P1.8 The development of forecast confidence measures using NCEP ensembles (2008

P1.8

The development of forecast confidence measures using NCEP ensembles

Robert Hart, Florida State University, Tallahassee, FL; and A. V. Durante and A. I. Watson

Ensemble model data can provide a wealth of knowledge to forecasters

especially in terms of forecast confidence. A model run where members

diverge corresponds to a low confidence forecast while a model run where

members converge corresponds to a forecast of high confidence. The current

NWS graphically based forecasts accessible to the public do not show this

measure of uncertainty and thus communicate an often exaggerated sense of

precision and confidence.

Starting August 2004 and extending into 2007, analysis of about 3 years of

individual global GFS ensemble data is occurring as part of a

COMETcooperative project with the NWS Office in Tallahassee. A climatology

for each GFS ensemble member is being developed as a function of variable,

location, time of year, and forecast length. Once the normalized

climatology distributions are calculated, forecast confidence/uncertainty

measures can be developed from comparing the normalized spread of the

real-time GFS ensemble members to the average spread of the GFS ensemble

climatology. This normalized spread will also be compared to the typical

spread for that time of year and location to arrive at a relative measure

of forecast uncertainty. If the current model ensemble uncertainty is

greater (less) than the uncertainty of the model ensemble climatology,

then there is a lower (higher) than normal confidence.

Confidence graphics have been developed and analyzed

(http://moe.met.fsu.edu/confidence) to examine how confidence values

behave within certain synoptic situations. Preliminary results have shown

that when the forecast confidence is above normal, the average NWS

forecast error is approximately 40 to 50% of the error when the forecast

confidence is below normal. A confidence climatology and several case

studies will be examined in this paper to illustrate how the forecast

confidence graphics can be used to not only speed up the forecast process

for the forecaster, but also to improve the accuracy of resulting

forecasts by knowing when using climatology for a forecast is most

appropriate.