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Seasonal Degree Day Outlooks

Seasonal Degree Day Outlooks. David A. Unger Climate Prediction Center Camp Springs, Maryland. Definitions. _. _. HDD = G 65 – t t < 65 F CDD = G t – 65 t > 65 F HD = HDD/N CD = CDD/N T = 65+CD-HD CD = T –65 +HD

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Seasonal Degree Day Outlooks

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  1. Seasonal Degree Day Outlooks David A. Unger Climate Prediction Center Camp Springs, Maryland

  2. Definitions _ _ HDD = G65 – t t < 65 F CDD = G t – 65 t > 65 F HD = HDD/N CD = CDD/N T = 65+CD-HD CD = T –65 +HD t = daily mean temperature, T=Monthly or Seasonal Mean N = Number of days in month or season _ _ _ _ _ _

  3. CPC Outlook

  4. CPC POE Outlooks

  5. Tools Overview Forecaster Input Skill: Heidke .10 RPS .02 Temperature Fcst Prob. Anom.For Tercile (Above, Near, Below) Model Skills, climatology Temperature POE Skill: CRPS .03 Downscaling(Regression Relationships) Temperature POE Downscaled Skill: CRPS .02 Temperature to Degree Day(Climatological Relationships) CRPS Skill: CDD .05 HDD .02 Degree Days HDD CDD POE Accumulation Algorithms Degree Days Flexible Region, Seasons CRPS Skill: CDD .06 HDD .02

  6. Temperature to Degree Days

  7. Rescaling Downscaling FD Seasonal CD Seasonal Disaggregation CD Monthly FD Monthly

  8. Downscaling • Regression • CD = a FD +b Equation’s coefficients are “inflated” (CD variance = climatological variance)

  9. Disaggregation - Seasonal to Monthly • Tm = a Ts + b Regression, inflated coefficients • Average 3 estimates M JFM + M FMA + M MAM 3 M =

  10. Verification note • Continuous Ranked Probability Score - Mean Absolute Error with provisions for uncertainty • Skill Score = 1. – - Percent Improvement over climatology CRPS CRPS Climo

  11. Continuous Ranked Probability Score

  12. CRPS Skill Scores: Temperature Skill .10 .05 .01 FD CD 3-Mo 1-Month Lead, All initial times 1-Mo

  13. CRPS Skill Scores: Heating and Cooling Degree Days Skill .10 .05 .02 1-Mo 12-Mo Cooling Heating

  14. Degree Day Forecast (Accumulations)

  15. Reliability

  16. Reliability

  17. Conclusions • Downscaled forecasts nearly as skillful as original temperature outlook • Skill better in Summer than Winter • Better understanding of season to season dependence will lead to improved forecasts for periods greater than 3-months.

  18. Testing • 50 – years of “perfect OCN” Forecast = decadal mean and standard deviation • Target year is included to assure skill. • Seasonal Forecasts on Forecast Divisions supplied How does the skill of the rescaled forecasts compare to the original

  19. CRPS Skill Scores – Downscaled and disaggregated Skill .10 .05 FD CD .01 Seasonal Monthly

  20. CRPS Skill Scores Temperature to Degree Days Skill .10 .05 T DD .01 Cooling Heating

  21. Accumulation Algorithm DD = DD + DD Independent (I) Dependent (D) From Climatology A+B A B F F F = 2 + 2 2 B A+B A F = F F + A+B A B F F < F < (I) A+B A+B (D) A+B F F A+B (I) F = F + F + F = ) K K( A+B F F (D) (D) (I) (D) (I)

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