A Physically Based Multisensor Quantitative Precipitation Estimation Approach for Gap-Filling Radar Coverage

Martinaitis, Steven M.; Osborne, Andrew P.; Simpson, Micheal J.; Zhang, Jian; Howard, Kenneth W.; Cocks, Stephen B.; Arthur, Ami; Langston, Carrie; Kaney, Brian T.

doi:10.1175/JHM-D-19-0264.1

Advanced Search

Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

Advanced Search
Custom Query

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Clear All

Query Builder

Query box

Clear All

For additional assistance using the Custom Query please check out our Help Page

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

i

A Physically Based Multisensor Quantitative Precipitation Estimation Approach for Gap-Filling Radar Coverage

2020
By Martinaitis, Steven M. ; Osborne, Andrew P. ; Simpson, Micheal J. ; ...
Source: J. Hydrometeor. (2020) 21 (7): 1485–1511.

[PDF-5.30 MB]

Select the Download button to view the document

This document is over 5mb in size and cannot be previewed

Details You May Also Like

Details:

Journal Title:

Journal of Hydrometeorology
Personal Author:

Martinaitis, Steven M. ; Osborne, Andrew P. ; Simpson, Micheal J. ; Zhang, Jian ; Howard, Kenneth W. ; ... More +

Martinaitis, Steven M. ; Osborne, Andrew P. ; Simpson, Micheal J. ; Zhang, Jian ; Howard, Kenneth W. ; Cocks, Stephen B. ; Arthur, Ami ; Langston, Carrie ; Kaney, Brian T. Less -
NOAA Program & Office:

CIMMS (Cooperative Institute for Mesoscale Meteorological Studies) ; OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; WPO (Weather Program Office)

CIMMS (Cooperative Institute for Mesoscale Meteorological Studies) ; OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; WPO (Weather Program Office) Less -
Description:

Weather radars and gauge observations are the primary observations to determine the coverage and magnitude of precipitation; however, radar and gauge networks have significant coverage gaps, which can underrepresent or even miss the occurrence of precipitation. This is especially noticeable in mountainous regions and in shallow precipitation regimes. The following study presents a methodology to improve spatial representations of precipitation by seamlessly blending multiple precipitation sources within the Multi-Radar Multi-Sensor (MRMS) system. A high spatiotemporal resolution multisensor merged quantitative precipitation estimation (QPE) product (MSQPE) is generated by using gauge-corrected radar QPE as a primary precipitation source with a combination of hourly gauge observations, monthly precipitation climatologies, numerical weather prediction short-term precipitation forecasts, and satellite observations to use in areas of insufficient radar coverage. The merging of the precipitation sources is dependent upon radar coverage based on an updated MRMS radar quality index, surface and atmospheric conditions, topography, gauge locations, and precipitation values. Evaluations of the MSQPE product over the western United States resulted in improved statistical measures over its individual input precipitation sources, particularly the locally gauge-corrected radar QPE. The MSQPE scheme demonstrated its ability to sufficiently fill in areas where radar alone failed to detect precipitation due to significant beam blockage or poor coverage while minimizing the generation of false precipitation and underestimation biases that resulted from radar overshooting precipitation.
Keywords:

[+]

Hydrometeorology Remote Sensing
Source:

J. Hydrometeor. (2020) 21 (7): 1485–1511.
DOI:

https://doi.org/10.1175/JHM-D-19-0264.1
Document Type:

Journal Article
Funding:

Grant no. NA17OAR4590175 ; Grant no. NA11OAR4320072

Grant no. NA17OAR4590175 ; Grant no. NA11OAR4320072 Less -
Rights Information:

Other
Compliance:

Submitted
Main Document Checksum:

[+]

urn:sha256:66c0ba12eefbd0002c3670529886f7391c406443f3374c6e416b31fc2b7fb88c
Download URL:

https://repository.library.noaa.gov/view/noaa/26144/noaa_26144_DS1.pdf
File Type:

No Additional Files

More +

A Physically Based Multisensor Quantitative Precipitation Estimation Approach for Gap-Filling Radar Coverage

Details:

Supporting Files

You May Also Like