Recurrent Neural Network for Rain Estimation Using Commercial Microwave Links

Hai Victor Habi, Hagit Messer

Research output: Contribution to journalArticlepeer-review


The use of recurrent neural networks (RNNtext{s}) to utilize measurements from commercial microwave links (CMLtext{s}) has recently gained attention. Whereas previous studies focused on the performance of methods for wet-dry classification, here we propose an RNN algorithm for estimating the rain-rate. We empirically analyzed the proposed algorithm, using real data, and compared it with the traditional power-law (PL)-based algorithm, commonly used for estimating rain from CML attenuation measurements. Our analysis shows that the data-driven RNN algorithm, when properly trained, outperforms the PL algorithm in terms of accuracy. On the other hand, the PL algorithm is simpler and more robust when dealing with a large variety of corruptions and adverse conditions. We then introduced a time normalization (TN) layer for controlling the trade-off between performance and robustness of the RNN methods, and demonstrated its performance.

Original languageEnglish
Article number9153027
Pages (from-to)3672-3681
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number5
StatePublished - May 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1980-2012 IEEE.


  • Commercial microwave links (CMLs)
  • GRU
  • power-law (PL)
  • rain estimation
  • recurrent neural network (RNN)
  • robustness


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