A Hybrid Method to Estimate Specific Differential Phase and Rainfall With Linear Programming and Physics Constraints
- 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
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.
- Research Organization:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- Grant/Contract Number:
- SC00112704
- OSTI ID:
- 1336090
- Report Number(s):
- BNL-112499-2016-JA; R&D Project: 2016-BNL-EE630EECA-Budg; KP1701000
- Journal Information:
- IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, Issue 1; ISSN 0196-2892
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
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