A Hybrid Method to Estimate Specific Differential Phase and Rainfall With Linear Programming and Physics Constraints
Abstract
A hybrid method of combining linear programming (LP) and physical constraints is developed to estimate specific differential phase (KDP) and to improve rain estimation. Moreover, the hybrid KDP estimator and the existing estimators of LP, least squares fitting, and a self-consistent relation of polarimetric radar variables are evaluated and compared using simulated data. Our simulation results indicate the new estimator's superiority, particularly in regions where backscattering phase (δhv) dominates. Further, a quantitative comparison between auto-weather-station rain-gauge observations and KDP-based radar rain estimates for a Meiyu event also demonstrate the superiority of the hybrid KDP estimator over existing methods.
- Authors:
-
- Nanjing Univ. (China). School of Atmospheric Sciences
- Nanjing Univ. (China). School of Atmospheric Sciences and School of Meteorology; Univ. of Oklahoma, Norman, OK (United States)
- Nanjing Univ. (China). School of Atmospheric Sciences
- Brookhaven National Lab. (BNL), Upton, NY (United States). Dept. of Environmental and Climate Sciences
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1336090
- Report Number(s):
- BNL-112499-2016-JA
Journal ID: ISSN 0196-2892; R&D Project: 2016-BNL-EE630EECA-Budg; KP1701000
- Grant/Contract Number:
- SC00112704
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE Transactions on Geoscience and Remote Sensing
- Additional Journal Information:
- Journal Volume: 55; Journal Issue: 1; Journal ID: ISSN 0196-2892
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; radar application; radar data processing
Citation Formats
Huang, Hao, Zhang, Guifu, Zhao, Kun, and Giangrande, Scott E. A Hybrid Method to Estimate Specific Differential Phase and Rainfall With Linear Programming and Physics Constraints. United States: N. p., 2016.
Web. doi:10.1109/TGRS.2016.2596295.
Huang, Hao, Zhang, Guifu, Zhao, Kun, & Giangrande, Scott E. A Hybrid Method to Estimate Specific Differential Phase and Rainfall With Linear Programming and Physics Constraints. United States. https://doi.org/10.1109/TGRS.2016.2596295
Huang, Hao, Zhang, Guifu, Zhao, Kun, and Giangrande, Scott E. Thu .
"A Hybrid Method to Estimate Specific Differential Phase and Rainfall With Linear Programming and Physics Constraints". United States. https://doi.org/10.1109/TGRS.2016.2596295. https://www.osti.gov/servlets/purl/1336090.
@article{osti_1336090,
title = {A Hybrid Method to Estimate Specific Differential Phase and Rainfall With Linear Programming and Physics Constraints},
author = {Huang, Hao and Zhang, Guifu and Zhao, Kun and Giangrande, Scott E.},
abstractNote = {A hybrid method of combining linear programming (LP) and physical constraints is developed to estimate specific differential phase (KDP) and to improve rain estimation. Moreover, the hybrid KDP estimator and the existing estimators of LP, least squares fitting, and a self-consistent relation of polarimetric radar variables are evaluated and compared using simulated data. Our simulation results indicate the new estimator's superiority, particularly in regions where backscattering phase (δhv) dominates. Further, a quantitative comparison between auto-weather-station rain-gauge observations and KDP-based radar rain estimates for a Meiyu event also demonstrate the superiority of the hybrid KDP estimator over existing methods.},
doi = {10.1109/TGRS.2016.2596295},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
number = 1,
volume = 55,
place = {United States},
year = {Thu Oct 20 00:00:00 EDT 2016},
month = {Thu Oct 20 00:00:00 EDT 2016}
}
Web of Science
Works referencing / citing this record:
Current Status and Future Challenges of Weather Radar Polarimetry: Bridging the Gap between Radar Meteorology/Hydrology/Engineering and Numerical Weather Prediction
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- Zhang, Guifu; Mahale, Vivek N.; Putnam, Bryan J.
- Advances in Atmospheric Sciences, Vol. 36, Issue 6
A Bayesian Hydrometeor Classification Algorithm for C-Band Polarimetric Radar
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- Yang, Ji; Zhao, Kun; Zhang, Guifu
- Remote Sensing, Vol. 11, Issue 16
Microphysical Characteristics of Three Convective Events with Intense Rainfall Observed by Polarimetric Radar and Disdrometer in Eastern China
journal, August 2019
- Chen, Gang; Zhao, Kun; Wen, Long
- Remote Sensing, Vol. 11, Issue 17