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Craig Brown, M.S. Thesis UW Atmospheric Sciences Advisers: David Battisti, Nate Mantua and Ed Sarachik

Regional Impacts of day-to-day changes in the large scale Pacific North America (PNA) pattern: observations and prospects for skillful 7-14 day lead-time weather risk forecasts. Craig Brown, M.S. Thesis UW Atmospheric Sciences Advisers: David Battisti, Nate Mantua and Ed Sarachik.

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Craig Brown, M.S. Thesis UW Atmospheric Sciences Advisers: David Battisti, Nate Mantua and Ed Sarachik

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  1. Regional Impacts of day-to-day changes in the large scale Pacific North America (PNA) pattern: observations and prospects for skillful 7-14 day lead-time weather risk forecasts Craig Brown, M.S. Thesis UW Atmospheric Sciences Advisers: David Battisti, Nate Mantua and Ed Sarachik

  2. Research Goals • For selected stations, quantify the extent of “extreme weather” sensitivity to changes in the large scale PNA circulation pattern (freeze events, heavy rain and snow, wind gusts, extreme temperatures) • Use Thompson and Wallace (1999) approach with AO • examine the potential for extending deterministic weather forecasts into skillful risk probability forecasts at lead times up to 14 days

  3. Tracking the PNA pattern Wallace and Gutzler (1981): with standardized 500mb height anomalies … PNA(t) = 1/4*(Hawaii - Aleutians + Alberta - Florida) PNA pattern

  4. Seasonal mean PNA index Created by Todd Mitchell, UW JISAO

  5. Daily PNA index PNA index values Frequency distributions St devs. St devs.

  6. Properties of the daily PNA index Distribution of the daily PNA index 1948-1999 • Gaussian • Impressive persistence, with an e-folding time scale ~7 days • Well-correlated with heights at each of the 4 “centers of action”

  7. Impacts of the day-to-day PNA variability • Composite reanalysis fields to gain insights into what PNA variations at day-to-day time scales might be doing to surface weather

  8. PNA 500 mb ht pattern: a dominant mode of cool season intraseasonal-to-interannual variability PNA + PNA -

  9. Composite surface wind fields for +/- PNA PNA + PNA -

  10. Composite surface wind and surface temperature anomaly fields for +/- PNA PNA + PNA -

  11. Craig’s Extreme Event Analysis • At 50 stations, daily data for 5 weather parameters, 1948-1999 • Tmin, Tmax, Snowfall, Precipitation, and peak wind gusts • Using Histograms: • Identify extreme events: >1.5 and 2.0 std devs • Compare event counts for PNA +/- days • Map the ratios of event counts for PNA-/PNA+ days

  12. Histograms of daily Oct-Mar Tmax at Stampede Pass, WA All days Avg=34.7 F PNA < -1 Avg=29.2 F PNA > +1 Avg=39.7 F

  13. Histograms of daily Oct-Mar Tmin at Juneau, AK All days Avg=26 F PNA < -1 Avg=18 F PNA > +1 Avg=33 F

  14. Daily PNA and Peak wind gusts

  15. Daily PNA and extreme high precipitation events

  16. Daily PNA and extreme high temperatures

  17. Daily PNA and extreme low temperatures

  18. Daily PNA and number of freeze events(Tmin < 0)

  19. Daily PNA and extreme snowfall events

  20. Seattle snowfall and the daily PNA

  21. Days with PNA < -1 Increased frequency of extreme cold temperatures, freeze events, heavy precipitation, low elevation snowfall, and high wind gusts Days with PNA > +1 Increased frequency of extremely warm winter temperatures Reduced frequency of extreme cold, freezes, low elevation snowfall, heavy precip., and high wind gusts PNA and PNW weather extremes summary

  22. Ensemble PNA forecasts and probabilistic risk assessments • Lorenz told us the “butterflies” will trash the skill of deterministic weather forecasts at lead times >14 days, no exceptions! • Due to the robust nature of PNA variability, and its strong tendency for persistence, it is the most predictable atmospheric pattern at lead times of 6-10 days (Renwick and Wallace (1995)). • Ensemble PNA forecasts, based on the output of global weather prediction models, show promising skill at lead times of 7-to-14 days

  23. NCEP 11 member ensemble forecasts for the daily PNA indexderived from Medium Range Forecast (MRF) model output

  24. PNA prediction skill Forecast lead time in days

  25. PNA-based risk forecast Step 1: Ensemble PNA forecast Step 2: Probability risk forecast Predicted probability Predicted probability -2 -1 0 1 2 0 1 2 3 4 5 6 7 8 Seattle Snowfall (inches)

  26. Filling the holes in the existing prediction system ENSO, QBO, PDO Interannual Intraseasonal … Madden-Julian Oscillation --> PNA ? PNA, AO/NAO, AAO Ensembles GFS MM5 Forecast Lead-Time

  27. fair weather Fall 2002 and the PNA

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