Climatological Precipitation Estimates Versus “Seasonal” Precipitation Totals & Trends Versus Real-Time Precipitatio

1. Real-time Estimation of Precipitation UsingWSR-88D Weather RadarsDavid R. Legates, Ph.D., C.C.M.Associate Professor and DirectorCenter for Climatic ResearchUniversity of DelawareNewark, Delaware 19716

2. THREE TYPES OF ANALYSES Climatological Precipitation Estimates Versus “Seasonal” Precipitation Totals & Trends Versus Real-Time Precipitation Estimates

3. High Resolution Weather Data System Originally Sponsored by Duke Energy Corporation of Charlotte, North Carolina Initial Application: Provide the “front-end” to Duke Energy’s River Management System of the Catawba River Basin for input to their Power Load Management System

4. High Resolution Weather Data System Station Data Products • Air Pressure SGage Precipitation • Air Temperature lSolar Radiation • Dew Point Temperature lWind Vector WSR-88D Radar Products • Radar-Based Precipitation • Composite Gage-Radar Precipitation Derived Products • 12-Hr Precipitation SPrecip. Difference Fields • 24-Hr Precipitation SStorm Total Precipitation • Relative Humidity lApparent Temperature • Evapotranspiration l Soil Moisture Content

21. National Weather Service WSR-88D Weather Radars “NEXRAD” 10 cm wavelength Doppler-based

30. Reflectivity – ZRainfall Rate – Rwhere D is the raindrop diameter, NB(D) and NG(D) are the dropsize distributions at the height of the beam and ground, respectively, and FT(D) is the terminal fall velocity.

31. WSR-88D Precipitation Processing Digital Precipitation Array (DPA) • Precipitation Processing Algorithms • Account for radar beam blockage • Check for spurious noise and outliers • Ground return/tilt test (0.5 versus 1.5 tilt angles) • Construction of Hybrid Scan • Precipitation Rate Algorithms • Z-R relationship is applied -- usually Z = 300 R1.4 • Simple averaging from 2km to 4km resolution • Time continuity checks • Precipitation Accumulation Algorithms • Scan and hourly accumulations • Missing data and outliers check • Precipitation Adjustment Algorithms • NOT IMPLEMENTED

32. Errors in Radar Precipitation Estimates Errors associated with reflectivity signrange • Ground Clutter Contamination * + Yes • Anomalous Propagation (Super-refractive conditions) * + No • Partial Beam Filling – Yes • Wet Radome Attenuation – No • Attenuation by Oxygen, Water Vapor, Clouds, Rainfall – Yes • Incorrect Hardware Calibration * +/– No Errors associated with the Z-R relationship • Variations in Dropsize Distribution +/– No • Hail, Mixed Precipitation, and Snow Events + No Errors associated with effects below the radar beam • Advection -- Strong Horizontal Winds +/– Yes • Virga -- Evaporation of Falling Precipitation + Yes • Condensation/Coalescence Below Radar Beam – Yes • Vertical Motions -- Updrafts and Downdrafts +/– No * WSR-88D system claims to specifically address these problems

33. Gage-Measured Precipitation Provides a good estimate of precipitation at a given point (when adjusted for gage measurement biases) Nearly all networks lack the gage densities needed to provide high-resolution estimates of storm-scale precipitation at an hourly time step Radar Precipitation Estimates Good spatial representation of precipitation is afforded by the DPA’s 4km x 4km resolution Accuracy of precipitation estimates is very low due to errors associated with reflectivity, below-beam effects, and the Z-R relationship Gage Measurements Versus Radar Estimates Calibration, therefore, uses the WSR-88D radar data for the “spatial footprint” of the storm and adjusts the radar reflectivities using the gage observations.

36. KHGX Radar Calibration Oct ‘94

37. Radar Calibration Procedure • Compute the “DPA-composite” reflectivity, Z’, from: Z’ = 300 R’ 1.4 or Z’ = 250 R’ 1.2 or Z’ = 200 R’ 1.6 (Standard) (Tropical) (Stratiform)where R’ is the precipitation estimate from the DPA. The appropriate equation is chosen from the Z-R relationship used by the NWS to derive the DPA. • Then, compute the Composite Gage-Radar precipitation estimate, R, using: Z’ = a Rb Dc where D is the range from the radar and a, b, and c are calibration parameters. Parameters are fit using weighted least-squares logarithmic regression and observed reflectivity-gage pairs.

38. Hourly Pair Calibration (Legates, 2000) where a, b, and c are constants and D is distance of the reflectivity from the radar Calibration is made using radar-gage pairs computed on an hourly interval

41. Problems in Estimating Snowfall using Weather Radar • See previous discussion about rainfall • Fall rate is smaller which accentuates the timing/advection problem • Reflectivity varies considerably between liquid and solid hydrometeors • Not all solid hydrometeors are equal! • Very few real-time observations of solid precipitation exist!!

42. Limitations in theLegates (2000) Method • Scatter is relatively small within storm events and increases as differing storm events are included • Distance adjustment is not always significant – owing to the different elevation angles chosen • System limits pair generation to one pair per update cycle per gage • Timing issues may exacerbate advection problems

43. A New Physically-Based Approach • Differentiate between storm events and regions within storms • Incorporate distance adjustments based on the selection of elevation angles with distance • Enhance pair generation to use all radar updates and more frequent gage observations • More stringent controls on timing issues to reduce advection problems • Include physical effects on reflectivity biases

44. A New Physically-Based Approach • Select pairs according to similar rainfall events using surface meteorological conditions Potential Temperature Equivalent Potential Temperature Air Temperature and Dew Point Range Wind shift Atmospheric Pressure

47. A New Physically-Based Approach where the integral holds over the radial from the radome to the cell Thus, a,b, c, d, andthe function f must be estimated, as well as a new selection of pairs.

48. Key Issues/Gap/Challenges • There is a definite need for real-time, high spatial resolution estimates of solid and mixed precipitation events • Onset/duration of the event AND • Semi-quantitative assessments of solid hydrometeor water equivalent may be all that is necessary • Weather radar may be the best solution to this problem

49. Real-time Estimation of Precipitation UsingWSR-88D Weather RadarsDavid R. Legates, Ph.D., C.C.M.Associate Professor and DirectorCenter for Climatic ResearchUniversity of DelawareNewark, Delaware 19716