Extreme Forecast Index (EFI) & Shift of Tails (SOT)
Potentially extreme weather can be identified with the powerful tools EFI and SOT. These indices provide ensemble-based measures of how likely and how extreme a weather event would be than usual, given as likelihood measure (not probability for a certain event).
efi: Extreme Forecast Index
sot: Shift Of Tails.
Both types of parameters are based on the Cumulative Distribution Function of the different models that make up the ensemble and refer to a certain time range over which they measure the tendency for extreme events. EFI values over 0.8 denote high likelihood for unusual conditions. SOT values signify their extremeness. Only for temperature, the shift of distribution tails is given for both the upper (default) and the lower tail (marked additionally).
Selection of time interval
These indices are queried together with a time range which they are valid for, i.e. t_2m_1d_efi:idx. Date/time stamps are right aligned.
Examples for time ranges:
The return period is an estimated average time between the occurrences of a defined event of a given severity for a selected area (e.g. extreme flooding event). The unit of the return period is years.
Example:<br/ >Defined event: Extremely high values of 24 hour accumulated precipitaton (1990 – 2020)
Return period: 10 years (mean of times between events)
Parameter & time period
Parameter to be analyzed
Start time of investigated data
End time of investigated data
Daily values that are above or below a certain threshold can be ignored. The orientation of the threshold has to be defined as well. The setting of a threshold is optional.
Value for a threshold
None, any numerical value (e.g. 50), quantiles (e.g. quantile0.25)
Defines the values that are kept
below (values above the threshold are set to invalid), above (values below the threshold are set to invalid)
Relevant time window of the year that can be defined for the analysis. For example, if floodings are investigated, a plausible risk window would be May 1st until October 1st.
Function used to accumulate/reduce daily valid values to single value per risk window
min, mean, max, median, sum
Start day of risk window
Start month of risk window
End day of risk window
End month of risk window
It has to be defined which orientation of extremes should be considered as severe conditions. The orientation can be either up, which takes large values into account, or down, which uses low values.
large (e.g. extreme rain) or low (e.g. frost) values indicate the rarest, most severe conditions
Dry conditions like in 2018 are expected to occur every 20 years on average.
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