the latter are determined using an iterative algorithm. The analysis of both cases is based on a 48-h rain event. The results reconfirm the validity of CML-based rain retrieval, showing a slight systematic performance improvement when an iterative algorithm is applied so each CML is represented by more than a single point, independent of the interpolation method.

On the Power of Microwave Communication Data to Monitor Rain for Agricultural Needs in Africa

Over the last two decades, prevalent technologies and Internet of Things (IoT) systems have been found to have potential for carrying out environmental monitoring. The data generated from these infrastructures are readily available and have the potential to provide massive spatial coverage. The costs involved in using these data are minimal since the records are already generated for the original uses of these systems. Commercial microwave links, which provide the underlying framework for data transfer between cellular network base stations, are one example of such a system and have been found useful for monitoring rainfall. Wireless infrastructure of this kind is deployed widely by communication providers across Africa and can thus be used as a rainfall monitoring device to complement the sparse proprietary resources that currently exist or to substitute for them where alternatives do not exist. Here we focus this approach’s potential to acquire valuable information required for agricultural needs across Africa using Kenya as an example.

RMWSPy (v 1.1): A Python code for spatial simulation and inversion for environmental applications

Spatial simulation and inversion problems are omnipresent in earth and environmental sciences. An open-source Python package (RMWSPy) for conditional spatial random field simulation and inversion based on a generalized implementation of the Random Mixing Whittaker-Shannon (RMWS) algorithm is presented in this paper. The RMWS algorithm has successfully been applied to a variety of environmental modelling problems, ranging from inverse groundwater flow and transport modelling to precipitation simulation incorporating incomplete observations. RMWSPy provides great flexibility due to its variety of linear and non-linear conditioning constraints. The generalized implementation isolates the core algorithm from the user-defined problem statement. In this paper, RMWSPy is introduced using a synthetic inversion example for spatial precipitation estimation which combines rain gauge data and integral rain rates obtained from Commercial Microwave Link data. The required Python scripts are described and the results of one precipitation event are presented and discussed.

Water Vapor Retrieval Using Commercial Microwave Links Based on the LSTM Network

In this article, a water vapor density inversion model based on the long short-term memory network is proposed for E-band commercial microwave links (CMLs). A full-duplex E-band microwave link located in Prague (two sublinks with frequencies of 73.5 and 83.5 GHz, both vertically polarized) was used to verify the performance of the model. The results show that the model inversion results are in good agreement with the water vapor density calculated by temperature and humidity sensors. Compared with previous water vapor inversion methods based on CMLs, this model has a higher temporal resolution and can realize real-time monitoring.

On the Use of Minimum and Maximum Attenuation for Retrieving Rainfall Intensity Through Commercial Microwave Links

On the Use of Minimum and Maximum Attenuation for Retrieving Rainfall Intensity Through Commercial Microwave Links

Atmospheric observations with E-band microwave links – challenges and opportunities

Abstract. Opportunistic sensing of rainfall and water vapor using commercial microwave links operated within cellular networks was conceived more than a decade ago. It has since been further investigated in numerous studies, predominantly concentrating on the frequency region of 15–40 GHz. This article provides the first evaluation of rainfall and water vapor sensing with microwave links operating at E-band frequencies (specifically 71–76 and 81–86 GHz). These microwave links are increasingly being updated (and are frequently replacing) older communication infrastructure. Attenuation–rainfall relations are investigated theoretically on drop size distribution data. Furthermore, quantitative rainfall estimates from six microwave links, operated within cellular backhaul, are compared with observed rainfall intensities. Finally, the capability to detect water vapor is demonstrated on the longest microwave link measuring 4.86 km in path length. The results show that E-band microwave links are markedly more sensitive to rainfall than devices operating in the 15–40 GHz range and can observe even light rainfalls, a feat practically impossible to achieve previously. The E-band links are, however, substantially more affected by errors related to variable drop size distribution. Water vapor retrieval might be possible from long E-band microwave links; nevertheless, the efficient separation of gaseous attenuation from other signal losses will be challenging in practice.

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

Abstract. There 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, 5 km×5 km 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 single-link 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.