Probabilistic precipitation rate estimates with ground-based radar networks

The uncertainty structure of radar quantitative precipitation estimation (QPE) is largely unknown at fine spatiotemporal scales near the radar measurement scale. By using the WSR‐88D radar network and gauge data sets across the conterminous US, an investigation of this subject has been carried out w...

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Veröffentlicht in:Water resources research Jg. 51; H. 3; S. 1422 - 1442
Hauptverfasser: Kirstetter, Pierre-Emmanuel, Gourley, Jonathan J., Hong, Yang, Zhang, Jian, Moazamigoodarzi, Saber, Langston, Carrie, Arthur, Ami
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Washington Blackwell Publishing Ltd 01.03.2015
John Wiley & Sons, Inc
Schlagworte:
Algorithms
conditional bias
Estimates
MRMS
NEXRAD
Precipitation rate
probabilistic quantitative precipitation estimation
Radar
uncertainty
ISSN:0043-1397, 1944-7973
Online-Zugang:Volltext
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Zusammenfassung:The uncertainty structure of radar quantitative precipitation estimation (QPE) is largely unknown at fine spatiotemporal scales near the radar measurement scale. By using the WSR‐88D radar network and gauge data sets across the conterminous US, an investigation of this subject has been carried out within the framework of the NOAA/NSSL ground radar‐based Multi‐Radar Multi‐Sensor (MRMS) QPE system. A new method is proposed and called PRORATE for probabilistic QPE using radar observations of rate and typology estimates. Probability distributions of precipitation rates are computed instead of deterministic values using a model quantifying the relation between radar reflectivity and the corresponding “true” precipitation. The model acknowledges the uncertainty arising from many factors operative at the radar measurement scale and from the correction algorithm. Ensembles of reflectivity‐to‐precipitation rate relationships accounting explicitly for precipitation typology were derived at a 5 min/1 km scale. This approach conditions probabilistic quantitative precipitation estimates (PQPE) on the precipitation rate and type. The model components were estimated on the basis of a 1 year long data sample over the CONUS. This PQPE model provides the basis for precipitation probability maps and the generation of radar precipitation ensembles. Maps of the precipitation exceedance probability for specific thresholds (e.g., precipitation return periods) are computed. Precipitation probability maps are accumulated to the hourly time scale and compare favorably to the deterministic QPE. As an essential property of precipitation, the impact of the temporal correlation on the hourly accumulation is examined. This approach to PQPE can readily apply to other systems including space‐based passive and active sensor algorithms. Key Points: Probabilistic radar QPE is derived at fine scale for precipitation types It is the basis for precipitation probability maps and precipitation ensembles It compares positively at the hourly time scale to the deterministic QPE
Bibliographie:istex:8C7ABC541E06D35C4B9DC691B62711AA7178B7AE
ark:/67375/WNG-QT90T8RW-7
NOAA/Office of Oceanic and Atmospheric Research
NOAA-University of Oklahoma Cooperative - No. NA17RJ1227
ArticleID:WRCR21333
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
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ISSN:0043-1397
1944-7973
DOI:10.1002/2014WR015672