By: Jeana Mascio. The Point Want to be more accurate with estimating rainfall amounts from Z/R relationships

By: Jeana Mascio

The PointWant 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





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

Rai

n ra

te 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

July 11 Rain Event

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

North (v-comp.) East (u-comp.)Observed 4.8 mm 1.1 mm

•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

North (u-comp.) East (v-comp.)Observed 4.8 mm 1.1 mm

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

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

Low CAPE High CAPEMean 10.73 13.6Variance 318.27 427.53P-Value 0.643

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

Zero Lightning Some LightningMean 10.55 22.36Variance 208.19 1188.19P-Value 0.105

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

Documents

By: Jeana Mascio. The Point Want to be more accurate with estimating rainfall amounts from Z/R relationships