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

Fencl, Martin; Dohnal, Michal; Valtr, Pável; Grábner, Martin; Bareš, Vojtěch

doi:10.5194/amt-2020-28

Cited by 7 publications

(11 citation statements)

References 21 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The return of baseline attenuation to previous dry-weather levels is attributed to the drying of the antenna radomes(Van Leth et al, 2018). The same behaviour was observed by new E-bands and reported recently inFencl et al (2020).…”

supporting

confidence: 91%

“…Although, to date, most CMLs use frequencies from 5 GHz to 40 GHz, the spectrum is currently further extended to 80 GHz. Recently, Fencl et al (2020) used PARSIVEL observations from the CoMMon dataset to simulate rainfall retrieval from an E-band CML which demonstrated that these may be promising tools for sensing light rainfall which is challenging for lower frequencies due to the quantization of the attenuation data (Berne and Schleiss, 2009). In a similar fashion, CMLs are "blind" regarding extremely high intensities as attenuation due to such high intensities drops below the receiver threshold of the hardware and causes outages of the CML (cf.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Špačková¹,

Bareš²,

Fencl³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Commercial microwave links (CML) 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. To address this issue, the CoMMon field experiment (COmmercial Microwave links for urban rainfall MONitoring) monitored a 38-GHz dual-polarized CML of 1.85 km at a high temporal resolution (4 s), as well as a collocated array of five disdrometers and three rain gauges over one year. The dataset is complemented with observations from five nearby weather stations. Raw and pre-processed data, which can be explored effortlessly with a custom static HTML viewer, are available at https://doi.org/10.5281/zenodo.4524632 (Špačková et al., 2020). The data quality is generally satisfactory 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.

show abstract

supporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

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

Špačková¹,

Bareš²,

Fencl³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In both studies, convolutional neural networks (CNNs) are considered one of the leading neural network architectures for image and time series classification. CNNs are inspired by the visual cortex of mammals, and they are designed to recognize objects or patterns, regardless of their location in images or time series (Fukushima, 1980). They are characterized by local connections of neurons, shared weights, and a large number of layers of neurons, involving pooling layers (LeCun et al, 2015).…”

Section: Data-driven Optimization Through Deep Learningmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

Abstract. 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.

show abstract

“…Recent acquisition method developments by Chwala et al (2016) and the project TEL4RAIN (2020) are promising in this direction. Moreover, the introduction of 5G cellular networks can lead to new opportunities as E‐band CMLs are an essential part of these networks (Ericsson, 2019; Fencl et al, 2020). Provided that the nowcasts are skillful on the relevant spatial resolutions and lead times, CML nowcasts can provide an opportunity to reduce fatalities and economic loss, e.g., by improving hazardous weather and (flash‐)flood early warning(s).…”

Section: Discussionmentioning

confidence: 99%

Rainfall Nowcasting Using Commercial Microwave Links

Imhoff

Overeem

Brauer

et al. 2020

Geophysical Research Letters

View full text Add to dashboard Cite

Accurate and timely precipitation forecasts are crucial for early warning. Rainfall nowcasting, the process of statistically extrapolating recent rainfall observations, is increasingly used for short-term forecasting. Nowcasts are generally constructed with high-resolution radar observations. As a proof of concept, we construct nowcasts with country-wide rainfall maps estimated from signal level data of commercial microwave links (CMLs) for 12 summer days in the Netherlands. CML nowcasts compare well to radar rainfall nowcasts. Provided well-calibrated CML rainfall estimates are employed, CML nowcasts can outperform unadjusted real-time radar nowcasts for high rainfall rates, which are underestimated as compared to a reference. Care should be taken with the sensitivity of the advection field derivation to areas with low CML coverage and the inherent measurement scale of CML data, which can be larger than the application scale. We see potential for rainfall nowcasting with CML data, for example, in regions where weather radars are absent. Plain Language Summary Capturing the amount, timing and location of rainfall are of key importance for water management and the agricultural sector. Nowcasting of rainfall, a computational approach where the most recent rainfall observations are spatially extrapolated, is increasingly used for short-term rainfall forecasts. The required rainfall observations normally originate from weather radars, but it has previously been demonstrated that rainfall can also be estimated from rain-induced signal attenuation from the commercial microwave links (CMLs) in cellular telecommunication networks, which are used worldwide. In this study, we use such estimates to construct rainfall nowcasts for 12 summer events in the Netherlands, which we compare to nowcasts using operational radars. The results demonstrate that there is potential for using rainfall estimates from CML data to nowcast rainfall up to a few hours ahead, for example, in (low-and middle-income) regions where high-resolution rainfall observations or weather radars are absent.

show abstract

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

Cited by 7 publications

References 21 publications

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

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

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

Rainfall Nowcasting Using Commercial Microwave Links

Contact Info

Product

Resources

About