Seasonal Degree Day Outlooks

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Presentation transcript:

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

Definitions HDD = G65 – t t < 65 F CDD = G t – 65 t > 65 F _ _ 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 _ _ _ _ _ _

CPC Outlook

CPC POE Outlooks

Flexible Region, Seasons Overview Tools 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

Temperature to Degree Days

Rescaling Downscaling FD Seasonal CD Seasonal Disaggregation CD Monthly FD Monthly

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

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

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

Continuous Ranked Probability Score

.031 .023 .028 .019 CRPS Skill Scores: Temperature Skill FD CD 3-Mo .051 .045 .041 .034 .027 .029 .026 .023 .013 .016 .027 .026 .002 .001 .011 .004 -.009 .002 -.006 -.008 .040 .036 .026 .030 .055 .059 .058 .020 .021 .024 .044 .038 .050 .047 Skill .035 .030 .012 .015 High Moderate Low None .094 .103 .074 .090 .10 .065 .055 .042 .035 .05 .01 FD CD .031 .023 .028 .019 3-Mo 1-Month Lead, All initial times 1-Mo

.049 .057 .018 .016 CRPS Skill Scores: Heating and Cooling Degree Days .040 .071 .036 .073 .114 .085 .019 .028 .058 .043 .021 -.011 .009 .022 .000 -0.16 -.004 .036 -.026 -.016 .101 .121 .014 .076 .090 .029 .035 .047 .102 .023 .048 .035 .014 .045 -.003 Skill .033 .051 .005 .003 High Moderate Low None .088 .115 .079 .111 .10 .044 .024 .046 .030 .05 .02 1-Mo 12-Mo .049 .057 .018 .016 Cooling Heating

Degree Day Forecast (Accumulations)

Reliability

Reliability

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.

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

.104 .109 .066 .057 CRPS Skill Scores – Downscaled and disaggregated .098 .081 .061 .042 .088 .092 .063 .039 .086 .083 .061 .059 .088 .085 .061 .055 .108 .105 .061 .060 .106 .019 .067 .077 .110 .086 .066 .074 .070 .052 .037 .109 .058 .055 Skill .138 .140 .086 .067 .198 .233 .106 .135 High Moderate Low None .10 .110 .087 .074 .044 .05 FD CD .01 .104 .109 .066 .057 Seasonal Monthly

.104 .095 .074 CRPS Skill Scores Temperature to Degree Days Skill T DD .098 -.027 .082 .088 -.006 .070 .086 .090 .053 .088 .093 .085 .108 .097 .066 .106 .081 .085 .110 .092 .060 .109 .038 .090 .074 .078 .049 Skill .138 .140 .102 .198 .197 .151 High Moderate Low None .10 .110 .076 .109 .05 T DD .01 .104 .095 .074 Cooling Heating

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)