A Bayesian Correction Approach for Improving Dual-frequency Precipitation Radar Rainfall Rate Estimates-Reference-Cited by-同舟云学术

A Bayesian Correction Approach for Improving Dual-frequency Precipitation Radar Rainfall Rate Estimates

Published:2020 Issue:3 Volume:98 Page:511-525
ISSN:0026-1165
Container-title:Journal of the Meteorological Society of Japan. Ser. II
language:en
Short-container-title:Journal of the Meteorological Society of Japan

Author:

MA Yingzhao¹,CHANDRASEKAR V.¹,BISWAS Sounak K.¹

Affiliation:

1. Colorado State University, Colorado, USA

Publisher

Meteorological Society of Japan

Subject

Atmospheric Science

Link

https://www.jstage.jst.go.jp/article/jmsj/98/3/98_2020-025/_pdf

Reference33 articles.

1. Battaglia, A., S. Tanelli, K. Mroz, and F. Tridon, 2015: Multiple scattering in observations of the GPM dual-frequency precipitation radar: Evidence and impact on retrievals. J. Geophys. Res.: Atmos., 120, 4090-4101.

2. Bedient, P. B., B. C. Hoblit, D. C. Gladwell, and B. E. Vieux, 2000: NEXRAD radar for flood prediction in Houston. J. Hydrol. Eng., 5, 269-277.

3. Biswas, S. K., and V. Chandrasekar, 2018: Cross-validation of observations between the GPM dual-frequency precipitation radar and ground based dual-polarization radars. Remote Sens., 10, 1773, doi:10.3390/rs10111773.

4. Bolen, S. M., and V. Chandrasekar, 2003: Methodology for aligning and comparing spaceborne radar and groundbased radar observations. J. Atmos. Oceanic Technol., 20, 647-659.

5. Bruno, F., D. Cocchi, F. Greco, and E. Scardovi, 2014: Spatial reconstruction of rainfall fields from rain gauge and radar data. Stoch. Environ. Res. Risk Assess., 28, 1235-1245.

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An Innovative Correction–Fusion Approach for Multi-Satellite Precipitation Products Conditioned by Gauge Background Fields over the Lancang River Basin;Remote Sensing;2024-05-21

2. Error modeling and hierarchical Bayesian fusion for spaceborne and ground radar rainfall data;Journal of Hydrology;2024-02

3. Validation of Spaceborne Precipitation Radar Data by Rain Gauges and Disdrometers over the Complex Topography of the Northeastern Indian Subcontinent;Journal of the Meteorological Society of Japan. Ser. II;2024

4. Rainfall Estimation From TEMPEST-D CubeSat Observations: A Machine-Learning Approach;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2022

5. Quantitative Precipitation Estimation using X-Band Radar for Orographic Rainfall in the San Francisco Bay Area;IEEE Transactions on Geoscience and Remote Sensing;2022