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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 Flood 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. 0-1 hr QPF:

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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 Flood 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. 0-1 hr QPF: High Resolution Precipitation Nowcaster (HPN) Targeted for FFMP application 0-6 hr QPF: Targeted primarily for RFC use, with potential use in Site Specific Model Layout: Recent performance of the HPN Detection of precipitation at 1 inch/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? 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 • Input from NMQ radar-only precipitation rate algorithm • Forecasts verified relative to subsequent NMQ radar-only precipitation estimates • Verified detection of ≥ 1 inch/25mm amounts • Documented performance relative to persistence forecast (initial-time rain rates) • Study setup • Conterminous US • 30 study hours over 19 days, 15 Sep-31 October

  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 ≥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 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 • Aim to provide updated training material

  9. Original requests for development from ABRFC Blending radar extrapolation and from RUC2 QPFs Extrapolation/advection model for precipitation rate fields: Initial condition: Precipitation rate from NMQ radar-only product (see succeeding NSSL presentation) Motion vector: 0-2 hour: radar echo motion (computed from NMQ radar-only data) 2-6 hour: morphed toward RUC2 700-500 hPa wind field forecast Model Output Statistics approach used to determine optimum weights between extrapolation and RUC2 QPFs – Stage IV gauge/radar serves as ground truth 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 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. 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 seasons, and subregions of the conterminous U.S. Create disaggregation logic to get QPFs for 1-h subintervals in 6-h period Operational implementation subject to field/management approval 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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