Exploring the uncertainty of weather generators’ extreme estimates in different practical available information scenarios

Figures & data

Figure 1. Monthly mean precipitation (grey) and number of annual maximum occurrences by month (black dots).

Table 1. Observation statistics.

Variable	Statistic	Value
Daily precipitation (Pd)	Mean	1.56 mm
	SD	6.81 mm
	No. Pd > 0.1	24.77%
	Maximum	206.94 mm
Annual Precipitation (Pa)	Mean	569.86 mm
Annual maximum daily precipitation (Pam)	Mean	73.35 mm
	CV	0.56
	Coefficient of Skewness	1.43
	Coefficient of Kurtosis	1.66

Figure 2. E-GPD upper tail decay for different ξ values.

Figure 3. Box plots of the estimated quantiles (standardized with the population quantiles) for the four information scenarios. Both the RRMSE (expressed in %) and the CV are shown on top of each box plot for each T.

Figure 4. Reduction of RRMSE for the three scenarios compared to scenario 0 (parameter ξ set to 0.05.

Figure 5. Box plots of the estimated quantiles (standardized with the population quantiles) for different calibration $X_T$ in scenario 2.

Figure 6. Box plots of the ξ values for different calibration $X_T$ values in scenario 2.

Figure 7. Box plots of the Monte Carlo simulation calibrated with the regional $X_{100}$ for populations with different extremality (i.e. ξ values of 0.09, 0.11, 0.13 and 0.25).

Figure 8. Box plots of the Monte Carlo simulation from 60-, 90- and 120-year samples – scenario 3 (ξ set to 0.11).