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A Climatology of Precipitation Efficiency in the Central Appalachian Mountain Region. James Morrow Nick Luchetti. Precipitation Efficiency = Precipitation/ Precipitable Water (i.e. moisture available through the depths of the atmosphere) - PowerPoint PPT Presentation
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A Climatology of Precipitation Efficiency in the Central Appalachian Mountain Region
James MorrowNick Luchetti
• Precipitation Efficiency = Precipitation/Precipitable Water (i.e. moisture available through the depths of the atmosphere)
• Ratio represents the the fraction of available water vapor in the atmosphere that is translated into precipitation reaching the surface.
Factors that alter the ratio: Storm Motion, Moisture Convergence, Coverage of Precipitation, Amount of lifting and instability release.
Objectives: • Highlight any trends that may signal a recent
change in the regional climate– Drought concerns– Heavy precipitation event concerns
• Highlight the inter-annual variability in seasonal precipitation efficiency – Identify climate system controllers
• Create a climatological visual display for operational forecasters to use when issuing watches and warnings.
Study Area
• Varying degrees of elevation• Flash flooding susceptibility • 25 weather stations
Methods
• 1982-2012
• Daily Precipitation data extracted from the National Climatic Data Center’s weather station database.
• Precipitable water data were gathered for a 2.5° latitude x 2.5° longitude area centered on Blacksburg, Virginia (elevation 640 m)– Re-analysis data base of NOAA’s Earth Systems Research Laboratory.
• 31-year median ratios were spatially plotted using GIS(Inverse Distance tool)
• Inter-annual median ratios vs. Multivariate ENSO Index(MEI) values were plotted.
Spring Season(March-May)
March April May25% 11.8 9.0 6.7
Median 34.2 27.1 19.975% 80.6 64.1 46.2
Summer Season(June-August)
June July August 25% 4.8 4.3 4.2
Median 14.5 13.0 12.675% 37.7 34.2 33.8
Fall Season(September-November)
September October November25% 4.6025 6.06 9.2
Median 15.435 20.83 28.6775% 46.705 55.53 74.16
Winter Season(December-February)
December January February25% 10.7 13.0 13.9
Median 31.9 37.4 36.375% 85.7 87.7 80.1
Full Season(January-December)
Potential Controller-Orographic Lift• Atmospheric instability• Lower-atmospheric evaporation of
hydrometers R^2 = .0049
• ENSO phase change alters synoptic scale storm motion
• Most evident in winter months
Potential Controller- El Niño/La Niña Cycles
Potential Controller- El Niño/La Niña Cycles• MEI + indicate El Niño– Wetter and cooler conditions
• MEI – indicate La Niña– Dryer and warmer conditions
Winter Classic El Nino Events (1982-1983, 1991-1992, 1997-1998, 2002-2003, 2009-2010)
(1.2,42%)
(1.6,35%)
(2.5,38%)
(1.1,51%)
(1.2,45%)
Winter Classic La Niña Events(1989-1990,1999-2000, 2000-2001, 2010-2012)
(-1.2,26%)
(-.909,22%)(-.6,34%)
(-1.12,33%)
Conclusions and Continued Research• Variation is evident spatially and temporally• Visually, aspect may be a potential controller • Extreme ENSO Phases could be a potential
controller • Use SWAT model to cross reference high flood
risk areas • Look into other large scale circulations(North
Atlantic Oscillation, Artic Oscillation)
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