Page History

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for d = 2015-01-01 to 2015-03-01 do
    print(d)  # each step is 1 day
end for

for d = 2015-01-01 to 2015-03-01 by 2 do
    print(d)  # each step is 2 days
end for

for d = 2015-01-01 to 2015-03-01 by hour(6) do
    print(d)  # each step is 6 hours
end for

Computing

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the precipitation rate at a point

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  particular location for each time step. The steps will be:

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The final calculation requires converting the time intervals into hours (because if the time difference between two steps is 7 hours, then the rate of precip per hour is the mean precip value divided by 7).

Computing a climatology

The supplied GRIB file era_t2m_jan_2010_2014.grib contains 2 metre temperature fields from the ERA Interim data set, interpolated onto a low-resolution 5x5 degree grid. The data are from years 2010 to 2014 and only include the month of January. The data are also from two times: 00:00 and 12:00. Check that all of this is true!

We will compute a small climatology dataset, which will simply be the mean of all these fields. Write a small macro to do this - it should be just 2 lines long: one to read the GRIB file, and one to compute the mean (simply the mean() function). Return or plot the result to confirm that it looks sensible.

Often, these climatological averages are computed individually for each time step. So in our case, we want to now produce two means: one for all the fields at 00:00 and one for all the fields at 12:00. Hint: use the GRIB Filter icon (and its equivalent Macro code) to extract all the fields where Time = 0 and compute their mean. Do the same with all the 12:00 fields. Concatenate the two mean fields into a 2-field fieldset.

Extracting dates from other data types

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