1. 11 Short-Range QPF for Flash Flood Prediction and Small Basin Forecasts Prediction Forecasts David Kitzmiller, Yu Zhang, Wanru Wu, Shaorong Wu, Feng Ding 1 Office of Hydrologic Development NOAA National Weather Service Silver Spring, Maryland 2 June 2010

2. 22  Recent performance of the High Resolution Precipitation Nowcaster (HPN) algorithm in 0-1 hour time frame  Detection of precipitation at 25mm h -1 thresholds  Verification at 16 km 2 grid resolution (4x4 km)  An approach to QPF in the 0-6-hour range  Does blending of physical and extrapolation model precipitation forecasts improve on either one, in the 0-6-hour time frame?  HPN was targeted for FFMP application  0-6h QPF targeted primarily for RFC use, but there are potential applications to Site Specific In this discussion:2

3. 33  Based purely on extrapolation of radar echoes  Implemented in OB9.0, following implementation of High- Resolution Precipitation Estimator (HPE)  Produces forecasts of: Rainfall rate at 15, 30, 45, and 60 minutes 1-hour rainfall total  Forecasts are computed on 4-km grid mesh, output on 1-km grid mesh  Can incorporate gauge/radar bias information from MPE  See WDTB flash flood modules: http://www.wdtb.noaa.gov/buildTraining/AWIPS_OB9/index.html HPN Extrapolation Forecasts in the 0-1 Hour Timeframe: 3

4. 4 HPN verification study: September-October 2009  HPN was run in offline mode over the conterminous U.S., during development of 0-6h QPF algorithm First two hours of the extrapolation forecast are from HPN algorithm  Input from NMQ radar-only precipitation rate algorithm  Forecasts verified relative to subsequent NMQ radar- only precipitation estimates  30 study hours over 15 days, 15 Sep-31 October  Verified detection of ≥12.5mm and ≥ 25mm amounts  Documented performance relative to persistence forecast (initial-time rain rates)

5. 5 Example HPN Input/Forecast/Verification Radar Rainrate 1845 UTC 24 Sep 2009 NMQ Estimate 1845-1945 UTC HPN Forecast 1845-1945 UTC

6. 6 HPN verification study: Detection of 4x4km rainfall 23.3 x 10 6 cases included in statistics ≥12.5mm ≥25mm

7. 7 HPN verification study: Forecast vs Radar-Estimated 4x4km rainfall 22,000 grid boxes with precipitation forecasted, northeastern U.S. 75 th pct 25 th pct Mean

8. 8 HPN Verification Study: Summary  HPN consistently improves on persistence forecasts in terms of POD and FAR: 40% more detections of 12.5- and 25-mm amounts 20% fewer false alarms  HPN QPF has little bias overall (0.9 to 1.1)  For HPN QPF > 10 mm: Expected (mean) observation is about 0.67 of the forecast amount  For HPN QPF > 10 mm: 25 th percentile observation is about 0.80 of the forecast amount

9. 99  Original requests for development from ABRFC  Designed to use a statistically-weighted combination of QPFs from radar extrapolation and from RUC2  Extrapolation/advection model for precipitation rate fields: Extrapolation based on recent radar echo motion for 0-2 hours Motion vector field is morphed toward RUC2 700-500 hPa wind field forecast for 3-6 hours  Radar precipitation rate input from NMQ radar-only product (see succeeding NSSL presentation)  Model Output Statistics approach used to determine optimum blend of extrapolation and RUC QPFs 0-6 Hour QPF From Radar Extrapolation and RUC forecasts 9

10. 1010 Radar Precipitation Rates,1715 UTC, 16 May 2009 From National Mosaic and Multisensor Quantitative Precipitation Estimation system (NMQ) Yellow: > 10mm 6-h -1 Red: > 25mm 6-h -1 Gray: > 38 mm 6-h -1 Blue: > 75 mm 6-h -1 Radar-Observed Precipitation Rates, 1715 UTC 15 May 2009

11. 1111 Extrapolation forecasts of rate field, 1715-2315 UTC:

12. 1212  Forecast products: Probability of 6-hour precipitation ≥ 0.25, 2.5, 12.5, 25, 50, 75 mm Precipitation amount forecast  Gridded forecasts, 4x4 km mesh length  Issue forecasts for periods 00-06, 06-12, 12-18, 18-00 UTC (cover entire day)  Forecasts use input from the hour preceding start of valid period  RUC-Satellite-Lightning equations will be applied in radar coverage gaps  Forecasts disseminated before start of valid period 0-6h QPF Product Characteristics12

13. 1313 Regression Equation for 0-6-h Precip Amount: Southeastern US Precipitation = 0.52 + 0.31 RADAR QPF(0-3h) + 0.24 RUC QPF (0-3h) + 0.26 RUC QPF (3-6h) + 0.17 RADAR QPF (3-6h) given RADAR and/or RUC QPF > 0; forecasts and predictors in mm, spatial area 4x4 km 13 Prediction equation based on 40,000 cases: Apr-Sep 2009, Southeastern United States. Mean observed precip = 1.9 mm; R 2 = 0.14

14. 1414 Regression (RUC2+Radar) Forecasts: Correlation to 6-H Rainfall, New England (17,300 cases Apr-Sep 2009 – 18-00 UTC) 14 Reduction of Variance (R 2 )

15. 1515  Explained variance is small at this small spatial scale. However skill increases as accumulating area increases.  Products combining RUC2 and extrapolation QPF could match or improve on skill of current operational guidance  Radar and numerical prediction models are clearly complementary for QPF in 0-6-hour range 0-6h QPF Findings 15

16. 1616  Collection of new forecast and verification data on a daily basis  Aim for 3 years’ development data  Creation of probability and amount equations for cool and warm season, and subregions of the conterminous U.S.  Create disaggregation logic to get QPFs for 1-h subintervals in 6-h period Ongoing Work – 0-6h QPF16

17. 1717 Questions? Suggestions? Thanks to collaborators in NOAA National Severe Storms Laboratory, Institute of Atmospheric Physics/Czech Republic Academy of Sciences 17

18. 18 Supplementary Slides

19. 19 HPN verification study: Detection of 8x8 km rainfall 11,100 grid boxes with precipitation observed or forecasted