Improved Water Vapor Density Estimation With Commercial Microwave Links Attenuation And Temperature

Itay Bragin*, Yoav Rubin, Pinhas Alpert, Jonatan Ostrometzky

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Water vapor measurement is beneficial for weather models. A machine learning support vector machine model for estimating water vapor density at a reference weather station location using measurements of the received signal level from commercial microwave link and trained with data from the reference weather station has already been proposed. In this paper, we propose an enhanced machine learning model that utilizes three commercial microwave links inside a given area, as well as additional temperature observations. This model can achieve higher accuracy of water vapor estimation (when compared to the weather station as ground truth). Specifically, we present preliminary results, and show that although certain uncertainties exist, the root mean square error achieved by the presented approach was more than twice as small as the error achieved when utilizing a model using a single commercial microwave link.

Original languageEnglish
Title of host publicationICASSPW 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350302615
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, ICASSPW 2023 - Rhodes Island, Greece
Duration: 4 Jun 202310 Jun 2023

Publication series

NameICASSPW 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, Proceedings

Conference

Conference2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, ICASSPW 2023
Country/TerritoryGreece
CityRhodes Island
Period4/06/2310/06/23

Keywords

  • Commercial Microwave Links
  • Humidity
  • Machine Learning
  • Water Vapor Density

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