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Time is a very important dimension in meteorology, and there are many things to keep in mind. Here we will explore how Metview handles the fourth this dimension.

Overlaying Time Steps

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As an exercise to put all of this together, we will write a new macro to compute the precipitation rate in mm per hour at a  a particular location for each time period. We'll do it step by step.

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Now, create an empty list (dates = nil). We will add each date variable to it when as we loop through the fields.

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dates = nil
for i = 1 to n do
   print(i) # we will put proper code here later in the next step!
end for

Extract the date and time from each field

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Convert these numbers into a date variable

The date (d) can be converted with the date() function into a proper date variable. You will need to divide the time variable (t) to convert it into a fraction of a day before adding it to the date variable (d). A time of 12:00 is returned as 1200, so we need to divide by 100 to get it into hours. The hourshour() function then converts it into a fraction of a day. The result can be converted with the date() function into a proper date; call the resulting variable dt and print it to check that it is   Add the two together to create a full date variable.:

dt = date(d) + hour(t/100)

Add the date to the list

If the date variable is dt, then we We add it to the list like this (inside the loop):

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Compute the differences between consecutive dates

Very This is very similar to computing the precipitation data earlier (ok, we know it's 3 hours, but in theory it could be anything). We do this after the previous loop:

date_diffs = dates[2, n] - dates[1, n-1]

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The nearest_gridpoint() function can be called in a number of ways, but we will use it like this (giving actual numbers for lat and lon) :

 values = nearest_gridpoint(precip_diffs, lat, lon)

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