Hello,
You should know or make assumptions on the distribution of the
precipitation. Let's say it is normally distributed (i.e. bell-shaped).
Then you can calculate the probability of exceeding the quantile /q/ by
pnorm(q, mean, sd, lower.tail = FALSE).
If you have several spatial points and a lot of measurments (stored in
columns of the sf/data.frame) for each of the points, then use
apply(X, MARGIN = 1, FUN = function(measurements) {return(pnorm(q, mean,
sd, lower.tail = FALSE))})
and you can display the probabilities in a map.
HTH,
Ákos
__________
Ákos Bede-Fazekas
Centre for Ecological Research, Hungary
2023.02.15. 1:28 keltezéssel, rain1290--- via R-sig-Geo írta:
> Hi there,
> I have climate data pertaining to extreme precipitation, as well as carbon
> emissions associated with those precipitation values in a dataframe.
> The goal of my analysis would be to determine the probability of exceeding
> specific thresholds of precipitation extremes, as well as showing this
> graphically (I am imagining this by placing extreme precipitation on the the
> x-axis and exceedance probabilities on the y-axis).
> My question is if anyone has an idea how to approach this, or a good starting
> place? I have looked online, but there is nothing specific to really draw on.
> Thank you for your time, and I look forward to your response!
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>
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