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Correlation Tests Between the Craven SigSvr Parameter and Severe Weather

Correlation Tests Between the Craven SigSvr Parameter and Severe Weather. Calvin Moorer EAS4803 Spring 09. Outline. Craven SigSvr Parameter Defined Data Source Plot with Linear Regression Pearson’s Correlation Coefficient Test Bootstrap Test of the Correlation Coefficient Conclusion.

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Correlation Tests Between the Craven SigSvr Parameter and Severe Weather

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  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 m3/s3. 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 y = Number of Severe Weather Events ans = 1.0000 0.7283 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

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