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Rainfall monitoring with opportunistic sensors
Group: Wageningen University
Promotor: Remko Uijlenhoet
Co-promotors: Hidde Leijnse & Aart Overeem
In this thesis I evaluate the potential of two opportunistic sensing techniques for operational rainfall monitoring, namely crowdsourcing personal weather stations (PWS) and rainfall estimates from power levels in commercial microwave links (CML). This work presents a validation study of PWS rainfall data in Amsterdam (Chapter 2), a validation study of CML rainfall estimates in the Netherlands (Chapter 3), a simulation study on PWS, CML and radar rainfall sampling in Amsterdam (Chapter 4), proposes a quality control methodology and shows the resulting improved accuracy on a PWS dataset in Amsterdam and the entire Netherlands (Chapter 5) and showcases the ability of various opportunistic sensing techniques, including CML and PWS to describe two weather events in Amsterdam (Chapter 6). PWS after quality control yields higher accuracies than CML in the Netherlands. While CML and PWS should not replace traditional sensors, they are able to improve rainfall monitoring, especially in urban areas.
By September we will present an overview of SENSE dissertations on this page, with links to the full texts of the dissertations.