Next, you will fit models for wind event data that have been separated by meteorological cause. Start with the non-thunderstorm data first (dataset “CID_Independent_NT-CID-WIND-SPEED”) and create a new Distribution Fitting Analysis called “PDS NT” in the same manner as the all storms dataset previously. Filter the data so that they are above 42 mi hr-1 and evaluate the goodness of fit of the collection of models when estimated using the method of L-moments. Record your results for the PDS generalized Pareto model in Table 1 and for the AMS generalized extreme value model in Table 2.
Table 1. Results for non-thunderstorm type, partial duration series.
Non-Thunderstorm Type – Partial Duration Series Generalized Pareto Distribution (L-moments) |
Parameter | Value |
Location (ξ) |
|
Scale (α) |
|
Shape (κ) |
|
Sample Size |
|
After fitting the partial duration series for the non-thunderstorm type, fit the annual maximum series in a new Distribution Fitting Analysis called “AMS NT” and filter to annual peaks the same way as in the "all storms" dataset in the previous task. Ensure the year starts on 01Oct.
Table 2. Results for non-thunderstorm type, annual maximum series.
Non-Thunderstorm Type – Annual Maximum Series Generalized Extreme Value Distribution (L-moments) |
Parameter | Value |
Location (ξ) |
|
Scale (α) |
|
Shape (κ) |
|
Sample Size |
|
Question 1: What is the average rate of non-thunderstorm type extreme wind events per year? Hint: you can compute it using the sample sizes of the PDS and AMS. What role does the rate of events per year play in an extreme value analysis?
Repeat the process for the thunderstorm data (dataset “CID_Independent_Thunderstorms-CID-WIND-SPEED”), creating a partial duration series “PDS Thunderstorm” and an annual maximum series “AMS Thunderstorm.” Do not forget to filter the partial duration winds so that the sample is above 42 mi hr-1 and the annual maximum winds so that the year starts on 01Oct. Record your results for the PDS generalized Pareto model in Table 3 and for the AMS generalized extreme value model in Table 4.
Table 3. Results for thunderstorm type, partial duration series.
Thunderstorm Type – Partial Duration Series Generalized Pareto Distribution (L-moments) |
Parameter | Value |
Location (ξ) |
|
Scale (α) |
|
Shape (κ) |
|
Sample Size |
|
Table 4. Results for thunderstorm type, annual maximum series.
Thunderstorm Type – Annual Maximum Series Generalized Extreme Value Distribution (L-moments) |
Parameter | Value |
Location (ξ) |
|
Scale (α) |
|
Shape (κ) |
|
Sample Size |
|
Question 2: What is the average rate of thunderstorm type extreme wind events per year? How does this compare to non-thunderstorm events?
Make sure to save your SSP project.
Next step: Task 3: Mixed Population Analysis