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Title: A Hybrid Method to Estimate Specific Differential Phase and Rainfall With Linear Programming and Physics Constraints

A hybrid method of combining linear programming (LP) and physical constraints is developed to estimate specific differential phase (K DP) and to improve rain estimation. Moreover, the hybrid K DP 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 (δ hv) dominates. Further, a quantitative comparison between auto-weather-station rain-gauge observations and K DP-based radar rain estimates for a Meiyu event also demonstrate the superiority of the hybrid K DP estimator over existing methods.
Authors:
 [1] ;  [2] ;  [3] ;  [4]
  1. Nanjing Univ. (China). School of Atmospheric Sciences
  2. Nanjing Univ. (China). School of Atmospheric Sciences and School of Meteorology; Univ. of Oklahoma, Norman, OK (United States)
  3. Nanjing Univ. (China). School of Atmospheric Sciences
  4. Brookhaven National Lab. (BNL), Upton, NY (United States). Dept. of Environmental and Climate Sciences
Publication Date:
Report Number(s):
BNL-112499-2016-JA
Journal ID: ISSN 0196-2892; R&D Project: 2016-BNL-EE630EECA-Budg; KP1701000
Grant/Contract Number:
SC00112704
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
Research Org:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; radar application; radar data processing
OSTI Identifier:
1336090

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., 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. doi:10.1109/TGRS.2016.2596295.
Huang, Hao, Zhang, Guifu, Zhao, Kun, and Giangrande, Scott E. 2016. "A Hybrid Method to Estimate Specific Differential Phase and Rainfall With Linear Programming and Physics Constraints". United States. doi: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 = {2016},
month = {10}
}