Figure. 3. Scatter plot diagrams of FAR as a function of warning frequency for (a) the current NWS verification method and (b) the proposed NWS verification method. The least-squares best-fit line and equation, as well as the correlation coefficient, are provided for each diagram.
Similar results are seen for both leadtime (Fig. 2) and FAR (Fig. 3). Both sets of diagrams show leadtime and FAR increase as WF increases. There is another more pertinent similarity between the two figures: There is a significant difference in the correlation coefficient between the two verification methods. In the case of leadtime, the relationship between WF and leadtime is relatively stronger for the polygon method than for the county method (Fig. 2). The opposite is true for FAR. In fact, not only is the polygon method correlation coefficient smaller, its value corresponds to little, if any, significant correlation between the variables. These data suggest that groups who issued more warnings had better leadtime but not necessarily higher FARs when using the polygon method. If these relationships hold up over continued analysis, then the polygon verification method holds some real promise. This new verification method offers a way to better distinguish a forecaster's skill at issuing warnings, a way for better forecasters to maintain high PODs while possibly reducing FARs. In other words, switching to the polygon verification system would have a positive impact on the warning verification goals outlined in the NWS Strategic Plan for 2005 (NWS, 1999).
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4.3 Continuing Analysis
Analysis on the WDM I warnings is on going. Besides the variables mentioned previously, many other variables will be compared to the standard verification statistics to better determine the skill level of the students. Some of these variables include warning length (WL), the reliance on spotter information for warning issuance (Spotter Percentage, or SP), and how often a forecaster's warning was accurate (Warning Accuracy, or WA). Data will also be collected from one of the classes at WDM II for comparison of NWS interns to more seasoned forecasters.
5. REFERENCES
Friday, E.W., 1994: The Modernization and Associated
Restructuring of the National Weather Service. Bull. Amer. Meteor. Soc., 75, 43-52.
National Weather Service, 1999: National Weather Service Strategic Plan for Weather, Water and Climate Services 2000-2005. Available on the Internet at:
http://www.nws.noaa.gov/sp/strplan.htm.
Office of Meteorology, NWS, 2000: Performance Measure Review: WFO and NCEP Products.
Operational Support Facility, Operations Training Branch, 1999: Distance Learning Operations Course. Available on the Internet at: http://www.wdtb.noaa.gov/courses/dloc/.
Polger, P.D., B.S. Goldsmith, R.C. Przywarty, and J.R.
Bocchieri, 1994: National Weather Service Warning
Performance Based on the WSR-88D. Bull. Amer.
Meteor. Soc., 75, 203-214.
Quoetone, E. M. and K. L. Huckabee, 1995: Anatomy of an
Effective Warning: Event Anticipation, Data
Integration, Feature Recognition. Preprints, 14th Conf.
on Weather Analysis. and Forecasting, AMS, Dallas,
Texas, 420-425.
Smith, R. D., 2000: The Warning Polygon Verification
Project: An Alternative Verification Scheme for Severe
Storm Warnings. Preprints, 20th Conf. on Severe
Local Storms, AMS, Orlando, Florida.
United States Census Bureau, 2000: Census 2000.
Available on the Internet at: http://www.census.gov/.
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