Li et al. (2026) The echo extrapolation of dual-polarization doppler radar based on the wind retrieval of radial velocity

Identification

• Journal: Natural Hazards
• Year: 2026
• Date: 2026-01-01
• Authors: Nan Li, Yongjiang Yu, Guo Wei, Yue Ruan
• DOI: 10.1007/s11069-025-07796-x

Research Groups

• School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
• Center for Applied Mathematics of Jiangsu Province, Nanjing University of Information Science and Technology, Nanjing, China
• Fujian Institute of Meteorological Science, Fuzhou, China
• Department of Mathematics and Computer Science, University of North Carolina at Pembroke, Pembroke, USA
• Fujian Meteorological Observatory, Fuzhou, China

Short Summary

This study proposes a nowcasting method for dual-polarization Doppler radar echoes by using wind fields retrieved from radial velocity data via the Velocity Volume Processing (VVP) algorithm as motion vectors. Applied to two precipitation events, the method demonstrates feasibility in extrapolating reflectivity and dual-polarization parameters, and in quantitative precipitation forecasting.

Objective

• To incorporate radial velocity data and dual-polarization data into precipitation nowcasting by using wind fields retrieved by the Velocity Volume Processing (VVP) algorithm as motion vectors to extrapolate dual-polarization Doppler weather radar echoes, including reflectivity (Z H), differential reflectivity (Z DR), correlation coefficient (CC), and specific differential phase (K DP).

Study Configuration

• Spatial Scale:
  • Radar station: Sanming, Fujian Province, China.
  • Range radius: 200 km.
  • Horizontal resolution: 5 km grid points.
  • Height of analysis: 1.5 km (around the top of the boundary layer).
• Temporal Scale:
  • Nowcasting duration: 1 hour (60 minutes).
  • Extrapolation time interval: 5 minutes (for VCP 11) or 6 minutes (for VCP 21).
  • Precipitation events: 4 April 2024 (08:00-09:00 UTC) and 12 June 2024 (01:00-02:00 UTC).

Methodology and Data

• Models used:
  • Wind retrieval: Velocity Volume Processing (VVP) algorithm.
  • Echo extrapolation: Backward extrapolation (Germann and Zawadzki, 2002).
  • Evaluation metrics: Contingency table method (Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI)), Mean Absolute Deviation (MAD).
  • Quantitative Precipitation Estimation (QPE): Cifelli et al. (2011) algorithm, using four equations based on Z H, Z DR, and K DP.
• Data sources:
  • China New Generation Radar (CINRAD) at Sanming, Fujian Province (S-band dual-polarization Doppler weather radar, upgraded November 2022).
  • Radar parameters used: Reflectivity (Z H, in dBZ), Radial velocity (V, in m/s), Differential reflectivity (Z DR, in dBZ), Correlation coefficient (CC, dimensionless), Specific differential phase (K DP, in degree/km).

Main Results

• The proposed nowcasting scheme, utilizing VVP-retrieved wind fields as motion vectors, is feasible and effective for extrapolating dual-polarization Doppler radar echoes.
• The nowcasting results for Z H, Z DR, CC, and K DP showed overall consistency with actual echoes for two heavy precipitation events in 2024.
• Quantitative evaluation using CSI and MAD indicated acceptable performance, although CSI generally decreased and MAD increased with longer forecasting times.
• Nowcasting of extreme value areas for dual-polarization parameters (Z DR, CC, K DP) was found to be more prone to position errors compared to reflectivity (Z H).
• The echo extrapolation of dual-polarization parameters should be combined with Z H extrapolation to accurately determine precipitation distribution.
• Quantitative Precipitation Forecasting (QPF) derived from the extrapolated echo parameters demonstrated practical significance by showing corresponding maximum values to actual QPE.
• Evaluation thresholds: Z H ≥ 10 dBZ (equivalent to 0.1 mm/h), Z DR ≥ 0 dBZ, CC ≥ 0.9, K DP ≥ 0 degree/km.

Contributions

• Introduces a novel approach for radar echo nowcasting by explicitly incorporating wind fields retrieved from Doppler radar radial velocity data using the VVP algorithm, addressing a gap in traditional and deep-learning methods.
• Demonstrates the feasibility of extrapolating dual-polarization radar parameters (Z DR, CC, K DP) in addition to reflectivity (Z H), which has been rarely reported in existing literature.
• Highlights the potential of radial velocity data from Doppler radars to play an important role in improving precipitation nowcasting.
• Enables Quantitative Precipitation Forecasting (QPF) by leveraging the advantages of dual-polarization radar parameters in nowcasting.

Funding

• Open Project of Center for Applied Mathematics of Jiangsu Province (Nanjing University of Information Science and Technology).
• Natural Science Foundation of Fujian Province (2023J011328).

Citation

@article{Li2026echo,
  author = {Li, Nan and Yu, Yongjiang and Wei, Guo and Ruan, Yue},
  title = {The echo extrapolation of dual-polarization doppler radar based on the wind retrieval of radial velocity},
  journal = {Natural Hazards},
  year = {2026},
  doi = {10.1007/s11069-025-07796-x},
  url = {https://doi.org/10.1007/s11069-025-07796-x}
}