Zhu, Jingxuan, Dai, Qiang, Xiao, Yuanyuan, Zhu, Shaonan, Zhuo, Lu, Zhang, Jun and Han, Dawei
2024.
Raindrop size distribution (DSD) retrieval from polarimetric radar observations using neural networks.
Atmospheric Research
310
, 107638.
10.1016/j.atmosres.2024.107638
Item availability restricted. |
PDF
- Accepted Post-Print Version
Restricted to Repository staff only until 14 August 2025 due to copyright restrictions. Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) |
Abstract
As a key component of rainfall estimation, the understanding of raindrop size distribution (DSD) is a long-standing goal in meteorology and hydrology. Given that weather radar can observe the precipitation microphysics over large spatial and temporal scales, it has been broadly applied in DSD estimation. Traditional polynomial regression algorithms that correlate DSD parameters and radar signatures are still widely applied due to their simple structure and acceptable accuracy. This study proposes a new DSD retrieval model using dual-polarization radar observations based on long short-term memory (LSTM) network techniques. Three schemes able to retrieve the parameters of a normalized gamma DSD (LSTM-D0, LSTM-Nw, and LSTM-μ) are proposed with different combinations of polarimetric radar measurement inputs. All LSTM estimators exhibit better performance than the polynomial regression method. The Nash-Sutcliffe efficiency coefficient for estimates of drop median diameter (D0) and intercept parameter (Nw) increases from 0.93 and 0.70 to 0.95 and 0.93 respectively at Chilbolton station. Poor estimates of the shape parameter (μ) using the polynomial regression estimator complicate real applications, whereas the remarkable improvement of LSTM model estimation facilitates practical applications. The temporal and spatial predictability is then estimated to investigate long-term estimator performance for various radars, or at least for all radar pixels of a single radar. The predictability, measured by the Nash coefficient, increases temporally by 0.08, 0.31, and 0.39 and spatially by 0.03, 0.19, and 0.23 for the parameters D0, log10Nw, and μ respectively. This study contributes to improving quantitative precipitation estimates from radar polarimetry, enabling a better understanding of precipitation microphysics.
Item Type: | Article |
---|---|
Date Type: | Publication |
Status: | Published |
Schools: | Earth and Environmental Sciences |
Publisher: | Elsevier |
ISSN: | 0169-8095 |
Date of First Compliant Deposit: | 23 August 2024 |
Date of Acceptance: | 13 August 2024 |
Last Modified: | 31 Aug 2024 08:46 |
URI: | https://orca.cardiff.ac.uk/id/eprint/171562 |
Actions (repository staff only)
Edit Item |