Validation of Z-R Relationships for Central Florida Thunderstorms

1. Sarah Tyson Validation of Z-R Relationships for Central Florida Thunderstorms

2. What is a Z-R relationship? What is z? What is r? Rainfall Rate (mm/hr) A calculated value relating the quantity of precipitation and time • Radar reflectivity (dBZ) • Microwave energy reflects off objects (e.g. hydrometeors) and the return is reflectivity

3. Who Cares? Z-R Relationships: • Provide a way to estimate the rainfall for a location without a rain gauge • Are important for hydrological applications such as flash flooding forecasting and agriculture

4. Z-R Relationships • Empirical relationship between Z and R follow the general relationship: Z= aRb • The WSR-88D Convective relationship is the standard Z-R relationship used by the Melbourne National Weather Service (NWS)

5. Objectives • Gain a better understanding of Z-R relationships in Central Florida • Evaluate factors that may cause departures from the standard Z-R relationship • Various meteorological parameters • Florida Tech horizontal rain gauge • Florida Tech test disdrometer

6. Methods • Storm chase to obtain precipitation samples from storms of varying intensity • Calculate rainfall rates and obtain reflectivity data • Create a Z-R plot with the rainfall rates and reflectivity from our samples • Compare the Z-R plot to the standard NWS Z-R relationship • Determine any meteorological factors that cause deviations from the NWS Z-R relationship

7. Methods: Instruments Davis Weather Station Horizontal Rain Gauge Standard 8 inch Gauge

8. Methods: Sampling • Deploy gauges ten minutes before the rain begins • Each gauge was leveled and the Davis station wind vane pointed to magnetic north • Start/end times and weather observations recorded • The amount of rain in each gauge was measured

9. Methods: Locations

10. Methods: Data Analysis • A five minute average rainfall rate was calculated • Reflectivity data obtained from the NEXRAD Information Distribution Service (NIDS)

11. Results: Time Series of 28 June 2010 Events 28 June 2010 Event 3 Event 1 Event 2

12. Z=300R1.4

13. Z=300R1.4

14. Z=300R1.4

15. Z=300R1.4

16. Z=300R1.4 +0.37 -1.75 -1.24

17. dBZ Departures for all events Average departure: -1.08dBZ

18. Factors Affecting Z-R Relationships • Wind • Angle of precipitation can effect R • Stronger winds = greater angle of the rain • The greater the angle of the rain, the less precipitation will fall into a vertical gauge

19. Factors Affecting Z-R Relationships • Humidity Variables • Evaporation could decrease R • Low humidity and/or dew point indicate evaporation is occurring

20. Factors Affecting Z-R Relationships • Distance From Radar • Radar beams curve away from surface with distance and Z may not be representative <http://oceanservice.noaa.gov/education/yos/resource/JetStream/doppler/baserefl.htm>

21. Statistical Analysis of dBZ Departures • ANOVA tests were run on the reflectivity departures, which were placed into different populations sets • Wind speed • Humidity (aloft and surface) • Dew point (surface) • Distance from the radar

22. Statistical Analysis Contiuned • α = 0.05 • Although none can be considered statistically significant, the wind and distance parameters have low p-values

23. Wind Speed • Low Winds ( < 8mph) • High Winds ( ≥ 8mph)

24. Distance from Radar • Short Distance ( < 50km) • Long Distance (> 5okm)

25. Case Study:NIDS vs. Archive Level 2 Data • NIDS Level III – coarse resolution data • Archive Level II – high resolution data • Single Event Example: • 28 June 2010, 15:27GMT

26. FloridaTech

27. Florida Tech

28. Florida Tech

29. Conclusion • Field measurements revealed large departures from the standard Z-R relationship • Meteorological Parameters • Wind speed and the distance from the radar are better discriminators than humidity and dew point • NIDS vs. Archive Level II • Archive Level II may be needed to filter out some events

30. Questions? Next: JeanaMascio