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NAEFS Forecasts for 814 Day Mean Temperatures

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Title: NAEFS Forecasts for 814 Day Mean Temperatures


1
NAEFS Forecasts for 8-14 Day Mean Temperatures
  • Dan Collins, Jon Hoopingarner, Edward Olenic,
    David Unger
  • NOAA/NWS/NCEP/CPC

2
Outline
  • Current 8-14 Day Product
  • NAEFS product
  • Calibration Procedure
  • Example Forecasts
  • Conclusions

3
CPC operational product
  • 8-14 Day Mean Temperature
  • Tercile Probabilities
  • Provide the probability of occurrence in the
    favored category, Below, Near, or Above Normal

4
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5
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6
Current Product
  • Issued daily at 3 PM
  • Human input (weekdays), Automated
    (Weekends)
  • GFS 12Z(y), 18Z(y), 00Z, 06Z
  • CMC 00Z
  • CDC Reforecast Model (1998 Version of the
    GFS)
  • Downscaling to Surface
  • Klein Specification Equations
  • Analogs with past cases
  • Calibrated Model Output
  • Neural Net Specifications

7
NAEFS Forecasts
  • Expand to include All North America
  • Objective procedure
  • Calibrated Model Output
  • (Models should be better at downscaling than
    the older CPC methods)

8
NAEFS Experimental Product
  • Must be comparable in skill to operational
    product
  • Must be consistent with operational product
  • Must exhibit reasonable temporal consistency.
    (00Z and 06Z for example)

9
Calibration
10
Ensemble Regression
  • A regression model designed for the kernel
    smoothing methodology
  • - Each member is equally likely to occur
  • - Each has the same conditional error
    distribution in the event it is
  • closest to the truth.
  • F Forecast, sF Forecast
    Standard Deviation
  • ObsObservations, sObs
    Standard Deviation of observations
  • RCorrelation between individual ensemble
    members and the observations
  • Rm Correlation between ensemble mean and
    observations
  • a1 , a0 Regression Coefficients,
  • F a0 a1 F

11
Time series estimation
  • Moving Average, Let X11 Be the 10-year running
    mean known on year 11. N10
  • X11 1/N(x1x2x3x4x5x6x7x9x9x10)
  • X12 X11 1/N(x11-x1)
  • XY1 XY 1/N(xY1-xY-10)
  • Exponential Moving Average, a 1/N
  • X12 X11 a(x11- X11)
  • XY1 (1- a)XY axY1

12
Adaptive Ensemble Regression
EMA estimates
  • F
  • F2
  • (Obs)
  • (Obs)2
  • F (Obs)
  • Fm2
  • (F-Fm)2

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14
NAEFS 8-14 day TemperatureIssued 30 Oct. 2006
Valid Nov 7-13, 2006 Washington DC 38N 77W
15
NAEFS 8-14 day TemperatureIssued 30 Oct. 2006
Valid Nov 7-13, 2006 Alaska 68N 150W
16
NCEP GFS CMC
17
NCEP GFS CMC
18
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20
NAEFS
Official
Observations
21
NCEP GFS CMC
22
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23
NAEFS Calibrated Temperatures
Official Forecast
24
Work to be done
  • Implement calibration algorithms
  • Smoothing/terrain corrections
  • Develop a strategy for consistency with
    operational product.
  • Design Probability of Exceedence (PoE) forecast
    format.
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