1. Correlation Tests Between the Craven SigSvr Parameter and Severe Weather Calvin Moorer EAS4803 Spring 09

2. Outline  Craven SigSvr Parameter Defined  Data Source  Plot with Linear Regression  Pearson’s Correlation Coefficient Test  Bootstrap Test of the Correlation Coefficient  Conclusion

3. Craven SigSvr Parameter  SigSvr = (mlCAPE J/kg)*(deep layer shear m/s)  CAPE(Convective Available Potential Energy) Measure instability and potential updraft strength  Deep Layer Shear- measures change in wind speed and or direction with height between 0- 6km  Most significant severe weather events occur when the SigSvr Parameter exceeds 20,000 m 3 /s 3. w1.spc.woc.noaa.gov

4. Data Source RUC-2 National Weather Service in Peachtree City

5. Linear Regression p = 0.0025 which is less than 0.05 Correlation is therefore significant.

6. Pearson’s Correlation Coefficient  corrcoef(x,y) x = SigSvr Parameter x = SigSvr Parameter y = Number of Severe Weather Events y = Number of Severe Weather Events ans = 1.0000 0.7283 1.0000 0.7283 0.7283 1.0000 0.7283 1.0000  r = 0.7283 indicates a positive correlation

7. Bootstrap Test of Correlation Coefficient Average Correlation Coefficient r computed using the mean function yields 0.7246 in agreement with 0.7283

8. Conclusion  Linear Regression, Pearson’s Correlation Coefficient and the Bootstrap Test of the Correlation all prove that the Craven SigSvr Parameter correlates with the number of severe weather events.  The Craven SigSvr Parameter is useful in forecasting severe weather.

9. QUESTIONS