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Atmospheric River Retrospective Forecasting Experiment: Forecasting West Coast Heavy Precipitation Events

This study evaluates the effectiveness of operational and experimental datasets in forecasting heavy precipitation events on the West Coast of the United States. The goal is to improve forecast information for extreme events, with a focus on the mid-range timeframe and the viability of probabilistic quantitative precipitation forecasts (PQPF). The study utilizes retrospective forecasts of atmospheric river events from September 17-28, 2012.

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Atmospheric River Retrospective Forecasting Experiment: Forecasting West Coast Heavy Precipitation Events

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  1. Initial Results From the Atmospheric River Retrospective Forecasting Experiment: Forecasting West Coast Heavy Precipitation Events Thomas E. Workoff1,2, Ellen Sukovich3, Benjamin Moore3, Michael J. Bodner1, Faye E. Barthold1,4, and David R. Novak1 1NOAA/NWS/Hydrometeorological Prediction Center 2Systems Research Group, Inc. 3CIRES/University of Colorado/NOAA Earth System Research Laboratory/Physical Sciences Division 4I.M. Systems Group, Inc.

  2. Motivation Atmospheric Rivers are the dominant form of meridional moisture transport in the atmosphere 7-15 times the moisture flux than at the mouth of the Mississippi River Increased understanding and predictability of ARs is beneficial in several areas Flood control, emergency management, water resources, etc. QPFs are challenging Underestimate amounts Location & timing difficult Influence on HPC products Excessive rainfall Medium Range QPF

  3. Atmospheric Rivers Retrospective Forecasting Experiment (ARRFEX)September 17-28 2012 • *Retrospectively forecast 8 cool-season AR events* • GOALS: • Evaluate operational and experimental datasets in forecasting West Coast heavy precipitation • Discuss ways to provide better forecast information to users in extreme events • With a focus on: • Mid-range timeframe (3-5 days) • Viability of probabilistic QPF (PQPF) • Benefits of experimental datasets in extreme precipitation forecasting

  4. Atmospheric Rivers Retrospective Forecasting Experiment (ARRFEX)September 17-28 2012 • Daily Activities: • 24 hr probability of QPF (PQPF) • Day 5 and Day 3 • 10% and 40% probability of >3” • 72 hr (Day 1-3) QPF • Forecast AR duration • Precipitation start/stop timing • Subjective Verification • Experimental forecasts • Deterministic/ensemble guidance

  5. Numerical Model Guidance 12Z 12Z 12Z 00Z 00Z 00Z 00Z 24 hr PQPF 24 hr PQPF 72 hr QPF

  6. Experimental Guidance • HMT Ensemble (9 km) • ARW core, multiple physics schemes • ESRL Reforecast Dataset • 2nd generation GEFS (version 9.0.1); 1985-2010 • 10 members plus control run; archive 00Z initializations • Ranked analog method at each grid point to find dates of closest 50 matches • NARR precipitation data (32 km) • 24 hr PQPF and mean QPF http://www.esrl.noaa.gov/psd/forecasts/reforecast2/

  7. Results: PQPF Verification GEFS CMCE ECENS MMENS RFCST HMT

  8. Results: PQPF Verification

  9. Results: PQPF Verification  Reforecast deemed ‘most helpful’ in 6 cases (CMCE: 1, HMT: 2)

  10. Results: 72 h QPF Verification Stage IV ECMWF NAM HMT

  11. Results: 72 h QPF Verification

  12. Preliminary Results: Objective Verification Day 3 24 h QPF Threat Score Threat Score Threshold (inches)

  13. Preliminary Results: Objective Verification Day 3 24 h QPF Bias Score Bias Score Threshold (inches)

  14. Results: Point Forecast Verification • How accurate can we really be with timing issues? • Do higher resolution models hurt more than help with this problem? “High” confidence (2 of 7 cases) did not lead to better results (50% success) • No ‘low’ confidence forecasts indicated

  15. Experiment Summary • Higher-resolution data is beneficial, especially in West Coast/terrain driven events • Reforecast (PQPF) and HMT-ensemble data largely considered the best guidance • HMT could be too wet (?) • HPC currently working with ESRL to make the reforecast dataset operational • PQPF seems to be a worthwhile way to explore extreme QPF at mid-range lead times • Forecasting AR duration is problematic  model timing issues Acknowledgments: Mary Ralph, PSD/ESRL Tom Hamill, PSD/ESRL Keith Brill, HPC

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