Articles | Volume 15, issue 2
Atmos. Meas. Tech., 15, 485–502, 2022
https://doi.org/10.5194/amt-15-485-2022
Atmos. Meas. Tech., 15, 485–502, 2022
https://doi.org/10.5194/amt-15-485-2022

Research article 27 Jan 2022

Research article | 27 Jan 2022

Rainfall retrieval algorithm for commercial microwave links: stochastic calibration

Wagner Wolff et al.

Data sets

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

Precipitation - 5 minute precipitation accumulations from climatological gauge-adjusted radar dataset for The Netherlands (1 km) in KNMI HDF5 format Aart Overeem https://dataplatform.knmi.nl/dataset/rad-nl25-rac-mfbs-5min-2-0

Model code and software

RAINLINK: Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network, R package version 1.21 Aart Overeem, Hidde Leijnse, and Lotte de Vos https://doi.org/10.5281/zenodo.5907524

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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.