Seasonal Variations in the Rainfall Kinetic Energy Estimation and the Dual-Polarization Radar Quantitative Precipitation Estimation Under Different Rainfall Types in the Tianshan Mountains, China

Raindrop size distribution (DSD) has an essential effect on rainfall kinetic energy estimation (RKEE) and dual-polarization radar quantitative precipitation estimation (QPE); DSD is a key factor for establishing a dual-polarization radar QPE scheme and RKEE scheme, particularly in mountainous areas. To improve the understanding of seasonal DSD-based RKEE, dual-polarization radar QPE, and the impact of rainfall types and classification methods, we investigated RKEE schemes and dual-polarimetric radar QPE algorithms across seasons and rainfall types based on two classic classification methods (BR09 and BR03) and DSD data from a disdrometer in the Tianshan Mountains during 2020–2022. Two RKEE schemes were established: the rainfall kinetic energy flux–rain rate (KEtime–R) and the rainfall kinetic energy content–mass-weighted mean diameter (KEmm–Dm). Both showed seasonal variation, whether it was stratiform rainfall or convective rainfall, under BR03 and BR09. Both schemes had excellent performance, especially the KEmm–Dm relationship across seasons and rainfall types. In addition, four QPE schemes for dual-polarimetric radar—R(Kdp), R(Zh), R(Kdp,Zdr), and R(Zh,Zdr)—were established, and exhibited characteristics that varied with season and rainfall type. Overall, the performance of the single-parameter algorithms was inferior to that of the double-parameter algorithms, and the performance of the R(Zh) algorithm was inferior to that of the R(Kdp) algorithm. The results of this study show that it is necessary to consider different rainfall types and seasons, as well as classification methods of rainfall types, when applying RKEE and dual-polarization radar QPE. In this process, choosing a suitable estimator—KEtime(R), KEmm(Dm), R(Kdp), R(Zh), R(Kdp,Zdr), or R(Zh,Zdr)—is key to improving the accuracy of estimating the rainfall KE and R.

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Publication Details

Journal
Remote Sensing
Published
2024-10-17
DOI
https://doi.org/10.3390/rs16203859
Citations
5
Primary Topic
Precipitation Measurement and Analysis
Type
article
Field-Weighted Citation Impact
1.88

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article

Seasonal Variations in the Rainfall Kinetic Energy Estimation and the Dual-Polarization Radar Quantitative Precipitation Estimation Under Different Rainfall Types in the Tianshan Mountains, China

Lianmei Yang, Abuduwaili Abulikemu, Zepeng Tong, Yufei Jiang et al.
5 citations
Remote Sensing
Precipitation Measurement and Analysis
1.88
article

Seasonal Variations in the Rainfall Kinetic Energy Estimation and the Dual-Polarization Radar Quantitative Precipitation Estimation Under Different Rainfall Types in the Tianshan Mountains, China

Lianmei Yang, Abuduwaili Abulikemu, Zepeng Tong, Yufei Jiang, Yong Zeng, Xiaomeng Li, Xinyu Lu
article en
5 citations

Abstract

Raindrop size distribution (DSD) has an essential effect on rainfall kinetic energy estimation (RKEE) and dual-polarization radar quantitative precipitation estimation (QPE); DSD is a key factor for establishing a dual-polarization radar QPE scheme and RKEE scheme, particularly in mountainous areas. To improve the understanding of seasonal DSD-based RKEE, dual-polarization radar QPE, and the impact of rainfall types and classification methods, we investigated RKEE schemes and dual-polarimetric radar QPE algorithms across seasons and rainfall types based on two classic classification methods (BR09 and BR03) and DSD data from a disdrometer in the Tianshan Mountains during 2020–2022. Two RKEE schemes were established: the rainfall kinetic energy flux–rain rate (KEtime–R) and the rainfall kinetic energy content–mass-weighted mean diameter (KEmm–Dm). Both showed seasonal variation, whether it was stratiform rainfall or convective rainfall, under BR03 and BR09. Both schemes had excellent performance, especially the KEmm–Dm relationship across seasons and rainfall types. In addition, four QPE schemes for dual-polarimetric radar—R(Kdp), R(Zh), R(Kdp,Zdr), and R(Zh,Zdr)—were established, and exhibited characteristics that varied with season and rainfall type. Overall, the performance of the single-parameter algorithms was inferior to that of the double-parameter algorithms, and the performance of the R(Zh) algorithm was inferior to that of the R(Kdp) algorithm. The results of this study show that it is necessary to consider different rainfall types and seasons, as well as classification methods of rainfall types, when applying RKEE and dual-polarization radar QPE. In this process, choosing a suitable estimator—KEtime(R), KEmm(Dm), R(Kdp), R(Zh), R(Kdp,Zdr), or R(Zh,Zdr)—is key to improving the accuracy of estimating the rainfall KE and R.

Remote SensingVol. 16(20)
China Meteorological Administration (CN), Xinjiang Institute of Water Resources and Hydropower Research (CN), Xinjiang University (CN)
Natural Science Foundation of Xinjiang Province, National Natural Science Foundation of China, China Meteorological Administration, Natural Science Foundation of Xinjiang, Institute of Desert Meteorology, China Meteorological Administration
Affordable and clean energy
Openalex Percentile: Top 15%
Precipitation Measurement and Analysis
1.88
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