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94 th American Meteorological Society Annual Meeting

Northern Hemisphere Forecast Skill During Extreme Winter Weather Regimes Ryan N. Maue (WeatherBELL Analytics - Tallahassee) and Rolf H. Langland (NRL- Monterey) maue@weatherbell.com. 94 th American Meteorological Society Annual Meeting

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94 th American Meteorological Society Annual Meeting

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  1. Northern Hemisphere Forecast Skill During Extreme Winter Weather RegimesRyan N. Maue (WeatherBELL Analytics - Tallahassee) and Rolf H. Langland (NRL- Monterey)maue@weatherbell.com 94th American Meteorological Society Annual Meeting Atlanta, GA February 2 – 6, 2014

  2. Use current NWP deterministic & ensemble systems to analyze large-scale flow patterns and relate to medium-range forecast skill “dropouts” Diagnose causes of low-predictability flow regimes including dropouts: inadequate observations, large analysis uncertainty, and/or model error growth Link to skill of Teleconnection Indices such as AO/NAO, EPO, WPO and PNA Forecast skill metrics: 5 or n-day 500 hPa geopotential anomaly correlation (NH: 20°-80°N), Forecast dropout: an individual or collection of several consecutive forecasts that have significantly lower 500 hPa geopotential anomaly correlation skill – compared to monthly/seasonal mean (AC < 0.8) Low-predictability regime: particular hemispheric-scale configuration of upper-level flow that leads to below average forecast skill Research Objectives Framework & Goals Definitions

  3. Anomaly Correlation: Definition 500-hPa geopotential height Northern Hemisphere 20°-80° N Forecast anomaly from climatology* at each grid point (m) Analysis anomaly • The AC is common forecast skill metric used by operational centers • Forecasts with AC > 0.6 are considered as providing potential positive skill • Not perfect metric, but used in concert w/ e.g. mean squared error • * NCEP CFSR + CDASv2 (reanalysis) 1979-2014 climatology

  4. Archive of Analysis and Forecast Fields • Historical record of analysis and forecasts from current NWP deterministic and ensemble systems • Key is archive and/or real-time access to forecast products • Valuable resource includes the ESRL/PSD GEFS Reforecasts Version 2 (Hamill et al. 2013) as frozen forecast model for post-processing purposes MODEL TIME PERIOD GRID/FIELDS SOURCE

  5. Anomaly Correlation: Forecast skill NH: 500hPa height AC -- 45-day running means 0.9

  6. Predictive Skill Regimes From N Hemi AC, ECMWF & GFS: Centered means of 30-days and 7-days are calculated The 7-day minus 30-day mean represents a seasonally adjusted measure of skill Above-average skill Below-average skill • Anom Corr / predictive skill is strongly dependent on atmosphericflow regime: • different models “dropout” on the same cases Models tend to have low and high skill on the same forecast(s)- but ECMWF skill drops off less than GFS

  7. Anomaly Correlation: GFS recent skill Periods of significant skill drop-off & success Nov 2013 Dec 2013 Jan 2013

  8. Anomaly Correlation: ECMWF 5-day Nov 2013 Dec 2013 Jan 2013

  9. Anomaly Correlation: ECMWF 6-day Compare to 5-day: Nov 2013 Dec 2013 Jan 2013

  10. Anomaly Correlation: ECMWF 10-day Nov 2013 Dec 2013 Jan 2013

  11. Anomaly Correlation: forecast skill NH: 120-hour 500-hPa height AC 45-day running means • Seasonal AC scores are highly correlated • NH Winter skill >> Summer Skill. • Both models showed record-high skill during winter 2010-2011 • Longer time series shows improvement in skill due to major model configuration changes: • Jan 26, 2010: ECMWF T799  T1279 • July 28, 2010: GFS T382  T574 and physics major upgrade • Significant Summer skill improvements 2012 & 2013 from GFS ECMWF GFS 5-day forecast skill leveling off during cold-season Still significant room for warm-season improvement

  12. Example: Arctic Oscillation • AO is the first EOF of sea-level pressure (1000 hPa geopotential height) variations north of 20°N latitude • How do NWP systems perform during the + and – phase of the AO, as well as through transitions (e.g. Archambault et al. 2010)– during the Northern Hemisphere cold season? POSITIVE PHASE NEGATIVE PHASE J. Wallace, University of Washington

  13. Arctic Oscillation Winter 2009 – 2010 Sustained Negative AO

  14. Arctic Oscillation Winter 2010 – 2011 November – January Negative AO, followed by strong Positive forecast Major Transitions

  15. Arctic Oscillation ECMWF – Winter 2009 – 2010 500 hPa NH Anomaly Correlation Arctic Oscillation Index AC AO Transitions Dec 2009 Feb 2010

  16. Arctic Oscillation ECMWF – Winter 2010 – 2011 500 hPa NH Anomaly Correlation Arctic Oscillation Index ECMWF – Winter 2010 – 2011 500 hPa NH Anomaly Correlation Arctic Oscillation Index AC AO Lower Skill Very High Skill Lower Skill Dec 2010 Feb 2011

  17. ESRL/PSD GEFS v2 Reforecasts T254 ensemble reforecasts based upon the historical CFS Reanalysis init conditions Run in real-time daily at 00z Advantage: Frozen Forecast Model 0.95 0.9 0.85 Nov 2010 Mar 2011 0.8

  18. ESRL/PSD GEFS v2 Reforecasts T254 ensemble reforecasts based upon the historical CFS Reanalysis init conditions Run in real-time daily at 00z Advantage: Frozen Forecast Model 0.95 0.9 0.85 Nov 2013 Jan 2014 0.8

  19. GEFSv2 Arctic Oscillation Daily Index Error 1 0 -1 Nov 2013 Jan 2014

  20. GEFSv2 Arctic Oscillation Daily Index Error Function of analysis phase 5-day Forecast Error 10-day Forecast Error

  21. Visualizing Arctic Oscillation Forecast Index • Box and Whiskery • Control, mean, std dev, extremes

  22. Visualizing Arctic Oscillation Forecast Index 2. Bubbles Analyzed AO, 10-day forecast bubbles

  23. Visualizing Arctic Oscillation Forecast Index 3. Color-coded members NCEP GEFS 16-day perturbed + control

  24. Summary and Future Directions • A multi-year archive of high-resolution operational center deterministic forecasts has been developed to study “dropouts” in medium-range forecast skill • The models tend to “dropout” during the same forecasts, in “lower-predictability” flow regimes • The Arctic Oscillation (AO) index is (to some extent) anti-correlated with medium-range forecast skill, as measured by the 5-day anomaly correlation of 500mb height • Value of frozen model w/many years of data (> 25) to evaluate model performance during particular large-scale flow regimes • Value of multi-model & ensemble forecast-forecast correlations for medium-range extreme events

  25. Acknowledgements Data: ECMWF TIGGE, NCEP/NOMADS, ESRL/PSD NRC Postdoc at NRL Monterey (2010-2012) advisor: Dr. Rolf H. Langland Updated work based on Langland and Maue (Tellus A, 2012): Recent Northern Hemisphere mid-latitude medium-range deterministic forecast skill Request: NCEP reforecast system similar to ECMWF hindcasts for post-processing of current model systems & “Model Climatology” for extreme events

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