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Office of Hydrologic Development NOAA National Weather Service Silver Spring, Maryland 2 June 2010

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

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Office of Hydrologic Development NOAA National Weather Service Silver Spring, Maryland 2 June 2010

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

  2. 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 km2 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 2

  3. 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 Forecastsin the 0-1 Hour Timeframe: 3 3

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

  6. HPN verification study: Detection of 4x4km rainfall ≥12.5mm ≥25mm 23.3 x 106 cases included in statistics

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

  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: 25th percentile observation is about 0.80 of the forecast amount

  9. 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 QPFFrom Radar Extrapolation and RUC forecasts 9 9

  10. Radar Precipitation Rates,1715 UTC, 16 May 2009 Radar-Observed Precipitation Rates, 1715 UTC 15 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 10

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

  12. 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 Characteristics 12 12

  13. Regression Equation for 0-6-hPrecip 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 Prediction equation based on 40,000 cases: Apr-Sep 2009, Southeastern United States. Mean observed precip = 1.9 mm; R2 = 0.14 13 13

  14. Regression (RUC2+Radar) Forecasts: Correlation to 6-H Rainfall, New England (17,300 cases Apr-Sep 2009 – 18-00 UTC) Reduction of Variance (R2) 14 14

  15. 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 15

  16. 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 QPF 16 16

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

  18. Supplementary Slides

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

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