By: Jeana Mascio

The Point Want to be more accurate with estimating rainfall amounts from Z/R relationships

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

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

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

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

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

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

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.

Calculating Terminal Velocity Wind velocity Rain rate Unknown… Infer a terminal velocity Rain Angle

Finding Mean Drop Size  Calculated terminal velocities can give a mean drop size  Mean drop size gives information on the DSD

July 11 Rain Event

Terminal Velocity that best matches 7/11 observations is between 4 and 4.6 m/s

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

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

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

CAPE  The potential an area of upper atmosphere has to produce convective storms  Higher CAPE  convection more likely Measured by upper-air balloon soundings

EL  The estimated temperature of possible storm cloud-top

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

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

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

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

CAPE T-test Results  No statistical support allows the statement that these two means are different

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

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

Questions? Next: Sarah Collins