Preprints
https://doi.org/10.5194/amt-2021-34
https://doi.org/10.5194/amt-2021-34

  12 Mar 2021

12 Mar 2021

Review status: this preprint is currently under review for the journal AMT.

Rainfall retrieval algorithm for commercial microwave links: stochastic calibration

Wagner Wolff1, Aart Overeem2,3, Hidde Leijnse2,3, and Remko Uijlenhoet3,4 Wagner Wolff et al.
  • 1Department of Biosystems Engineering, University of São Paulo/“Luiz de Queiroz” College of Agriculture (ESALQ/USP)
  • 2R&D Observations and Data Technology, Royal Netherlands Meteorological Institute (KNMI)
  • 3Hydrology and Quantitative Water Management Group, Wageningen University & Research (WUR)
  • 4Department of Water Management, Delft University of Technology (TU Delft)

Abstract. During the last decade, rainfall monitoring using signal level data from commercial microwave links (CMLs) in cellular communication networks has been proposed as a complementary way to estimate rainfall for large areas. Path-averaged rainfall is retrieved between the transmitting and receiving cellular antenna of a CML. One rainfall estimation algorithm for CMLs is RAINLINK, which has been employed in different regions (e.g., Brazil, Italy, the Netherlands, and Pakistan) with satisfactory results. However, the RAINLINK parameters have been calibrated for a unique optimum solution, which is inconsistent with the fact that multiple similar or equivalent solutions may exist due to uncertainties in algorithm structure, input data, and parameters. Here, we show how CML rainfall estimates can be improved by calibrating all parameters of the algorithm systematically and simultaneously with the stochastic optimization method Particle Swarm Optimisation, which is used for the numerical maximization of the objective function. An open dataset of approximately 2,800 sub-links of minimum and maximum received signal levels over 15-minute intervals covering the Netherlands (~35,500 km2) is employed, where 12 days are used for calibration and 3 months for validation. A gauge-adjusted radar rainfall dataset is utilized as reference. Verification of path-average daily rainfall shows a reasonable improvement for the stochastically calibrated parameters with respect to RAINLINK's default parameter settings. Results further improve when averaged over the Netherlands. Moreover, the method provides a better underpinning of the chosen parameter values and is therefore of general interest for calibration of RAINLINK's parameters for other climates and cellular communication networks.

Wagner Wolff et al.

Status: open (until 07 May 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-34', Anonymous Referee #1, 08 Apr 2021 reply
  • RC2: 'Comment on amt-2021-34', Anonymous Referee #2, 14 Apr 2021 reply

Wagner Wolff et al.

Data sets

Commercial microwave link data for rainfall monitoring A. Overeem https://doi.org/10.4121/uuid:323587ea-82b7-4cff-b123-c660424345e5

Wagner Wolff et al.

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Short summary
The existing infrastructure for cellular communication is promising for ground-based rainfall remote sensing. The rain-induced signal attenuation is used in dedicated algorithms for retrieving rainfall depth along commercial microwave links (CMLs) between cellphone towers. This processing is 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.