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Published byGeorge Sutton Modified over 9 years ago
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By: Jeana Mascio
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The Point Want to be more accurate with estimating rainfall amounts from Z/R relationships
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The Point Want to be more accurate with estimating rainfall amounts from Z/R relationships Drop Size Distribution (DSD) variations in storms causes most inaccuracies
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The Point Want to be more accurate with estimating rainfall amounts from Z/R relationships Drop Size Distribution (DSD) variations in storms causes most inaccuracies Use meteorological parameters that may infer DSD
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The Point Want to be more accurate with estimating rainfall amounts from Z/R relationships Drop Size Distribution (DSD) variations in storms causes most inaccuracies Use meteorological parameters that may infer DSD Determine if these parameters can explain the discrepancies from Z/R relationship
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The Point Want to be more accurate with estimating rainfall amounts from Z/R relationships Drop Size Distribution (DSD) variations in storms causes most inaccuracies Use meteorological parameters that may infer DSD Determine if these parameters can explain the discrepancies from Z/R relationship If results are found, could change the relationship
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Drop Size Distribution (DSD) Defines hydrometeor size, shape, orientation and phase Each storm type, as well as phase of storm, has a different DSD Affects Z/R relationship Box 2 will give the greater rainfall Both boxes have the same reflectivity measurement
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Using the Horizontal Rain Gage Horizontal gages collect different rain angles Different directions represent the u- and v- components North = + v South = - v East = + u West = - u
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How Horizontal Gage Works Example: If rain came directly from the North, this direction gage would only collect rain… only v-component would have a value.
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Calculating Terminal Velocity Wind velocity Rain rate Unknown… Infer a terminal velocity Rain Angle
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Finding Mean Drop Size Calculated terminal velocities can give a mean drop size Mean drop size gives information on the DSD
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July 11 Rain Event
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Terminal Velocity that best matches 7/11 observations is between 4 and 4.6 m/s
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Terminal Velocity that best matches 7/11 observations is between 4 and 4.6 m/s From previous table: 4.03 m/s 1.0 mm mean drop size
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Using Drop Size Data Could classify measured drop sizes into storm types and storm phases if more data was collected Use classification to compare to the Z/R relationship Possible correlations to either an over- or under-estimation of rainfall from relationship
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Use Lightning Metrics as a Proxy Lightning Metrics : Convective Available Potential Energy (CAPE) Equilibrium Level temperature (EL) Lightning Flash Rate (LFR) All help to determine if storms are convectively active
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CAPE The potential an area of upper atmosphere has to produce convective storms Higher CAPE convection more likely Measured by upper-air balloon soundings
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EL The estimated temperature of possible storm cloud-top
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Lightning Flash Rate (LFR) Measured by the U.S. National Lightning Detection Network Database (NLDN) Collects location, time, polarity and amplitude of each cloud-to-ground strike Methods: Tabulated flash count for each system Specified radius (5, 10 km) for varying circular areas
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Comparing Metrics to Z/R Compared data to rainfall rate departure (shown with red arrows on a cut-off portion of Z/R relationship graph) = difference between the observed rainfall rate and rate that the reflectivities estimated by NWS relationship
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Comparing Metrics to Z/R Compared data to rainfall rate departure Best results came from CAPE and 10 km LFR Divided CAPE/10 km LFR into 2 groups: CAPE: high and low (dividing value = 2950 J/kg) 10 km LFR: zero and some lightning
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Statistical Analysis Statistical T-tests completed for CAPE and 10 km LFR Determined if there is any statistical difference between mean departures of groups for both metrics P-value less than or equal to 0.05 allows rejection that groups are equal
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CAPE T-test Results No statistical support allows the statement that these two means are different
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10 km LFR T-test Results There is about 90% confidence that these two means are different Not enough for the 0.05 confidence value
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Conclusions Rainfall rate mean departures for both groups in both metrics cannot be claimed different But results of 10 km LFR were close to confidence value No new Z/R relationships can be inferred from the results Could study other seasons throughout entire year; different storm types Measure DSD with a disdrometer
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Questions? Next: Sarah Collins
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