Radar-Derived Precipitation Part 4

1. Radar-Derived Precipitation Part 4 I. Radar Representation of Precipitation II. WSR-88D, PPS III. PPS Adjustment, Limitations IV. Effective Use COMET Hydrometeorology 00-1 Matt Kelsch Tuesday, 19 October 1999 kelsch@comet.ucar.edu

2. V. Effective UseStage I PPSStrengths • Numerous quality control steps to minimize limitations both in the radar estimate of precipitation, and the rain gauge representation of precipitation. • Spatial and temporal resolution are excellent for the mesoscale detail of precipitation systems. • Spatial detail over a large area • Monitor evolution of events between gauge sites • Real time information

10. Stage 1 PPS:Strengths (cont.) • Opportunity for important rainfall information in remote, poorly instrumented areas. • Adaptation parameters provide some flexibility for different locations and climate regimes. • Has the versatility to evolve into a better algorithm that can effectively account for variability on a geographic, seasonal, and even hourly basis. • Offers important input for a comprehensive, multi-sensor system.

11. Radar-Derived Precip:When changing Z-R coefficients is not the real solution: • Range degradation, overshooting low-levels • Problem associated with propagation of beam, not Z-R. • Snowfall • More complexity than liquid hydrometeors. • Phase changes and mixed phases exist over small space/time scales. • Range degradation often co-exists. • Phase change: hail, melting snow • Radical storm-scale changes in Z to R relationship. • Minimal proof that hail correction can be done with Z-R. • Inconsistent relationship between Z-R and hail occurrence.

12. Radar-Derived Precip:When changing Z-R may help: • Consistently different average DSD (climate) • Tropical versus mid-latitude (warm vs. cold process) • Maritime versus continental • Consistently different average DSD (season) • Convective versus stratiform • Precip System character • Identify Convective versus Stratiform signature • Identify warm versus cold rain signature • Identify maritime versus continental

13. Why can’t the adaptation parameters and bias adjustment procedure solve all the limitations? • Radar bias adjustment is only one uniform adjustment. It depends on adequate representation of precip by the local gauge network. • Adaptation parameters can greatly help the algorithm performance for a given site and/or season. The parameters “tune” the algorithm for the typical scenario. Atypical events, such as unusually high rainfall rates, may not be diagnosed well. • The most effective use of PPS is to make it a function of meteorology, not the “normal” climatology.

14. Can we account for the important atypical events without degrading the guidance for the more common typical events? • Meteorological information from soundings, profilers, and surface reports are a few examples of data sources that can assist with real-time adjustment of adaptation parameters. • Information from other NEXRAD algorithms, such as HAIL or VIL, may provide some guidance. • The most effective use of PPS is to make it a function of meteorology, not the “normal” climatology.

15. DATA: Soundings and Rainfall RatesWhat are reasonable maximum rainfall rates expected?

19. Radar-derived Precipitation:A Summary Of Major Points • Radar provides one of several useful methods for sampling precipitation • Quantitative reliability issues are related to the fact that radar is sampling some volume at some elevation to estimate precipitation at the ground • Radar-derived precipitation is most reliably modeled for liquid hydrometeors; hail and snow add complexity • The above two points are not effectively corrected by changing Z-R coefficients; Z-R changes should be related to Drop Size Distribution knowledge. • Radars and rain gauges do not measure equal samples • Rain gauges do not provide a good representation of precipitation distribution, especially convective precip. • Radar provides excellent information about the spatial and temporal evolution of precipitation systems.