23 June 2003 4:30 PM 23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Por Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Por tland, OR tland, OR Toward Short-Range Ensemble Prediction of Mesoscale Forecast Skill Eric P. Grimit Clifford F. Mass University of Washington Supported by: NWS Western Region/UCAR-COMET Student-Career Experience Program (SCEP) DoD Multi-Disciplinary University Research Initiative (MURI)
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23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, OR Toward Short-Range Ensemble Prediction of Mesoscale.
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23 June 2003 4:30 PM23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, ORSession 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, OR
Toward Short-Range Ensemble Prediction of Mesoscale Forecast Skill
Eric P. GrimitClifford F. Mass
University of Washington
Supported by:NWS Western Region/UCAR-COMET Student-Career Experience Program (SCEP)
DoD Multi-Disciplinary University Research Initiative (MURI)
23 June 2003 4:30 PM23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, ORSession 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, OR
Forecasting Forecast Error
Like any other scientific prediction or measurement, weather forecasts should be accompanied by error bounds, or a statement of uncertainty.
Forecast error changes from day-to-day,
and is dependent on:Atmospheric predictability – a function of
the sensitivity of the flow to:Magnitude/orientation of initial state errors
Numerical model errors / deficiencies
T2m = 3 °C ± 2 °C P(T2m < 0 °C) = 6.7 %
23 June 2003 4:30 PM23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, ORSession 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, OR
Operational forecasters need this crucial information to know how much to trust model forecast guidance
Current uncertainty knowledge is partial, and largely subjective
End users could greatly benefit from knowing the expected forecast reliability
Allows sophisticated users to make optimal decisions in the face of uncertainty (economic cost-loss or utility)
Common users of weather forecasts – confidence index
Value of Forecast Error Prediction
ShowersLow 46°FHigh 54°F
FRI
88
AM ShowersLow 47°FHigh 57°F
SAT
55
Take protective action if: P(T2m < 0 °C) > cost/loss
23 June 2003 4:30 PM23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, ORSession 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, OR
Probabilistic Weather Forecasts
One approach to estimating forecast uncertainty is to use a collection of different forecasts—an ensemble.
Ensemble weather forecasting diagnoses the sensitivity of the predicted flow to initial-state and model errors—provided they are well-sampled.
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23 June 2003 4:30 PM23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, ORSession 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, OR
Probabilistic Weather Forecasts
Agreement/disagreement among ensemble member forecasts provides information about forecast certainty/uncertainty.
agreement disagreement
better forecast worse forecast
reliability reliability
use ensemble forecast variance as a predictor of forecast error
23 June 2003 4:30 PM23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, ORSession 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, OR
Observed Error Predictions: A Disappointment
[c.f. Goerss 2000] [c.f. Hou et al. 2001][c.f. Hamill and Colucci 1998]
•Unique 5-member short-range ensemble developed in 2000 showed promise•Spread-error correlations near 0.6, higher for cases with extreme spread
23 June 2003 4:30 PM23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, ORSession 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, OR
Definition of forecast errorError metric – user-dependencySpecifics of the forecast verification approach
Day-to-day forecast spread variability
An accurately forecast probability distribution is requiredIn practice, the PDF is not well forecast
Unaccounted for sources of uncertainty• Sub-grid scale processes
Under-sampling (distribution tails not well captured)Systematic forecast biases
Must find ways to extract flow-dependent uncertainty information from current (suboptimal) ensembles
Why Forecast Error Prediction is Limited
1- exp(-2)2(,|E|) = ; =std(ln )
2 1-exp(-2)2
23 June 2003 4:30 PM23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, ORSession 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, OR
Project Goal
Develop a short-range forecast error prediction system using an imperfect mesoscale ensemble
short-range = 0 – 48 h
imperfect = suboptimal; cannot correctly forecast the true PDF
Estimate the upper-bound of forecast error predictability using a simple statistical model
Use existing UW MM5 SREF system – a unique resourceInitialized using an international collection of large-scale analysesSpatial resolution (12-km grid spacing)
Include spatially- and temporally-dependent forecast bias correction
Use temporal ensemble spread as a secondary predictor of forecast error, if viable
Test a variety of metrics of spread and error
23 June 2003 4:30 PM23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, ORSession 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, OR
STD-AEM correlation STD-RMS correlationSimple Statistical Model Spread-Error Correlations
spreadSTD =Standard
Deviation
errorRMS= Root-Mean
Square errorAEM= Absolute Error
of the ensemble Mean
UW’s Ensemble of Ensembles
# of EF Initial Forecast Forecast Name Members Type Conditions Model(s) Cycle Domain
Verification Period: Oct 2002 – Mar 2003(130 cases)
Verification Strategy: Interpolate Model to Observations
Variable: 10-m Wind Direction
23 June 2003 4:30 PM23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, ORSession 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, OR
Spread-Error Correlations for Temperature
ACMEcore ACMEcore+
23 June 2003 4:30 PM23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, ORSession 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, OR
Summary
Forecast error predictability depends largely on the definition of error itself.
User-dependent needs
Spread-error correlation is sensitive to the spread and error metrics
For mesoscale wind and temperature forecast errors, the UW MM5 SREF spread appears to be a viable predictor—especially using the multi-analysis, mixed-physics ensemble (ACMEcore+).
Incorporation of a simple method of forecast bias correction is expected to further improve spread-error correlations.
Temporal ensemble spread has not proven to be a consistently skillful secondary predictor of forecast error.
23 June 2003 4:30 PM23 June 2003 4:30 PM Session 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, ORSession 2: Mesoscale Predictability I; 10th Mesoscale Conference; Portland, OR
“No forecast is complete without a forecast of forecast skill!”