1 / 30

Refining Rainfall Estimation Using Meteorological Parameters in Central Florida

This study explores the relationship between reflectivity and rainfall along with the impact of Drop Size Distribution variations during storms on rainfall estimation accuracy. It investigates using meteorological parameters to explain discrepancies in Z/R relationships and assesses the influence of hydrometeor characteristics on rainfall estimation. The research examines the use of Lightning Metrics as a proxy to determine convective activity in storms. Statistical analyses are conducted to evaluate the effectiveness of the proposed methods in enhancing rainfall estimation accuracy.

sage-gibbs
Download Presentation

Refining Rainfall Estimation Using Meteorological Parameters in Central Florida

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. By: Jeana Mascio Radar Reflectivity (Z) and Rainfall (R) Relationships in Central FloridaPart II

  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 Both boxes have the same reflectivity measurement Box 2 will give the greater rainfall

  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 Rain Angle Unknown… Rain rate Infer a terminal velocity Wind velocity

  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

  13. July 11 Rain Event

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

  15. 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

  16. 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

  17. 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

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

  19. EL Measured by upper-air balloon soundings • The estimated temperature of possible storm cloud-top

  20. 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

  21. Comparing Metrics to Z/R • Compared data to rainfall rate departure = difference between the observed rainfall rate and rate that the reflectivities estimated by NWS relationship (shown with red arrows on a cut-off portion of Z/R relationship graph)

  22. 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

  23. 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

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

  25. 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

  26. 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

  27. Questions? Next: Sarah Collins

More Related