Splitting cases into wet and dry events

Following the information I found from the mid level (3.5km) dew point, I split the cases into wet and dry events, using -30C as a benchmark.

I reanalyzed all of the variables in the empirical formula given the class of the event (wet/dry), for a new average, standard deviation, and correlation to the actual data.

In addition, I added the dew point variable itself into the formula given that especially the dry cases show a strong correlation to the wind magnitude (drier mid levels = stronger winds).

The latest formula incorporates the following variables:

• Cordova-Anchorage SLP gradient
• Depth of the mid-level temperature inversion
• 2.5-5km shear magnitude
• Change in low-level cross-barrier flow over time
• 3km temperature
• 3.5km dew point

The results are very good. Of most significance, the new formula reduces the error of the outliers.

For the wet cases: Correlation = 0.73, Error = 5.0kt, 6.7%

For the dry cases: Correlation = 0.83, Error = 3.9kt, 5.2%

Overall: Correlation = 0.75, Error = 4.6kt, 6.1%

The 0.83 correlation for the dry cases is excellent.