Improving Satellite Quantitative Precipitation Estimation Using GOES-Retrieved Cloud Optical Depth

Stenz, Ronald; Dong, Xiquan; Xi, Baike; Feng, Zhe; Kuligowski, Robert J.

doi:10.1175/jhm-d-15-0057.1

i

Improving Satellite Quantitative Precipitation Estimation Using GOES-Retrieved Cloud Optical Depth

2016
By Stenz, Ronald ; Dong, Xiquan ; Xi, Baike ; ...

Details

Journal Title:

Journal of Hydrometeorology
Personal Author:

Stenz, Ronald ; Dong, Xiquan ; Xi, Baike ; Feng, Zhe ; Kuligowski, Robert J.
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research)
Description:

To address gaps in ground-based radar coverage and rain gauge networks in the United States, geostationary satellite quantitative precipitation estimation (QPE) such as the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) can be used to fill in both spatial and temporal gaps of ground-based measurements. Additionally, with the launch of Geostationary Operational Environmental Satellite R series (GOES-R), the temporal resolution of satellite QPEs may be comparable to Weather Surveillance Radar-1988 Doppler (WSR-88D) volume scans as GOES images will be available every 5 min.
Keywords:

Algorithms Convective Storms Hydrology Observational Techniques And Algorithms Physical Meteorology And Climatology Radars/Radar Observations Remote Sensing Satellite Observations
Source:

Journal of Hydrometeorology, 17(2), 557-570
DOI:

https://doi.org/10.1175/jhm-d-15-0057.1
Document Type:

Journal Article
Funding:

Grant no. NA11NES440004
Rights Information:

Other
Compliance:

Submitted
Main Document Checksum:

urn:sha256:7e737dc91373a08cd539ed6add3cb1da560627987b3906e0244903de4d6e6da4
Download URL:

https://repository.library.noaa.gov/view/noaa/41040/noaa_41040_DS1.pdf
File Type:

[PDF - 2.63 MB ]

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