Rainfall retrieval using commercial microwave links

Rainfall retrieval using commercial microwave links: Effect of sampling strategy on retrieval accuracy

Pudashine, Jayaram (Monash University)
Guyot, Adrien (Monash University)
Overeem, A. (Wageningen University & Research; Royal Netherlands Meteorological Institute (KNMI))
Pauwels, Valentijn R.N. (Monash University)
Seed, Alan (Griffith University)
Uijlenhoet, R. (TU Delft Water Resources)
Prakash, Mahesh (Data 61-CSIRO)

2021

This study presents the first evaluation of using commercial microwave link (CML) data for rainfall measurements in Australia, with the test site being the greater Melbourne Metropolitan area. More than 100 CMLs with microwave frequency ranging between 10 and 40 GHz have been used for the rainfall retrieval. The 15-minute received signal levels (RSLs) for each CML based on two sampling strategies (average and minimum/maximum) collected for 2 years provided a unique dataset to compare performances of rainfall retrievals. The open source algorithm RAINLINK was used for deriving rainfall from the 15-minute RSL data. From two years of data, a subset of 30 rainy days distributed across this period were used for calibrating the RAINLINK parameters, with the remaining data used for validation. For this study, only path-averaged rainfall intensities were validated based on a gauge-adjusted radar product serving as the reference. The result of the wet-dry classification showed that the minimum and maximum RSL data performed better, with lower probability of false detection and higher Matthews correlation coefficient than average RSL data. For the rainfall retrieval, both datasets showed similar correlation with the gauge adjusted radar product. However, based on other statistics (RMSE, bias and CV) minimum and maximum RSL data outperformed average for the rainfall retrieval. Overall, this study highlights the robust accuracy of commercial microwave links for rainfall retrieval while using only minimum and maximum RSL data.

Cellular communication networks
Microwave links
Opportunistic sensing
Rainfall
Remote sensing

http://resolver.tudelft.nl/uuid:e668571d-ee31-44cc-a025-f1924e66ce83

https://doi.org/10.1016/j.jhydrol.2021.126909

2022-03-14

0022-1694

Journal of Hydrology, 603 (Part B), 1-18

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2021 Jayaram Pudashine, Adrien Guyot, A. Overeem, Valentijn R.N. Pauwels, Alan Seed, R. Uijlenhoet, Mahesh Prakash