1. By: Jeana Mascio

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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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.

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

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

12. July 11 Rain Event

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

16. 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

17. 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

18. 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

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

20. EL  The estimated temperature of possible storm cloud-top

21. 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

22. 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

23. 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

26. 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

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

28. 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

29. 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

30. Questions? Next: Sarah Collins