Calibration of the reflectivity-rainfall rate (Z-R) relationship using long-term radar reflectivity factor over the entire South Korea region in a Bayesian perspective-Reference-Cited by-同舟云学术

Calibration of the reflectivity-rainfall rate (Z-R) relationship using long-term radar reflectivity factor over the entire South Korea region in a Bayesian perspective

Published:2021-02 Issue: Volume:593 Page:125790
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Kim Tae-Jeong^ORCID,Kwon Hyun-Han^ORCID,Kim Kue Bum^ORCID

Funder

Ministry of Environment

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference137 articles.

1. Modeling Radar Rainfall Estimation Uncertainties: Random Error Model;AghaKouchak;J. Hydrol. Eng.,2010

2. Time-dependent Z-R relationships for estimating rainfall fields from radar measurements;Alfieri;Nat. Hazard Earth Sys.,2010

3. High-resolution rainfall estimation from X-band polarimetric radar measurements;Anagnostou;J. Hydrometeorol.,2004

4. Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations;Anagnostou;Remote Sens-Basel,2018

5. Identification of Vertical Profiles of Radar Reflectivity for Hydrological Applications Using an Inverse Method. 1. Formulation;Andrieu;J. Appl. Meteorol.,1995

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