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Managing Uncertainty Due to a Fundamental Error Source Arising from Scatterer Distribution Complexity in Radar Remote Sensing of PrecipitationThe assumption that cloud and rain drops are spatially distributed according to a Poisson distribution within a scattering volume probed by a radar being used to estimate precipitation has represented bedrock theory in establishing 'rules of the game' for pulse averaging--the process needed to beat down noise to an acceptable level in the measurement of radar reflectivity factor. Based on relatively recent observations of 'realistic' spatial distributions of hydrometeor scatterers in a cloudy atmosphere motivates a renewed examination of the consequences of using a too simplified assumption underlying volume scattering--particularly in regards to the standard pulse averaging rule. Our investigation addresses two extremes, simple to complex, insofar as allowed for complexities in an underlying scatterer distribution. It is demonstrated that as the spatial distribution ranges from Poisson (a narrow distribution) to multi-fractal (much broader distribution), uncertainty in a measurement increases if the rule for pulse averaging goes unchanged from its Poisson distribution reference county. [A bounded cascade is used for the multi-fractal distribution, a regularly observed distribution vis-a-vis cloud liquid water content.] The resultant measurement uncertainty leads to a fundamental source of error in the estimation of rain rate from radar measurements, one that has been disregarded since the early 1950s when radar sets first began to be used for rainfall measuring. It is shown how this source of error can be 'managed'--under the assumption that number of data analysis experiments would be carried out, experiments involving pulse-by-pulse measurements obtained from a radar set modified to output individual pulses of reflectivity factor. For practical applications, a new parameter called normalized k-sample intensity invariance is developed to enable defining the required pulse average count according to a preferred degree of uncertainty.
Document ID
20080030358
Acquisition Source
Goddard Space Flight Center
Document Type
Conference Paper
Authors
Smith, Eric A.
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Kuo, Kwo-Sen
(Caelum Research Corp. Rockville, MD, United States)
Meneghini, Robert
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Mugnai, Alberto
(National Research Council Rome, Italy)
Date Acquired
August 24, 2013
Publication Date
November 10, 2007
Subject Category
Earth Resources And Remote Sensing
Meeting Information
Meeting: 9th International Precipitation Conference
Location: Paris
Country: France
Start Date: November 10, 2007
End Date: November 17, 2007
Sponsors: American Geophysical Union, European Geosciences Union
Distribution Limits
Public
Copyright
Other

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