Reanalysis of Validation Data for the 2009 Convective Season

Pryor (2009) presented validation results for the GOES Microburst Windspeed Potential Index (MWPI) for the 2009 convective season over the U.S. southern Great Plains. Further statistical analysis of a dataset built by comparing wind gust speeds recorded by Oklahoma Mesonet stations to adjacent MWPI values for 35 downburst events has yielded some favorable results. Correlation has been computed between key parameters in downburst process, including temperature lapse rate (LR) and dewpoint depression difference (DDD) between two levels (670mb/850mb), CAPE, and radar reflectivity (Z). The first important finding is a statistically significant negative correlation (r=-.34) between lapse rate and radar reflectivity, as shown in Figure 1. Similar to the findings of Srivastava (1985), for lapse rates greater than 8 K/km, downburst occurrence is nearly independent of radar reflectivity. For lapse rates less than 8 K/km, downburst occurrence was associated with high reflectivity (> 50 dBZ) storms. The majority of downbursts occurred in sub-cloud environments with lapse rates greater than 8.5 K/km. Adding the dewpoint depression difference to lapse rate yielded an even greater negative correlation (r=- .42) when compared to radar reflectivity, as demonstrated in Figure 2. Finally, comparing the sum of LR and DDD (the former hybrid microburst index (HMI)) to CAPE resulted in the strongest negative correlation (r=-.82), with a confidence level above 99%. This emphasizes the complementary nature of the HMI and CAPE in generating a robust and physically meaningful MWPI value. This result also shows that CAPE can serve as an adequate proxy variable for precipitation loading (expressed as radar reflectivity) in the MWPI . The strong negative correlation, or negative functional relationship, between HMI and CAPE terms in the MWPI algorithm indicates that the MWPI should be effective in capturing both negative buoyancy generation and precipitation loading as downburst forcing mechanisms.

Figure 1. Scatterplot of lapse rate versus radar reflectivity.

Figure 2.Scatterplot of the sum of lapse rate and DDD versus radar reflectivity.

Figure 3. Scatterplot of the sum of lapse rate and DDD versus CAPE.

References

Pryor, K.L., 2009:Microburst windspeed potential assessment: progress and recent developments.arXiv:0910.5166v1 [physics.ao-ph]

Srivastava, R.C., 1985: A simple model of evaporatively driven downdraft: Application to microburst downdraft. J. Atmos. Sci., 42, 1004-1023.