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Merging dual-polarization X-band radar network intelligence for improved microscale observation of summer rainfall in south Sweden

Hosseini, Seyyed Hasan LU ; Hashemi, Hossein LU orcid ; Larsson, Rolf LU and Berndtsson, Ronny LU orcid (2023) In Journal of Hydrology 617(Part C).
Abstract
Compact dual-polarization doppler X-band weather radars (X-WRs) have recently gained attention in Scandinavia for sub-km and minute scale rainfall observations. This study develops a method for merging data from two X-WRs in Dalby and Helsingborg, southern Sweden (operated at five and one elevation angle levels, respectively) to improve the accuracy of rainfall observations. In total, 87 rainfall events from May-September 2021, observed by 38 tipping bucket gauges in the overlapping coverage of the X-WRs, were used for ground truth. The gauges were classified into four zones. An artificial neural network using doppler and dual-polarization variables (ANN) and a regression-based hybrid of RATEs (single-level rainfall products built-in to... (More)
Compact dual-polarization doppler X-band weather radars (X-WRs) have recently gained attention in Scandinavia for sub-km and minute scale rainfall observations. This study develops a method for merging data from two X-WRs in Dalby and Helsingborg, southern Sweden (operated at five and one elevation angle levels, respectively) to improve the accuracy of rainfall observations. In total, 87 rainfall events from May-September 2021, observed by 38 tipping bucket gauges in the overlapping coverage of the X-WRs, were used for ground truth. The gauges were classified into four zones. An artificial neural network using doppler and dual-polarization variables (ANN) and a regression-based hybrid of RATEs (single-level rainfall products built-in to the X-WRs) based on the Marshall-Palmer equation (RMP) were calibrated for each zone. The calibrated models at 5-min scale significantly outperformed RATEs for all zones verified by Gilbert skill score (GSS), relative bias (rBIAS), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) not using the calibration data. Quantile-quantile plots confirmed a considerable improvement of the statistical distribution of the merged rainfall estimates for Zone I (closest to Dalby), II (mid-way between Dalby and Helsingborg), and IV (similar range as II for Dalby but farthest to Helsingborg) especially using ANN. Zone III (farthest to Dalby and closest to Helsingborg) was problematic for all RATEs, ANN, and RMP. The lowest-level elevation angle for both X-WRs showed the most erroneous RATEs. Consequently, the problems with Zone III can be solved if multiple levels of Helsingborg X-WR at higher levels are available. (Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial intelligence (AI), FURUNO, Prediction, Quantitative precipitation estimation (QPE), Skåne, Urban hydrology
in
Journal of Hydrology
volume
617
issue
Part C
article number
129090
pages
18 pages
publisher
Elsevier
external identifiers
  • scopus:85147122548
ISSN
1879-2707
DOI
10.1016/j.jhydrol.2023.129090
language
English
LU publication?
yes
id
4ab25e3d-1dcf-49d2-bf06-f5af89aa9f86
date added to LUP
2023-01-12 23:48:46
date last changed
2023-11-21 15:31:33
@article{4ab25e3d-1dcf-49d2-bf06-f5af89aa9f86,
  abstract     = {{Compact dual-polarization doppler X-band weather radars (X-WRs) have recently gained attention in Scandinavia for sub-km and minute scale rainfall observations. This study develops a method for merging data from two X-WRs in Dalby and Helsingborg, southern Sweden (operated at five and one elevation angle levels, respectively) to improve the accuracy of rainfall observations. In total, 87 rainfall events from May-September 2021, observed by 38 tipping bucket gauges in the overlapping coverage of the X-WRs, were used for ground truth. The gauges were classified into four zones. An artificial neural network using doppler and dual-polarization variables (ANN) and a regression-based hybrid of RATEs (single-level rainfall products built-in to the X-WRs) based on the Marshall-Palmer equation (RMP) were calibrated for each zone. The calibrated models at 5-min scale significantly outperformed RATEs for all zones verified by Gilbert skill score (GSS), relative bias (rBIAS), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) not using the calibration data. Quantile-quantile plots confirmed a considerable improvement of the statistical distribution of the merged rainfall estimates for Zone I (closest to Dalby), II (mid-way between Dalby and Helsingborg), and IV (similar range as II for Dalby but farthest to Helsingborg) especially using ANN. Zone III (farthest to Dalby and closest to Helsingborg) was problematic for all RATEs, ANN, and RMP. The lowest-level elevation angle for both X-WRs showed the most erroneous RATEs. Consequently, the problems with Zone III can be solved if multiple levels of Helsingborg X-WR at higher levels are available.}},
  author       = {{Hosseini, Seyyed Hasan and Hashemi, Hossein and Larsson, Rolf and Berndtsson, Ronny}},
  issn         = {{1879-2707}},
  keywords     = {{Artificial intelligence (AI); FURUNO; Prediction; Quantitative precipitation estimation (QPE); Skåne; Urban hydrology}},
  language     = {{eng}},
  number       = {{Part C}},
  publisher    = {{Elsevier}},
  series       = {{Journal of Hydrology}},
  title        = {{Merging dual-polarization X-band radar network intelligence for improved microscale observation of summer rainfall in south Sweden}},
  url          = {{http://dx.doi.org/10.1016/j.jhydrol.2023.129090}},
  doi          = {{10.1016/j.jhydrol.2023.129090}},
  volume       = {{617}},
  year         = {{2023}},
}