...
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_20102009_20142013.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 2009 to 2014 2013 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!
...
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 and plot it.
Extracting dates from other data types
...
If you have time, try the following.
Computing
...
monthly anomalies
Continuing from the section "Computing a climatology", we will now take some data from 2014 and compute its difference from the climatology data we produced.
Examine the supplied GRIB file monthly_temperatureera_t2m_jan_2014.grib. It contains low-resolution temperature fields (4x4 degree) over an area of Europe for each day in January 20152014 at time steps 00:00 and 12:00. Try the following in a new macro:
- compute the monthly mean temperature field .for the whole month
- separate notice that the data contains two different time steps: 00:00 and 12:00. Monthly means are normally computed separately for each time step. Separate the data into the two different time steps and compute the mean field for each (hint: use the Grib Filter icon and its equivalent macro code). The end result should be two fields - one is the mean of all the 00:00 fields and the other is the mean of all the 12:00 fields.
- for each of the original fieldstime step, compute the difference between it the 2014 mean and the climatological mean computed earlier (you may wish to combine both macros into a single macro at this point)
- note that the two data sets are on different grids - you will need to change one of them to the other's grid
- plot the result (it should be two fields) corresponding mean field for that time step. Plot it with Contouring icons appropriate for showing temperature anomalies.
Your result shows the monthly anomalies for January 2014 compared with the previous 5 years.