WHAT IS Z?  Radar reflectivity (dBZ)  Microwave energy reflects off objects (e.g. hydrometeors) and the return is reflectivity WHAT IS R?  Rainfall Rate (mm/hr)  A calculated value relating the quantity of precipitation and time

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

 Empirical relationship between Z and R follow the general relationship: Z= aR b  The WSR-88D Convective relationship is the standard Z-R relationship used by the Melbourne National Weather Service (NWS) RELATIONSHIP:OPTIMAL FOR: Marshall-Palmer Z=200R 1.6 General stratiform precipitation East-Cool Stratiform Z=130R 2.0 Winter stratiform precipitation – east of continental divide West-Cool Stratiform Z=75R 2.0 Winter stratiform precipitation – west of continental divide WSR-88D Convective Z=300R 1.4 Summer deep convection Rosenfield Tropical Z=250R 1.2 Tropical convective systems

 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

 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

Davis Weather Station Horizontal Rain Gauge Standard 8 inch Gauge

 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

 A five minute average rainfall rate was calculated  Reflectivity data obtained from the NEXRAD Information Distribution Service (NIDS)

Event 1 Event 2 Event 3 28 June 2010

Z=300R 1.4

Average departure: -1.08dBZ

 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

 Humidity Variables  Evaporation could decrease R  Low humidity and/or dew point indicate evaporation is occurring

 Distance From Radar  Radar beams curve away from surface with distance and Z may not be representative

 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

 α = 0.05  Although none can be considered statistically significant, the wind and distance parameters have low p-values DBZ DEPARTURES P-Value Low Winds 0.18 High Winds Short Distance 0.12 Long Distance Low Dew point (SFC) 0.15 High Dew point (SFC) Low Humidity (SFC) 0.71 High Humidity (SFC) Low Humidity (Aloft) 0.23 High Humidity (Aloft)

 Low Winds ( < 8mph)  High Winds ( ≥ 8mph)

 Short Distance ( < 50km)  Long Distance (> 5okm)

 NIDS Level III – coarse resolution data  Archive Level II – high resolution data  Single Event Example:  28 June 2010, 15:27GMT NIDSArchive Level 2 Reflectivity (dBZ/10)

 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