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Use of HPC Confidence Interval Forecasts to Produce a Hydrologic Ensemble of River Forecasts

Use of HPC Confidence Interval Forecasts to Produce a Hydrologic Ensemble of River Forecasts. John Halquist NOAA / NWS / NCRFC Chanhassen, MN. The problem:. River Forecasts (deterministic) are typically based on one possible hydrometeorologic scenario

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Use of HPC Confidence Interval Forecasts to Produce a Hydrologic Ensemble of River Forecasts

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  1. Use of HPC Confidence Interval Forecasts to Produce a Hydrologic Ensemble of River Forecasts John Halquist NOAA / NWS / NCRFC Chanhassen, MN

  2. The problem: • River Forecasts (deterministic) are typically based on one possible hydrometeorologic scenario • Changing or dynamic weather patterns result in different rainfall amounts or distribution than forecast • Users want access to more potential scenarios • Some want more future rain, while others want less

  3. A solution: • Use additional possible Quantitative Precipitation Forecasts (QPF) to produce an ensemble of possible River Forecasts • Maintain connection to realistic meteorologic conditions • Utilize Short Range Ensemble Forecasts (SREFs) and Hydrometeorological Prediction Center (HPC) expertise

  4. HPC Confidence Interval QPF • Confidence Intervals (CI) derived by analyzing SREF QPF spread and HPC QPF absolute errors (AE) • 95% Confidence Interval QPF calculated twice per day (1200 and 0000 UTC)

  5. HPC Confidence Interval QPF

  6. River Forecast Ensemble • Mean areal QPF are computed from the HPC 95% CI QPF – Maximum and Minimum • Independent hydrologic model simulations are produced using these QPF as input • Resultant hydrologic time-series are provided in Scalable Vector Graphics (SVG) • Allows for interactive query of data represented in plots

  7. Sample ensemble display Mean Areal QPF (local) Hydrograph w/HPC 95% CI Maximum QPF Observed data Hydrograph w/ HPC deterministic QPF Hydrograph w/HPC 95% CI Minimum QPF

  8. Example event Observed Traditional Forecast

  9. Analysis • Performed for April – September 2005 • Locations: Cedar River, Iowa • 11,593 forecast vs observation pairs • 49,211 modeled vs observation pairs

  10. Analysis • Measures: • BIAS • Probability of Detection (POD) • False Alarm Ratio (FAR) • Critical Success Index (CSI) • Percent Correct (PC) • Mean Error (ME) • Mean Absolute Error (MAE) • Root Mean Square Error (RMSE)

  11. Analysis

  12. Std error: .7822 R2 : .9197 Std error: 1.198 R2 : .1419 Std error: .7822 R2 : .9197

  13. What next? • Proceed and provide ensembles for representative locations to WFOs • Continue evaluation • Add locations • Investigate additional ensemble members • Forecast temperatures • Additional hours of QPF • QPF time shifting

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