Articles | Volume 15, issue 2
https://doi.org/10.5194/amt-15-485-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-485-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jan 2022
Research article |
| 27 Jan 2022
| 27 Jan 2022
Rainfall retrieval algorithm for commercial microwave links: stochastic calibration
Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture (ESALQ/USP), Piracicaba, Brazil
Aart Overeem
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Hydrology and Quantitative Water Management Group, Wageningen University & Research (WUR), Wageningen, the Netherlands
Hidde Leijnse
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Hydrology and Quantitative Water Management Group, Wageningen University & Research (WUR), Wageningen, the Netherlands
Remko Uijlenhoet
Hydrology and Quantitative Water Management Group, Wageningen University & Research (WUR), Wageningen, the Netherlands
Department of Water Management, Delft University of Technology (TU Delft), Delft, the Netherlands
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Xuan Chen, Job Augustijn van der Werf, Arjan Droste, Miriam Coenders-Gerrits, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 29, 3447–3480, https://doi.org/10.5194/hess-29-3447-2025, https://doi.org/10.5194/hess-29-3447-2025, 2025
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The review highlights the need to integrate urban land surface and hydrological models to better predict and manage compound climate events in cities. We find that inadequate representation of water surfaces, hydraulic systems and detailed building representations are key areas for improvement in future models. Coupled models show promise but face challenges at regional and neighbourhood scales. Interdisciplinary communication is crucial to enhance urban hydrometeorological simulations.
Claudia C. Brauer, Ruben O. Imhoff, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2025-1712, https://doi.org/10.5194/egusphere-2025-1712, 2025
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In lowland catchments, flood severity is determined by both the amount of rain and how wet the soil is prior to the rain event. We investigated the trade-off between these two factors and how this affects peaks in the river discharge, for both the current and future climate. We found that with climate change floods will increase in winter and spring, but decease in fall. The total number and severity of floods will increase. This can help water managers to design climate robust water management.
Nathalie Rombeek, Markus Hrachowitz, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2025-1502, https://doi.org/10.5194/egusphere-2025-1502, 2025
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On 29 October 2024 Valencia (Spain) was struck by torrential rainfall, triggering devastating floods in this area. In this study, we quantify and describe the spatial and temporal structure of this rainfall event using personal weather stations (PWSs). These PWSs provide near real-time observations at a temporal resolution of ~5 min. This study shows the potential of PWSs for real-time rainfall monitoring and potentially flood early warning systems by complementing dedicated rain gauge networks.
Luuk D. van der Valk, Oscar K. Hartogensis, Miriam Coenders-Gerrits, Rolf W. Hut, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2025-1128, https://doi.org/10.5194/egusphere-2025-1128, 2025
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Commercial microwave links (CMLs), part of mobile phone networks, transmit comparable signals as instruments specially designed to estimate evaporation. Therefore, we investigate if CMLs could be used to estimate evaporation, even though they have not been designed for this purpose. Our results illustrate the potential of using CMLs to estimate evaporation, especially given their global coverage, but also outline some major drawbacks, often a consequence of unfavourable design choices for CMLs.
Aart Overeem, Hidde Leijnse, Mats Veldhuizen, and Bastiaan Anker
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-160, https://doi.org/10.5194/essd-2025-160, 2025
Revised manuscript accepted for ESSD
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The Dutch real-time gauge-adjusted radar product provides 5 min precipitation accumulations every 5 min covering the Netherlands and the area around it. It plays a key role in hydrological decision-support systems and as input for short-term weather forecasts. Major changes were implemented on 31 January 2023 and the associated quality improvement is presented. Moreover, the employed radar and rain gauge datasets and the algorithms needed to produce this real-time radar product are described.
Luuk D. van der Valk, Oscar K. Hartogensis, Miriam Coenders-Gerrits, Rolf W. Hut, Bas Walraven, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2024-2974, https://doi.org/10.5194/egusphere-2024-2974, 2025
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Commercial microwave links (CMLs), part of mobile phone networks, transmit comparable signals as instruments specially designed to estimate evaporation. Therefore, we investigate if CMLs could be used to estimate evaporation, even though they have not been designed for this purpose. Our results illustrate the potential of using CMLs to estimate evaporation, especially given their global coverage, but also outline some major drawbacks, often a consequence of unfavourable design choices for CMLs.
Nathalie Rombeek, Markus Hrachowitz, Arjan Droste, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2024-3207, https://doi.org/10.5194/egusphere-2024-3207, 2024
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Rain gauge networks from personal weather stations (PWSs) have a network density 100 times higher than dedicated rain gauge networks in the Netherlands. However, PWSs are prone to several sources of error, as they are generally not installed and maintained according to international guidelines. This study systematically quantifies and describes the uncertainties arising from PWS rainfall estimates. In particular, the focus is on the highest rainfall accumulations.
Abbas El Hachem, Jochen Seidel, Tess O'Hara, Roberto Villalobos Herrera, Aart Overeem, Remko Uijlenhoet, András Bárdossy, and Lotte de Vos
Hydrol. Earth Syst. Sci., 28, 4715–4731, https://doi.org/10.5194/hess-28-4715-2024, https://doi.org/10.5194/hess-28-4715-2024, 2024
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This study presents an overview of open-source quality control (QC) algorithms for rainfall data from personal weather stations (PWSs). The methodology and usability along technical and operational guidelines for using every QC algorithm are presented. All three QC algorithms are available for users to explore in the OpenSense sandbox. They were applied in a case study using PWS data from the Amsterdam region in the Netherlands. The results highlight the necessity for data quality control.
Athanasios Tsiokanos, Martine Rutten, Ruud J. van der Ent, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 28, 3327–3345, https://doi.org/10.5194/hess-28-3327-2024, https://doi.org/10.5194/hess-28-3327-2024, 2024
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We focus on past high-flow events to find flood drivers in the Geul. We also explore flood drivers’ trends across various timescales and develop a new method to detect the main direction of a trend. Our results show that extreme 24 h precipitation alone is typically insufficient to cause floods. The combination of extreme rainfall and wet initial conditions determines the chance of flooding. Precipitation that leads to floods increases in winter, whereas no consistent trends are found in summer.
Luuk D. van der Valk, Miriam Coenders-Gerrits, Rolf W. Hut, Aart Overeem, Bas Walraven, and Remko Uijlenhoet
Atmos. Meas. Tech., 17, 2811–2832, https://doi.org/10.5194/amt-17-2811-2024, https://doi.org/10.5194/amt-17-2811-2024, 2024
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Microwave links, often part of mobile phone networks, can be used to measure rainfall along the link path by determining the signal loss caused by rainfall. We use high-frequency data of multiple microwave links to recreate commonly used sampling strategies. For time intervals up to 1 min, the influence of sampling strategies on estimated rainfall intensities is relatively little, while for intervals longer than 5–15 min, the sampling strategy can have significant influences on the estimates.
Aart Overeem, Hidde Leijnse, Gerard van der Schrier, Else van den Besselaar, Irene Garcia-Marti, and Lotte Wilhelmina de Vos
Hydrol. Earth Syst. Sci., 28, 649–668, https://doi.org/10.5194/hess-28-649-2024, https://doi.org/10.5194/hess-28-649-2024, 2024
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Ground-based radar precipitation products typically need adjustment with rain gauge accumulations to achieve a reasonable accuracy. Crowdsourced rain gauge networks have a much higher density than conventional ones. Here, a 1-year personal weather station (PWS) gauge dataset is obtained. After quality control, the 1 h PWS gauge accumulations are merged with pan-European radar accumulations. The potential of crowdsourcing to improve radar precipitation products in (near) real time is confirmed.
Louise J. Schreyers, Tim H. M. van Emmerik, Thanh-Khiet L. Bui, Khoa L. van Thi, Bart Vermeulen, Hong-Q. Nguyen, Nicholas Wallerstein, Remko Uijlenhoet, and Martine van der Ploeg
Hydrol. Earth Syst. Sci., 28, 589–610, https://doi.org/10.5194/hess-28-589-2024, https://doi.org/10.5194/hess-28-589-2024, 2024
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River plastic emissions into the ocean are of global concern, but the transfer dynamics between fresh water and the marine environment remain poorly understood. We developed a simple Eulerian approach to estimate the net and total plastic transport in tidal rivers. Applied to the Saigon River, Vietnam, we found that net plastic transport amounted to less than one-third of total transport, highlighting the need to better integrate tidal dynamics in plastic transport and emission models.
Linda Bogerd, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet
Atmos. Meas. Tech., 17, 247–259, https://doi.org/10.5194/amt-17-247-2024, https://doi.org/10.5194/amt-17-247-2024, 2024
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Algorithms merge satellite radiometer data from various frequency channels, each tied to a different footprint size. We studied the uncertainty associated with sampling (over the Netherlands using 4 years of data) as precipitation is highly variable in space and time by simulating ground-based data as satellite footprints. Though sampling affects precipitation estimates, it doesn’t explain all discrepancies. Overall, uncertainties in the algorithm seem more influential than how data is sampled.
Bich Ngoc Tran, Johannes van der Kwast, Solomon Seyoum, Remko Uijlenhoet, Graham Jewitt, and Marloes Mul
Hydrol. Earth Syst. Sci., 27, 4505–4528, https://doi.org/10.5194/hess-27-4505-2023, https://doi.org/10.5194/hess-27-4505-2023, 2023
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Satellite data are increasingly used to estimate evapotranspiration (ET) or the amount of water moving from plants, soils, and water bodies into the atmosphere over large areas. Uncertainties from various sources affect the accuracy of these calculations. This study reviews the methods to assess the uncertainties of such ET estimations. It provides specific recommendations for a comprehensive assessment that assists in the potential uses of these data for research, monitoring, and management.
Aart Overeem, Else van den Besselaar, Gerard van der Schrier, Jan Fokke Meirink, Emiel van der Plas, and Hidde Leijnse
Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, https://doi.org/10.5194/essd-15-1441-2023, 2023
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EURADCLIM is a new precipitation dataset covering a large part of Europe. It is based on weather radar data to provide local precipitation information every hour and combined with rain gauge data to obtain good precipitation estimates. EURADCLIM provides a much better reference for validation of weather model output and satellite precipitation datasets. It also allows for climate monitoring and better evaluation of extreme precipitation events and their impact (landslides, flooding).
Femke A. Jansen, Remko Uijlenhoet, Cor M. J. Jacobs, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 26, 2875–2898, https://doi.org/10.5194/hess-26-2875-2022, https://doi.org/10.5194/hess-26-2875-2022, 2022
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We studied the controls on open water evaporation with a focus on Lake IJssel, the Netherlands, by analysing eddy covariance observations over two summer periods at two locations at the borders of the lake. Wind speed and the vertical vapour pressure gradient can explain most of the variation in observed evaporation, which is in agreement with Dalton's model. We argue that the distinct characteristics of inland waterbodies need to be taken into account when parameterizing their evaporation.
Ruben Imhoff, Claudia Brauer, Klaas-Jan van Heeringen, Hidde Leijnse, Aart Overeem, Albrecht Weerts, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 25, 4061–4080, https://doi.org/10.5194/hess-25-4061-2021, https://doi.org/10.5194/hess-25-4061-2021, 2021
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Significant biases in real-time radar rainfall products limit the use for hydrometeorological forecasting. We introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors to correct radar rainfall products and to benchmark other correction algorithms. When tested for 12 Dutch basins, estimated rainfall and simulated discharges with CARROTS generally outperform those using the operational mean field bias adjustments.
Simone Gelsinari, Valentijn R. N. Pauwels, Edoardo Daly, Jos van Dam, Remko Uijlenhoet, Nicholas Fewster-Young, and Rebecca Doble
Hydrol. Earth Syst. Sci., 25, 2261–2277, https://doi.org/10.5194/hess-25-2261-2021, https://doi.org/10.5194/hess-25-2261-2021, 2021
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Estimates of recharge to groundwater are often driven by biophysical processes occurring in the soil column and, particularly in remote areas, are also always affected by uncertainty. Using data assimilation techniques to merge remotely sensed observations with outputs of numerical models is one way to reduce this uncertainty. Here, we show the benefits of using such a technique with satellite evapotranspiration rates and coupled hydrogeological models applied to a semi-arid site in Australia.
Jolijn van Engelenburg, Erik van Slobbe, Adriaan J. Teuling, Remko Uijlenhoet, and Petra Hellegers
Drink. Water Eng. Sci., 14, 1–43, https://doi.org/10.5194/dwes-14-1-2021, https://doi.org/10.5194/dwes-14-1-2021, 2021
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This study analysed the impact of extreme weather events, water quality deterioration, and a growing drinking water demand on the sustainability of drinking water supply in the Netherlands. The results of the case studies were compared to sustainability issues for drinking water supply that are experienced worldwide. This resulted in a set of sustainability characteristics describing drinking water supply on a local scale in terms of hydrological, technical, and socio-economic characteristics.
Cited articles
Abdelaziz, R. and Zambrano-Bigiarini, M.: Particle Swarm Optimization for inverse modeling of solute transport in fractured gneiss aquifer, J. Contam. Hydrol., 164, 285–298, https://doi.org/10.1016/j.jconhyd.2014.06.003, 2014. a
Atlas, D. and Ulbrich, C. W.: Path- and area-integrated rainfall measurement
by microwave attenuation in the 1–3 cm band, J. Appl. Meteorol., 16, 1322–1331, https://doi.org/10.1175/1520-0450(1977)016<1322:PAAIRM>2.0.CO;2, 1977. a
Bianchi, B., van Leeuwen, P. J., Hogan, R. J., and Berne, A.: A variational approach to retrieve rain rate by combining information from rain gauges, radars, and microwave links, J. Hydrometeorol., 14, 1897–1909, https://doi.org/10.1175/JHM-D-12-094.1, 2013. a
Bisselink, B., Zambrano-Bigiarini, M., Burek, P., and de Roo, A.: Assessing
the role of uncertain precipitation estimates on the robustness of
hydrological model parameters under highly variable climate conditions,
Journal of Hydrology: Regional Studies, 8, 112–129,
https://doi.org/10.1016/j.ejrh.2016.09.003, 2016. a
Brauer, C. C., Overeem, A., Leijnse, H., and Uijlenhoet, R.: The effect of
differences between rainfall measurement techniques on groundwater and
discharge simulations in a lowland catchment, Hydrol. Process., 30,
3885–3900, https://doi.org/10.1002/hyp.10898, 2016. a
Chicco, D. and Jurman, G.: The advantages of the Matthews correlation
coefficient (MCC) over F1 score and accuracy in binary classification
evaluation, BMC Genomics, 21, 6, https://doi.org/10.1186/s12864-019-6413-7, 2020. a
Chwala, C. and Kunstmann, H.: Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges, WIREs Water, 6, e1337, https://doi.org/10.1002/wat2.1337, 2019. a, b, c
Clerc, M.: Standard Particle Swarm Optimisation, Hal Open Sci., 15 pp., available at: https://hal.archives-ouvertes.fr/hal-00764996 (last access: 21 September 2020), 2012. a
de Vos, L., Overeem, A., Leijnse, H., and Uijlenhoet, R.: Rainfall estimation accuracy of a nation-wide instantaneously sampling commercial microwave link network: error-dependency on known characteristics, J. Atmos. Ocean. Tech., 36, 1267–1283, https://doi.org/10.1175/JTECH-D-18-0197.1, 2019. a, b, c, d, e, f, g, h, i, j, k
Fencl, M., Rieckermann, J., Sýkora, P., Stránský, D., and
Bareš, V.: Commercial microwave links instead of rain gauges: fiction
or reality?, Water Sci. Technol., 71, 31,
https://doi.org/10.2166/wst.2014.466, 2015. a
Fencl, M., Valtr, P., Kvicera, M., and Bares, V.: Quantifying wet antenna
attenuation in 38-GHz commercial microwave links of cellular backhaul, IEEE
Geosci. Remote S., 16, 514–518, https://doi.org/10.1109/LGRS.2018.2876696, 2019. a
Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data, Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, 2020. a
GSMA: Mobile technology for rural climate resilience: The role of mobile
operators in bridging the data gap, tech. Rep., London, UK, available at:
https://www.gsma.com/mobilefordevelopment/wp-content/uploads/2019/10/GSMA_AgriTech_Climate_Report.pdf (last access: 13 August 2020), 2019. a
Habi, H. V. and Messer, H.: Recurrent neural network for rain estimation using commercial microwave links, IEEE T. Geosci. Remote, 59, 3672–3681, https://doi.org/10.1109/TGRS.2020.3010305, 2021. a
International Telecommunication Union: Specific attenuation model for rain
for use in prediction methods, Tech. rep., International Telecommunication
Union, available at: https://www.itu.int/dms_pubrec/itu-r/rec/p/R-REC-P.838-3-200503-I!!PDF-E.pdf (last access: 8 August 2021),
2005. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol.,
424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012. a, b
Leijnse, H.: Hydrometeorological application of microwave links: measurement
of evaporation and precipitation, PhD thesis, Wageningen University, ISBN 978-90-8504-775-9, 2007. a
Leijnse, H., Uijlenhoet, R., and Stricker, J. N. M.: Rainfall measurement
using radio links from cellular communication networks, Water Resour.
Res., 43, W03201, https://doi.org/10.1029/2006WR005631, 2007. a, b
Leijnse, H., Uijlenhoet, R., and Stricker, J.: Microwave link rainfall
estimation: effects of link length and frequency, temporal sampling, power
resolution, and wet antenna attenuation, Adv. Water Resour., 31,
1481–1493, https://doi.org/10.1016/j.advwatres.2008.03.004, 2008. a, b, c, d
Liberman, Y., Samuels, R., Alpert, P., and Messer, H.: New algorithm for integration between wireless microwave sensor network and radar for improved rainfall measurement and mapping, Atmos. Meas. Tech., 7, 3549–3563, https://doi.org/10.5194/amt-7-3549-2014, 2014. a
Matthews, B.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme, BBA-Protein Struct., 405, 442–451, https://doi.org/10.1016/0005-2795(75)90109-9, 1975. a
Messer, H.: Environmental monitoring by wireless communication networks,
Science, 312, 713–713, https://doi.org/10.1126/science.1120034, 2006. a
Messer, H. and Sendik, O.: A new approach to precipitation monitoring: a
critical survey of existing technologies and challenges, IEEE Signal
Proc. Mag., 32, 110–122, https://doi.org/10.1109/MSP.2014.2309705, 2015. a, b
Olsen, R., Rogers, D., and Hodge, D.: The aRb relation in the calculation
of rain attenuation, IEEE T. Antenn. Propag., 26, 318–329, https://doi.org/10.1109/TAP.1978.1141845, 1978. a
Ostrometzky, J. and Messer, H.: Statistical signal processing approach for rain estimation based on measurements from network management systems, in: ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4 May 2020, Barcelona, Spain, IEEE, 9026–9030, https://doi.org/10.1109/ICASSP40776.2020.9054652, 2020. a
Overeem, A.: Precipitation – 5 minute precipitation accumulations from climatological gauge-adjusted radar dataset for The Netherlands (1 km) in KNMI HDF5 format, KNMI Data Services [data set], available at: https://dataplatform.knmi.nl/dataset/rad-nl25-rac-mfbs-5min-2-0 (last access: 7 May 2020), 2017. a
Overeem, A.: Commercial microwave link data for rainfall monitoring, 4TU.ResearchData [data set], https://doi.org/10.4121/uuid:323587ea-82b7-4cff-b123-c660424345e5, 2019. a
Overeem, A., Buishand, T. A., and Holleman, I.: Extreme rainfall analysis and estimation of depth-duration-frequency curves using weather radar, Water
Resour. Res., 45, 1–15, https://doi.org/10.1029/2009WR007869, 2009a. a
Overeem, A., Holleman, I., and Buishand, A.: Derivation of a 10-year
radar-based climatology of rainfall, J. Appl. Meteorol. Clim., 48, 1448–1463, https://doi.org/10.1175/2009JAMC1954.1, 2009b. a
Overeem, A., Leijnse, H., and de Vos Lotte: RAINLINK: Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network, R package version 1.21, GitHub [code], available at: https://github.com/overeem11/RAINLINK, last access: 14 July 2021a. a
Overeem, A., Leijnse, H., van Leth, T. C., Bogerd, L., Priebe, J., Tricarico,
D., Droste, A., and Uijlenhoet, R.: Tropical rainfall monitoring with
commercial microwave links in Sri Lanka, Environ. Res. Lett., 16,
074058, https://doi.org/10.1088/1748-9326/ac0fa6, 2021b. a
Overeem, A., Leijnse, H., and de Vos, L.: RAINLINK: Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network, R package version 1.21, Zenodo [code], https://doi.org/10.5281/zenodo.5907524, 2022. a, b
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007. a
Pijl, A., Brauer, C. C., Sofia, G., Teuling, A. J., and Tarolli, P.:
Hydrologic impacts of changing land use and climate in the Veneto lowlands
of Italy, Anthropocene, 22, 20–30, https://doi.org/10.1016/j.ancene.2018.04.001,
2018. a
Polz, J., Chwala, C., Graf, M., and Kunstmann, H.: Rain event detection in commercial microwave link attenuation data using convolutional neural networks, Atmos. Meas. Tech., 13, 3835–3853, https://doi.org/10.5194/amt-13-3835-2020, 2020. a, b
Pudashine, J., Guyot, A., Petitjean, F., Pauwels, V. R. N., Uijlenhoet, R.,
Seed, A., Prakash, M., and Walker, J. P.: Deep learning for an improved
prediction of rainfall retrievals from commercial microwave links, Water
Resour. Res., 56, 1–10, https://doi.org/10.1029/2019WR026255, 2020. a
Pudashine, J., Guyot, A., Overeem, A., Pauwels, V. R., Seed, A., Uijlenhoet,
R., Prakash, M., and Walker, J. P.: Rainfall retrieval using commercial
microwave links: Effect of sampling strategy on retrieval accuracy, J. Hydrol., 603, 126909, https://doi.org/10.1016/j.jhydrol.2021.126909, 2021. a
R Core Team: R: A Language and Environment for Statistical Computing, Version 3.4.4, available at: http://www.R-project.org/ (last access: 7 July 2019), 2018. a
Rios Gaona, M. F., Overeem, A., Raupach, T. H., Leijnse, H., and Uijlenhoet, R.: Rainfall retrieval with commercial microwave links in São Paulo, Brazil, Atmos. Meas. Tech., 11, 4465–4476, https://doi.org/10.5194/amt-11-4465-2018, 2018. a
Roversi, G., Alberoni, P. P., Fornasiero, A., and Porcù, F.: Commercial microwave links as a tool for operational rainfall monitoring in Northern Italy, Atmos. Meas. Tech., 13, 5779–5797, https://doi.org/10.5194/amt-13-5779-2020, 2020. a
Schneider, U., Finger, P., Meyer-Christoffer, A., Ziese, M., and Becker, A.:
Global Precipitation Analysis Products of the GPCC, Tech. Rep. 9, National
Meteorological Service of Germany, Offenbach am Main, available at: https://opendata.dwd.de/climate_environment/GPCC/PDF/GPCC_intro_products_v2020.pdf,
last access: 7 September 2021. a, b
Smiatek, G., Keis, F., Chwala, C., Fersch, B., and Kunstmann, H.: Potential of commercial microwave link network derived rainfall for river runoff
simulations, Environ. Res. Lett., 12, 034026,
https://doi.org/10.1088/1748-9326/aa5f46, 2017. a
Sohail Afzal, M., Shah, S. H. H., Cheema, M. J. M., and Ahmad, R.: Real time rainfall estimation using microwave signals of cellular communication networks: a case study of Faisalabad, Pakistan, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2017-740, 2018. a
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.-L.: A
review of global precipitation data sets: data sources, estimation, and
intercomparisons, Rev. Geophys., 56, 79–107,
https://doi.org/10.1002/2017RG000574, 2018. a
Uijlenhoet, R., Overeem, A., and Leijnse, H.: Opportunistic remote sensing of rainfall using microwave links from cellular communication networks, WIREs Water, 5, 1–15, https://doi.org/10.1002/wat2.1289, 2018.
a, b
Upton, G., Holt, A., Cummings, R., Rahimi, A., and Goddard, J.: Microwave
links: The future for urban rainfall measurement?, Atmos. Res., 77,
300–312, https://doi.org/10.1016/j.atmosres.2004.10.009, 2005. a
Van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., and
Srinivasan, R.: A global sensitivity analysis tool for the parameters of
multi-variable catchment models, J. Hydrol., 324, 10–23,
https://doi.org/10.1016/j.jhydrol.2005.09.008, 2006. a, b, c, d
Zambrano-Bigiarini, M. and Rojas, R.: A model-independent Particle Swarm
Optimisation software for model calibration, Environ. Model. Softw., 43, 5–25, https://doi.org/10.1016/j.envsoft.2013.01.004, 2013. a
Zinevich, A., Messer, H., and Alpert, P.: Frontal rainfall observation by a
commercial microwave communication network, Jo. Appl. Meteorol. Clim., 48, 1317–1334, https://doi.org/10.1175/2008JAMC2014.1, 2009. a
Short summary
The existing infrastructure for cellular communication is promising for ground-based rainfall remote sensing. Rain-induced signal attenuation is used in dedicated algorithms for retrieving rainfall depth along commercial microwave links (CMLs) between cell phone towers. This processing is a source of many uncertainties about input data, algorithm structures, parameters, CML network, and local climate. Application of a stochastic optimization method leads to improved CML rainfall estimates.
The existing infrastructure for cellular communication is promising for ground-based rainfall...
