Radar Hydrology in the U.S. National Weather Service: A Flash Flood Prediction- Centric Overview

1. Radar Hydrology in the U.S. National Weather Service: A Flash Flood Prediction-Centric Overview Presented by D.-J. Seo Hydrology Laboratory Office of Hydrologic Development National Weather Service National Oceanic and Atmospheric Administration

2. In this presentation • What is radar hydrology? • A flash flood prediction-centric overview • Science issues, where NWS is headed • Closing remarks • Discussion

3. Flash Flood Forecasting Presented by D.-J. Seo Hydrologic Science and Modeling Branch Hydrology Laboratory Office of Hydrologic Development National Weather Service USWRP Warm Season Workshop, Mar 2002

4. Future Directions for Flash Flood Prediction D.-J. Seo Hydrologic Science and Modeling Branch Hydrology Laboratory National Weather Service NWS PQPF Science Strategy Workshop June 3-4, 2002

5. QPE and Short-Range QPF for Flash Flood Forecasting and Decision Making: Outstanding Issues and Future Directions D.-J. Seo Hydrologic Science and Modeling Branch Hydrology Laboratory National Weather Service NWS Flash Flood Workshop Aug 27-29, 2002

6. Multisensor Precipitation Estimator (MPE) Presented by D.-J. Seo Hydrologic Science and Modeling Branch Hydrology Laboratory National Weather Service Silver Spring, MD NWS Flash Flood Workshop, Aug 27-29, 2002

7. Distributed models as a flash flood forecasting tool Presented by D.-J. Seo Hydrologic Science and Modeling Branch Hydrology Laboratory Office of Hydrologic Development National Weather Service NWS Flash Flood Workshop, Aug 27-29, 2002

8. What is radar hydrology?

9. Hydrology 001 Meteorology, Climatology precipitation evapo-transpiration Hydrometeorology, Hydroclimatology Surface runoff Surfacewater hydrology Soil moisture dS/dt=I-O Subsurface runoff Deep percolation Groundwater hydrology

10. Data Model structure/physics Model parameters Initial conditions Observed boundary conditions Future boundary conditions Q/C, data assimilation Modeling Parameter estimation Data assimilation QPE QPF Post processing Sources of error, areas to be addressed

11. What’s driving radar hydrology? • Better weather, water and climate prediction services • Finer scale • Improved accuracy • Longer lead time

12. From the NWS strategic plan

13. Radar hydrology - Current practice • Flash flood prediction and monitoring • River stage prediction and monitoring • NWP • Climate prediction and monitoring • Hydrometeorological design

14. Flash Flood Guidance (FFG) Rainfall Soil moisture accounting model (SAC, API) Excess rainfall (threshold runoff) Routing model (unit hydrograph) Peak flow (Qpeak) Flood stage (>bankfull)

15. Current Paradigm • QPE + QPF - FFG > some threshold

16. Flash Flood Monitoring and Prediction System (FFMP)

17. Issues, where we’re headed • Quantitative Precipitation Estimation (QPE) • Quantitative Precipitation Forecast (QPF) • Hydrologic (soil moisture accounting) modeling • Hydraulic (routing) modeling (including inundation mapping) • Data assimilation • Dealing with uncertainty

18. Questions • Where can we gain lead time? • Where can we gain accuracy? • Where can we gain (spatial) resolution? • How can one verify all this each step of the way?

19. Forecast error

20. Scale, nonlinear dynamics and uncertainty Errors due to neglecting spatio-temporal variability Goodness of forecast Nonlinear accretion of errors Scale

21. Challenges of Distributed Modeling Space/Time Variability • Does accounting for the space/time variability of input data and parameters guarantee better results? Effect of noisy rainfall data on the peak volume at different simulation scales. 75% 50% Error 25% Increasing resolution From Koren et al. 2001, Smith et al. 2002

22. Quantitative Precipitation Estimation • Radar QPE • Multisensor QPE • Polarimetric QPE

23. DPA WSR-88D DHR/DSP ORPG/PPS Hydro-Estimator Rain Gauges Multi-Sensor Precipitation Estimator (MPE) Lightning NWP model output WFO-MPE WFO-MPE WFO RFC

24. Real-time Hydrologic and Hydrometeorological Data From Cedrone 2002

25. Effect of Bias Adjustment From Seo et al. 1999

26. After correction Before correction

27. Sampling Geometry - Topography - Reflectivity Morphology From Seo et al. 2000

28. Vertical Profiles of Reflectivity Slant Range vs Adjustment Factor (Tilts 1 thru 3) From Seo et al. 2000

29. Storm Total Rainfall - KATX, Unadjusted From Seo et al. 2000

30. Storm Total Rainfall - KATX, Adjusted From Seo et al. 2000

31. Extreme events • Radar QPE susceptible to errors due to; • Uncertainty in microphysical parameters (Z-R, hail, etc.) • Sampling problem associated with low centroid echos • Orographic enhancement • (Partial) Beam blockage

32. Better rainfall estimates • Heavy rain • Discriminating rain from hail • Recovering areas of partial beam blockage • Removal of AP, clutter, birds, insects • October 2002 event • 1” to 3” gauge observations • Good estimates with polarimetric variable KDP • Large overestimates with Z-R • Partial beam blockage mitigated Rainfall Comparisons with the Oklahoma Mesonet J O I N T P O L A R I Z A T I O N E X P E R I M E N T From Schuur et al. 2003

33. Statistical Summary Algorithm Mean Absolute Error Root Mean Squared Error Z 1.18" 1.99 Z-ZDR 1.29" 2.20 ZDR-KDP -0.21" 0.63 KDP -0.26" 0.61 Statistical Summary of Rainfall Estimation J O I N T P O L A R I Z A T I O N E X P E R I M E N T • The KDP algorithm significantly outperformed the Z algorithm, particularly in regions of heavy rainfall. • While the Z algorithm showed a bias toward overestimation, the KDP algorithm showed no consistent bias. • While partial beam blockage hindered the Z algorithm performance, no corresponding beam blockage was noted in the KDP algorithm output. From Schuur et al. 2003

34. Snow Stratiform Convective Rain hail HCA – Including Snow Categories 16 June 2002 MCS (same as in conference preprint volume) Results include HCA improvements since 16 June 2002 AP – Ground clutter / AP BS - Biological scatterers DS – Dry snow WS – Wet snow SR – Stratiform rain CR – Convective rain RH – Rain / hail mixture J O I N T P O L A R I Z A T I O N E X P E R I M E N T From Schuur et al. 2003

35. QPF • Warm-season QPF • Arguably the most difficult part of radar hydrology • Scale • Uncertainty • Probabilistic QPF (PQPF)

36. Quantification of predictability of rainfall From Fulton and Seo 1999

37. The ESP Process QPF, QTF Corrects bias, accounts for meteorological uncertainty ESP Pre-Processor Ensemble traces of precipitation, temperature Hydrologic model Ensemble traces of streamflow Corrects bias, accounts for hydrologic uncertainty ESP Post-Processor Ensemble traces of streamflow Reflects both uncertainties

38. Ensemble traces of precipitation

39. Ensemble traces of streamflow

40. Ensemble traces of post-processed streamflow (stochastic)

41. Hydrologic, hydraulic modeling • Distributed modeling • Data assimilation • Ensemble prediction

42. FFMP basin size vs. NWSRFS calibrated basins From Smith 2002

43. Kansas Missouri Oklahoma Arkansas Blue River Basin Texas Distributed modeling Test Basin Blue River Basin, OK Area: 1233 km2 From Zhang et al. 2001

44. Soil moisture accounting

45. Examples of Gridded Parameters From Zhang et al. 2002

46. Hillslope Routing From Reed et al. 2002 Drainage Density Illustrated ~ 1.07

47. Channel Routing From Reed et al. 2002

48. Test Results Hydrographs @ Interior Points A B Basin Outlet C From Zhang et al. 2002

49. 0 - 1 1 - 2 2 - 4 4 - 8 8 - 18 18 - 45 Precipitation Distribution from NEXRAD Feb. 12, 1997 6:00 (mm) From Smith et al. 2002

50. 0 - 1 1 - 10 10 - 15 15 - 20 20 - 25 25 - 30 30 - 40 40 - 45 45 - 59 Distribution of Upper Zone Free Water Feb. 12, 1997 6:00 (mm) From Smith et al. 2002