Seasonal Degree Day Outlooks

David A. Unger

Climate Prediction Center

Camp Springs, Maryland

Definitions

HDD = 65 – t t < 65 F

CDD = t – 65 t > 65 F

HD = HDD/N CD = CDD/NT = 65+CD-HDCD = T –65 +HDt = daily mean temperature, T=Monthly or Seasonal Mean

N = Number of days in month or season

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CPC Outlook

CPC POE Outlooks

OverviewTools

Temperature FcstProb. Anom.For Tercile

(Above, Near, Below)

Temperature POE

Degree DaysHDD CDD POE

Degree DaysFlexible Region, Seasons

Forecaster Input

Model Skills, climatology

Downscaling (Regression Relationships)

Temperature POEDownscaled Temperature to Degree Day

(Climatological Relationships)

Accumulation Algorithms

Skill: Heidke .10

RPS .02

Skill: CRPS .02

CRPS Skill: CDD .05

HDD .02

Skill: CRPS .03

CRPS Skill: CDD .06

HDD .02

Temperature to Degree Days

Rescaling

FD Seasonal

FD Monthly

CD Seasonal

CD Monthly

Downscaling

Disaggregation

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

Climo

CRPS

CRPS

Continuous Ranked Probability Score

.031

.023

.028 .019

.040 .036

.026 .030

.094 .103

.074 .090

.035 .030

.012 .015

1-Mo

FD CD

3-Mo

CRPS Skill Scores: Temperature

-.009 .002

-.006 -.008

.002 .001

.011 .004

.044 .038

.050 .047

.013 .016

.027 .026

.055 .059

.055 .058

.027 .029

.026 .023

.020 .021

.024 .024

.051 .045

.041 .034

.065 .055

.042 .035

High

Moderate

Low

None

Skill

.10

.05

.01

1-Month Lead, All initial times

.049

.057

.018 .016

.101 .121

.014 .076

.088 .115

.079 .111

.033 .051

.005 .003

Heating

1-Mo 12-Mo

Cooling

CRPS Skill Scores: Heating and Cooling Degree Days

-.004 .036

-.026 -.016

.009 .022

.000 -0.16

.035 .014

.045 -.003

.058 .043

.021 -.011

.090 .090

.029 .035

.114 .085

.019 .028

.047 .102

.023 .048

.040 .071

.036 .073

.044 .024

.046 .030

High

Moderate

Low

None

Skill

.10

.05

.02

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

.106 .019

.067 .077

.198 .233

.106 .135

.138 .140

.086 .067

Seasonal

FD CD

Monthly

CRPS Skill Scores – Downscaled and disaggregated

.108 .105

.061 .060

.088 .085

.061 .055

.074 .070

.052 .037

.086 .083

.061 .059

.110 .086

.066 .066

.088 .092

.063 .039

.109 .109

.058 .055

.098 .081

.061 .042

.110 .087

.074 .044

SkillHigh

Moderate

Low

None

.10

.05

.01

.104

.095

.104 .074

.106 .081

.106 .085

.198 .197

.198 .151

.138 .140

.138 .102

Heating

T DD

Cooling

CRPS Skill Scores Temperature to Degree Days

.108 .097

.108 .066

.088 .093

.088 .085

.074 .078

.074 .049

.086 .090

.086 .053

.110 .092

.110 .060

.088 -.006

.088 .070

.109 .038

.109 .090

.098 -.027

.098 .082

.110 .076

.110 .109

High

Moderate

Low

None

Skill

.10

.05

.01

Accumulation Algorithm

DD = DD + DD

Independent (I)

Dependent (D)

From Climatology

=

<

A+B

(I) (D)

A+B = A B

A+B =A B

+

+

+

2 2

A+B

A+B

<

A+B

2

A+B A B

KA+B

(I)(I)

(I) (D) =

(D)(D)K( )+